diff --git a/.gitignore b/.gitignore index 31f9738..f4921bb 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,9 @@ +# Data files +*.csv + +# Picture files +*.png + # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] diff --git a/.travis.yml b/.travis.yml index d0bc8fa..e5416cc 100644 --- a/.travis.yml +++ b/.travis.yml @@ -18,9 +18,10 @@ services: # Set the language language: python python: - - "2.7" + - "2.7" before_script: + - sudo apt-get install python-dev # - psql -c 'create database mimic_test;' -U postgres # - psql -c 'create schema mimiciii;' -d mimic_test -U postgres # - psql target-db -U -p -h -c "\copy source-table from 'source-table.csv' with DELIMITER ','" @@ -28,9 +29,9 @@ before_script: # command to install dependencies install: - - pip install psycopg2 --quiet - - pip install pandas --quiet - - pip install MySQL-python --quiet + - pip install psycopg2 --quiet + - pip install pandas --quiet + - pip install MySQL-python --quiet # - pip install . # - pip install -r requirements.txt diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..12791ea --- /dev/null +++ b/Makefile @@ -0,0 +1,75 @@ +## ------------------------------------------------------------------ +## Title: Top-level build file +## Description: Automated import of data and SQL scripts +## ------------------------------------------------------------------ + +## Parameters ## + +DBNAME=mimic +DBUSER=mimic +SCHEMA=mimiciii +DATADIR= + +# path of this makefile - used to locate concepts subfolder +ROOT_DIR:=$(shell "dirname" $(realpath $(lastword $(MAKEFILE_LIST)))) + +## Commands ## + +PSQL=psql "dbname=$(DBNAME) options=--search_path=$(SCHEMA)" --username=$(DBUSER) + + +## Export ## +# Parameters given in this Makefile take precedence over those defined in each +# individual Makefile (due to specifying the -e option and the export command +# here) +export + +## Build targets ## +help: + @echo '---------------------------------------------------------------------------' + @echo 'mimic-download: Download data from PhysioNet' + @echo 'mimic-gz: Import data into a local PostgreSQL database using .csv.gz files' + @echo 'mimic: Import data into a local PostgreSQL database using .csv files' + @echo 'concepts: Create community contributed materialized views' + @echo '--------------------------------------------------------------------------- ' + @echo ' Download MIMIC-III from PhysioNet to the /path/to/data/ directory - ' + @echo ' ' + @echo ' make mimic-download physionetuser=USERNAME datadir="/path/to/data/" ' + @echo ' ' + @echo ' e.g. make mimic-download physionetuser=me@email.com datadir="/data/" ' + @echo ' ' + @echo ' Build MIMIC-III using the CSV files in the /path/to/data directory - ' + @echo ' ' + @echo ' make mimic datadir="/path/to/data/" ' + @echo ' ' + @echo ' e.g. make mimic datadir="/data/mimic/v1_4/" ' + @echo ' ' + @echo ' Build MIMIC-III using the .csv.gz files in the /path/to/data directory - ' + @echo ' ' + @echo ' make mimic-gz datadir="/path/to/data/" ' + @echo ' ' + @echo ' e.g. make mimic-gz datadir="/data/mimic/v1_4/" ' + @echo '--------------------------------------------------------------------------- ' + +mimic: mimic-build mimic-check +mimic-gz: mimic-build-gz mimic-check-gz + +mimic-download: + @$(MAKE) -e -C buildmimic/postgres mimic-download + +mimic-build: + @$(MAKE) -e -C buildmimic/postgres mimic-build + +mimic-check: + @$(MAKE) -e -C buildmimic/postgres mimic-check + +mimic-build-gz: + @$(MAKE) -e -C buildmimic/postgres mimic-build-gz + +mimic-check-gz: + @$(MAKE) -e -C buildmimic/postgres mimic-check-gz + +concepts: + @cd $(ROOT_DIR)/concepts && $(PSQL) -f make-concepts.sql + +.PHONY: help mimic mimic-build mimic-download mimic-check mimic-gz mimic-build-gz mimic-check-gz concepts diff --git a/Makefile.md b/Makefile.md new file mode 100644 index 0000000..c6d02c6 --- /dev/null +++ b/Makefile.md @@ -0,0 +1,40 @@ + +## Introduction to the build system + +The build system for mimic code uses the GNU Makefile system. From a user's point of view this makes the whole process very straightforward. +Starting from a fresh system which has both GNU Make installed, PostgreSQL installed, and a local copy of this repository, an instance of the MIMIC database can be imported from PhysioNet by running the following: + +``` +make mimic-download +make mimic-gz DATADIR=/path/to/data +``` + +Note that if you have already downloaded the data, you can skip the `make mimic-download`. If you have already decompressed the data into `.csv` files, then you can run `make mimic DATADIR=/path/to/data`. + +Optionally, additional contributed materialized views can be created afterward by running: + +``` +make concepts +``` + +Note that you may want to modify parameters at the top of the Makefile - e.g. the username (see below "non-standard username or database name"). + +### Authentication +In order to avoid the prompts for your database password each time, you may create a file in your home directory called .pgpass containing the following: + +``` +localhost:5432:*:mimic:password +``` + +Replace ```mimic``` with your username and ```password``` with your password. Note that this is storage of your database password in the clear and so we would recommend only doing this for installation. + +### Non-standard username or database name +If you need to use a username or database name other than ```mimic```, then you will need to specify this by modifying the top-level Makefile: + +``` +DBNAME=mimic +DBUSER=mimic +``` + +## Contributing +If you would like to contribute code to create a materialized view to the `concepts` folder, the existence of this makefile places an additional (and hopefully very minor) burden to ensure that your views are included in this build system. The top-level Makefile (i.e. the one in the root of this repository) is a wrapper that calls `concepts/make-concepts.sql`: simply add a command which calls the script to this file. The format is fairly straightforward: e.g. adding the `\i sepsis/angus.sql` line informs the script to call the `concepts/sepsis/angus.sql` file. diff --git a/README.md b/README.md index d9d3a7d..28c97c4 100644 --- a/README.md +++ b/README.md @@ -2,6 +2,10 @@ This is a repository of code shared by the research community. The repository is intended to be a central hub for sharing, refining, and reusing code used for analysis of the [MIMIC critical care database](https://mimic.physionet.org). To find out more about MIMIC, please see: https://mimic.physionet.org +## Acknowledgement + +[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.821872.svg)](https://doi.org/10.5281/zenodo.821872) + ## How to contribute Our team has worked hard to create and share the MIMIC dataset. We encourage you to share the code that you use for data processing and analysis. Sharing code helps to make studies reproducible and promotes collaborative research. To contribute, please: @@ -10,10 +14,16 @@ Our team has worked hard to create and share the MIMIC dataset. We encourage you - Commit your changes to the forked repository. - Submit a pull request to the [MIMIC code repository](https://github.com/MIT-LCP/mimic-code), using the method described at: https://help.github.com/articles/using-pull-requests/ -## Coding style +We encourage users to share concepts they have extracted by writing code which generates a materialized view. These materialized views can then be used by researchers around the world to speed up data extraction. For example, ventilation durations can be acquired by creating the ventdurations view in [etc/ventilation-durations.sql](https://github.com/MIT-LCP/mimic-code/blob/master/concepts/ventilation-durations.sql). -Please refer to the [style guide](https://github.com/MIT-LCP/mimic-code/blob/master/styleguide.md) for guidelines on formatting your code for the repository. +## License +By committing your code to the [MIMIC Code Repository](https://github.com/mit-lcp/mimic-code) you agree to release the code under the [MIT License attached to the repository](https://github.com/mit-lcp/mimic-code/blob/master/LICENSE). + +## Coding style +Please refer to the [style guide](https://github.com/MIT-LCP/mimic-code/blob/master/styleguide.md) for guidelines on formatting your code for the repository. +## Building MIMIC automatically +A Makefile build system has been created to facilitate the building of the MIMIC database, and optionally contributed views from the community. Please refer to the [Makefile guide](https://github.com/MIT-LCP/mimic-code/blob/master/Makefile.md) for more details. diff --git a/buildmimic/apache-drill/drill_create_data.sql b/buildmimic/apache-drill/drill_create_data.sql index 132394b..9afa3c0 100644 --- a/buildmimic/apache-drill/drill_create_data.sql +++ b/buildmimic/apache-drill/drill_create_data.sql @@ -1,9 +1,7 @@ --- ------------------------------------------------------------------ +-- ------------------------------------------------------------------ -- Title: Create the MIMIC-III tables --- Description: More detailed description explaining the purpose. --- MIMIC version: MIMIC-III v1.3 --- Created by: paris nicolas --- ------------------------------------------------------------------ +-- Description: More detailed description explaining the purpose. +-- ------------------------------------------------------------------ @@ -35,7 +33,6 @@ case when EDREGTIME = '' then cast(NULL as TIMESTAMP(0)) else cast(EDREGTIME as case when EDOUTTIME = '' then cast(NULL as TIMESTAMP(0)) else cast(EDOUTTIME as TIMESTAMP(0)) end as EDOUTTIME, DIAGNOSIS, case when HOSPITAL_EXPIRE_FLAG = '' then cast(NULL as INT) else cast(HOSPITAL_EXPIRE_FLAG as INT) end as HOSPITAL_EXPIRE_FLAG, -case when HAS_IOEVENTS_DATA = '' then cast(NULL as INT ) else cast(HAS_IOEVENTS_DATA as INT) end as HAS_IOEVENTS_DATA, case when HAS_CHARTEVENTS_DATA = '' then cast(NULL as INT ) else cast(HAS_CHARTEVENTS_DATA as INT) end as HAS_CHARTEVENTS_DATA FROM dfs.tmp.`ADMISSIONS.csv` ; @@ -76,7 +73,7 @@ FROM dfs.tmp.`CALLOUT.csv`; -------------------------------------------------------- CREATE TABLE dfs.mimiciii.`CAREGIVERS` AS -SELECT +SELECT case when ROW_ID = '' then cast(NULL as INT ) else cast(ROW_ID as INT) end as ROW_ID, case when CGID = '' then cast(NULL as INT ) else cast(CGID as INT) end as CGID, LABEL, @@ -270,7 +267,7 @@ case when LAST_WARDID = '' then cast(NULL as INT ) else cast(LAST_WARDID as INT) case when INTIME = '' then cast(NULL as TIMESTAMP(0)) else cast(INTIME as TIMESTAMP(0)) end as INTIME, case when OUTTIME = '' then cast(NULL as TIMESTAMP(0)) else cast(OUTTIME as TIMESTAMP(0)) end as OUTTIME, case when LOS = '' then cast(NULL as DOUBLE ) else cast(LOS as DOUBLE) end as LOS -FROM dfs.tmp.`ICUSTAYS.csv`; +FROM dfs.tmp.`ICUSTAYS.csv`; -------------------------------------------------------- -- DDL for Table INPUTEVENTS_CV @@ -461,7 +458,7 @@ DOSE_VAL_RX, DOSE_UNIT_RX, FORM_VAL_DISP, FORM_UNIT_DISP, -ROUTE +ROUTE FROM dfs.tmp.`PRESCRIPTIONS.csv`; @@ -545,4 +542,3 @@ case when INTIME = '' then cast(NULL as TIMESTAMP(0)) else cast(INTIME as TIMEST case when OUTTIME = '' then cast(NULL as TIMESTAMP(0)) else cast(OUTTIME as TIMESTAMP(0)) end as OUTTIME, case when LOS = '' then cast(NULL as DOUBLE ) else cast(LOS as DOUBLE) end as LOS FROM dfs.tmp.`TRANSFERS.csv`; - diff --git a/buildmimic/docker/Dockerfile b/buildmimic/docker/Dockerfile index e2404fb..1a61883 100644 --- a/buildmimic/docker/Dockerfile +++ b/buildmimic/docker/Dockerfile @@ -1,16 +1,24 @@ FROM postgres:latest -MAINTAINER Aaron J. Masino - +# in the docker initialization, we do not build the data ENV BUILD_MIMIC 0 RUN apt-get update +RUN apt-get install -y vim git + +# clone the postgres build scripts into a local folder +RUN mkdir mimic-code +RUN cd mimic-code +RUN git init +RUN git remote add -f origin https://github.com/MIT-lcp/mimic-code +RUN git config core.sparseCheckout true +RUN echo "buildmimic/postgres/" >> .git/info/sparse-checkout +RUN git pull origin master -RUN apt-get install -y vim +# copy the build scripts into a different folder +RUN cp -r buildmimic /docker-entrypoint-initdb.d/ +# make a directory for the data RUN mkdir /mimic_data ADD setup.sh /docker-entrypoint-initdb.d/ - -ADD mimic_build_files /docker-entrypoint-initdb.d/mimic_build_files - diff --git a/buildmimic/docker/README.md b/buildmimic/docker/README.md index 511d4e1..f25cb74 100644 --- a/buildmimic/docker/README.md +++ b/buildmimic/docker/README.md @@ -1,22 +1,19 @@ -# Building the MIMIC database in PostgreSQL - -This directory contains scripts that can be used to create a new instance of the MIMIC-III -Clinical Database. The scripts can be executed on a host system PostgreSQL ("postgres") database. - # Building the MIMIC database with Docker -Alternatively, a Docker file is provided that can be used to build a +A Docker file is provided that can be used to build a [Docker](https://www.docker.com/) image and subsequently deploy a Docker container (i.e. a lightweight virtual machine) with PostgreSQL installed and the MIMIC database tables loaded automatically as described below. This documentation assumes you have Docker installed on your host machine. Docker -installation instructions can be found [here](https://docs.docker.com/). +installation instructions can be found [here](https://docs.docker.com/). Note that +the Docker install sometimes requires a reboot (in order for the Daemon to start properly). +You can ensure your docker is working correctly on Linux/OS X using `docker images`. ## Step 0: Clone this repository to your host machine This document assumes that this .git repo has been cloned to your host in the directory -*/mimic_code* +`/mimic_code` ## Step 1: Obtain the MIMIC csv data files @@ -27,23 +24,29 @@ Once, access has been granted, download all of the .csv files [from here](https://physionet.org/works/MIMICIIIClinicalDatabase/files/). Place all of the files in a directory on your host machine. This document will assume -they are in the directory */HOST/mimic/csv* - -Unzip all files and set permissions to read and write for all groups and users (this is -necessary to allow the container postgres server to read the files for loading into -the database). On Linux and MAC systems this can be accomplished with the *unzip_csv.sh* -script provide in this directory. At a terminal -command line enter the following: - - cd /mimic_code/buildmimic/postgres +they are in the directory `/HOST/mimic/csv`. The docker build can be done using either +plain-text `.csv` files, or compressed `.csv.gz files`. Specifically, the build will +check for `ADMISSIONS.csv.gz`: if this file exists, it will build using compressed files, +otherwise it will build using plain `csv` files. It's likely easier to keep the files +compressed, and move on to step 2. + +If you choose to unzip all the files (or already have), be sure to set permissions to read and write for all groups and users +(this is necessary to allow the container postgres server to read the files for loading into the database). +On Linux and MAC systems, a script to automtically to automatically unzip and set the appropriate permissions +is provided in this directory: `unzip_csv.sh`. +Run this at a terminal command line by entering the following: + + cd /mimic_code/buildmimic/docker source unzip_csv.sh /HOST/mimic/csv +... where `/HOST/mimic/csv` is the data folder you would like to work in. Remember this folder name for later. + ## Step 2: Build the Docker image Assuming Docker is installed on your host, you can build the image by entering the following at a terminal command line (or Docker Quickstart Terminal on Windows): - cd /mimic_code/buildmimic/postgres + cd /mimic_code/buildmimic/docker docker build -t postgres/mimic . Please note the "." at the end is necessary. @@ -54,8 +57,8 @@ To see that the docker image was successfully generated, enter at the command line. The output should include an entry similar to the following: - REPOSITORY TAG IMAGE ID CREATED VIRTUAL SIZE - postgres/mimic latest a664dd0d7238 6 hours ago 304 MB + REPOSITORY TAG IMAGE ID CREATED SIZE + postgres/mimic latest a664dd0d7238 2 minutes ago 349.4 MB ## Step 3: Deploy the container @@ -63,10 +66,10 @@ Once the image has been built, it is necessary to start a Docker container (esse running instance of the image). The following example command can be used to start the container and build the MIMIC III database from the CSV data files. When the container starts, a bash script is automatically executed that will run the SQL scripts in the -*mimic_build_files* directory (if BUILD_MIMIC=1, see below). These scripts create -the *mimic* user and create the *mimic* database, with a *mimiciii* schema, create all -of the tables in the *mimiciii* schema and copy the data from the CSV files. The *mimic* user -is the owner of the *mimic* database. Note that these scripts may take several hours to complete. +`mimic_build_files` directory (if BUILD_MIMIC=1, see below). These scripts create +the `mimic` user and create the `mimic` database, with a `mimiciii` schema, create all +of the tables in the `mimiciii` schema and copy the data from the CSV files. The `mimic` user +is the owner of the `mimic` database. Note that these scripts may take several hours to complete. docker run \ --name mimic \ @@ -80,24 +83,24 @@ is the owner of the *mimic* database. Note that these scripts may take several h In detail, this command: -* names the container *mimic* +* names the container `mimic` -* maps the container's port 5432 to the *HOST_PORT* (replace this with appropriate port number) +* maps the container's port 5432 to the `HOST_PORT` (replace this with appropriate port number) to enable remote connections to the DB -* sets the container's *BUILD_MIMIC* environment variable to 1 which indicates that the MIMIC database +* sets the container's `BUILD_MIMIC` environment variable to 1 which indicates that the MIMIC database tables need to be created and that the data from the CSV files should be loaded to the database. If the CSV data has been previously loaded to the Postgres database and the container just needs to connect -to it, use *BUILD_MIMIC=0* in the above command. +to it, use `BUILD_MIMIC=0` in the above command. -* sets the *postgres* user password to POSTGRES_USER_PASSWORD +* sets the `postgres` user password to POSTGRES_USER_PASSWORD -* sets the *mimic* user password to MIMIC_USER_PASSWORD +* sets the `mimic` user password to MIMIC_USER_PASSWORD -* maps the container's */mimic_data* directory to the host's */HOST/mimic_data/csv* directory +* maps the container's `/mimic_data` directory to the host's `/HOST/mimic_data/csv` directory so that it can find the MIMIC III CSV data files. -* maps the container's /var/lib/PostgreSQL/data directory to the host's */HOST/PG_DATA* +* maps the container's /var/lib/PostgreSQL/data directory to the host's `/HOST/PG_DATA` directory so that data is persisted on the host (not the container). This is to prevent data loss if the container is removed and restarted later. @@ -105,7 +108,7 @@ Note that on Windows systems, the host paths will need to be prefixed by an extr docker run \ --name mimic \ - -p 5432:5432 \ + -p 5555:5432 \ -e BUILD_MIMIC=1 \ -e POSTGRES_PASSWORD=postgres \ -e MIMIC_PASSWORD=mimic \ @@ -113,6 +116,18 @@ Note that on Windows systems, the host paths will need to be prefixed by an extr -v //d/mimic/pgdata:/var/lib/PostgreSQL/data \ -d postgres/mimic +... and here is an example of a working command on Ubuntu 16.04 running Docker v1.12.1: + + docker run \ + --name mimic \ + -p 5555:5432 \ + -e BUILD_MIMIC=1 \ + -e POSTGRES_PASSWORD=postgres \ + -e MIMIC_PASSWORD=mimic \ + -v /data/mimic3/version_1_4:/mimic_data \ + -v /data/docker/mimic:/var/lib/PostgreSQL/data \ + -d postgres/mimic + To view the Docker log file for this container (and monitor progress of the SQL scripts that load the data to the database from the CSV files) enter the following at the command line @@ -123,6 +138,9 @@ This will generate output that contains something similar to CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES YOUR_CONTAINER_ID postgres/mimic "/docker-entrypoint. 3 days ago Up 3 days 0.0.0.0:32777->5432/tcp mimic +You can remove containers by running `docker rm `, and images using `docker rmi `. +This is useful if you receive an error stating that the name "mimic" is already in use, or if you are rebuilding the image/containers. + To view the log files, enter the following: docker logs -f YOUR_CONTAINER_ID diff --git a/buildmimic/docker/setup.sh b/buildmimic/docker/setup.sh index ef918c9..5bedd37 100644 --- a/buildmimic/docker/setup.sh +++ b/buildmimic/docker/setup.sh @@ -2,26 +2,71 @@ echo 'CREATING MIMIC ... ' +# this flag allows us to initialize the docker repo without building the data if [ $BUILD_MIMIC -eq 1 ] then - echo "running create mimic user" +echo "running create mimic user" - gosu postgres pg_ctl stop +gosu postgres pg_ctl stop - gosu postgres pg_ctl -D "$PGDATA" \ - -o "-c listen_addresses='' -c checkpoint_segments=256 -c checkpoint_timeout=600" \ - -w start +gosu postgres pg_ctl -D "$PGDATA" \ + -o "-c listen_addresses='' -c checkpoint_timeout=600" \ + -w start - source /docker-entrypoint-initdb.d/mimic_build_files/create_mimic_user.sh +gosu postgres psql <<- EOSQL + CREATE USER MIMIC WITH PASSWORD '$MIMIC_PASSWORD'; + CREATE DATABASE MIMIC OWNER MIMIC; + \c mimic; + CREATE SCHEMA MIMICIII; + ALTER SCHEMA MIMICIII OWNER TO MIMIC; +EOSQL - echo "$0: running postgres_create_tables.sql" - psql --username "$POSTGRES_USER" --dbname mimic < /docker-entrypoint-initdb.d/mimic_build_files/postgres_create_tables.sql +# check for the admissions to set the extension +if [ -e "/mimic_data/ADMISSIONS.csv.gz" ]; then + COMPRESSED=1 + EXT='.csv.gz' +elif [ -e "/mimic_data/ADMISSIONS.csv" ]; then + COMPRESSED=0 + EXT='.csv' +else + echo "Unable to find a MIMIC data file (ADMISSIONS) in /mimic_data" + echo "Did you map a local directory using `docker run -v /path/to/mimic/data:/mimic_data` ?" + exit 1 +fi + +# check for all the tables, exit if we are missing any +ALLTABLES='admissions callout caregivers chartevents cptevents datetimeevents d_cpt diagnoses_icd d_icd_diagnoses d_icd_procedures d_items d_labitems drgcodes icustays inputevents_cv inputevents_mv labevents microbiologyevents noteevents outputevents patients prescriptions procedureevents_mv procedures_icd services transfers' + +for TBL in $ALLTABLES; do + if [ ! -e "/mimic_data/${TBL^^}$EXT" ]; + then + echo "Unable to find ${TBL^^}$EXT in /mimic_data" + exit 1 + fi + echo "Found all tables in /mimic_data - beginning import from $EXT files." +done + +# checks passed - begin building the database +echo "$0: running postgres_create_tables.sql" +psql --username "$POSTGRES_USER" --dbname mimic < /docker-entrypoint-initdb.d/buildmimic/postgres/postgres_create_tables.sql + +if [ $COMPRESSED -eq 1 ]; then +echo "$0: running postgres_load_data_gz.sql" +psql --username "$POSTGRES_USER" --dbname mimic -v mimic_data_dir=/mimic_data < /docker-entrypoint-initdb.d/buildmimic/postgres/postgres_load_data_gz.sql +else +echo "$0: running postgres_load_data.sql" +psql --username "$POSTGRES_USER" --dbname mimic -v mimic_data_dir=/mimic_data < /docker-entrypoint-initdb.d/buildmimic/postgres/postgres_load_data.sql +fi + +echo "$0: running postgres_add_indexes.sql" +psql --username "$POSTGRES_USER" --dbname mimic < /docker-entrypoint-initdb.d/buildmimic/postgres/postgres_add_indexes.sql + +echo "$0: running postgres_add_constraints.sql" +psql --username "$POSTGRES_USER" --dbname mimic < /docker-entrypoint-initdb.d/buildmimic/postgres/postgres_add_constraints.sql - echo "$0: running postgres_add_indexes.sql" - psql --username "$POSTGRES_USER" --dbname mimic < /docker-entrypoint-initdb.d/mimic_build_files/postgres_add_indexes.sql - echo "$0: running postgres_add_constraints.sql" - psql --username "$POSTGRES_USER" --dbname mimic < /docker-entrypoint-initdb.d/mimic_build_files/postgres_add_constraints.sql +echo "$0: running postgres_checks.sql (all rows should return PASSED)" +psql --username "$POSTGRES_USER" --dbname mimic < /docker-entrypoint-initdb.d/buildmimic/postgres/postgres_checks.sql fi echo 'Done!' diff --git a/buildmimic/hana/hana_create_tables.sql b/buildmimic/hana/hana_create_tables.sql index 02bd993..082739b 100644 --- a/buildmimic/hana/hana_create_tables.sql +++ b/buildmimic/hana/hana_create_tables.sql @@ -4,8 +4,12 @@ -- -- ------------------------------------------------------------------------------- +-- Before running this script, please do a find and replace: +-- find: '/path/to/csv/' +-- replace it with your actual CSV path. + -------------------------------------------------------- --- File created - Wednesday-November-20-2015 +-- File created - Wednesday-November-20-2015 -------------------------------------------------------- -- Create the schema @@ -82,7 +86,6 @@ SET SCHEMA MIMICIII; EDOUTTIME LONGDATE CS_LONGDATE, DIAGNOSIS VARCHAR(255), HOSPITAL_EXPIRE_FLAG SMALLINT, - HAS_IOEVENTS_DATA SMALLINT NOT NULL, HAS_CHARTEVENTS_DATA SMALLINT NOT NULL, CONSTRAINT adm_rowid_pk PRIMARY KEY (ROW_ID), CONSTRAINT adm_hadm_unique UNIQUE (HADM_ID) @@ -90,7 +93,7 @@ SET SCHEMA MIMICIII; -- Example command for importing from a CSV to a table -IMPORT FROM CSV FILE '/path/to/csv/file/ADMISSIONS.csv' +IMPORT FROM CSV FILE '/path/to/csv/ADMISSIONS.csv' INTO ADMISSIONS WITH THREADS 8 BATCH 2000 @@ -101,7 +104,7 @@ SKIP FIRST 1 ROW RECORD DELIMITED BY '\n' FIELD DELIMITED BY ',' OPTIONALLY ENCLOSED BY '"' -ERROR LOG '/path/to/log/file/ADMISSIONS.log' +ERROR LOG '/path/to/csv/ADMISSIONS.log' --FAIL ON INVALID DATA ; @@ -138,7 +141,7 @@ CREATE COLUMN TABLE CALLOUT ); -- Example command for importing from a CSV to a table -IMPORT FROM CSV FILE '/path/to/csv/file/CALLOUT.csv' +IMPORT FROM CSV FILE '/path/to/csv/CALLOUT.csv' INTO CALLOUT WITH THREADS 8 BATCH 2000 @@ -149,7 +152,7 @@ SKIP FIRST 1 ROW RECORD DELIMITED BY '\n' FIELD DELIMITED BY ',' OPTIONALLY ENCLOSED BY '"' -ERROR LOG '/path/to/log/file/CALLOUT.log' +ERROR LOG '/path/to/csv/CALLOUT.log' --FAIL ON INVALID DATA ; @@ -167,7 +170,7 @@ ERROR LOG '/path/to/log/file/CALLOUT.log' ) ; -- Example command for importing from a CSV to a table -IMPORT FROM CSV FILE '/path/to/csv/file/CAREGIVERS.csv' +IMPORT FROM CSV FILE '/path/to/csv/CAREGIVERS.csv' INTO CAREGIVERS WITH THREADS 8 BATCH 2000 @@ -178,7 +181,7 @@ SKIP FIRST 1 ROW RECORD DELIMITED BY '\n' FIELD DELIMITED BY ',' OPTIONALLY ENCLOSED BY '"' -ERROR LOG '/path/to/log/file/CAREGIVERS.log' +ERROR LOG '/path/to/csv/CAREGIVERS.log' --FAIL ON INVALID DATA ; @@ -206,7 +209,7 @@ ERROR LOG '/path/to/log/file/CAREGIVERS.log' ) ; -- Example command for importing from a CSV to a table -IMPORT FROM CSV FILE '/path/to/csv/file/CHARTEVENTS.csv' +IMPORT FROM CSV FILE '/path/to/csv/CHARTEVENTS.csv' INTO CHARTEVENTS WITH THREADS 8 BATCH 2000 @@ -217,7 +220,7 @@ SKIP FIRST 1 ROW RECORD DELIMITED BY '\n' FIELD DELIMITED BY ',' OPTIONALLY ENCLOSED BY '"' -ERROR LOG '/path/to/log/file/CHARTEVENTS.log' +ERROR LOG '/path/to/csv/CHARTEVENTS.log' --FAIL ON INVALID DATA ; @@ -242,7 +245,7 @@ ERROR LOG '/path/to/log/file/CHARTEVENTS.log' ) ; -- Example command for importing from a CSV to a table -IMPORT FROM CSV FILE '/path/to/csv/file/CPTEVENTS.csv' +IMPORT FROM CSV FILE '/path/to/csv/CPTEVENTS.csv' INTO CPTEVENTS WITH THREADS 8 BATCH 2000 @@ -253,7 +256,7 @@ SKIP FIRST 1 ROW RECORD DELIMITED BY '\n' FIELD DELIMITED BY ',' OPTIONALLY ENCLOSED BY '"' -ERROR LOG '/path/to/log/file/CPTEVENTS.log' +ERROR LOG '/path/to/csv/CPTEVENTS.log' --FAIL ON INVALID DATA ; @@ -280,7 +283,7 @@ ERROR LOG '/path/to/log/file/CPTEVENTS.log' ) ; -- Example command for importing from a CSV to a table -IMPORT FROM CSV FILE '/path/to/csv/file/DATETIMEEVENTS.csv' +IMPORT FROM CSV FILE '/path/to/csv/DATETIMEEVENTS.csv' INTO DATETIMEEVENTS WITH THREADS 8 BATCH 2000 @@ -291,7 +294,7 @@ SKIP FIRST 1 ROW RECORD DELIMITED BY '\n' FIELD DELIMITED BY ',' OPTIONALLY ENCLOSED BY '"' -ERROR LOG '/path/to/log/file/DATETIMEEVENTS.log' +ERROR LOG '/path/to/csv/DATETIMEEVENTS.log' --FAIL ON INVALID DATA ; @@ -309,7 +312,7 @@ ERROR LOG '/path/to/log/file/DATETIMEEVENTS.log' ) ; -- Example command for importing from a CSV to a table -IMPORT FROM CSV FILE '/path/to/csv/file/DIAGNOSES_ICD.csv' +IMPORT FROM CSV FILE '/path/to/csv/DIAGNOSES_ICD.csv' INTO DIAGNOSES_ICD WITH THREADS 8 BATCH 2000 @@ -320,7 +323,7 @@ SKIP FIRST 1 ROW RECORD DELIMITED BY '\n' FIELD DELIMITED BY ',' OPTIONALLY ENCLOSED BY '"' -ERROR LOG '/path/to/log/file/DIAGNOSES_ICD.log' +ERROR LOG '/path/to/csv/DIAGNOSES_ICD.log' --FAIL ON INVALID DATA ; @@ -341,7 +344,7 @@ ERROR LOG '/path/to/log/file/DIAGNOSES_ICD.log' ) ; -- Example command for importing from a CSV to a table -IMPORT FROM CSV FILE '/path/to/csv/file/DRGCODES.csv' +IMPORT FROM CSV FILE '/path/to/csv/DRGCODES.csv' INTO DRGCODES WITH THREADS 8 BATCH 2000 @@ -352,7 +355,7 @@ SKIP FIRST 1 ROW RECORD DELIMITED BY '\n' FIELD DELIMITED BY ',' OPTIONALLY ENCLOSED BY '"' -ERROR LOG '/path/to/log/file/DRGCODES.log' +ERROR LOG '/path/to/csv/DRGCODES.log' --FAIL ON INVALID DATA ; @@ -375,7 +378,7 @@ ERROR LOG '/path/to/log/file/DRGCODES.log' ) ; -- Example command for importing from a CSV to a table -IMPORT FROM CSV FILE '/path/to/csv/file/D_CPT.csv' +IMPORT FROM CSV FILE '/path/to/csv/D_CPT.csv' INTO D_CPT WITH THREADS 8 BATCH 2000 @@ -386,7 +389,7 @@ SKIP FIRST 1 ROW RECORD DELIMITED BY '\n' FIELD DELIMITED BY ',' OPTIONALLY ENCLOSED BY '"' -ERROR LOG '/path/to/log/file/D_CPT.log' +ERROR LOG '/path/to/csv/D_CPT.log' --FAIL ON INVALID DATA ; @@ -404,7 +407,7 @@ ERROR LOG '/path/to/log/file/D_CPT.log' ) ; -- Example command for importing from a CSV to a table -IMPORT FROM CSV FILE '/path/to/csv/file/D_ICD_DIAGNOSES.csv' +IMPORT FROM CSV FILE '/path/to/csv/D_ICD_DIAGNOSES.csv' INTO D_ICD_DIAGNOSES WITH THREADS 8 BATCH 2000 @@ -415,7 +418,7 @@ SKIP FIRST 1 ROW RECORD DELIMITED BY '\n' FIELD DELIMITED BY ',' OPTIONALLY ENCLOSED BY '"' -ERROR LOG '/path/to/log/file/D_ICD_DIAGNOSES.log' +ERROR LOG '/path/to/csv/D_ICD_DIAGNOSES.log' --FAIL ON INVALID DATA ; @@ -433,7 +436,7 @@ ERROR LOG '/path/to/log/file/D_ICD_DIAGNOSES.log' ) ; -- Example command for importing from a CSV to a table -IMPORT FROM CSV FILE '/path/to/csv/file/D_ICD_PROCEDURES.csv' +IMPORT FROM CSV FILE '/path/to/csv/D_ICD_PROCEDURES.csv' INTO D_ICD_PROCEDURES WITH THREADS 8 BATCH 2000 @@ -444,7 +447,7 @@ SKIP FIRST 1 ROW RECORD DELIMITED BY '\n' FIELD DELIMITED BY ',' OPTIONALLY ENCLOSED BY '"' -ERROR LOG '/path/to/log/file/D_ICD_PROCEDURES.log' +ERROR LOG '/path/to/csv/D_ICD_PROCEDURES.log' --FAIL ON INVALID DATA ; @@ -468,7 +471,7 @@ ERROR LOG '/path/to/log/file/D_ICD_PROCEDURES.log' ) ; -- Example command for importing from a CSV to a table -IMPORT FROM CSV FILE '/path/to/csv/file/D_ITEMS.csv' +IMPORT FROM CSV FILE '/path/to/csv/D_ITEMS.csv' INTO D_ITEMS WITH THREADS 8 BATCH 2000 @@ -479,7 +482,7 @@ SKIP FIRST 1 ROW RECORD DELIMITED BY '\n' FIELD DELIMITED BY ',' OPTIONALLY ENCLOSED BY '"' -ERROR LOG '/path/to/log/file/D_ITEMS.log' +ERROR LOG '/path/to/csv/D_ITEMS.log' --FAIL ON INVALID DATA ; @@ -499,7 +502,7 @@ ERROR LOG '/path/to/log/file/D_ITEMS.log' ) ; -- Example command for importing from a CSV to a table -IMPORT FROM CSV FILE '/path/to/csv/file/D_LABITEMS.csv' +IMPORT FROM CSV FILE '/path/to/csv/D_LABITEMS.csv' INTO D_LABITEMS WITH THREADS 8 BATCH 2000 @@ -510,7 +513,7 @@ SKIP FIRST 1 ROW RECORD DELIMITED BY '\n' FIELD DELIMITED BY ',' OPTIONALLY ENCLOSED BY '"' -ERROR LOG '/path/to/log/file/D_LABITEMS.log' +ERROR LOG '/path/to/csv/D_LABITEMS.log' --FAIL ON INVALID DATA ; @@ -536,7 +539,7 @@ ERROR LOG '/path/to/log/file/D_LABITEMS.log' ) ; -- Example command for importing from a CSV to a table -IMPORT FROM CSV FILE '/path/to/csv/file/ICUSTAYS.csv' +IMPORT FROM CSV FILE '/path/to/csv/ICUSTAYS.csv' INTO ICUSTAYS WITH THREADS 8 BATCH 2000 @@ -547,7 +550,7 @@ SKIP FIRST 1 ROW RECORD DELIMITED BY '\n' FIELD DELIMITED BY ',' OPTIONALLY ENCLOSED BY '"' -ERROR LOG '/path/to/log/file/ICUSTAYS.log' +ERROR LOG '/path/to/csv/ICUSTAYS.log' --FAIL ON INVALID DATA ; @@ -583,7 +586,7 @@ ERROR LOG '/path/to/log/file/ICUSTAYS.log' ) ; -- Example command for importing from a CSV to a table -IMPORT FROM CSV FILE '/path/to/csv/file/INPUTEVENTS_CV.csv' +IMPORT FROM CSV FILE '/path/to/csv/INPUTEVENTS_CV.csv' INTO INPUTEVENTS_CV WITH THREADS 8 BATCH 2000 @@ -594,7 +597,7 @@ SKIP FIRST 1 ROW RECORD DELIMITED BY '\n' FIELD DELIMITED BY ',' OPTIONALLY ENCLOSED BY '"' -ERROR LOG '/path/to/log/file/INPUTEVENTS_CV.log' +ERROR LOG '/path/to/csv/INPUTEVENTS_CV.log' --FAIL ON INVALID DATA ; @@ -638,7 +641,7 @@ ERROR LOG '/path/to/log/file/INPUTEVENTS_CV.log' ) ; -- Example command for importing from a CSV to a table -IMPORT FROM CSV FILE '/path/to/csv/file/INPUTEVENTS_MV.csv' +IMPORT FROM CSV FILE '/path/to/csv/INPUTEVENTS_MV.csv' INTO INPUTEVENTS_MV WITH THREADS 8 BATCH 2000 @@ -649,7 +652,7 @@ SKIP FIRST 1 ROW RECORD DELIMITED BY '\n' FIELD DELIMITED BY ',' OPTIONALLY ENCLOSED BY '"' -ERROR LOG '/path/to/log/file/INPUTEVENTS_MV.log' +ERROR LOG '/path/to/csv/INPUTEVENTS_MV.log' --FAIL ON INVALID DATA ; @@ -671,7 +674,7 @@ ERROR LOG '/path/to/log/file/INPUTEVENTS_MV.log' ) ; -- Example command for importing from a CSV to a table -IMPORT FROM CSV FILE '/path/to/csv/file/LABEVENTS.csv' +IMPORT FROM CSV FILE '/path/to/csv/LABEVENTS.csv' INTO LABEVENTS WITH THREADS 8 BATCH 2000 @@ -682,7 +685,7 @@ SKIP FIRST 1 ROW RECORD DELIMITED BY '\n' FIELD DELIMITED BY ',' OPTIONALLY ENCLOSED BY '"' -ERROR LOG '/path/to/log/file/LABEVENTS.log' +ERROR LOG '/path/to/csv/LABEVENTS.log' --FAIL ON INVALID DATA ; @@ -711,7 +714,7 @@ ERROR LOG '/path/to/log/file/LABEVENTS.log' ) ; -- Example command for importing from a CSV to a table -IMPORT FROM CSV FILE '/path/to/csv/file/MICROBIOLOGYEVENTS.csv' +IMPORT FROM CSV FILE '/path/to/csv/MICROBIOLOGYEVENTS.csv' INTO MICROBIOLOGYEVENTS WITH THREADS 8 BATCH 2000 @@ -722,7 +725,7 @@ SKIP FIRST 1 ROW RECORD DELIMITED BY '\n' FIELD DELIMITED BY ',' OPTIONALLY ENCLOSED BY '"' -ERROR LOG '/path/to/log/file/MICROBIOLOGYEVENTS.log' +ERROR LOG '/path/to/csv/MICROBIOLOGYEVENTS.log' --FAIL ON INVALID DATA ; @@ -746,7 +749,7 @@ ERROR LOG '/path/to/log/file/MICROBIOLOGYEVENTS.log' ) ; -- Example command for importing from a CSV to a table -IMPORT FROM CSV FILE '/path/to/csv/file/NOTEEVENTS.csv' +IMPORT FROM CSV FILE '/path/to/csv/NOTEEVENTS.csv' INTO NOTEEVENTS WITH THREADS 8 BATCH 2000 @@ -757,7 +760,7 @@ SKIP FIRST 1 ROW RECORD DELIMITED BY '\n' FIELD DELIMITED BY ',' OPTIONALLY ENCLOSED BY '"' -ERROR LOG '/path/to/log/file/NOTEEVENTS.log' +ERROR LOG '/path/to/csv/NOTEEVENTS.log' --FAIL ON INVALID DATA ; @@ -783,7 +786,7 @@ ERROR LOG '/path/to/log/file/NOTEEVENTS.log' ) ; -- Example command for importing from a CSV to a table -IMPORT FROM CSV FILE '/path/to/csv/file/OUTPUTEVENTS.csv' +IMPORT FROM CSV FILE '/path/to/csv/OUTPUTEVENTS.csv' INTO OUTPUTEVENTS WITH THREADS 8 BATCH 2000 @@ -794,7 +797,7 @@ SKIP FIRST 1 ROW RECORD DELIMITED BY '\n' FIELD DELIMITED BY ',' OPTIONALLY ENCLOSED BY '"' -ERROR LOG '/path/to/log/file/OUTPUTEVENTS.log' +ERROR LOG '/path/to/csv/OUTPUTEVENTS.log' --FAIL ON INVALID DATA ; @@ -816,7 +819,7 @@ ERROR LOG '/path/to/log/file/OUTPUTEVENTS.log' ) ; -- Example command for importing from a CSV to a table -IMPORT FROM CSV FILE '/path/to/csv/file/PATIENTS.csv' +IMPORT FROM CSV FILE '/path/to/csv/PATIENTS.csv' INTO PATIENTS WITH THREADS 8 BATCH 2000 @@ -827,7 +830,7 @@ SKIP FIRST 1 ROW RECORD DELIMITED BY '\n' FIELD DELIMITED BY ',' OPTIONALLY ENCLOSED BY '"' -ERROR LOG '/path/to/log/file/PATIENTS.log' +ERROR LOG '/path/to/csv/PATIENTS.log' --FAIL ON INVALID DATA ; @@ -859,7 +862,7 @@ ERROR LOG '/path/to/log/file/PATIENTS.log' ) ; -- Example command for importing from a CSV to a table -IMPORT FROM CSV FILE '/path/to/csv/file/PRESCRIPTIONS.csv' +IMPORT FROM CSV FILE '/path/to/csv/PRESCRIPTIONS.csv' INTO PRESCRIPTIONS WITH THREADS 8 BATCH 2000 @@ -870,7 +873,7 @@ SKIP FIRST 1 ROW RECORD DELIMITED BY '\n' FIELD DELIMITED BY ',' OPTIONALLY ENCLOSED BY '"' -ERROR LOG '/path/to/log/file/PRESCRIPTIONS.log' +ERROR LOG '/path/to/csv/PRESCRIPTIONS.log' --FAIL ON INVALID DATA ; @@ -910,7 +913,7 @@ ERROR LOG '/path/to/log/file/PRESCRIPTIONS.log' ) ; -- Example command for importing from a CSV to a table -IMPORT FROM CSV FILE '/path/to/csv/file/PROCEDUREEVENTS_MV.csv' +IMPORT FROM CSV FILE '/path/to/csv/PROCEDUREEVENTS_MV.csv' INTO PROCEDUREEVENTS_MV WITH THREADS 8 BATCH 2000 @@ -921,7 +924,7 @@ SKIP FIRST 1 ROW RECORD DELIMITED BY '\n' FIELD DELIMITED BY ',' OPTIONALLY ENCLOSED BY '"' -ERROR LOG '/path/to/log/file/PROCEDUREEVENTS_MV.log' +ERROR LOG '/path/to/csv/PROCEDUREEVENTS_MV.log' --FAIL ON INVALID DATA ; @@ -939,7 +942,7 @@ ERROR LOG '/path/to/log/file/PROCEDUREEVENTS_MV.log' ) ; -- Example command for importing from a CSV to a table -IMPORT FROM CSV FILE '/path/to/csv/file/PROCEDURES_ICD.csv' +IMPORT FROM CSV FILE '/path/to/csv/PROCEDURES_ICD.csv' INTO PROCEDURES_ICD WITH THREADS 8 BATCH 2000 @@ -950,7 +953,7 @@ SKIP FIRST 1 ROW RECORD DELIMITED BY '\n' FIELD DELIMITED BY ',' OPTIONALLY ENCLOSED BY '"' -ERROR LOG '/path/to/log/file/PROCEDURES_ICD.log' +ERROR LOG '/path/to/csv/PROCEDURES_ICD.log' --FAIL ON INVALID DATA ; @@ -969,7 +972,7 @@ ERROR LOG '/path/to/log/file/PROCEDURES_ICD.log' ) ; -- Example command for importing from a CSV to a table -IMPORT FROM CSV FILE '/path/to/csv/file/SERVICES.csv' +IMPORT FROM CSV FILE '/path/to/csv/SERVICES.csv' INTO SERVICES WITH THREADS 8 BATCH 2000 @@ -980,7 +983,7 @@ SKIP FIRST 1 ROW RECORD DELIMITED BY '\n' FIELD DELIMITED BY ',' OPTIONALLY ENCLOSED BY '"' -ERROR LOG '/path/to/log/file/SERVICES.log' +ERROR LOG '/path/to/csv/SERVICES.log' --FAIL ON INVALID DATA ; @@ -1006,7 +1009,7 @@ ERROR LOG '/path/to/log/file/SERVICES.log' ) ; -- Example command for importing from a CSV to a table -IMPORT FROM CSV FILE '/path/to/csv/file/TRANSFERS.csv' +IMPORT FROM CSV FILE '/path/to/csv/TRANSFERS.csv' INTO TRANSFERS WITH THREADS 8 BATCH 2000 @@ -1017,6 +1020,6 @@ SKIP FIRST 1 ROW RECORD DELIMITED BY '\n' FIELD DELIMITED BY ',' OPTIONALLY ENCLOSED BY '"' -ERROR LOG '/path/to/log/file/TRANSFERS.log' +ERROR LOG '/path/to/csv/TRANSFERS.log' --FAIL ON INVALID DATA ; diff --git a/buildmimic/mimic-iii.yaml b/buildmimic/mimic-iii.yaml new file mode 100644 index 0000000..be29096 --- /dev/null +++ b/buildmimic/mimic-iii.yaml @@ -0,0 +1,2339 @@ +# Configuration file for the MIMIC-III schema +# Table ADMISSIONS +databaseChangeLog: + - preConditions: + - runningAs: + username: postgres + - changeSet: + id: 1 + author: lcp + changes: + - createTable: + tableName: ADMISSIONS + columns: + - column: + name: ROW_ID + type: INT + constraints: + nullable: false + - column: + name: SUBJECT_ID + type: INT + constraints: + nullable: false + - column: + name: HADM_ID + type: INT + constraints: + nullable: false + - column: + name: ADMITTIME + type: TIMESTAMP(0) + constraints: + nullable: false + - column: + name: DISCHTIME + type: TIMESTAMP(0) + constraints: + nullable: false + - column: + name: DEATHTIME + type: TIMESTAMP(0) + - column: + name: ADMISSION_TYPE + type: VARCHAR(50) + constraints: + nullable: false + - column: + name: ADMISSION_LOCATION + type: VARCHAR(50) + constraints: + nullable: false + - column: + name: DISCHARGE_LOCATION + type: VARCHAR(50) + constraints: + nullable: false + - column: + name: INSURANCE + type: VARCHAR(255) + constraints: + nullable: false + - column: + name: LANGUAGE + type: VARCHAR(10) + - column: + name: RELIGION + type: VARCHAR(50) + - column: + name: MARITAL_STATUS + type: VARCHAR(50) + - column: + name: ETHNICITY + type: VARCHAR(200) + constraints: + nullable: false + - column: + name: EDREGTIME + type: TIMESTAMP(0) + - column: + name: EDOUTTIME + type: TIMESTAMP(0) + - column: + name: DIAGNOSIS + type: VARCHAR(255) + - column: + name: HOSPITAL_EXPIRE_FLAG + type: SMALLINT + - column: + name: HAS_CHARTEVENTS_DATA + type: SMALLINT + constraints: + nullable: false +# Table CALLOUT + - changeSet: + id: 2 + author: lcp + changes: + - createTable: + tableName: CALLOUT + columns: + - column: + name: ROW_ID + type: INT + constraints: + nullable: false + - column: + name: SUBJECT_ID + type: INT + constraints: + nullable: false + - column: + name: HADM_ID + type: INT + constraints: + nullable: false + - column: + name: SUBMIT_WARDID + type: INT + - column: + name: SUBMIT_CAREUNIT + type: VARCHAR(15) + - column: + name: CURR_WARDID + type: INT + - column: + name: CURR_CAREUNIT + type: VARCHAR(15) + - column: + name: CALLOUT_WARDID + type: INT + - column: + name: CALLOUT_SERVICE + type: VARCHAR(10) + constraints: + nullable: false + - column: + name: REQUEST_TELE + type: SMALLINT + constraints: + nullable: false + - column: + name: REQUEST_RESP + type: SMALLINT + constraints: + nullable: false + - column: + name: REQUEST_CDIFF + type: SMALLINT + constraints: + nullable: false + - column: + name: REQUEST_MRSA + type: SMALLINT + constraints: + nullable: false + - column: + name: REQUEST_VRE + type: SMALLINT + constraints: + nullable: false + - column: + name: CALLOUT_STATUS + type: VARCHAR(20) + constraints: + nullable: false + - column: + name: CALLOUT_OUTCOME + type: VARCHAR(20) + constraints: + nullable: false + - column: + name: DISCHARGE_WARDID + type: INT + - column: + name: ACKNOWLEDGE_STATUS + type: VARCHAR(20) + constraints: + nullable: false + - column: + name: CREATETIME + type: TIMESTAMP(0) + constraints: + nullable: false + - column: + name: UPDATETIME + type: TIMESTAMP(0) + constraints: + nullable: false + - column: + name: ACKNOWLEDGETIME + type: TIMESTAMP(0) + - column: + name: OUTCOMETIME + type: TIMESTAMP(0) + constraints: + nullable: false + - column: + name: FIRSTRESERVATIONTIME + type: TIMESTAMP(0) + - column: + name: CURRENTRESERVATIONTIME + type: TIMESTAMP(0) +# Table CAREGIVERS + - changeSet: + id: 3 + author: lcp + changes: + - createTable: + tableName: CAREGIVERS + columns: + - column: + name: ROW_ID + type: INT + constraints: + nullable: false + - column: + name: CGID + type: INT + constraints: + nullable: false + - column: + name: LABEL + type: VARCHAR(15) + - column: + name: DESCRIPTION + type: VARCHAR(30) +# Table CHARTEVENTS + - changeSet: + id: 4 + author: lcp + changes: + - createTable: + tableName: CHARTEVENTS + columns: + - column: + name: ROW_ID + type: INT + constraints: + nullable: false + - column: + name: SUBJECT_ID + type: INT + constraints: + nullable: false + - column: + name: HADM_ID + type: INT + - column: + name: ICUSTAY_ID + type: INT + - column: + name: ITEMID + type: INT + - column: + name: CHARTTIME + type: TIMESTAMP(0) + - column: + name: STORETIME + type: TIMESTAMP(0) + - column: + name: CGID + type: INT + - column: + name: VALUE + type: VARCHAR(255) + - column: + name: VALUENUM + type: DOUBLE PRECISION + - column: + name: VALUEUOM + type: VARCHAR(50) + - column: + name: WARNING + type: INT + - column: + name: ERROR + type: INT + - column: + name: RESULTSTATUS + type: VARCHAR(50) + - column: + name: STOPPED + type: VARCHAR(50) + # PARTITION for Table CHARTEVENTS + # -- CREATE CHARTEVENTS TABLE + # CREATE TABLE chartevents_1 ( CHECK ( itemid >= 1 AND itemid < 210 )) INHERITS (chartevents); + # CREATE TABLE chartevents_2 ( CHECK ( itemid >= 210 AND itemid < 250 )) INHERITS (chartevents); + # CREATE TABLE chartevents_3 ( CHECK ( itemid >= 250 AND itemid < 614 )) INHERITS (chartevents); + # CREATE TABLE chartevents_4 ( CHECK ( itemid >= 614 AND itemid < 640 )) INHERITS (chartevents); + # CREATE TABLE chartevents_5 ( CHECK ( itemid >= 640 AND itemid < 742 )) INHERITS (chartevents); + # CREATE TABLE chartevents_6 ( CHECK ( itemid >= 742 AND itemid < 1800 )) INHERITS (chartevents); + # CREATE TABLE chartevents_7 ( CHECK ( itemid >= 1800 AND itemid < 2700 )) INHERITS (chartevents); + # CREATE TABLE chartevents_8 ( CHECK ( itemid >= 2700 AND itemid < 3700 )) INHERITS (chartevents); + # CREATE TABLE chartevents_9 ( CHECK ( itemid >= 3700 AND itemid < 4700 )) INHERITS (chartevents); + # CREATE TABLE chartevents_10 ( CHECK ( itemid >= 4700 AND itemid < 6000 )) INHERITS (chartevents); + # CREATE TABLE chartevents_11 ( CHECK ( itemid >= 6000 AND itemid < 7000 )) INHERITS (chartevents); + # CREATE TABLE chartevents_12 ( CHECK ( itemid >= 7000 AND itemid < 8000 )) INHERITS (chartevents); + # CREATE TABLE chartevents_13 ( CHECK ( itemid >= 8000 AND itemid < 220074 )) INHERITS (chartevents); + # CREATE TABLE chartevents_14 ( CHECK ( itemid >= 220074 AND itemid < 323769 )) INHERITS (chartevents); + # + # -- CREATE CHARTEVENTS TRIGGER + # CREATE OR REPLACE FUNCTION chartevents_insert_trigger() + # RETURNS TRIGGER AS $$ + # BEGIN + # IF ( NEW.itemid >= 1 AND NEW.itemid < 210 ) THEN INSERT INTO chartevents_1 VALUES (NEW.*); + # ELSIF ( NEW.itemid >= 210 AND NEW.itemid < 250 ) THEN INSERT INTO chartevents_2 VALUES (NEW.*); + # ELSIF ( NEW.itemid >= 250 AND NEW.itemid < 614 ) THEN INSERT INTO chartevents_3 VALUES (NEW.*); + # ELSIF ( NEW.itemid >= 614 AND NEW.itemid < 640 ) THEN INSERT INTO chartevents_4 VALUES (NEW.*); + # ELSIF ( NEW.itemid >= 640 AND NEW.itemid < 742 ) THEN INSERT INTO chartevents_5 VALUES (NEW.*); + # ELSIF ( NEW.itemid >= 742 AND NEW.itemid < 1800 ) THEN INSERT INTO chartevents_6 VALUES (NEW.*); + # ELSIF ( NEW.itemid >= 1800 AND NEW.itemid < 2700 ) THEN INSERT INTO chartevents_7 VALUES (NEW.*); + # ELSIF ( NEW.itemid >= 2700 AND NEW.itemid < 3700 ) THEN INSERT INTO chartevents_8 VALUES (NEW.*); + # ELSIF ( NEW.itemid >= 3700 AND NEW.itemid < 4700 ) THEN INSERT INTO chartevents_9 VALUES (NEW.*); + # ELSIF ( NEW.itemid >= 4700 AND NEW.itemid < 6000 ) THEN INSERT INTO chartevents_10 VALUES (NEW.*); + # ELSIF ( NEW.itemid >= 6000 AND NEW.itemid < 7000 ) THEN INSERT INTO chartevents_11 VALUES (NEW.*); + # ELSIF ( NEW.itemid >= 7000 AND NEW.itemid < 8000 ) THEN INSERT INTO chartevents_12 VALUES (NEW.*); + # ELSIF ( NEW.itemid >= 8000 AND NEW.itemid < 220074 ) THEN INSERT INTO chartevents_13 VALUES (NEW.*); + # ELSIF ( NEW.itemid >= 220074 AND NEW.itemid < 323769 ) THEN INSERT INTO chartevents_14 VALUES (NEW.*); + # ELSE + # INSERT INTO chartevents_null VALUES (NEW.*); + # END IF; + # RETURN NULL; + # END; + # $$ + # LANGUAGE plpgsql; + # + # CREATE TRIGGER insert_chartevents_trigger + # BEFORE INSERT ON chartevents + # FOR EACH ROW EXECUTE PROCEDURE chartevents_insert_trigger(); + # +# #Table CPTEVENTS + - changeSet: + id: 5 + author: lcp + changes: + - createTable: + tableName: CPTEVENTS + columns: + - column: + name: ROW_ID + type: INT + constraints: + nullable: false + - column: + name: SUBJECT_ID + type: INT + constraints: + nullable: false + - column: + name: HADM_ID + type: INT + constraints: + nullable: false + - column: + name: COSTCENTER + type: VARCHAR(10) + constraints: + nullable: false + - column: + name: CHARTDATE + type: TIMESTAMP(0) + - column: + name: CPT_CD + type: VARCHAR(10) + constraints: + nullable: false + - column: + name: CPT_NUMBER + type: INT + - column: + name: CPT_SUFFIX + type: VARCHAR(5) + - column: + name: TICKET_ID_SEQ + type: INT + - column: + name: SECTIONHEADER + type: VARCHAR(50) + - column: + name: SUBSECTIONHEADER + type: VARCHAR(255) + - column: + name: DESCRIPTION + type: VARCHAR(200) +# Table DATETIMEEVENTS + - changeSet: + id: 6 + author: lcp + changes: + - createTable: + tableName: DATETIMEEVENTS + columns: + - column: + name: ROW_ID + type: INT + constraints: + nullable: false + - column: + name: SUBJECT_ID + type: INT + constraints: + nullable: false + - column: + name: HADM_ID + type: INT + - column: + name: ICUSTAY_ID + type: INT + - column: + name: ITEMID + type: INT + constraints: + nullable: false + - column: + name: CHARTTIME + type: TIMESTAMP(0) + constraints: + nullable: false + - column: + name: STORETIME + type: TIMESTAMP(0) + constraints: + nullable: false + - column: + name: CGID + type: INT + constraints: + nullable: false + - column: + name: VALUE + type: TIMESTAMP(0) + - column: + name: VALUEUOM + type: VARCHAR(50) + constraints: + nullable: false + - column: + name: WARNING + type: SMALLINT + - column: + name: ERROR + type: SMALLINT + - column: + name: RESULTSTATUS + type: VARCHAR(50) + - column: + name: STOPPED + type: VARCHAR(50) +# Table DIAGNOSES_ICD + - changeSet: + id: 7 + author: lcp + changes: + - createTable: + tableName: DIAGNOSES_ICD + columns: + - column: + name: ROW_ID + type: INT + constraints: + nullable: false + - column: + name: SUBJECT_ID + type: INT + constraints: + nullable: false + - column: + name: HADM_ID + type: INT + constraints: + nullable: false + - column: + name: SEQ_NUM + type: INT + - column: + name: ICD9_CODE + type: VARCHAR(20) +# Table DRGCODES + - changeSet: + id: 8 + author: lcp + changes: + - createTable: + tableName: DRGCODES + columns: + - column: + name: ROW_ID + type: INT + constraints: + nullable: false + - column: + name: SUBJECT_ID + type: INT + constraints: + nullable: false + - column: + name: HADM_ID + type: INT + constraints: + nullable: false + - column: + name: DRG_TYPE + type: VARCHAR(20) + constraints: + nullable: false + - column: + name: DRG_CODE + type: VARCHAR(20) + constraints: + nullable: false + - column: + name: DESCRIPTION + type: VARCHAR(255) + - column: + name: DRG_SEVERITY + type: SMALLINT + - column: + name: DRG_MORTALITY + type: SMALLINT +# Table D_CPT + - changeSet: + id: 9 + author: lcp + changes: + - createTable: + tableName: D_CPT + columns: + - column: + name: ROW_ID + type: INT + constraints: + nullable: false + - column: + name: CATEGORY + type: SMALLINT + constraints: + nullable: false + - column: + name: SECTIONRANGE + type: VARCHAR(100) + constraints: + nullable: false + - column: + name: SECTIONHEADER + type: VARCHAR(50) + constraints: + nullable: false + - column: + name: SUBSECTIONRANGE + type: VARCHAR(100) + constraints: + nullable: false + - column: + name: SUBSECTIONHEADER + type: VARCHAR(255) + constraints: + nullable: false + - column: + name: CODESUFFIX + type: VARCHAR(5) + - column: + name: MINCODEINSUBSECTION + type: INT + constraints: + nullable: false + - column: + name: MAXCODEINSUBSECTION + type: INT + constraints: + nullable: false +# Table D_ICD_DIAGNOSES + - changeSet: + id: 10 + author: lcp + changes: + - createTable: + tableName: D_ICD_DIAGNOSES + columns: + - column: + name: ROW_ID + type: INT + constraints: + nullable: false + - column: + name: ICD9_CODE + type: VARCHAR(10) + constraints: + nullable: false + - column: + name: SHORT_TITLE + type: VARCHAR(50) + constraints: + nullable: false + - column: + name: LONG_TITLE + type: VARCHAR(255) + constraints: + nullable: false +# Table D_ICD_PROCEDURES + - changeSet: + id: 11 + author: lcp + changes: + - createTable: + tableName: D_ICD_PROCEDURES + columns: + - column: + name: ROW_ID + type: INT + constraints: + nullable: false + - column: + name: ICD9_CODE + type: VARCHAR(10) + constraints: + nullable: false + - column: + name: SHORT_TITLE + type: VARCHAR(50) + constraints: + nullable: false + - column: + name: LONG_TITLE + type: VARCHAR(255) + constraints: + nullable: false +# Table D_ITEMS + - changeSet: + id: 12 + author: lcp + changes: + - createTable: + tableName: D_ITEMS + columns: + - column: + name: ROW_ID + type: INT + constraints: + nullable: false + - column: + name: ITEMID + type: INT + constraints: + nullable: false + - column: + name: LABEL + type: VARCHAR(200) + - column: + name: ABBREVIATION + type: VARCHAR(100) + - column: + name: DBSOURCE + type: VARCHAR(20) + - column: + name: LINKSTO + type: VARCHAR(50) + - column: + name: CATEGORY + type: VARCHAR(100) + - column: + name: UNITNAME + type: VARCHAR(100) + - column: + name: PARAM_TYPE + type: VARCHAR(30) + - column: + name: CONCEPTID + type: INT +# Table D_LABITEMS + - changeSet: + id: 13 + author: lcp + changes: + - createTable: + tableName: D_LABITEMS + columns: + - column: + name: ROW_ID + type: INT + constraints: + nullable: false + - column: + name: ITEMID + type: INT + constraints: + nullable: false + - column: + name: LABEL + type: VARCHAR(100) + constraints: + nullable: false + - column: + name: FLUID + type: VARCHAR(100) + constraints: + nullable: false + - column: + name: CATEGORY + type: VARCHAR(100) + constraints: + nullable: false + - column: + name: LOINC_CODE + type: VARCHAR(100) +# Table ICUSTAYS + - changeSet: + id: 14 + author: lcp + changes: + - createTable: + tableName: ICUSTAYS + columns: + - column: + name: ROW_ID + type: INT + constraints: + nullable: false + - column: + name: SUBJECT_ID + type: INT + constraints: + nullable: false + - column: + name: HADM_ID + type: INT + constraints: + nullable: false + - column: + name: ICUSTAY_ID + type: INT + constraints: + nullable: false + - column: + name: DBSOURCE + type: VARCHAR(20) + constraints: + nullable: false + - column: + name: FIRST_CAREUNIT + type: VARCHAR(20) + constraints: + nullable: false + - column: + name: LAST_CAREUNIT + type: VARCHAR(20) + constraints: + nullable: false + - column: + name: FIRST_WARDID + type: SMALLINT + constraints: + nullable: false + - column: + name: LAST_WARDID + type: SMALLINT + constraints: + nullable: false + - column: + name: INTIME + type: TIMESTAMP(0) + constraints: + nullable: false + - column: + name: OUTTIME + type: TIMESTAMP(0) + - column: + name: LOS + type: DOUBLE PRECISION +# Table INPUTEVENTS_CV + - changeSet: + id: 15 + author: lcp + changes: + - createTable: + tableName: INPUTEVENTS_CV + columns: + - column: + name: ROW_ID + type: INT + constraints: + nullable: false + - column: + name: SUBJECT_ID + type: INT + constraints: + nullable: false + - column: + name: HADM_ID + type: INT + - column: + name: ICUSTAY_ID + type: INT + - column: + name: CHARTTIME + type: TIMESTAMP(0) + - column: + name: ITEMID + type: INT + - column: + name: AMOUNT + type: DOUBLE PRECISION + - column: + name: AMOUNTUOM + type: VARCHAR(30) + - column: + name: RATE + type: DOUBLE PRECISION + - column: + name: RATEUOM + type: VARCHAR(30) + - column: + name: STORETIME + type: TIMESTAMP(0) + - column: + name: CGID + type: INT + - column: + name: ORDERID + type: INT + - column: + name: LINKORDERID + type: INT + - column: + name: STOPPED + type: VARCHAR(30) + - column: + name: NEWBOTTLE + type: INT + - column: + name: ORIGINALAMOUNT + type: DOUBLE PRECISION + - column: + name: ORIGINALAMOUNTUOM + type: VARCHAR(30) + - column: + name: ORIGINALROUTE + type: VARCHAR(30) + - column: + name: ORIGINALRATE + type: DOUBLE PRECISION + - column: + name: ORIGINALRATEUOM + type: VARCHAR(30) + - column: + name: ORIGINALSITE + type: VARCHAR(30) +# Table INPUTEVENTS_MV + - changeSet: + id: 16 + author: lcp + changes: + - createTable: + tableName: INPUTEVENTS_MV + columns: + - column: + name: ROW_ID + type: INT + constraints: + nullable: false + - column: + name: SUBJECT_ID + type: INT + constraints: + nullable: false + - column: + name: HADM_ID + type: INT + - column: + name: ICUSTAY_ID + type: INT + - column: + name: STARTTIME + type: TIMESTAMP(0) + - column: + name: ENDTIME + type: TIMESTAMP(0) + - column: + name: ITEMID + type: INT + - column: + name: AMOUNT + type: DOUBLE PRECISION + - column: + name: AMOUNTUOM + type: VARCHAR(30) + - column: + name: RATE + type: DOUBLE PRECISION + - column: + name: RATEUOM + type: VARCHAR(30) + - column: + name: STORETIME + type: TIMESTAMP(0) + - column: + name: CGID + type: INT + - column: + name: ORDERID + type: INT + - column: + name: LINKORDERID + type: INT + - column: + name: ORDERCATEGORYNAME + type: VARCHAR(100) + - column: + name: SECONDARYORDERCATEGORYNAME + type: VARCHAR(100) + - column: + name: ORDERCOMPONENTTYPEDESCRIPTION + type: VARCHAR(200) + - column: + name: ORDERCATEGORYDESCRIPTION + type: VARCHAR(50) + - column: + name: PATIENTWEIGHT + type: DOUBLE PRECISION + - column: + name: TOTALAMOUNT + type: DOUBLE PRECISION + - column: + name: TOTALAMOUNTUOM + type: VARCHAR(50) + - column: + name: ISOPENBAG + type: SMALLINT + - column: + name: CONTINUEINNEXTDEPT + type: SMALLINT + - column: + name: CANCELREASON + type: SMALLINT + - column: + name: STATUSDESCRIPTION + type: VARCHAR(30) + - column: + name: COMMENTS_EDITEDBY + type: VARCHAR(30) + - column: + name: COMMENTS_CANCELEDBY + type: VARCHAR(40) + - column: + name: COMMENTS_DATE + type: TIMESTAMP(0) + - column: + name: ORIGINALAMOUNT + type: DOUBLE PRECISION + - column: + name: ORIGINALRATE + type: DOUBLE PRECISION +# Table LABEVENTS + - changeSet: + id: 17 + author: lcp + changes: + - createTable: + tableName: LABEVENTS + columns: + - column: + name: ROW_ID + type: INT + constraints: + nullable: false + - column: + name: SUBJECT_ID + type: INT + constraints: + nullable: false + - column: + name: HADM_ID + type: INT + - column: + name: ITEMID + type: INT + constraints: + nullable: false + - column: + name: CHARTTIME + type: TIMESTAMP(0) + - column: + name: VALUE + type: VARCHAR(200) + - column: + name: VALUENUM + type: DOUBLE PRECISION + - column: + name: VALUEUOM + type: VARCHAR(20) + - column: + name: FLAG + type: VARCHAR(20) +# Table MICROBIOLOGYEVENTS + - changeSet: + id: 18 + author: lcp + changes: + - createTable: + tableName: MICROBIOLOGYEVENTS + columns: + - column: + name: ROW_ID + type: INT + constraints: + nullable: false + - column: + name: SUBJECT_ID + type: INT + constraints: + nullable: false + - column: + name: HADM_ID + type: INT + - column: + name: CHARTDATE + type: TIMESTAMP(0) + - column: + name: CHARTTIME + type: TIMESTAMP(0) + - column: + name: SPEC_ITEMID + type: INT + - column: + name: SPEC_TYPE_DESC + type: VARCHAR(100) + - column: + name: ORG_ITEMID + type: INT + - column: + name: ORG_NAME + type: VARCHAR(100) + - column: + name: ISOLATE_NUM + type: SMALLINT + - column: + name: AB_ITEMID + type: INT + - column: + name: AB_NAME + type: VARCHAR(30) + - column: + name: DILUTION_TEXT + type: VARCHAR(10) + - column: + name: DILUTION_COMPARISON + type: VARCHAR(20) + - column: + name: DILUTION_VALUE + type: DOUBLE PRECISION + - column: + name: INTERPRETATION + type: VARCHAR(5) +# Table NOTEEVENTS + - changeSet: + id: 19 + author: lcp + changes: + - createTable: + tableName: NOTEEVENTS + columns: + - column: + name: ROW_ID + type: INT + constraints: + nullable: false + - column: + name: SUBJECT_ID + type: INT + constraints: + nullable: false + - column: + name: HADM_ID + type: INT + - column: + name: CHARTDATE + type: TIMESTAMP(0) + - column: + name: CHARTTIME + type: TIMESTAMP(0) + - column: + name: STORETIME + type: TIMESTAMP(0) + - column: + name: CATEGORY + type: VARCHAR(50) + - column: + name: DESCRIPTION + type: VARCHAR(255) + - column: + name: CGID + type: INT + - column: + name: ISERROR + type: CHAR(1) + - column: + name: TEXT + type: TEXT +# Table OUTPUTEVENTS + - changeSet: + id: 20 + author: lcp + changes: + - createTable: + tableName: OUTPUTEVENTS + columns: + - column: + name: ROW_ID + type: INT + constraints: + nullable: false + - column: + name: SUBJECT_ID + type: INT + constraints: + nullable: false + - column: + name: HADM_ID + type: INT + - column: + name: ICUSTAY_ID + type: INT + - column: + name: CHARTTIME + type: TIMESTAMP(0) + - column: + name: ITEMID + type: INT + - column: + name: VALUE + type: DOUBLE PRECISION + - column: + name: VALUEUOM + type: VARCHAR(30) + - column: + name: STORETIME + type: TIMESTAMP(0) + - column: + name: CGID + type: INT + - column: + name: STOPPED + type: VARCHAR(30) + - column: + name: NEWBOTTLE + type: CHAR(1) + - column: + name: ISERROR + type: INT +# Table PATIENTS + - changeSet: + id: 21 + author: lcp + changes: + - createTable: + tableName: PATIENTS + columns: + - column: + name: ROW_ID + type: INT + constraints: + nullable: false + - column: + name: SUBJECT_ID + type: INT + constraints: + nullable: false + - column: + name: GENDER + type: VARCHAR(5) + constraints: + nullable: false + - column: + name: DOB + type: TIMESTAMP(0) + constraints: + nullable: false + - column: + name: DOD + type: TIMESTAMP(0) + - column: + name: DOD_HOSP + type: TIMESTAMP(0) + - column: + name: DOD_SSN + type: TIMESTAMP(0) + - column: + name: EXPIRE_FLAG + type: INT + constraints: + nullable: false +# Table PRESCRIPTIONS + - changeSet: + id: 22 + author: lcp + changes: + - createTable: + tableName: PRESCRIPTIONS + columns: + - column: + name: ROW_ID + type: INT + constraints: + nullable: false + - column: + name: SUBJECT_ID + type: INT + constraints: + nullable: false + - column: + name: HADM_ID + type: INT + constraints: + nullable: false + - column: + name: ICUSTAY_ID + type: INT + - column: + name: STARTDATE + type: TIMESTAMP(0) + - column: + name: ENDDATE + type: TIMESTAMP(0) + - column: + name: DRUG_TYPE + type: VARCHAR(100) + constraints: + nullable: false + - column: + name: DRUG + type: VARCHAR(100) + constraints: + nullable: false + - column: + name: DRUG_NAME_POE + type: VARCHAR(100) + - column: + name: DRUG_NAME_GENERIC + type: VARCHAR(100) + - column: + name: FORMULARY_DRUG_CD + type: VARCHAR(120) + - column: + name: GSN + type: VARCHAR(200) + - column: + name: NDC + type: VARCHAR(120) + - column: + name: PROD_STRENGTH + type: VARCHAR(120) + - column: + name: DOSE_VAL_RX + type: VARCHAR(120) + - column: + name: DOSE_UNIT_RX + type: VARCHAR(120) + - column: + name: FORM_VAL_DISP + type: VARCHAR(120) + - column: + name: FORM_UNIT_DISP + type: VARCHAR(120) + - column: + name: ROUTE + type: VARCHAR(120) +# Table PROCEDUREEVENTS_MV + - changeSet: + id: 23 + author: lcp + changes: + - createTable: + tableName: PROCEDUREEVENTS_MV + columns: + - column: + name: ROW_ID + type: INT + constraints: + nullable: false + - column: + name: SUBJECT_ID + type: INT + constraints: + nullable: false + - column: + name: HADM_ID + type: INT + constraints: + nullable: false + - column: + name: ICUSTAY_ID + type: INT + - column: + name: STARTTIME + type: TIMESTAMP(0) + - column: + name: ENDTIME + type: TIMESTAMP(0) + - column: + name: ITEMID + type: INT + - column: + name: VALUE + type: DOUBLE PRECISION + - column: + name: VALUEUOM + type: VARCHAR(30) + - column: + name: LOCATION + type: VARCHAR(30) + - column: + name: LOCATIONCATEGORY + type: VARCHAR(30) + - column: + name: STORETIME + type: TIMESTAMP(0) + - column: + name: CGID + type: INT + - column: + name: ORDERID + type: INT + - column: + name: LINKORDERID + type: INT + - column: + name: ORDERCATEGORYNAME + type: VARCHAR(100) + - column: + name: SECONDARYORDERCATEGORYNAME + type: VARCHAR(100) + - column: + name: ORDERCATEGORYDESCRIPTION + type: VARCHAR(50) + - column: + name: ISOPENBAG + type: SMALLINT + - column: + name: CONTINUEINNEXTDEPT + type: SMALLINT + - column: + name: CANCELREASON + type: SMALLINT + - column: + name: STATUSDESCRIPTION + type: VARCHAR(30) + - column: + name: COMMENTS_EDITEDBY + type: VARCHAR(30) + - column: + name: COMMENTS_CANCELEDBY + type: VARCHAR(30) + - column: + name: COMMENTS_DATE + type: TIMESTAMP(0) +# Table PROCEDURES_ICD + - changeSet: + id: 24 + author: lcp + changes: + - createTable: + tableName: PROCEDURES_ICD + columns: + - column: + name: ROW_ID + type: INT + constraints: + nullable: false + - column: + name: SUBJECT_ID + type: INT + constraints: + nullable: false + - column: + name: HADM_ID + type: INT + constraints: + nullable: false + - column: + name: SEQ_NUM + type: INT + constraints: + nullable: false + - column: + name: ICD9_CODE + type: VARCHAR(20) + constraints: + nullable: false +# Table SERVICES + - changeSet: + id: 25 + author: lcp + changes: + - createTable: + tableName: SERVICES + columns: + - column: + name: ROW_ID + type: INT + constraints: + nullable: false + - column: + name: SUBJECT_ID + type: INT + constraints: + nullable: false + - column: + name: HADM_ID + type: INT + constraints: + nullable: false + - column: + name: TRANSFERTIME + type: TIMESTAMP(0) + constraints: + nullable: false + - column: + name: PREV_SERVICE + type: VARCHAR(20) + - column: + name: CURR_SERVICE + type: VARCHAR(20) +# Table TRANSFERS + - changeSet: + id: 26 + author: lcp + changes: + - createTable: + tableName: TRANSFERS + columns: + - column: + name: ROW_ID + type: INT + constraints: + nullable: false + - column: + name: SUBJECT_ID + type: INT + constraints: + nullable: false + - column: + name: HADM_ID + type: INT + constraints: + nullable: false + - column: + name: ICUSTAY_ID + type: INT + - column: + name: DBSOURCE + type: VARCHAR(20) + - column: + name: EVENTTYPE + type: VARCHAR(20) + - column: + name: PREV_CAREUNIT + type: VARCHAR(20) + - column: + name: CURR_CAREUNIT + type: VARCHAR(20) + - column: + name: PREV_WARDID + type: SMALLINT + - column: + name: CURR_WARDID + type: SMALLINT + - column: + name: INTIME + type: TIMESTAMP(0) + - column: + name: OUTTIME + type: TIMESTAMP(0) + - column: + name: LOS + type: DOUBLE PRECISION +# ---------------------------- # +# Primary keys and constraints # +# ---------------------------- # + - changeSet: + id: add-admissions-constraints + author: lcp + changes: + - addPrimaryKey: + columnNames: ROW_ID + constraintName: adm_rowid_pk + tableName: admissions + - addUniqueConstraint: + columnNames: HADM_ID + constraintName: adm_hadm_unique + tableName: admissions + - changeSet: + id: add-callout-constraints + author: lcp + changes: + - addPrimaryKey: + columnNames: ROW_ID + constraintName: callout_rowid_pk + tableName: callout + - changeSet: + id: add-caregivers-constraints + author: lcp + changes: + - addPrimaryKey: + columnNames: ROW_ID + constraintName: cg_rowid_pk + tableName: caregivers + - addUniqueConstraint: + columnNames: CGID + constraintName: cg_cgid_unique + tableName: caregivers + - changeSet: + id: add-chartevents-constraints + author: lcp + changes: + - addPrimaryKey: + columnNames: ROW_ID + constraintName: chartevents_rowid_pk + tableName: chartevents + - changeSet: + id: add-cptevents-constraints + author: lcp + changes: + - addPrimaryKey: + columnNames: ROW_ID + constraintName: cpt_rowid_pk + tableName: cptevents + - changeSet: + id: add-datetimeevents-constraints + author: lcp + changes: + - addPrimaryKey: + columnNames: ROW_ID + constraintName: datetime_rowid_pk + tableName: datetimeevents + - changeSet: + id: add-diagnoses_icd-constraints + author: lcp + changes: + - addPrimaryKey: + columnNames: ROW_ID + constraintName: diagnosesicd_rowid_pk + tableName: diagnoses_icd + - changeSet: + id: add-drgcodes-constraints + author: lcp + changes: + - addPrimaryKey: + columnNames: ROW_ID + constraintName: drg_rowid_pk + tableName: drgcodes + - changeSet: + id: add-d_cpt-constraints + author: lcp + changes: + - addPrimaryKey: + columnNames: ROW_ID + constraintName: dcpt_rowid_pk + tableName: d_cpt + - addUniqueConstraint: + columnNames: SUBSECTIONRANGE + constraintName: dcpt_ssrange_unique + tableName: d_cpt + - changeSet: + id: add-d_icd_diagnoses-constraints + author: lcp + changes: + - addPrimaryKey: + columnNames: ROW_ID + constraintName: d_icd_diag_rowid_pk + tableName: d_icd_diagnoses + - addUniqueConstraint: + columnNames: ICD9_CODE + constraintName: d_icd_diag_code_unique + tableName: d_icd_diagnoses + - changeSet: + id: add-d_icd_procedures-constraints + author: lcp + changes: + - addPrimaryKey: + columnNames: ROW_ID + constraintName: d_icd_proc_rowid_pk + tableName: d_icd_procedures + - addUniqueConstraint: + columnNames: ICD9_CODE + constraintName: d_icd_proc_code_unique + tableName: d_icd_procedures + - changeSet: + id: add-d_items-constraints + author: lcp + changes: + - addPrimaryKey: + columnNames: ROW_ID + constraintName: ditems_rowid_pk + tableName: d_items + - addUniqueConstraint: + columnNames: ITEMID + constraintName: ditems_itemid_unique + tableName: d_items + - changeSet: + id: add-d_labitems-constraints + author: lcp + changes: + - addPrimaryKey: + columnNames: ROW_ID + constraintName: dlabitems_rowid_pk + tableName: d_labitems + - addUniqueConstraint: + columnNames: ITEMID + constraintName: dlabitems_itemid_unique + tableName: d_labitems + - changeSet: + id: add-icustays-constraints + author: lcp + changes: + - addPrimaryKey: + columnNames: ROW_ID + constraintName: icustay_rowid_pk + tableName: icustays + - addUniqueConstraint: + columnNames: ICUSTAY_ID + constraintName: icustay_icustayid_unique + tableName: icustays + - changeSet: + id: add-inputevents_cv-constraints + author: lcp + changes: + - addPrimaryKey: + columnNames: ROW_ID + constraintName: inputevents_cv_rowid_pk + tableName: inputevents_cv + - changeSet: + id: add-inputevents_mv-constraints + author: lcp + changes: + - addPrimaryKey: + columnNames: ROW_ID + constraintName: inputevents_mv_rowid_pk + tableName: inputevents_mv + - changeSet: + id: add-labevents-constraints + author: lcp + changes: + - addPrimaryKey: + columnNames: ROW_ID + constraintName: labevents_rowid_pk + tableName: labevents + - changeSet: + id: add-microbiologyevents-constraints + author: lcp + changes: + - addPrimaryKey: + columnNames: ROW_ID + constraintName: micro_rowid_pk + tableName: microbiologyevents + - changeSet: + id: add-noteevents-constraints + author: lcp + changes: + - addPrimaryKey: + columnNames: ROW_ID + constraintName: noteevents_rowid_pk + tableName: noteevents + - changeSet: + id: add-outputevents-constraints + author: lcp + changes: + - addPrimaryKey: + columnNames: ROW_ID + constraintName: outputevents_cv_rowid_pk + tableName: outputevents + - changeSet: + id: add-patients-constraints + author: lcp + changes: + - addPrimaryKey: + columnNames: ROW_ID + constraintName: pat_rowid_pk + tableName: patients + - addUniqueConstraint: + columnNames: SUBJECT_ID + constraintName: pat_subid_unique + tableName: patients + - changeSet: + id: add-prescriptions-constraints + author: lcp + changes: + - addPrimaryKey: + columnNames: ROW_ID + constraintName: prescription_rowid_pk + tableName: prescriptions + - changeSet: + id: add-procedureevents-constraints + author: lcp + changes: + - addPrimaryKey: + columnNames: ROW_ID + constraintName: procedureevents_mv_rowid_pk + tableName: procedureevents + - changeSet: + id: add-procedures_icd-constraints + author: lcp + changes: + - addPrimaryKey: + columnNames: ROW_ID + constraintName: proceduresicd_rowid_pk + tableName: procedures_icd + - changeSet: + id: add-services-constraints + author: lcp + changes: + - addPrimaryKey: + columnNames: ROW_ID + constraintName: services_rowid_pk + tableName: services + - changeSet: + id: add-transfers-constraints + author: lcp + changes: + - addPrimaryKey: + columnNames: ROW_ID + constraintName: transfers_rowid_pk + tableName: transfers +# --------------------------------- # +# Indexes # +# --------------------------------- # + - changeSet: + id: createIndex-ADMISSIONS + author: lcp + changes: + - createIndex: + columns: + - column: + name: subject_id + type: int + indexName: ADMISSIONS_IDX01 + tableName: ADMISSIONS + unique: false + - createIndex: + columns: + - column: + name: hadm_id + type: int + indexName: ADMISSIONS_IDX02 + tableName: ADMISSIONS + unique: false + - changeSet: + id: createIndex-CALLOUT + author: lcp + changes: + - createIndex: + columns: + - column: + name: subject_id + type: int + indexName: CALLOUT_IDX01 + tableName: CALLOUT + unique: false + - createIndex: + columns: + - column: + name: hadm_id + type: int + indexName: CALLOUT_IDX02 + tableName: CALLOUT + unique: false +# TODO: chartevents + - changeSet: + id: createIndex-CPTEVENTS + author: lcp + changes: + - createIndex: + columns: + - column: + name: subject_id + type: int + indexName: CPTEVENTS_idx01 + tableName: CPTEVENTS + unique: false + - createIndex: + columns: + - column: + name: CPT_CD + type: varchar(10) + indexName: CPTEVENTS_idx02 + tableName: CPTEVENTS + unique: false + - changeSet: + id: createIndex-D_ICD_DIAG + author: lcp + changes: + - createIndex: + columns: + - column: + name: ICD9_CODE + type: varchar(10) + indexName: D_ICD_DIAG_idx01 + tableName: D_ICD_DIAGNOSES + unique: false + - createIndex: + columns: + - column: + name: LONG_TITLE + type: varchar(255) + indexName: D_ICD_DIAG_idx02 + tableName: D_ICD_DIAGNOSES + unique: false + - changeSet: + id: createIndex-D_ICD_PROC + author: lcp + changes: + - createIndex: + columns: + - column: + name: ICD9_CODE + type: varchar(10) + indexName: D_ICD_PROC_idx01 + tableName: D_ICD_PROCEDURES + unique: false + - createIndex: + columns: + - column: + name: LONG_TITLE + type: varchar(255) + indexName: D_ICD_PROC_idx02 + tableName: D_ICD_PROCEDURES + unique: false + - changeSet: + id: createIndex-D_ITEMS + author: lcp + changes: + - createIndex: + columns: + - column: + name: itemid + type: int + indexName: D_ITEMS_idx01 + tableName: D_ITEMS + unique: false + - createIndex: + columns: + - column: + name: LABEL + type: varchar(100) + indexName: D_ITEMS_idx02 + tableName: D_ITEMS + unique: false + - changeSet: + id: createIndex-D_LABITEMS + author: lcp + changes: + - createIndex: + columns: + - column: + name: itemid + type: int + indexName: D_LABITEMS_idx01 + tableName: D_LABITEMS + unique: false + - createIndex: + columns: + - column: + name: LABEL + type: varchar(100) + indexName: D_LABITEMS_idx02 + tableName: D_LABITEMS + unique: false + - createIndex: + columns: + - column: + name: LOINC_CODE + type: ??? + indexName: D_LABITEMS_idx03 + tableName: D_LABITEMS + unique: false + - changeSet: + id: createIndex-DATETIMEEVENTS + author: lcp + changes: + - createIndex: + columns: + - column: + name: subject_id + type: int + indexName: DATETIMEEVENTS_idx01 + tableName: DATETIMEEVENTS + unique: false + - createIndex: + columns: + - column: + name: itemid + type: int + indexName: DATETIMEEVENTS_idx02 + tableName: DATETIMEEVENTS + unique: false + - createIndex: + columns: + - column: + name: icustay_id + type: int + indexName: DATETIMEEVENTS_idx03 + tableName: DATETIMEEVENTS + unique: false + - createIndex: + columns: + - column: + name: hadm_id + type: int + indexName: DATETIMEEVENTS_idx04 + tableName: DATETIMEEVENTS + unique: false + - changeSet: + id: createIndex-DIAGNOSES_ICD + author: lcp + changes: + - createIndex: + columns: + - column: + name: subject_id + type: int + indexName: DIAGNOSES_ICD_idx01 + tableName: DIAGNOSES_ICD + unique: false + - createIndex: + columns: + - column: + name: ICD9_CODE + type: varchar(10) + indexName: DIAGNOSES_ICD_idx02 + tableName: DIAGNOSES_ICD + unique: false + - createIndex: + columns: + - column: + name: hadm_id + type: int + indexName: DIAGNOSES_ICD_idx03 + tableName: DIAGNOSES_ICD + unique: false + - changeSet: + id: createIndex-DRGCODES + author: lcp + changes: + - createIndex: + columns: + - column: + name: subject_id + type: int + indexName: DRGCODES_idx01 + tableName: DRGCODES + unique: false + - createIndex: + columns: + - column: + name: DRG_CODE + type: varchar(20) + indexName: DRGCODES_idx02 + tableName: DRGCODES + unique: false + - createIndex: + columns: + - column: + name: DESCRIPTION + type: varchar(255) + indexName: DRGCODES_idx03 + tableName: DRGCODES + unique: false + - changeSet: + id: createIndex-ICUSTAYS + author: lcp + changes: + - createIndex: + columns: + - column: + name: subject_id + type: int + indexName: ICUSTAYS_idx01 + tableName: ICUSTAYS + unique: false + - createIndex: + columns: + - column: + name: icustay_id + type: int + indexName: ICUSTAYS_idx02 + tableName: ICUSTAYS + unique: false + - createIndex: + columns: + - column: + name: hadm_id + type: int + indexName: ICUSTAYS_idx03 + tableName: ICUSTAYS + unique: false + - changeSet: + id: createIndex-INPUTEVENTS_CV + author: lcp + changes: + - createIndex: + columns: + - column: + name: subject_id + type: int + indexName: INPUTEVENTS_CV_idx01 + tableName: INPUTEVENTS_CV + unique: false + - createIndex: + columns: + - column: + name: hadm_id + type: int + indexName: INPUTEVENTS_CV_idx02 + tableName: INPUTEVENTS_CV + unique: false + - createIndex: + columns: + - column: + name: icustay_id + type: int + indexName: INPUTEVENTS_CV_idx03 + tableName: INPUTEVENTS_CV + unique: false + - createIndex: + columns: + - column: + name: itemid + type: int + indexName: INPUTEVENTS_CV_idx04 + tableName: INPUTEVENTS_CV + unique: false + - changeSet: + id: createIndex-INPUTEVENTS_MV + author: lcp + changes: + - createIndex: + columns: + - column: + name: subject_id + type: int + indexName: INPUTEVENTS_MV_idx01 + tableName: INPUTEVENTS_MV + unique: false + - createIndex: + columns: + - column: + name: hadm_id + type: int + indexName: INPUTEVENTS_MV_idx02 + tableName: INPUTEVENTS_MV + unique: false + - createIndex: + columns: + - column: + name: icustay_id + type: int + indexName: INPUTEVENTS_MV_idx03 + tableName: INPUTEVENTS_MV + unique: false + - createIndex: + columns: + - column: + name: itemid + type: int + indexName: INPUTEVENTS_MV_idx04 + tableName: INPUTEVENTS_MV + unique: false + - changeSet: + id: createIndex-LABEVENTS + author: lcp + changes: + - createIndex: + columns: + - column: + name: subject_id + type: int + indexName: LABEVENTS_idx01 + tableName: LABEVENTS + unique: false + - createIndex: + columns: + - column: + name: hadm_id + type: int + indexName: LABEVENTS_idx02 + tableName: LABEVENTS + unique: false + - createIndex: + columns: + - column: + name: itemid + type: int + indexName: LABEVENTS_idx03 + tableName: LABEVENTS + unique: false + - changeSet: + id: createIndex-MICROBIOLOGYEVENTS + author: lcp + changes: + - createIndex: + columns: + - column: + name: subject_id + type: int + indexName: MICROBIOLOGYEVENTS_idx01 + tableName: MICROBIOLOGYEVENTS + unique: false + - createIndex: + columns: + - column: + name: hadm_id + type: int + indexName: MICROBIOLOGYEVENTS_idx02 + tableName: MICROBIOLOGYEVENTS + unique: false + - changeSet: + id: createIndex-NOTEEVENTS + author: lcp + changes: + - createIndex: + columns: + - column: + name: subject_id + type: int + indexName: NOTEEVENTS_idx01 + tableName: NOTEEVENTS + unique: false + - createIndex: + columns: + - column: + name: hadm_id + type: int + indexName: NOTEEVENTS_idx02 + tableName: NOTEEVENTS + unique: false + - createIndex: + columns: + - column: + name: CATEGORY + type: varchar(50) + indexName: NOTEEVENTS_idx03 + tableName: NOTEEVENTS + unique: false + - changeSet: + id: createIndex-OUTPUTEVENTS + author: lcp + changes: + - createIndex: + columns: + - column: + name: subject_id + type: int + indexName: OUTPUTEVENTS_idx01 + tableName: OUTPUTEVENTS + unique: false + - createIndex: + columns: + - column: + name: hadm_id + type: int + indexName: OUTPUTEVENTS_idx02 + tableName: OUTPUTEVENTS + unique: false + - createIndex: + columns: + - column: + name: icustay_id + type: int + indexName: OUTPUTEVENTS_idx03 + tableName: OUTPUTEVENTS + unique: false + - createIndex: + columns: + - column: + name: itemid + type: int + indexName: OUTPUTEVENTS_idx04 + tableName: OUTPUTEVENTS + unique: false + - changeSet: + id: createIndex-PRESCRIPTIONS + author: lcp + changes: + - createIndex: + columns: + - column: + name: subject_id + type: int + indexName: PRESCRIPTIONS_idx01 + tableName: PRESCRIPTIONS + unique: false + - createIndex: + columns: + - column: + name: hadm_id + type: int + indexName: PRESCRIPTIONS_idx02 + tableName: PRESCRIPTIONS + unique: false + - createIndex: + columns: + - column: + name: icustay_id + type: int + indexName: PRESCRIPTIONS_idx03 + tableName: PRESCRIPTIONS + unique: false + - createIndex: + columns: + - column: + name: DRUG_TYPE + type: varchar(100) + indexName: PRESCRIPTIONS_idx04 + tableName: PRESCRIPTIONS + unique: false + - createIndex: + columns: + - column: + name: DRUG + type: varchar(100) + indexName: PRESCRIPTIONS_idx05 + tableName: PRESCRIPTIONS + unique: false + - changeSet: + id: createIndex-PROCEDUREEVENTS_MV + author: lcp + changes: + - createIndex: + columns: + - column: + name: subject_id + type: int + indexName: PROCEDUREEVENTS_MV_idx01 + tableName: PROCEDUREEVENTS_MV + unique: false + - createIndex: + columns: + - column: + name: hadm_id + type: int + indexName: PROCEDUREEVENTS_MV_idx02 + tableName: PROCEDUREEVENTS_MV + unique: false + - createIndex: + columns: + - column: + name: icustay_id + type: int + indexName: PROCEDUREEVENTS_MV_idx03 + tableName: PROCEDUREEVENTS_MV + unique: false + - createIndex: + columns: + - column: + name: itemid + type: int + indexName: PROCEDUREEVENTS_MV_idx04 + tableName: PROCEDUREEVENTS_MV + unique: false + - changeSet: + id: createIndex-PROCEDURES_ICD + author: lcp + changes: + - createIndex: + columns: + - column: + name: subject_id + type: int + indexName: PROCEDURES_ICD_idx01 + tableName: PROCEDURES_ICD + unique: false + - createIndex: + columns: + - column: + name: ICD9_CODE + type: varchar(10) + indexName: PROCEDURES_ICD_idx02 + tableName: PROCEDURES_ICD + unique: false + - createIndex: + columns: + - column: + name: hadm_id + type: int + indexName: PROCEDURES_ICD_idx03 + tableName: PROCEDURES_ICD + unique: false + - changeSet: + id: createIndex-SERVICES + author: lcp + changes: + - createIndex: + columns: + - column: + name: subject_id + type: int + indexName: SERVICES_idx01 + tableName: SERVICES + unique: false + - createIndex: + columns: + - column: + name: hadm_id + type: int + indexName: SERVICES_idx02 + tableName: SERVICES + unique: false + - changeSet: + id: createIndex-TRANSFERS + author: lcp + changes: + - createIndex: + columns: + - column: + name: subject_id + type: int + indexName: TRANSFERS_idx01 + tableName: TRANSFERS + unique: false + - createIndex: + columns: + - column: + name: hadm_id + type: int + indexName: TRANSFERS_idx02 + tableName: TRANSFERS + unique: false + - createIndex: + columns: + - column: + name: icustay_id + type: int + indexName: TRANSFERS_idx03 + tableName: TRANSFERS + unique: false +# ---------------------------------- # +# Comments # +# ---------------------------------- # +# -------------------------------- # +# Checks # +# -------------------------------- # diff --git a/buildmimic/monetdb/README.md b/buildmimic/monetdb/README.md index a1c198b..df26bb1 100644 --- a/buildmimic/monetdb/README.md +++ b/buildmimic/monetdb/README.md @@ -34,8 +34,16 @@ DBeaver works great with monetDB. Download and install it from here: [http://dbe ## Install Mimic Data +Note that the Windows instructions require the data to be unzipped, but the *nix instructions allow you to build from zipped data. It is possible to import the data on Windows when it is compressed, but + + ### Windows +(Optional) These instructions require uncompressing the data. In principle it is possible to install the data directly from the compressed files. First you must install a command line tool which can unzip data and add it to your environment path in command prompt (good examples include 7zip or GnuWin32 gzip). After that you'll need to create a .bat script to use the command line tool, e.g.: + +`mclient -d mimic -s "COPY INTO MIMICIII.ADMISSIONS FROM STDIN USING DELIMITERS ',','\n','\"' NULL AS ''" - < gzip -dck /path/to/ADMISSIONS.csv.gz` + +You'll also need to add an appropriate fix for CHARTEVENTS and NOTEEVENTS that escapes backslashes with an extra backslash (see `monetdb_load_data.sh`). (Optional) You may want to change where MonetDB stores the data, which is accomplished by modifying the .bat files directly. Open up WordPad by right clicking and selecting "Run as Administrator" (needed in order to edit the .bat file). Open up M5server.bat, and add the following after `:skipuservar`: @@ -71,13 +79,18 @@ $ monetdb create mimic $ monetdb start mimic ``` -In DBeaver, connect to the database. -1. Open `monetdb_create_tables.sql` (SQL Editor -> Load SQL script or Ctrl+O ), execute the script -2. Open `monetdb_load_data.sql`, **modify the path used to load the data** -6. Execute the `monetdb_load_data.sql` script +1. Copy both `monetdb_create_tables.sql` \& `monetdb_load_data.sh` into the mimic compressed files directory +1. Go in that folder +1. Create a `.monetdb` file containing: +``` +user=monetdb +password=monetdb +``` +1. Run `mclient -d mimic < monetdb_create_tables.sql` +1. Execute `monetdb_load_data.sh` ## Notes * there is no need to add indexes, monetdb indexes itself after loading -* there are some issues with mimic v1.3 backslashes in tables chartevents & noteevents. For now, removing them thanks to `sed -i 's/\\/g' table.csv` is a workaround. +* monetDB has issues importing backslashes in tables chartevents & noteevents. For now, escaping them thanks to `sed -i 's/\\/\\\\/g' table.csv` is a workaround. diff --git a/buildmimic/monetdb/monetdb_create_tables.sql b/buildmimic/monetdb/monetdb_create_tables.sql index 533477c..41d8a30 100644 --- a/buildmimic/monetdb/monetdb_create_tables.sql +++ b/buildmimic/monetdb/monetdb_create_tables.sql @@ -1,9 +1,7 @@ --- ------------------------------------------------------------------ +-- ------------------------------------------------------------------ -- Title: Create the MIMIC-III tables --- Description: More detailed description explaining the purpose. --- MIMIC version: MIMIC-III v1.3 --- Created by: paris nicolas --- ------------------------------------------------------------------ +-- Description: More detailed description explaining the purpose. +-- ------------------------------------------------------------------ CREATE SCHEMA mimiciii; @@ -40,7 +38,6 @@ CREATE TABLE MIMICIII.ADMISSIONS EDOUTTIME TIMESTAMP(0), DIAGNOSIS VARCHAR(255), HOSPITAL_EXPIRE_FLAG SMALLINT, - HAS_IOEVENTS_DATA SMALLINT NOT NULL, HAS_CHARTEVENTS_DATA SMALLINT NOT NULL, CONSTRAINT adm_rowid_pk PRIMARY KEY (ROW_ID), CONSTRAINT adm_hadm_unique UNIQUE (HADM_ID) @@ -560,4 +557,3 @@ CREATE TABLE MIMICIII.TRANSFERS LOS DOUBLE PRECISION, CONSTRAINT transfers_rowid_pk PRIMARY KEY (ROW_ID) ) ; - diff --git a/buildmimic/monetdb/monetdb_load_data.sh b/buildmimic/monetdb/monetdb_load_data.sh new file mode 100755 index 0000000..70f901a --- /dev/null +++ b/buildmimic/monetdb/monetdb_load_data.sh @@ -0,0 +1,27 @@ +#!/bin/bash +gzip -dck ./ADMISSIONS.csv.gz | sed 1d | mclient -d mimic -s "COPY INTO MIMICIII.ADMISSIONS FROM STDIN USING DELIMITERS ',','\n','\"' NULL AS ''" - && +gzip -dck ./DATETIMEEVENTS.csv.gz | sed 1d | mclient -d mimic -s "COPY INTO MIMICIII.DATETIMEEVENTS FROM STDIN USING DELIMITERS ',','\n','\"' NULL AS ''" - && +gzip -dck ./D_ITEMS.csv.gz | sed 1d | mclient -d mimic -s "COPY INTO MIMICIII.D_ITEMS FROM STDIN USING DELIMITERS ',','\n','\"' NULL AS ''" - && +gzip -dck ./INPUTEVENTS_MV.csv.gz | sed 1d | mclient -d mimic -s "COPY INTO MIMICIII.INPUTEVENTS_MV FROM STDIN USING DELIMITERS ',','\n','\"' NULL AS ''" - && +gzip -dck ./PATIENTS.csv.gz | sed 1d | mclient -d mimic -s "COPY INTO MIMICIII.PATIENTS FROM STDIN USING DELIMITERS ',','\n','\"' NULL AS ''" - && +gzip -dck ./TRANSFERS.csv.gz | sed 1d | mclient -d mimic -s "COPY INTO MIMICIII.TRANSFERS FROM STDIN USING DELIMITERS ',','\n','\"' NULL AS ''" - && +gzip -dck ./CALLOUT.csv.gz | sed 1d | mclient -d mimic -s "COPY INTO MIMICIII.CALLOUT FROM STDIN USING DELIMITERS ',','\n','\"' NULL AS ''" - && +gzip -dck ./D_CPT.csv.gz | sed 1d | mclient -d mimic -s "COPY INTO MIMICIII.D_CPT FROM STDIN USING DELIMITERS ',','\n','\"' NULL AS ''" - && +gzip -dck ./D_LABITEMS.csv.gz | sed 1d | mclient -d mimic -s "COPY INTO MIMICIII.D_LABITEMS FROM STDIN USING DELIMITERS ',','\n','\"' NULL AS ''" - && +gzip -dck ./LABEVENTS.csv.gz | sed 1d | mclient -d mimic -s "COPY INTO MIMICIII.LABEVENTS FROM STDIN USING DELIMITERS ',','\n','\"' NULL AS ''" - && +gzip -dck ./PRESCRIPTIONS.csv.gz | sed 1d | mclient -d mimic -s "COPY INTO MIMICIII.PRESCRIPTIONS FROM STDIN USING DELIMITERS ',','\n','\"' NULL AS ''" - && +gzip -dck ./CAREGIVERS.csv.gz | sed 1d | mclient -d mimic -s "COPY INTO MIMICIII.CAREGIVERS FROM STDIN USING DELIMITERS ',','\n','\"' NULL AS ''" - && +gzip -dck ./DIAGNOSES_ICD.csv.gz | sed 1d | mclient -d mimic -s "COPY INTO MIMICIII.DIAGNOSES_ICD FROM STDIN USING DELIMITERS ',','\n','\"' NULL AS ''" - && +gzip -dck ./DRGCODES.csv.gz | sed 1d | mclient -d mimic -s "COPY INTO MIMICIII.DRGCODES FROM STDIN USING DELIMITERS ',','\n','\"' NULL AS ''" - && +gzip -dck ./MICROBIOLOGYEVENTS.csv.gz | sed 1d | mclient -d mimic -s "COPY INTO MIMICIII.MICROBIOLOGYEVENTS FROM STDIN USING DELIMITERS ',','\n','\"' NULL AS ''" - && +gzip -dck ./PROCEDUREEVENTS_MV.csv.gz | sed 1d | mclient -d mimic -s "COPY INTO MIMICIII.PROCEDUREEVENTS_MV FROM STDIN USING DELIMITERS ',','\n','\"' NULL AS ''" - && +gzip -dck ./CHARTEVENTS.csv.gz | sed 's/\\/\\\\/g' | sed 1d | mclient -d mimic -s "COPY INTO MIMICIII.CHARTEVENTS FROM STDIN USING DELIMITERS ',','\n','\"' NULL AS ''" - && +gzip -dck ./D_ICD_DIAGNOSES.csv.gz | sed 1d | mclient -d mimic -s "COPY INTO MIMICIII.D_ICD_DIAGNOSES FROM STDIN USING DELIMITERS ',','\n','\"' NULL AS ''" - && +gzip -dck ./ICUSTAYS.csv.gz | sed 1d | mclient -d mimic -s "COPY INTO MIMICIII.ICUSTAYS FROM STDIN USING DELIMITERS ',','\n','\"' NULL AS ''" - && +gzip -dck ./NOTEEVENTS.csv.gz | sed 's/\\/\\\\/g' | sed 1d | mclient -d mimic -s "COPY INTO MIMICIII.NOTEEVENTS FROM STDIN USING DELIMITERS ',','\n','\"' NULL AS ''" - && +gzip -dck ./PROCEDURES_ICD.csv.gz | sed 1d | mclient -d mimic -s "COPY INTO MIMICIII.PROCEDURES_ICD FROM STDIN USING DELIMITERS ',','\n','\"' NULL AS ''" - && +gzip -dck ./CPTEVENTS.csv.gz | sed 1d | mclient -d mimic -s "COPY INTO MIMICIII.CPTEVENTS FROM STDIN USING DELIMITERS ',','\n','\"' NULL AS ''" - && +gzip -dck ./D_ICD_PROCEDURES.csv.gz | sed 1d | mclient -d mimic -s "COPY INTO MIMICIII.D_ICD_PROCEDURES FROM STDIN USING DELIMITERS ',','\n','\"' NULL AS ''" - && +gzip -dck ./INPUTEVENTS_CV.csv.gz | sed 1d | mclient -d mimic -s "COPY INTO MIMICIII.INPUTEVENTS_CV FROM STDIN USING DELIMITERS ',','\n','\"' NULL AS ''" - && +gzip -dck ./OUTPUTEVENTS.csv.gz | sed 1d | mclient -d mimic -s "COPY INTO MIMICIII.OUTPUTEVENTS FROM STDIN USING DELIMITERS ',','\n','\"' NULL AS ''" - && +gzip -dck ./SERVICES.csv.gz | sed 1d | mclient -d mimic -s "COPY INTO MIMICIII.SERVICES FROM STDIN USING DELIMITERS ',','\n','\"' NULL AS ''" - diff --git a/buildmimic/monetdb/monetdb_load_data.sql b/buildmimic/monetdb/monetdb_load_data.sql index 831698a..bcadd8f 100644 --- a/buildmimic/monetdb/monetdb_load_data.sql +++ b/buildmimic/monetdb/monetdb_load_data.sql @@ -1,9 +1,7 @@ --- ------------------------------------------------------------------ --- Title: Load data into the MIMIC-III schema --- Description: More detailed description explaining the purpose. --- MIMIC version: MIMIC-III v1.3 --- Created by: paris nicolas --- ------------------------------------------------------------------ +-- ------------------------------------------------------------------ +-- Title: Load data into the MIMIC-III schema +-- Description: More detailed description explaining the purpose. +-- ------------------------------------------------------------------ /* Set the mimic_data_dir variable to point to directory containing @@ -35,7 +33,7 @@ COPY 7567 OFFSET 2 RECORDS INTO MIMICIII.CAREGIVERS FROM '/path/to/CAREGIVERS.cs -- Load Data for Table CHARTEVENTS -------------------------------------------------------- -COPY 263201375 OFFSET 2 RECORDS INTO MIMICIII.CHARTEVENTS FROM '/path/to/CHARTEVENTS.csv' USING DELIMITERS ',','\n','"' NULL AS ''; +COPY 330712483 OFFSET 2 RECORDS INTO MIMICIII.CHARTEVENTS FROM '/path/to/CHARTEVENTS.csv' USING DELIMITERS ',','\n','"' NULL AS ''; ---------------------------------------------------------------------------------------------------------------- -- Load Data for Table CPTEVENTS @@ -47,13 +45,13 @@ COPY 573146 OFFSET 2 RECORDS INTO MIMICIII.CPTEVENTS FROM '/path/to/CPTEVENTS.cs -- Load Data for Table DATETIMEEVENTS -------------------------------------------------------- -COPY 4486049 OFFSET 2 RECORDS INTO MIMICIII.DATETIMEEVENTS FROM '/path/to/DATETIMEEVENTS.csv' USING DELIMITERS ',','\n','"' NULL AS ''; +COPY 4485937 OFFSET 2 RECORDS INTO MIMICIII.DATETIMEEVENTS FROM '/path/to/DATETIMEEVENTS.csv' USING DELIMITERS ',','\n','"' NULL AS ''; -------------------------------------------------------- -- Load Data for Table DIAGNOSES_ICD -------------------------------------------------------- -COPY 651048 OFFSET 2 RECORDS INTO MIMICIII.DIAGNOSES_ICD FROM '/path/to/DIAGNOSES_ICD.csv' USING DELIMITERS ',','\n','"' NULL AS ''; +COPY 651047 OFFSET 2 RECORDS INTO MIMICIII.DIAGNOSES_ICD FROM '/path/to/DIAGNOSES_ICD.csv' USING DELIMITERS ',','\n','"' NULL AS ''; -------------------------------------------------------- -- Load Data for Table DRGCODES @@ -89,7 +87,7 @@ COPY 12478 OFFSET 2 RECORDS INTO MIMICIII.D_ITEMS FROM '/path/to/D_ITEMS.csv' US -- Load Data for Table D_LABITEMS -------------------------------------------------------- -COPY 755 OFFSET 2 RECORDS INTO MIMICIII.D_LABITEMS FROM '/path/to/D_LABITEMS.csv' USING DELIMITERS ',','\n','"' NULL AS ''; +COPY 753 OFFSET 2 RECORDS INTO MIMICIII.D_LABITEMS FROM '/path/to/D_LABITEMS.csv' USING DELIMITERS ',','\n','"' NULL AS ''; -------------------------------------------------------- -- Load Data for Table ICUSTAYS @@ -101,7 +99,7 @@ COPY 61532 OFFSET 2 RECORDS INTO MIMICIII.ICUSTAYS FROM '/path/to/ICUSTAYS.csv' -- Load Data for Table INPUTEVENTS_CV -------------------------------------------------------- -COPY 17528894 OFFSET 2 RECORDS INTO MIMICIII.INPUTEVENTS_CV FROM '/path/to/INPUTEVENTS_CV.csv' USING DELIMITERS ',','\n','"' NULL AS ''; +COPY 17527935 OFFSET 2 RECORDS INTO MIMICIII.INPUTEVENTS_CV FROM '/path/to/INPUTEVENTS_CV.csv' USING DELIMITERS ',','\n','"' NULL AS ''; -------------------------------------------------------- -- Load Data for Table INPUTEVENTS_MV @@ -113,25 +111,25 @@ COPY 3618991 OFFSET 2 RECORDS INTO MIMICIII.INPUTEVENTS_MV FROM '/path/to/INPUTE -- Load Data for Table LABEVENTS -------------------------------------------------------- -COPY 27872575 OFFSET 2 RECORDS INTO MIMICIII.LABEVENTS FROM '/path/to/LABEVENTS.csv' USING DELIMITERS ',','\n','"' NULL AS ''; +COPY 27854055 OFFSET 2 RECORDS INTO MIMICIII.LABEVENTS FROM '/path/to/LABEVENTS.csv' USING DELIMITERS ',','\n','"' NULL AS ''; -------------------------------------------------------- -- Load Data for Table MICROBIOLOGYEVENTS -------------------------------------------------------- -COPY 328446 OFFSET 2 RECORDS INTO MIMICIII.MICROBIOLOGYEVENTS FROM '/path/to/MICROBIOLOGYEVENTS.csv' USING DELIMITERS ',','\n','"' NULL AS ''; +COPY 631726 OFFSET 2 RECORDS INTO MIMICIII.MICROBIOLOGYEVENTS FROM '/path/to/MICROBIOLOGYEVENTS.csv' USING DELIMITERS ',','\n','"' NULL AS ''; -------------------------------------------------------- -- Load Data for Table NOTEEVENTS -------------------------------------------------------- -COPY 2078704 OFFSET 2 RECORDS INTO MIMICIII.NOTEEVENTS FROM '/path/to/NOTEEVENTS.csv' USING DELIMITERS ',','\n','"' NULL AS ''; +COPY 2083180 OFFSET 2 RECORDS INTO MIMICIII.NOTEEVENTS FROM '/path/to/NOTEEVENTS.csv' USING DELIMITERS ',','\n','"' NULL AS ''; -------------------------------------------------------- -- Load Data for Table OUTPUTEVENTS -------------------------------------------------------- -COPY 349339 OFFSET 2 RECORDS INTO MIMICIII.OUTPUTEVENTS FROM '/path/to/OUTPUTEVENTS.csv' USING DELIMITERS ',','\n','"' NULL AS ''; +COPY 4349218 OFFSET 2 RECORDS INTO MIMICIII.OUTPUTEVENTS FROM '/path/to/OUTPUTEVENTS.csv' USING DELIMITERS ',','\n','"' NULL AS ''; -------------------------------------------------------- -- Load Data for Table PATIENTS @@ -143,7 +141,7 @@ COPY 46520 OFFSET 2 RECORDS INTO MIMICIII.PATIENTS FROM '/path/to/PATIENTS.csv' -- Load Data for Table PRESCRIPTIONS -------------------------------------------------------- -COPY 156848 OFFSET 2 RECORDS INTO MIMICIII.PRESCRIPTIONS FROM '/path/to/PRESCRIPTIONS.csv' USING DELIMITERS ',','\n','"' NULL AS ''; +COPY 4156450 OFFSET 2 RECORDS INTO MIMICIII.PRESCRIPTIONS FROM '/path/to/PRESCRIPTIONS.csv' USING DELIMITERS ',','\n','"' NULL AS ''; -------------------------------------------------------- -- Load Data for Table PROCEDUREEVENTS_MV diff --git a/buildmimic/mysql/1-define.sql b/buildmimic/mysql/1-define.sql index 07e85ab..081f767 100644 --- a/buildmimic/mysql/1-define.sql +++ b/buildmimic/mysql/1-define.sql @@ -54,7 +54,6 @@ CREATE TABLE ADMISSIONS ( -- rows=58976 EDOUTTIME DATETIME, DIAGNOSIS TEXT, -- max=189 HOSPITAL_EXPIRE_FLAG TINYINT UNSIGNED NOT NULL, - HAS_IOEVENTS_DATA TINYINT UNSIGNED NOT NULL, HAS_CHARTEVENTS_DATA TINYINT UNSIGNED NOT NULL, UNIQUE KEY ADMISSIONS_ROW_ID (ROW_ID), -- nvals=58976 UNIQUE KEY ADMISSIONS_HADM_ID (HADM_ID) -- nvals=58976 @@ -62,10 +61,10 @@ CREATE TABLE ADMISSIONS ( -- rows=58976 CHARACTER SET = UTF8; LOAD DATA LOCAL INFILE 'ADMISSIONS.csv' INTO TABLE ADMISSIONS - FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' + FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n' IGNORE 1 LINES - (@ROW_ID,@SUBJECT_ID,@HADM_ID,@ADMITTIME,@DISCHTIME,@DEATHTIME,@ADMISSION_TYPE,@ADMISSION_LOCATION,@DISCHARGE_LOCATION,@INSURANCE,@LANGUAGE,@RELIGION,@MARITAL_STATUS,@ETHNICITY,@EDREGTIME,@EDOUTTIME,@DIAGNOSIS,@HOSPITAL_EXPIRE_FLAG,@HAS_IOEVENTS_DATA,@HAS_CHARTEVENTS_DATA) + (@ROW_ID,@SUBJECT_ID,@HADM_ID,@ADMITTIME,@DISCHTIME,@DEATHTIME,@ADMISSION_TYPE,@ADMISSION_LOCATION,@DISCHARGE_LOCATION,@INSURANCE,@LANGUAGE,@RELIGION,@MARITAL_STATUS,@ETHNICITY,@EDREGTIME,@EDOUTTIME,@DIAGNOSIS,@HOSPITAL_EXPIRE_FLAG,@HAS_CHARTEVENTS_DATA) SET ROW_ID = @ROW_ID, SUBJECT_ID = @SUBJECT_ID, @@ -85,7 +84,6 @@ LOAD DATA LOCAL INFILE 'ADMISSIONS.csv' INTO TABLE ADMISSIONS EDOUTTIME = IF(@EDOUTTIME='', NULL, @EDOUTTIME), DIAGNOSIS = IF(@DIAGNOSIS='', NULL, @DIAGNOSIS), HOSPITAL_EXPIRE_FLAG = @HOSPITAL_EXPIRE_FLAG, - HAS_IOEVENTS_DATA = @HAS_IOEVENTS_DATA, HAS_CHARTEVENTS_DATA = @HAS_CHARTEVENTS_DATA; DROP TABLE IF EXISTS CALLOUT; @@ -120,7 +118,7 @@ CREATE TABLE CALLOUT ( -- rows=34499 CHARACTER SET = UTF8; LOAD DATA LOCAL INFILE 'CALLOUT.csv' INTO TABLE CALLOUT - FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' + FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n' IGNORE 1 LINES (@ROW_ID,@SUBJECT_ID,@HADM_ID,@SUBMIT_WARDID,@SUBMIT_CAREUNIT,@CURR_WARDID,@CURR_CAREUNIT,@CALLOUT_WARDID,@CALLOUT_SERVICE,@REQUEST_TELE,@REQUEST_RESP,@REQUEST_CDIFF,@REQUEST_MRSA,@REQUEST_VRE,@CALLOUT_STATUS,@CALLOUT_OUTCOME,@DISCHARGE_WARDID,@ACKNOWLEDGE_STATUS,@CREATETIME,@UPDATETIME,@ACKNOWLEDGETIME,@OUTCOMETIME,@FIRSTRESERVATIONTIME,@CURRENTRESERVATIONTIME) @@ -162,7 +160,7 @@ CREATE TABLE CAREGIVERS ( -- rows=7567 CHARACTER SET = UTF8; LOAD DATA LOCAL INFILE 'CAREGIVERS.csv' INTO TABLE CAREGIVERS - FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' + FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n' IGNORE 1 LINES (@ROW_ID,@CGID,@LABEL,@DESCRIPTION) @@ -194,7 +192,7 @@ CREATE TABLE CHARTEVENTS ( -- rows=263201375 CHARACTER SET = UTF8; LOAD DATA LOCAL INFILE 'CHARTEVENTS.csv' INTO TABLE CHARTEVENTS - FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' + FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n' IGNORE 1 LINES (@ROW_ID,@SUBJECT_ID,@HADM_ID,@ICUSTAY_ID,@ITEMID,@CHARTTIME,@STORETIME,@CGID,@VALUE,@VALUENUM,@VALUEUOM,@WARNING,@ERROR,@RESULTSTATUS,@STOPPED) @@ -234,7 +232,7 @@ CREATE TABLE CPTEVENTS ( -- rows=573146 CHARACTER SET = UTF8; LOAD DATA LOCAL INFILE 'CPTEVENTS.csv' INTO TABLE CPTEVENTS - FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' + FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n' IGNORE 1 LINES (@ROW_ID,@SUBJECT_ID,@HADM_ID,@COSTCENTER,@CHARTDATE,@CPT_CD,@CPT_NUMBER,@CPT_SUFFIX,@TICKET_ID_SEQ,@SECTIONHEADER,@SUBSECTIONHEADER,@DESCRIPTION) @@ -273,7 +271,7 @@ CREATE TABLE DATETIMEEVENTS ( -- rows=4486049 CHARACTER SET = UTF8; LOAD DATA LOCAL INFILE 'DATETIMEEVENTS.csv' INTO TABLE DATETIMEEVENTS - FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' + FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n' IGNORE 1 LINES (@ROW_ID,@SUBJECT_ID,@HADM_ID,@ICUSTAY_ID,@ITEMID,@CHARTTIME,@STORETIME,@CGID,@VALUE,@VALUEUOM,@WARNING,@ERROR,@RESULTSTATUS,@STOPPED) @@ -305,7 +303,7 @@ CREATE TABLE DIAGNOSES_ICD ( -- rows=651047 CHARACTER SET = UTF8; LOAD DATA LOCAL INFILE 'DIAGNOSES_ICD.csv' INTO TABLE DIAGNOSES_ICD - FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' + FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n' IGNORE 1 LINES (@ROW_ID,@SUBJECT_ID,@HADM_ID,@SEQ_NUM,@ICD9_CODE) @@ -331,7 +329,7 @@ CREATE TABLE DRGCODES ( -- rows=125557 CHARACTER SET = UTF8; LOAD DATA LOCAL INFILE 'DRGCODES.csv' INTO TABLE DRGCODES - FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' + FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n' IGNORE 1 LINES (@ROW_ID,@SUBJECT_ID,@HADM_ID,@DRG_TYPE,@DRG_CODE,@DESCRIPTION,@DRG_SEVERITY,@DRG_MORTALITY) @@ -363,7 +361,7 @@ CREATE TABLE D_CPT ( -- rows=134 CHARACTER SET = UTF8; LOAD DATA LOCAL INFILE 'D_CPT.csv' INTO TABLE D_CPT - FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' + FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n' IGNORE 1 LINES (@ROW_ID,@CATEGORY,@SECTIONRANGE,@SECTIONHEADER,@SUBSECTIONRANGE,@SUBSECTIONHEADER,@CODESUFFIX,@MINCODEINSUBSECTION,@MAXCODEINSUBSECTION) @@ -390,7 +388,7 @@ CREATE TABLE D_ICD_DIAGNOSES ( -- rows=14567 CHARACTER SET = UTF8; LOAD DATA LOCAL INFILE 'D_ICD_DIAGNOSES.csv' INTO TABLE D_ICD_DIAGNOSES - FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' + FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n' IGNORE 1 LINES (@ROW_ID,@ICD9_CODE,@SHORT_TITLE,@LONG_TITLE) @@ -413,7 +411,7 @@ CREATE TABLE D_ICD_PROCEDURES ( -- rows=3882 CHARACTER SET = UTF8; LOAD DATA LOCAL INFILE 'D_ICD_PROCEDURES.csv' INTO TABLE D_ICD_PROCEDURES - FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' + FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n' IGNORE 1 LINES (@ROW_ID,@ICD9_CODE,@SHORT_TITLE,@LONG_TITLE) @@ -441,7 +439,7 @@ CREATE TABLE D_ITEMS ( -- rows=12478 CHARACTER SET = UTF8; LOAD DATA LOCAL INFILE 'D_ITEMS.csv' INTO TABLE D_ITEMS - FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' + FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n' IGNORE 1 LINES (@ROW_ID,@ITEMID,@LABEL,@ABBREVIATION,@DBSOURCE,@LINKSTO,@CATEGORY,@UNITNAME,@PARAM_TYPE,@CONCEPTID) @@ -471,7 +469,7 @@ CREATE TABLE D_LABITEMS ( -- rows=755 CHARACTER SET = UTF8; LOAD DATA LOCAL INFILE 'D_LABITEMS.csv' INTO TABLE D_LABITEMS - FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' + FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n' IGNORE 1 LINES (@ROW_ID,@ITEMID,@LABEL,@FLUID,@CATEGORY,@LOINC_CODE) @@ -503,7 +501,7 @@ CREATE TABLE ICUSTAYS ( -- rows=61532 CHARACTER SET = UTF8; LOAD DATA LOCAL INFILE 'ICUSTAYS.csv' INTO TABLE ICUSTAYS - FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' + FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n' IGNORE 1 LINES (@ROW_ID,@SUBJECT_ID,@HADM_ID,@ICUSTAY_ID,@DBSOURCE,@FIRST_CAREUNIT,@LAST_CAREUNIT,@FIRST_WARDID,@LAST_WARDID,@INTIME,@OUTTIME,@LOS) @@ -550,7 +548,7 @@ CREATE TABLE INPUTEVENTS_CV ( -- rows=17528894 CHARACTER SET = UTF8; LOAD DATA LOCAL INFILE 'INPUTEVENTS_CV.csv' INTO TABLE INPUTEVENTS_CV - FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' + FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n' IGNORE 1 LINES (@ROW_ID,@SUBJECT_ID,@HADM_ID,@ICUSTAY_ID,@CHARTTIME,@ITEMID,@AMOUNT,@AMOUNTUOM,@RATE,@RATEUOM,@STORETIME,@CGID,@ORDERID,@LINKORDERID,@STOPPED,@NEWBOTTLE,@ORIGINALAMOUNT,@ORIGINALAMOUNTUOM,@ORIGINALROUTE,@ORIGINALRATE,@ORIGINALRATEUOM,@ORIGINALSITE) @@ -616,7 +614,7 @@ CREATE TABLE INPUTEVENTS_MV ( -- rows=3618991 CHARACTER SET = UTF8; LOAD DATA LOCAL INFILE 'INPUTEVENTS_MV.csv' INTO TABLE INPUTEVENTS_MV - FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' + FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n' IGNORE 1 LINES (@ROW_ID,@SUBJECT_ID,@HADM_ID,@ICUSTAY_ID,@STARTTIME,@ENDTIME,@ITEMID,@AMOUNT,@AMOUNTUOM,@RATE,@RATEUOM,@STORETIME,@CGID,@ORDERID,@LINKORDERID,@ORDERCATEGORYNAME,@SECONDARYORDERCATEGORYNAME,@ORDERCOMPONENTTYPEDESCRIPTION,@ORDERCATEGORYDESCRIPTION,@PATIENTWEIGHT,@TOTALAMOUNT,@TOTALAMOUNTUOM,@ISOPENBAG,@CONTINUEINNEXTDEPT,@CANCELREASON,@STATUSDESCRIPTION,@COMMENTS_EDITEDBY,@COMMENTS_CANCELEDBY,@COMMENTS_DATE,@ORIGINALAMOUNT,@ORIGINALRATE) @@ -669,7 +667,7 @@ CREATE TABLE LABEVENTS ( -- rows=27872575 CHARACTER SET = UTF8; LOAD DATA LOCAL INFILE 'LABEVENTS.csv' INTO TABLE LABEVENTS - FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' + FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n' IGNORE 1 LINES (@ROW_ID,@SUBJECT_ID,@HADM_ID,@ITEMID,@CHARTTIME,@VALUE,@VALUENUM,@VALUEUOM,@FLAG) @@ -694,8 +692,8 @@ CREATE TABLE MICROBIOLOGYEVENTS ( -- rows=328446 SPEC_ITEMID MEDIUMINT UNSIGNED, SPEC_TYPE_DESC VARCHAR(255) NOT NULL, -- max=56 ORG_ITEMID MEDIUMINT UNSIGNED, - ORG_NAME VARCHAR(255) NOT NULL, -- max=70 - ISOLATE_NUM TINYINT UNSIGNED NOT NULL, + ORG_NAME VARCHAR(255), -- max=70 + ISOLATE_NUM TINYINT UNSIGNED, AB_ITEMID MEDIUMINT UNSIGNED, AB_NAME VARCHAR(255), -- max=20 DILUTION_TEXT VARCHAR(255), -- max=6 @@ -707,7 +705,7 @@ CREATE TABLE MICROBIOLOGYEVENTS ( -- rows=328446 CHARACTER SET = UTF8; LOAD DATA LOCAL INFILE 'MICROBIOLOGYEVENTS.csv' INTO TABLE MICROBIOLOGYEVENTS - FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' + FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n' IGNORE 1 LINES (@ROW_ID,@SUBJECT_ID,@HADM_ID,@CHARTDATE,@CHARTTIME,@SPEC_ITEMID,@SPEC_TYPE_DESC,@ORG_ITEMID,@ORG_NAME,@ISOLATE_NUM,@AB_ITEMID,@AB_NAME,@DILUTION_TEXT,@DILUTION_COMPARISON,@DILUTION_VALUE,@INTERPRETATION) @@ -720,8 +718,8 @@ LOAD DATA LOCAL INFILE 'MICROBIOLOGYEVENTS.csv' INTO TABLE MICROBIOLOGYEVENTS SPEC_ITEMID = IF(@SPEC_ITEMID='', NULL, @SPEC_ITEMID), SPEC_TYPE_DESC = @SPEC_TYPE_DESC, ORG_ITEMID = IF(@ORG_ITEMID='', NULL, @ORG_ITEMID), - ORG_NAME = @ORG_NAME, - ISOLATE_NUM = @ISOLATE_NUM, + ORG_NAME = IF(@ORG_NAME='', NULL, @ORG_NAME), + ISOLATE_NUM = IF(@ISOLATE_NUM='', NULL, @ISOLATE_NUM), AB_ITEMID = IF(@AB_ITEMID='', NULL, @AB_ITEMID), AB_NAME = IF(@AB_NAME='', NULL, @AB_NAME), DILUTION_TEXT = IF(@DILUTION_TEXT='', NULL, @DILUTION_TEXT), @@ -747,7 +745,7 @@ CREATE TABLE NOTEEVENTS ( -- rows=2078705 CHARACTER SET = UTF8; LOAD DATA LOCAL INFILE 'NOTEEVENTS.csv' INTO TABLE NOTEEVENTS - FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' + FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n' IGNORE 1 LINES (@ROW_ID,@SUBJECT_ID,@HADM_ID,@CHARTDATE,@CHARTTIME,@STORETIME,@CATEGORY,@DESCRIPTION,@CGID,@ISERROR,@TEXT) @@ -784,7 +782,7 @@ CREATE TABLE OUTPUTEVENTS ( -- rows=4349339 CHARACTER SET = UTF8; LOAD DATA LOCAL INFILE 'OUTPUTEVENTS.csv' INTO TABLE OUTPUTEVENTS - FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' + FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n' IGNORE 1 LINES (@ROW_ID,@SUBJECT_ID,@HADM_ID,@ICUSTAY_ID,@CHARTTIME,@ITEMID,@VALUE,@VALUEUOM,@STORETIME,@CGID,@STOPPED,@NEWBOTTLE,@ISERROR) @@ -819,7 +817,7 @@ CREATE TABLE PATIENTS ( -- rows=46520 CHARACTER SET = UTF8; LOAD DATA LOCAL INFILE 'PATIENTS.csv' INTO TABLE PATIENTS - FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' + FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n' IGNORE 1 LINES (@ROW_ID,@SUBJECT_ID,@GENDER,@DOB,@DOD,@DOD_HOSP,@DOD_SSN,@EXPIRE_FLAG) @@ -859,7 +857,7 @@ CREATE TABLE PRESCRIPTIONS ( -- rows=4156848 CHARACTER SET = UTF8; LOAD DATA LOCAL INFILE 'PRESCRIPTIONS.csv' INTO TABLE PRESCRIPTIONS - FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' + FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n' IGNORE 1 LINES (@ROW_ID,@SUBJECT_ID,@HADM_ID,@ICUSTAY_ID,@STARTDATE,@ENDDATE,@DRUG_TYPE,@DRUG,@DRUG_NAME_POE,@DRUG_NAME_GENERIC,@FORMULARY_DRUG_CD,@GSN,@NDC,@PROD_STRENGTH,@DOSE_VAL_RX,@DOSE_UNIT_RX,@FORM_VAL_DISP,@FORM_UNIT_DISP,@ROUTE) @@ -917,7 +915,7 @@ CREATE TABLE PROCEDUREEVENTS_MV ( -- rows=258066 CHARACTER SET = UTF8; LOAD DATA LOCAL INFILE 'PROCEDUREEVENTS_MV.csv' INTO TABLE PROCEDUREEVENTS_MV - FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' + FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n' IGNORE 1 LINES (@ROW_ID,@SUBJECT_ID,@HADM_ID,@ICUSTAY_ID,@STARTTIME,@ENDTIME,@ITEMID,@VALUE,@VALUEUOM,@LOCATION,@LOCATIONCATEGORY,@STORETIME,@CGID,@ORDERID,@LINKORDERID,@ORDERCATEGORYNAME,@SECONDARYORDERCATEGORYNAME,@ORDERCATEGORYDESCRIPTION,@ISOPENBAG,@CONTINUEINNEXTDEPT,@CANCELREASON,@STATUSDESCRIPTION,@COMMENTS_EDITEDBY,@COMMENTS_CANCELEDBY,@COMMENTS_DATE) @@ -960,7 +958,7 @@ CREATE TABLE PROCEDURES_ICD ( -- rows=240095 CHARACTER SET = UTF8; LOAD DATA LOCAL INFILE 'PROCEDURES_ICD.csv' INTO TABLE PROCEDURES_ICD - FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' + FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n' IGNORE 1 LINES (@ROW_ID,@SUBJECT_ID,@HADM_ID,@SEQ_NUM,@ICD9_CODE) @@ -984,7 +982,7 @@ CREATE TABLE SERVICES ( -- rows=73343 CHARACTER SET = UTF8; LOAD DATA LOCAL INFILE 'SERVICES.csv' INTO TABLE SERVICES - FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' + FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n' IGNORE 1 LINES (@ROW_ID,@SUBJECT_ID,@HADM_ID,@TRANSFERTIME,@PREV_SERVICE,@CURR_SERVICE) @@ -1016,7 +1014,7 @@ CREATE TABLE TRANSFERS ( -- rows=261897 CHARACTER SET = UTF8; LOAD DATA LOCAL INFILE 'TRANSFERS.csv' INTO TABLE TRANSFERS - FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' + FIELDS TERMINATED BY ',' ESCAPED BY '\\' OPTIONALLY ENCLOSED BY '"' LINES TERMINATED BY '\n' IGNORE 1 LINES (@ROW_ID,@SUBJECT_ID,@HADM_ID,@ICUSTAY_ID,@DBSOURCE,@EVENTTYPE,@PREV_CAREUNIT,@CURR_CAREUNIT,@PREV_WARDID,@CURR_WARDID,@INTIME,@OUTTIME,@LOS) @@ -1034,4 +1032,3 @@ LOAD DATA LOCAL INFILE 'TRANSFERS.csv' INTO TABLE TRANSFERS INTIME = IF(@INTIME='', NULL, @INTIME), OUTTIME = IF(@OUTTIME='', NULL, @OUTTIME), LOS = IF(@LOS='', NULL, @LOS); - diff --git a/buildmimic/oracle/README.md b/buildmimic/oracle/README.md new file mode 100644 index 0000000..62e19a7 --- /dev/null +++ b/buildmimic/oracle/README.md @@ -0,0 +1,47 @@ +# Loading data in Oracle + +This folder contains scripts for loading the data into an Oracle database system. +There are three steps to loading the data into Oracle: + +1. The tables are created using an SQL script +2. `sqlldr` is used to load data into these tables (either individually or with a shell script) +3. Indices and constraints are added using an SQL script + +The largest challenge in loading in data in Oracle is NOTEEVENTS. Oracle cannot handle reasonably standard formatted CSVs: instead it requires a special character to delineate a newline from a new row (most database systems can handle newlines if they are quoted, which is how the data for MIMIC-III is distributed). As a result, we have provided an additional Python script which will run through NOTEEVENTS and append a unique string of characters to the end of each row. The script can be called as follows: + +```python +python add_oracle_rowdelimiter.py -d ',' -i 'NOTEEVENTS.csv' -r '><><><> -d -r ') + sys.exit(2) + for opt, arg in opts: + if opt == '-h': + print('remove_newlines.py -i -d -r ') + sys.exit() + elif opt in ("-i", "--ifile"): + fn_in = arg + elif opt in ("-d", "--delimiter"): + delimiter = arg + elif opt in ("-r", "--row-delimiter"): + oracle_newrow = arg + + # input argument checking + if os.path.isfile(fn_in) == 1: + print('Using input file {}'.format(fn_in)) + else: + print('Cannot find input file {}'.format(fn_in)) + sys.exit(2) + + fn_out=fn_in.strip('.csv')+'_output.csv' + print('\n'+'~'*40) + print('Input filename = {}'.format(fn_in)) + print('Delimiter = {}'.format(delimiter)) + print('New row character(s) = {}'.format(oracle_newrow)) + print('Output filename = {}'.format(fn_out)) + print('Please note all output fields will be double quoted.') + print('~'*40+'\n') + #raw_input('Press any key to continue.') + + with open(fn_in , 'rb') as input_file: + reader = csv.reader(input_file, delimiter=',', + doublequote=True, + quoting=csv.QUOTE_MINIMAL) + # QUOTE_NONNUMERIC doesn't work because it tries to convert dates are floats + # consequently, all fields are output quoted + # not a big deal for oracle, which has optionally enclosed by double quotes parameter + + with open(fn_out,'wb') as fout: + out = csv.writer(fout, doublequote=True, quoting=csv.QUOTE_NONNUMERIC, + lineterminator=oracle_newrow + '\r\n') + for row in reader: + out.writerow(row) + if reader.line_num % 100000 == 0: + print('Finished {} million lines.'.format(reader.line_num / 1000000)) + + # Summarise output + print('\n'+'~'*40) + print('Merging complete\n') + print('Number of rows processed: {}'.format(reader.line_num)) + print('New file created: {}'.format(fout.name)) + print('~'*40) + +if __name__ == "__main__": + main(sys.argv[1:]) diff --git a/buildmimic/oracle/build_mimic_oracle.sh b/buildmimic/oracle/build_mimic_oracle.sh new file mode 100644 index 0000000..49c73cf --- /dev/null +++ b/buildmimic/oracle/build_mimic_oracle.sh @@ -0,0 +1,72 @@ +#!/bin/bash + +# This script attempts to build MIMIC on an Oracle instance. +# You will likely need to modify it to fit your own system, for example, +# you may need to change the authentication, e.g. replace '\ as SYSDBA' with 'myusername/mypassword' +# The script requires sqlldr and sqlplus + + +# Create the tables + +sqlplus '\ as SYSDBA' << EOF +WHENEVER OSERROR EXIT 9; +WHENEVER SQLERROR EXIT SQL.SQLCODE; + +ALTER SESSION SET CURRENT_SCHEMA = MIMICIII; +@oracle_create_tables.sql + +EOF + +# Alternatively, you could specify a username/password here, and use the below snippet + +#db_username= +#db_password= + +#sqlplus -s /nolog << EOF +#WHENEVER OSERROR EXIT 9; +#WHENEVER SQLERROR EXIT SQL.SQLCODE; +#CONNECT ${db_username}/${db_password}; +#@oracle_create_tables.sql +#EOF + + +# Call sqlldr to load the data + +sqlldr '\ as SYSDBA' control='controlfiles/admissions.ctl' log=admissions.log parallel=true direct=true multithreading=true skip_index_maintenance=true +sqlldr '\ as SYSDBA' control='controlfiles/callout.ctl' log=callout.log parallel=true direct=true multithreading=true skip_index_maintenance=true +sqlldr '\ as SYSDBA' control='controlfiles/caregivers.ctl' log=caregivers.log parallel=true direct=true multithreading=true skip_index_maintenance=true +sqlldr '\ as SYSDBA' control='controlfiles/chartevents.ctl' log=chartevents.log parallel=true direct=true multithreading=true skip_index_maintenance=true +sqlldr '\ as SYSDBA' control='controlfiles/cptevents.ctl' log=cptevents.log parallel=true direct=true multithreading=true skip_index_maintenance=true +sqlldr '\ as SYSDBA' control='controlfiles/d_cpt.ctl' log=d_cpt.log parallel=true direct=true multithreading=true skip_index_maintenance=true +sqlldr '\ as SYSDBA' control='controlfiles/d_icd_diagnoses.ctl' log=d_icd_diagnoses.log parallel=true direct=true multithreading=true skip_index_maintenance=true +sqlldr '\ as SYSDBA' control='controlfiles/d_icd_procedures.ctl' log=d_icd_procedures.log parallel=true direct=true multithreading=true skip_index_maintenance=true +sqlldr '\ as SYSDBA' control='controlfiles/d_items.ctl' log=d_items.log parallel=true direct=true multithreading=true skip_index_maintenance=true +sqlldr '\ as SYSDBA' control='controlfiles/d_labitems.ctl' log=d_labitems.log parallel=true direct=true multithreading=true skip_index_maintenance=true +sqlldr '\ as SYSDBA' control='controlfiles/datetimeevents.ctl' log=datetimeevents.log parallel=true direct=true multithreading=true skip_index_maintenance=true +sqlldr '\ as SYSDBA' control='controlfiles/diagnoses_icd.ctl' log=diagnoses_icd.log parallel=true direct=true multithreading=true skip_index_maintenance=true +sqlldr '\ as SYSDBA' control='controlfiles/drgcodes.ctl' log=drgcodes.log parallel=true direct=true multithreading=true skip_index_maintenance=true +sqlldr '\ as SYSDBA' control='controlfiles/icustays.ctl' log=icustays.log parallel=true direct=true multithreading=true skip_index_maintenance=true +sqlldr '\ as SYSDBA' control='controlfiles/inputevents_cv.ctl' log=inputevents_cv.log parallel=true direct=true multithreading=true skip_index_maintenance=true +sqlldr '\ as SYSDBA' control='controlfiles/inputevents_mv.ctl' log=inputevents_mv.log parallel=true direct=true multithreading=true skip_index_maintenance=true +sqlldr '\ as SYSDBA' control='controlfiles/labevents.ctl' log=labevents.log parallel=true direct=true multithreading=true skip_index_maintenance=true +sqlldr '\ as SYSDBA' control='controlfiles/microbiologyevents.ctl' log=microbiologyevents.log parallel=true direct=true multithreading=true skip_index_maintenance=true +sqlldr '\ as SYSDBA' control='controlfiles/noteevents_output.ctl' log=noteevents_output.log parallel=true direct=true multithreading=true skip_index_maintenance=true +sqlldr '\ as SYSDBA' control='controlfiles/outputevents.ctl' log=outputevents.log parallel=true direct=true multithreading=true skip_index_maintenance=true +sqlldr '\ as SYSDBA' control='controlfiles/patients.ctl' log=patients.log parallel=true direct=true multithreading=true skip_index_maintenance=true +sqlldr '\ as SYSDBA' control='controlfiles/prescriptions.ctl' log=prescriptions.log parallel=true direct=true multithreading=true skip_index_maintenance=true +sqlldr '\ as SYSDBA' control='controlfiles/procedureevents_mv.ctl' log=procedureevents_mv.log parallel=true direct=true multithreading=true skip_index_maintenance=true +sqlldr '\ as SYSDBA' control='controlfiles/procedures_icd.ctl' log=procedures_icd.log parallel=true direct=true multithreading=true skip_index_maintenance=true +sqlldr '\ as SYSDBA' control='controlfiles/services.ctl' log=services.log parallel=true direct=true multithreading=true skip_index_maintenance=true +sqlldr '\ as SYSDBA' control='controlfiles/transfers.ctl' log=transfers.log parallel=true direct=true multithreading=true skip_index_maintenance=true + +# Now, create the indexes and constraints + +sqlplus '\ as SYSDBA' << EOF +WHENEVER OSERROR EXIT 9; +WHENEVER SQLERROR EXIT SQL.SQLCODE; + +ALTER SESSION SET CURRENT_SCHEMA = MIMICIII; +@oracle_add_indexes.sql +@oracle_add_constraints.sql + +EOF diff --git a/buildmimic/oracle/controlfiles/noteevents.ctl b/buildmimic/oracle/controlfiles/noteevents.ctl index c034c9c..7739582 100644 --- a/buildmimic/oracle/controlfiles/noteevents.ctl +++ b/buildmimic/oracle/controlfiles/noteevents.ctl @@ -2,7 +2,7 @@ OPTIONS ( skip=1, errors=0, direct=true, -multithreading=true +multithreading=true ) LOAD DATA INFILE 'NOTEEVENTS.csv' "str '\n'" @@ -14,7 +14,7 @@ FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY '"' AND '"' TRAILING nullcols ( -ROW_ID, +ROW_ID, SUBJECT_ID, HADM_ID, CHARTDATE DATE "YYYY-MM-DD HH24:MI:SS", @@ -24,5 +24,5 @@ CATEGORY, DESCRIPTION, CGID, ISERROR, -TEXT -) \ No newline at end of file +TEXT +) diff --git a/buildmimic/oracle/controlfiles/noteevents_output.ctl b/buildmimic/oracle/controlfiles/noteevents_output.ctl new file mode 100644 index 0000000..5c8aa55 --- /dev/null +++ b/buildmimic/oracle/controlfiles/noteevents_output.ctl @@ -0,0 +1,28 @@ +OPTIONS ( +skip=1, +errors=0, +direct=true, +multithreading=true +) +LOAD DATA +INFILE 'NOTEEVENTS.csv' "str '\n'><>/dev/null; then @@ -27,8 +28,16 @@ fi # SUDO='sudo -u postgres' # fi -$SUDO psql postgres > /dev/null <<- EOSQL - CREATE USER $MIMIC_USER WITH PASSWORD '$MIMIC_PASSWORD'; - DROP DATABASE IF EXISTS $MIMIC_DB; - CREATE DATABASE $MIMIC_DB OWNER $MIMIC_USER; -EOSQL +if [ "$MIMIC_USER" != "postgres" ]; then + # create user + psql postgres -c "DROP USER IF EXISTS $MIMIC_USER;" + psql postgres -c "CREATE USER $MIMIC_USER WITH PASSWORD '$MIMIC_PASSWORD';" +fi + +# create database +psql postgres -c "DROP DATABASE IF EXISTS $MIMIC_DB;" +psql postgres -c "CREATE DATABASE $MIMIC_DB OWNER $MIMIC_USER;" + +# create schema on database +export PGPASSWORD=$MIMIC_PASSWORD +psql -U $MIMIC_USER -d ${MIMIC_DB} -c "CREATE SCHEMA $MIMIC_SCHEMA AUTHORIZATION $MIMIC_USER;" diff --git a/buildmimic/postgres/postgres_add_comments.sql b/buildmimic/postgres/postgres_add_comments.sql index dc985cb..fd9cec3 100644 --- a/buildmimic/postgres/postgres_add_comments.sql +++ b/buildmimic/postgres/postgres_add_comments.sql @@ -40,7 +40,10 @@ NOTES: */ -SET search_path TO mimiciii; +\set ON_ERROR_STOP 1 + +-- If running scripts individually, you can set the schema where all tables are created as follows: +-- SET search_path TO mimiciii; -------------- --ADMISSIONS-- diff --git a/buildmimic/postgres/postgres_add_constraints.sql b/buildmimic/postgres/postgres_add_constraints.sql index 705bbbb..e040a0b 100644 --- a/buildmimic/postgres/postgres_add_constraints.sql +++ b/buildmimic/postgres/postgres_add_constraints.sql @@ -4,8 +4,10 @@ -- -- ---------------------------------------------------------------- --- The below command defines the schema where the data should reside -SET search_path TO mimiciii; +\set ON_ERROR_STOP 1 + +-- If running scripts individually, you can set the schema where all tables are created as follows: +-- SET search_path TO mimiciii; -- Restoring the search path to its default value can be accomplished as follows: -- SET search_path TO "$user",public; diff --git a/buildmimic/postgres/postgres_add_indexes.sql b/buildmimic/postgres/postgres_add_indexes.sql index 672d25d..d4d9988 100644 --- a/buildmimic/postgres/postgres_add_indexes.sql +++ b/buildmimic/postgres/postgres_add_indexes.sql @@ -4,8 +4,10 @@ -- -- ---------------------------------------------------------------- --- Define the schema where all the indexes are created -SET search_path TO mimiciii; +\set ON_ERROR_STOP 1 + +-- If running scripts individually, you can set the schema where all tables are created as follows: +-- SET search_path TO mimiciii; -- Restoring the search path to its default value can be accomplished as follows: -- SET search_path TO "$user",public; @@ -75,127 +77,127 @@ CREATE INDEX CALLOUT_IDX02 -- index on itemid -- -drop index IF EXISTS chartevents_1_idx01; +drop index IF EXISTS chartevents_1_idx01; CREATE INDEX chartevents_1_idx01 ON chartevents_1 (itemid); -drop index IF EXISTS chartevents_2_idx01; +drop index IF EXISTS chartevents_2_idx01; CREATE INDEX chartevents_2_idx01 ON chartevents_2 (itemid); -drop index IF EXISTS chartevents_3_idx01; +drop index IF EXISTS chartevents_3_idx01; CREATE INDEX chartevents_3_idx01 ON chartevents_3 (itemid); -drop index IF EXISTS chartevents_4_idx01; +drop index IF EXISTS chartevents_4_idx01; CREATE INDEX chartevents_4_idx01 ON chartevents_4 (itemid); -drop index IF EXISTS chartevents_5_idx01; +drop index IF EXISTS chartevents_5_idx01; CREATE INDEX chartevents_5_idx01 ON chartevents_5 (itemid); -drop index IF EXISTS chartevents_6_idx01; +drop index IF EXISTS chartevents_6_idx01; CREATE INDEX chartevents_6_idx01 ON chartevents_6 (itemid); -drop index IF EXISTS chartevents_7_idx01; +drop index IF EXISTS chartevents_7_idx01; CREATE INDEX chartevents_7_idx01 ON chartevents_7 (itemid); -drop index IF EXISTS chartevents_8_idx01; +drop index IF EXISTS chartevents_8_idx01; CREATE INDEX chartevents_8_idx01 ON chartevents_8 (itemid); -drop index IF EXISTS chartevents_9_idx01; +drop index IF EXISTS chartevents_9_idx01; CREATE INDEX chartevents_9_idx01 ON chartevents_9 (itemid); -drop index IF EXISTS chartevents_10_idx01; +drop index IF EXISTS chartevents_10_idx01; CREATE INDEX chartevents_10_idx01 ON chartevents_10 (itemid); -drop index IF EXISTS chartevents_11_idx01; +drop index IF EXISTS chartevents_11_idx01; CREATE INDEX chartevents_11_idx01 ON chartevents_11 (itemid); -drop index IF EXISTS chartevents_12_idx01; +drop index IF EXISTS chartevents_12_idx01; CREATE INDEX chartevents_12_idx01 ON chartevents_12 (itemid); -drop index IF EXISTS chartevents_13_idx01; +drop index IF EXISTS chartevents_13_idx01; CREATE INDEX chartevents_13_idx01 ON chartevents_13 (itemid); -drop index IF EXISTS chartevents_14_idx01; +drop index IF EXISTS chartevents_14_idx01; CREATE INDEX chartevents_14_idx01 ON chartevents_14 (itemid); -- index on subject_id -- -drop index IF EXISTS chartevents_1_idx02; +drop index IF EXISTS chartevents_1_idx02; CREATE INDEX chartevents_1_idx02 ON chartevents_1 (SUBJECT_ID); -drop index IF EXISTS chartevents_2_idx02; +drop index IF EXISTS chartevents_2_idx02; CREATE INDEX chartevents_2_idx02 ON chartevents_2 (SUBJECT_ID); -drop index IF EXISTS chartevents_3_idx02; +drop index IF EXISTS chartevents_3_idx02; CREATE INDEX chartevents_3_idx02 ON chartevents_3 (SUBJECT_ID); -drop index IF EXISTS chartevents_4_idx02; +drop index IF EXISTS chartevents_4_idx02; CREATE INDEX chartevents_4_idx02 ON chartevents_4 (SUBJECT_ID); -drop index IF EXISTS chartevents_5_idx02; +drop index IF EXISTS chartevents_5_idx02; CREATE INDEX chartevents_5_idx02 ON chartevents_5 (SUBJECT_ID); -drop index IF EXISTS chartevents_6_idx02; +drop index IF EXISTS chartevents_6_idx02; CREATE INDEX chartevents_6_idx02 ON chartevents_6 (SUBJECT_ID); -drop index IF EXISTS chartevents_7_idx02; +drop index IF EXISTS chartevents_7_idx02; CREATE INDEX chartevents_7_idx02 ON chartevents_7 (SUBJECT_ID); -drop index IF EXISTS chartevents_8_idx02; +drop index IF EXISTS chartevents_8_idx02; CREATE INDEX chartevents_8_idx02 ON chartevents_8 (SUBJECT_ID); -drop index IF EXISTS chartevents_9_idx02; +drop index IF EXISTS chartevents_9_idx02; CREATE INDEX chartevents_9_idx02 ON chartevents_9 (SUBJECT_ID); -drop index IF EXISTS chartevents_10_idx02; +drop index IF EXISTS chartevents_10_idx02; CREATE INDEX chartevents_10_idx02 ON chartevents_10 (SUBJECT_ID); -drop index IF EXISTS chartevents_11_idx02; +drop index IF EXISTS chartevents_11_idx02; CREATE INDEX chartevents_11_idx02 ON chartevents_11 (SUBJECT_ID); -drop index IF EXISTS chartevents_12_idx02; +drop index IF EXISTS chartevents_12_idx02; CREATE INDEX chartevents_12_idx02 ON chartevents_12 (SUBJECT_ID); -drop index IF EXISTS chartevents_13_idx02; +drop index IF EXISTS chartevents_13_idx02; CREATE INDEX chartevents_13_idx02 ON chartevents_13 (SUBJECT_ID); -drop index IF EXISTS chartevents_14_idx02; +drop index IF EXISTS chartevents_14_idx02; CREATE INDEX chartevents_14_idx02 ON chartevents_14 (SUBJECT_ID); -- index on hadm_id -- -drop index IF EXISTS chartevents_1_idx04; +drop index IF EXISTS chartevents_1_idx04; CREATE INDEX chartevents_1_idx04 ON chartevents_1 (HADM_ID); -drop index IF EXISTS chartevents_2_idx04; +drop index IF EXISTS chartevents_2_idx04; CREATE INDEX chartevents_2_idx04 ON chartevents_2 (HADM_ID); -drop index IF EXISTS chartevents_3_idx04; +drop index IF EXISTS chartevents_3_idx04; CREATE INDEX chartevents_3_idx04 ON chartevents_3 (HADM_ID); -drop index IF EXISTS chartevents_4_idx04; +drop index IF EXISTS chartevents_4_idx04; CREATE INDEX chartevents_4_idx04 ON chartevents_4 (HADM_ID); -drop index IF EXISTS chartevents_5_idx04; +drop index IF EXISTS chartevents_5_idx04; CREATE INDEX chartevents_5_idx04 ON chartevents_5 (HADM_ID); -drop index IF EXISTS chartevents_6_idx04; +drop index IF EXISTS chartevents_6_idx04; CREATE INDEX chartevents_6_idx04 ON chartevents_6 (HADM_ID); -drop index IF EXISTS chartevents_7_idx04; +drop index IF EXISTS chartevents_7_idx04; CREATE INDEX chartevents_7_idx04 ON chartevents_7 (HADM_ID); -drop index IF EXISTS chartevents_8_idx04; +drop index IF EXISTS chartevents_8_idx04; CREATE INDEX chartevents_8_idx04 ON chartevents_8 (HADM_ID); -drop index IF EXISTS chartevents_9_idx04; +drop index IF EXISTS chartevents_9_idx04; CREATE INDEX chartevents_9_idx04 ON chartevents_9 (HADM_ID); -drop index IF EXISTS chartevents_10_idx04; +drop index IF EXISTS chartevents_10_idx04; CREATE INDEX chartevents_10_idx04 ON chartevents_10 (HADM_ID); -drop index IF EXISTS chartevents_11_idx04; +drop index IF EXISTS chartevents_11_idx04; CREATE INDEX chartevents_11_idx04 ON chartevents_11 (HADM_ID); -drop index IF EXISTS chartevents_12_idx04; +drop index IF EXISTS chartevents_12_idx04; CREATE INDEX chartevents_12_idx04 ON chartevents_12 (HADM_ID); -drop index IF EXISTS chartevents_13_idx04; +drop index IF EXISTS chartevents_13_idx04; CREATE INDEX chartevents_13_idx04 ON chartevents_13 (HADM_ID); -drop index IF EXISTS chartevents_14_idx04; +drop index IF EXISTS chartevents_14_idx04; CREATE INDEX chartevents_14_idx04 ON chartevents_14 (HADM_ID); -- index on icustay_id -- -drop index IF EXISTS chartevents_1_idx06; +drop index IF EXISTS chartevents_1_idx06; CREATE INDEX chartevents_1_idx06 ON chartevents_1 (ICUSTAY_ID); -drop index IF EXISTS chartevents_2_idx06; +drop index IF EXISTS chartevents_2_idx06; CREATE INDEX chartevents_2_idx06 ON chartevents_2 (ICUSTAY_ID); -drop index IF EXISTS chartevents_3_idx06; +drop index IF EXISTS chartevents_3_idx06; CREATE INDEX chartevents_3_idx06 ON chartevents_3 (ICUSTAY_ID); -drop index IF EXISTS chartevents_4_idx06; +drop index IF EXISTS chartevents_4_idx06; CREATE INDEX chartevents_4_idx06 ON chartevents_4 (ICUSTAY_ID); -drop index IF EXISTS chartevents_5_idx06; +drop index IF EXISTS chartevents_5_idx06; CREATE INDEX chartevents_5_idx06 ON chartevents_5 (ICUSTAY_ID); -drop index IF EXISTS chartevents_6_idx06; +drop index IF EXISTS chartevents_6_idx06; CREATE INDEX chartevents_6_idx06 ON chartevents_6 (ICUSTAY_ID); -drop index IF EXISTS chartevents_7_idx06; +drop index IF EXISTS chartevents_7_idx06; CREATE INDEX chartevents_7_idx06 ON chartevents_7 (ICUSTAY_ID); -drop index IF EXISTS chartevents_8_idx06; +drop index IF EXISTS chartevents_8_idx06; CREATE INDEX chartevents_8_idx06 ON chartevents_8 (ICUSTAY_ID); -drop index IF EXISTS chartevents_9_idx06; +drop index IF EXISTS chartevents_9_idx06; CREATE INDEX chartevents_9_idx06 ON chartevents_9 (ICUSTAY_ID); -drop index IF EXISTS chartevents_10_idx06; +drop index IF EXISTS chartevents_10_idx06; CREATE INDEX chartevents_10_idx06 ON chartevents_10 (ICUSTAY_ID); -drop index IF EXISTS chartevents_11_idx06; +drop index IF EXISTS chartevents_11_idx06; CREATE INDEX chartevents_11_idx06 ON chartevents_11 (ICUSTAY_ID); -drop index IF EXISTS chartevents_12_idx06; +drop index IF EXISTS chartevents_12_idx06; CREATE INDEX chartevents_12_idx06 ON chartevents_12 (ICUSTAY_ID); -drop index IF EXISTS chartevents_13_idx06; +drop index IF EXISTS chartevents_13_idx06; CREATE INDEX chartevents_13_idx06 ON chartevents_13 (ICUSTAY_ID); -drop index IF EXISTS chartevents_14_idx06; +drop index IF EXISTS chartevents_14_idx06; CREATE INDEX chartevents_14_idx06 ON chartevents_14 (ICUSTAY_ID); diff --git a/buildmimic/postgres/postgres_checks.sql b/buildmimic/postgres/postgres_checks.sql new file mode 100644 index 0000000..0a28719 --- /dev/null +++ b/buildmimic/postgres/postgres_checks.sql @@ -0,0 +1,79 @@ +-- this query runs a few simple checks to make sure the database has loaded in OK +-- These checks are designed for MIMIC-III v1.4 + +-- If running scripts individually, you can set the schema where all tables are created as follows: +-- SET search_path TO mimiciii; + +\set ON_ERROR_STOP 1 + +with expected as +( +select 'admissions' as tbl, 58976 as row_count UNION ALL +select 'callout' as tbl, 34499 as row_count UNION ALL +select 'caregivers' as tbl, 7567 as row_count UNION ALL +select 'chartevents' as tbl, 330712483 as row_count UNION ALL +select 'cptevents' as tbl, 573146 as row_count UNION ALL +select 'd_cpt' as tbl, 134 as row_count UNION ALL +select 'd_icd_diagnoses' as tbl, 14567 as row_count UNION ALL +select 'd_icd_procedures' as tbl, 3882 as row_count UNION ALL +select 'd_items' as tbl, 12487 as row_count UNION ALL +select 'd_labitems' as tbl, 753 as row_count UNION ALL +select 'datetimeevents' as tbl, 4485937 as row_count UNION ALL +select 'diagnoses_icd' as tbl, 651047 as row_count UNION ALL +select 'drgcodes' as tbl, 125557 as row_count UNION ALL +select 'icustays' as tbl, 61532 as row_count UNION ALL +select 'inputevents_cv' as tbl, 17527935 as row_count UNION ALL +select 'inputevents_mv' as tbl, 3618991 as row_count UNION ALL +select 'labevents' as tbl, 27854055 as row_count UNION ALL +select 'microbiologyevents' as tbl, 631726 as row_count UNION ALL +select 'noteevents' as tbl, 2083180 as row_count UNION ALL +select 'outputevents' as tbl, 4349218 as row_count UNION ALL +select 'patients' as tbl, 46520 as row_count UNION ALL +select 'prescriptions' as tbl, 4156450 as row_count UNION ALL +select 'procedureevents_mv' as tbl, 258066 as row_count UNION ALL +select 'procedures_icd' as tbl, 240095 as row_count UNION ALL +select 'services' as tbl, 73343 as row_count UNION ALL +select 'transfers' as tbl, 261897 as row_count +) +, observed as +( + select 'admissions' as tbl, count(*) as row_count from admissions UNION ALL + select 'callout' as tbl, count(*) as row_count from callout UNION ALL + select 'caregivers' as tbl, count(*) as row_count from caregivers UNION ALL + select 'chartevents' as tbl, count(*) as row_count from chartevents UNION ALL + select 'cptevents' as tbl, count(*) as row_count from cptevents UNION ALL + select 'd_cpt' as tbl, count(*) as row_count from d_cpt UNION ALL + select 'd_icd_diagnoses' as tbl, count(*) as row_count from d_icd_diagnoses UNION ALL + select 'd_icd_procedures' as tbl, count(*) as row_count from d_icd_procedures UNION ALL + select 'd_items' as tbl, count(*) as row_count from d_items UNION ALL + select 'd_labitems' as tbl, count(*) as row_count from d_labitems UNION ALL + select 'datetimeevents' as tbl, count(*) as row_count from datetimeevents UNION ALL + select 'diagnoses_icd' as tbl, count(*) as row_count from diagnoses_icd UNION ALL + select 'drgcodes' as tbl, count(*) as row_count from drgcodes UNION ALL + select 'icustays' as tbl, count(*) as row_count from icustays UNION ALL + select 'inputevents_cv' as tbl, count(*) as row_count from inputevents_cv UNION ALL + select 'inputevents_mv' as tbl, count(*) as row_count from inputevents_mv UNION ALL + select 'labevents' as tbl, count(*) as row_count from labevents UNION ALL + select 'microbiologyevents' as tbl, count(*) as row_count from microbiologyevents UNION ALL + select 'noteevents' as tbl, count(*) as row_count from noteevents UNION ALL + select 'outputevents' as tbl, count(*) as row_count from outputevents UNION ALL + select 'patients' as tbl, count(*) as row_count from patients UNION ALL + select 'prescriptions' as tbl, count(*) as row_count from prescriptions UNION ALL + select 'procedureevents_mv' as tbl, count(*) as row_count from procedureevents_mv UNION ALL + select 'procedures_icd' as tbl, count(*) as row_count from procedures_icd UNION ALL + select 'services' as tbl, count(*) as row_count from services UNION ALL + select 'transfers' as tbl, count(*) as row_count from transfers +) +select + exp.tbl + , exp.row_count as expected_count + , obs.row_count as observed_count + , case + when exp.row_count = obs.row_count + then 'PASSED' + else 'FAILED' + end as ROW_COUNT_CHECK +from expected exp +inner join observed obs + on exp.tbl = obs.tbl +order by exp.tbl; diff --git a/buildmimic/postgres/postgres_create_tables.sql b/buildmimic/postgres/postgres_create_tables.sql index c97784b..592cd6d 100644 --- a/buildmimic/postgres/postgres_create_tables.sql +++ b/buildmimic/postgres/postgres_create_tables.sql @@ -8,9 +8,10 @@ -- File created - Thursday-November-28-2015 -------------------------------------------------------- --- Define the schema where all tables are created -CREATE SCHEMA IF NOT EXISTS mimiciii; -SET search_path TO mimiciii; +\set ON_ERROR_STOP 1 + +-- If running scripts individually, you can set the schema where all tables are created as follows: +-- SET search_path TO mimiciii; -- Restoring the search path to its default value can be accomplished as follows: -- SET search_path TO "$user",public; @@ -48,7 +49,6 @@ CREATE TABLE ADMISSIONS EDOUTTIME TIMESTAMP(0), DIAGNOSIS VARCHAR(255), HOSPITAL_EXPIRE_FLAG SMALLINT, - HAS_IOEVENTS_DATA SMALLINT NOT NULL, HAS_CHARTEVENTS_DATA SMALLINT NOT NULL, CONSTRAINT adm_rowid_pk PRIMARY KEY (ROW_ID), CONSTRAINT adm_hadm_unique UNIQUE (HADM_ID) @@ -60,32 +60,33 @@ CREATE TABLE ADMISSIONS DROP TABLE IF EXISTS CALLOUT CASCADE; CREATE TABLE CALLOUT - ( ROW_ID INT NOT NULL, - SUBJECT_ID INT NOT NULL, - HADM_ID INT NOT NULL, - SUBMIT_WARDID INT, - SUBMIT_CAREUNIT VARCHAR(15), - CURR_WARDID INT, - CURR_CAREUNIT VARCHAR(15), - CALLOUT_WARDID INT, - CALLOUT_SERVICE VARCHAR(10) NOT NULL, - REQUEST_TELE SMALLINT NOT NULL, - REQUEST_RESP SMALLINT NOT NULL, - REQUEST_CDIFF SMALLINT NOT NULL, - REQUEST_MRSA SMALLINT NOT NULL, - REQUEST_VRE SMALLINT NOT NULL, - CALLOUT_STATUS VARCHAR(20) NOT NULL, - CALLOUT_OUTCOME VARCHAR(20) NOT NULL, - DISCHARGE_WARDID INT, - ACKNOWLEDGE_STATUS VARCHAR(20) NOT NULL, - CREATETIME TIMESTAMP(0) NOT NULL, - UPDATETIME TIMESTAMP(0) NOT NULL, - ACKNOWLEDGETIME TIMESTAMP(0), - OUTCOMETIME TIMESTAMP(0) NOT NULL, - FIRSTRESERVATIONTIME TIMESTAMP(0), - CURRENTRESERVATIONTIME TIMESTAMP(0), - CONSTRAINT callout_rowid_pk PRIMARY KEY (ROW_ID) - ); +( + ROW_ID INT NOT NULL, + SUBJECT_ID INT NOT NULL, + HADM_ID INT NOT NULL, + SUBMIT_WARDID INT, + SUBMIT_CAREUNIT VARCHAR(15), + CURR_WARDID INT, + CURR_CAREUNIT VARCHAR(15), + CALLOUT_WARDID INT, + CALLOUT_SERVICE VARCHAR(10) NOT NULL, + REQUEST_TELE SMALLINT NOT NULL, + REQUEST_RESP SMALLINT NOT NULL, + REQUEST_CDIFF SMALLINT NOT NULL, + REQUEST_MRSA SMALLINT NOT NULL, + REQUEST_VRE SMALLINT NOT NULL, + CALLOUT_STATUS VARCHAR(20) NOT NULL, + CALLOUT_OUTCOME VARCHAR(20) NOT NULL, + DISCHARGE_WARDID INT, + ACKNOWLEDGE_STATUS VARCHAR(20) NOT NULL, + CREATETIME TIMESTAMP(0) NOT NULL, + UPDATETIME TIMESTAMP(0) NOT NULL, + ACKNOWLEDGETIME TIMESTAMP(0), + OUTCOMETIME TIMESTAMP(0) NOT NULL, + FIRSTRESERVATIONTIME TIMESTAMP(0), + CURRENTRESERVATIONTIME TIMESTAMP(0), + CONSTRAINT callout_rowid_pk PRIMARY KEY (ROW_ID) +) ; -------------------------------------------------------- -- DDL for Table CAREGIVERS @@ -93,13 +94,14 @@ CREATE TABLE CALLOUT DROP TABLE IF EXISTS CAREGIVERS CASCADE; CREATE TABLE CAREGIVERS - ( ROW_ID INT NOT NULL, +( + ROW_ID INT NOT NULL, CGID INT NOT NULL, LABEL VARCHAR(15), DESCRIPTION VARCHAR(30), CONSTRAINT cg_rowid_pk PRIMARY KEY (ROW_ID), CONSTRAINT cg_cgid_unique UNIQUE (CGID) - ) ; +) ; -------------------------------------------------------- -- DDL for Table CHARTEVENTS @@ -107,7 +109,8 @@ CREATE TABLE CAREGIVERS DROP TABLE IF EXISTS CHARTEVENTS CASCADE; CREATE TABLE CHARTEVENTS - ( ROW_ID INT NOT NULL, +( + ROW_ID INT NOT NULL, SUBJECT_ID INT NOT NULL, HADM_ID INT, ICUSTAY_ID INT, @@ -123,8 +126,7 @@ CREATE TABLE CHARTEVENTS RESULTSTATUS VARCHAR(50), STOPPED VARCHAR(50), CONSTRAINT chartevents_rowid_pk PRIMARY KEY (ROW_ID) - ); - +) ; -------------------------------------------------------- -- PARTITION for Table CHARTEVENTS @@ -150,8 +152,6 @@ CREATE TABLE chartevents_14 ( CHECK ( itemid >= 220074 AND itemid < 323769 )) I CREATE OR REPLACE FUNCTION chartevents_insert_trigger() RETURNS TRIGGER AS $$ BEGIN - - IF ( NEW.itemid >= 1 AND NEW.itemid < 210 ) THEN INSERT INTO chartevents_1 VALUES (NEW.*); ELSIF ( NEW.itemid >= 210 AND NEW.itemid < 250 ) THEN INSERT INTO chartevents_2 VALUES (NEW.*); ELSIF ( NEW.itemid >= 250 AND NEW.itemid < 614 ) THEN INSERT INTO chartevents_3 VALUES (NEW.*); @@ -166,9 +166,9 @@ ELSIF ( NEW.itemid >= 6000 AND NEW.itemid < 7000 ) THEN INSERT INTO chartevents_ ELSIF ( NEW.itemid >= 7000 AND NEW.itemid < 8000 ) THEN INSERT INTO chartevents_12 VALUES (NEW.*); ELSIF ( NEW.itemid >= 8000 AND NEW.itemid < 220074 ) THEN INSERT INTO chartevents_13 VALUES (NEW.*); ELSIF ( NEW.itemid >= 220074 AND NEW.itemid < 323769 ) THEN INSERT INTO chartevents_14 VALUES (NEW.*); - ELSE - INSERT INTO chartevents_null VALUES (NEW.*); - END IF; +ELSE + INSERT INTO chartevents_null VALUES (NEW.*); +END IF; RETURN NULL; END; $$ @@ -178,30 +178,14 @@ CREATE TRIGGER insert_chartevents_trigger BEFORE INSERT ON chartevents FOR EACH ROW EXECUTE PROCEDURE chartevents_insert_trigger(); --- For consistency, index creation codes are moved to the postgres_add_index.sql - ---CREATE INDEX chartevents_1_idx01 ON chartevents_1 (itemid); ---CREATE INDEX chartevents_2_idx01 ON chartevents_2 (itemid); ---CREATE INDEX chartevents_3_idx01 ON chartevents_3 (itemid); ---CREATE INDEX chartevents_4_idx01 ON chartevents_4 (itemid); ---CREATE INDEX chartevents_5_idx01 ON chartevents_5 (itemid); ---CREATE INDEX chartevents_6_idx01 ON chartevents_6 (itemid); ---CREATE INDEX chartevents_7_idx01 ON chartevents_7 (itemid); ---CREATE INDEX chartevents_8_idx01 ON chartevents_8 (itemid); ---CREATE INDEX chartevents_9_idx01 ON chartevents_9 (itemid); ---CREATE INDEX chartevents_10_idx01 ON chartevents_10 (itemid); ---CREATE INDEX chartevents_11_idx01 ON chartevents_11 (itemid); ---CREATE INDEX chartevents_12_idx01 ON chartevents_12 (itemid); ---CREATE INDEX chartevents_13_idx01 ON chartevents_13 (itemid); ---CREATE INDEX chartevents_14_idx01 ON chartevents_14 (itemid); - -------------------------------------------------------- -- DDL for Table CPTEVENTS -------------------------------------------------------- DROP TABLE IF EXISTS CPTEVENTS CASCADE; CREATE TABLE CPTEVENTS - ( ROW_ID INT NOT NULL, +( + ROW_ID INT NOT NULL, SUBJECT_ID INT NOT NULL, HADM_ID INT NOT NULL, COSTCENTER VARCHAR(10) NOT NULL, @@ -214,7 +198,7 @@ CREATE TABLE CPTEVENTS SUBSECTIONHEADER VARCHAR(255), DESCRIPTION VARCHAR(200), CONSTRAINT cpt_rowid_pk PRIMARY KEY (ROW_ID) - ) ; +) ; -------------------------------------------------------- -- DDL for Table DATETIMEEVENTS @@ -222,7 +206,8 @@ CREATE TABLE CPTEVENTS DROP TABLE IF EXISTS DATETIMEEVENTS CASCADE; CREATE TABLE DATETIMEEVENTS - ( ROW_ID INT NOT NULL, +( + ROW_ID INT NOT NULL, SUBJECT_ID INT NOT NULL, HADM_ID INT, ICUSTAY_ID INT, @@ -237,7 +222,7 @@ CREATE TABLE DATETIMEEVENTS RESULTSTATUS VARCHAR(50), STOPPED VARCHAR(50), CONSTRAINT datetime_rowid_pk PRIMARY KEY (ROW_ID) - ) ; +) ; -------------------------------------------------------- -- DDL for Table DIAGNOSES_ICD @@ -245,13 +230,14 @@ CREATE TABLE DATETIMEEVENTS DROP TABLE IF EXISTS DIAGNOSES_ICD CASCADE; CREATE TABLE DIAGNOSES_ICD - ( ROW_ID INT NOT NULL, +( + ROW_ID INT NOT NULL, SUBJECT_ID INT NOT NULL, HADM_ID INT NOT NULL, SEQ_NUM INT, - ICD9_CODE VARCHAR(20), + ICD9_CODE VARCHAR(10), CONSTRAINT diagnosesicd_rowid_pk PRIMARY KEY (ROW_ID) - ) ; +) ; -------------------------------------------------------- -- DDL for Table DRGCODES @@ -259,7 +245,8 @@ CREATE TABLE DIAGNOSES_ICD DROP TABLE IF EXISTS DRGCODES CASCADE; CREATE TABLE DRGCODES - ( ROW_ID INT NOT NULL, +( + ROW_ID INT NOT NULL, SUBJECT_ID INT NOT NULL, HADM_ID INT NOT NULL, DRG_TYPE VARCHAR(20) NOT NULL, @@ -268,7 +255,7 @@ CREATE TABLE DRGCODES DRG_SEVERITY SMALLINT, DRG_MORTALITY SMALLINT, CONSTRAINT drg_rowid_pk PRIMARY KEY (ROW_ID) - ) ; +) ; -------------------------------------------------------- -- DDL for Table D_CPT @@ -276,7 +263,8 @@ CREATE TABLE DRGCODES DROP TABLE IF EXISTS D_CPT CASCADE; CREATE TABLE D_CPT - ( ROW_ID INT NOT NULL, +( + ROW_ID INT NOT NULL, CATEGORY SMALLINT NOT NULL, SECTIONRANGE VARCHAR(100) NOT NULL, SECTIONHEADER VARCHAR(50) NOT NULL, @@ -285,9 +273,9 @@ CREATE TABLE D_CPT CODESUFFIX VARCHAR(5), MINCODEINSUBSECTION INT NOT NULL, MAXCODEINSUBSECTION INT NOT NULL, - CONSTRAINT dcpt_ssrange_unique UNIQUE (SUBSECTIONRANGE), - CONSTRAINT dcpt_rowid_pk PRIMARY KEY (ROW_ID) - ) ; + CONSTRAINT dcpt_ssrange_unique UNIQUE (SUBSECTIONRANGE), + CONSTRAINT dcpt_rowid_pk PRIMARY KEY (ROW_ID) +) ; -------------------------------------------------------- -- DDL for Table D_ICD_DIAGNOSES @@ -295,13 +283,14 @@ CREATE TABLE D_CPT DROP TABLE IF EXISTS D_ICD_DIAGNOSES CASCADE; CREATE TABLE D_ICD_DIAGNOSES - ( ROW_ID INT NOT NULL, +( + ROW_ID INT NOT NULL, ICD9_CODE VARCHAR(10) NOT NULL, SHORT_TITLE VARCHAR(50) NOT NULL, LONG_TITLE VARCHAR(255) NOT NULL, - CONSTRAINT d_icd_diag_code_unique UNIQUE (ICD9_CODE), - CONSTRAINT d_icd_diag_rowid_pk PRIMARY KEY (ROW_ID) - ) ; + CONSTRAINT d_icd_diag_code_unique UNIQUE (ICD9_CODE), + CONSTRAINT d_icd_diag_rowid_pk PRIMARY KEY (ROW_ID) +) ; -------------------------------------------------------- -- DDL for Table D_ICD_PROCEDURES @@ -309,13 +298,14 @@ CREATE TABLE D_ICD_DIAGNOSES DROP TABLE IF EXISTS D_ICD_PROCEDURES CASCADE; CREATE TABLE D_ICD_PROCEDURES - ( ROW_ID INT NOT NULL, +( + ROW_ID INT NOT NULL, ICD9_CODE VARCHAR(10) NOT NULL, SHORT_TITLE VARCHAR(50) NOT NULL, LONG_TITLE VARCHAR(255) NOT NULL, - CONSTRAINT d_icd_proc_code_unique UNIQUE (ICD9_CODE), - CONSTRAINT d_icd_proc_rowid_pk PRIMARY KEY (ROW_ID) - ) ; + CONSTRAINT d_icd_proc_code_unique UNIQUE (ICD9_CODE), + CONSTRAINT d_icd_proc_rowid_pk PRIMARY KEY (ROW_ID) +) ; -------------------------------------------------------- -- DDL for Table D_ITEMS @@ -323,19 +313,20 @@ CREATE TABLE D_ICD_PROCEDURES DROP TABLE IF EXISTS D_ITEMS CASCADE; CREATE TABLE D_ITEMS - ( ROW_ID INT NOT NULL, - ITEMID INT NOT NULL, - LABEL VARCHAR(200), - ABBREVIATION VARCHAR(100), - DBSOURCE VARCHAR(20), - LINKSTO VARCHAR(50), - CATEGORY VARCHAR(100), - UNITNAME VARCHAR(100), - PARAM_TYPE VARCHAR(30), - CONCEPTID INT, - CONSTRAINT ditems_itemid_unique UNIQUE (ITEMID), - CONSTRAINT ditems_rowid_pk PRIMARY KEY (ROW_ID) - ) ; +( + ROW_ID INT NOT NULL, + ITEMID INT NOT NULL, + LABEL VARCHAR(200), + ABBREVIATION VARCHAR(100), + DBSOURCE VARCHAR(20), + LINKSTO VARCHAR(50), + CATEGORY VARCHAR(100), + UNITNAME VARCHAR(100), + PARAM_TYPE VARCHAR(30), + CONCEPTID INT, + CONSTRAINT ditems_itemid_unique UNIQUE (ITEMID), + CONSTRAINT ditems_rowid_pk PRIMARY KEY (ROW_ID) +) ; -------------------------------------------------------- -- DDL for Table D_LABITEMS @@ -343,15 +334,16 @@ CREATE TABLE D_ITEMS DROP TABLE IF EXISTS D_LABITEMS CASCADE; CREATE TABLE D_LABITEMS - ( ROW_ID INT NOT NULL, +( + ROW_ID INT NOT NULL, ITEMID INT NOT NULL, LABEL VARCHAR(100) NOT NULL, FLUID VARCHAR(100) NOT NULL, CATEGORY VARCHAR(100) NOT NULL, LOINC_CODE VARCHAR(100), - CONSTRAINT dlabitems_itemid_unique UNIQUE (ITEMID), - CONSTRAINT dlabitems_rowid_pk PRIMARY KEY (ROW_ID) - ) ; + CONSTRAINT dlabitems_itemid_unique UNIQUE (ITEMID), + CONSTRAINT dlabitems_rowid_pk PRIMARY KEY (ROW_ID) +) ; -------------------------------------------------------- -- DDL for Table ICUSTAYS @@ -359,7 +351,8 @@ CREATE TABLE D_LABITEMS DROP TABLE IF EXISTS ICUSTAYS CASCADE; CREATE TABLE ICUSTAYS - ( ROW_ID INT NOT NULL, +( + ROW_ID INT NOT NULL, SUBJECT_ID INT NOT NULL, HADM_ID INT NOT NULL, ICUSTAY_ID INT NOT NULL, @@ -371,9 +364,9 @@ CREATE TABLE ICUSTAYS INTIME TIMESTAMP(0) NOT NULL, OUTTIME TIMESTAMP(0), LOS DOUBLE PRECISION, - CONSTRAINT icustay_icustayid_unique UNIQUE (ICUSTAY_ID), - CONSTRAINT icustay_rowid_pk PRIMARY KEY (ROW_ID) - ) ; + CONSTRAINT icustay_icustayid_unique UNIQUE (ICUSTAY_ID), + CONSTRAINT icustay_rowid_pk PRIMARY KEY (ROW_ID) +) ; -------------------------------------------------------- -- DDL for Table INPUTEVENTS_CV @@ -381,7 +374,8 @@ CREATE TABLE ICUSTAYS DROP TABLE IF EXISTS INPUTEVENTS_CV CASCADE; CREATE TABLE INPUTEVENTS_CV - ( ROW_ID INT NOT NULL, +( + ROW_ID INT NOT NULL, SUBJECT_ID INT NOT NULL, HADM_ID INT, ICUSTAY_ID INT, @@ -404,7 +398,7 @@ CREATE TABLE INPUTEVENTS_CV ORIGINALRATEUOM VARCHAR(30), ORIGINALSITE VARCHAR(30), CONSTRAINT inputevents_cv_rowid_pk PRIMARY KEY (ROW_ID) - ) ; +) ; -------------------------------------------------------- -- DDL for Table INPUTEVENTS_MV @@ -412,7 +406,8 @@ CREATE TABLE INPUTEVENTS_CV DROP TABLE IF EXISTS INPUTEVENTS_MV CASCADE; CREATE TABLE INPUTEVENTS_MV - ( ROW_ID INT NOT NULL, +( + ROW_ID INT NOT NULL, SUBJECT_ID INT NOT NULL, HADM_ID INT, ICUSTAY_ID INT, @@ -444,7 +439,7 @@ CREATE TABLE INPUTEVENTS_MV ORIGINALAMOUNT DOUBLE PRECISION, ORIGINALRATE DOUBLE PRECISION, CONSTRAINT inputevents_mv_rowid_pk PRIMARY KEY (ROW_ID) - ) ; +) ; -------------------------------------------------------- -- DDL for Table LABEVENTS @@ -452,7 +447,8 @@ CREATE TABLE INPUTEVENTS_MV DROP TABLE IF EXISTS LABEVENTS CASCADE; CREATE TABLE LABEVENTS - ( ROW_ID INT NOT NULL, +( + ROW_ID INT NOT NULL, SUBJECT_ID INT NOT NULL, HADM_ID INT, ITEMID INT NOT NULL, @@ -462,7 +458,7 @@ CREATE TABLE LABEVENTS VALUEUOM VARCHAR(20), FLAG VARCHAR(20), CONSTRAINT labevents_rowid_pk PRIMARY KEY (ROW_ID) - ) ; +) ; -------------------------------------------------------- -- DDL for Table MICROBIOLOGYEVENTS @@ -470,7 +466,8 @@ CREATE TABLE LABEVENTS DROP TABLE IF EXISTS MICROBIOLOGYEVENTS CASCADE; CREATE TABLE MICROBIOLOGYEVENTS - ( ROW_ID INT NOT NULL, +( + ROW_ID INT NOT NULL, SUBJECT_ID INT NOT NULL, HADM_ID INT, CHARTDATE TIMESTAMP(0), @@ -487,7 +484,7 @@ CREATE TABLE MICROBIOLOGYEVENTS DILUTION_VALUE DOUBLE PRECISION, INTERPRETATION VARCHAR(5), CONSTRAINT micro_rowid_pk PRIMARY KEY (ROW_ID) - ) ; +) ; -------------------------------------------------------- -- DDL for Table NOTEEVENTS @@ -495,7 +492,8 @@ CREATE TABLE MICROBIOLOGYEVENTS DROP TABLE IF EXISTS NOTEEVENTS CASCADE; CREATE TABLE NOTEEVENTS - ( ROW_ID INT NOT NULL, +( + ROW_ID INT NOT NULL, SUBJECT_ID INT NOT NULL, HADM_ID INT, CHARTDATE TIMESTAMP(0), @@ -507,7 +505,7 @@ CREATE TABLE NOTEEVENTS ISERROR CHAR(1), TEXT TEXT, CONSTRAINT noteevents_rowid_pk PRIMARY KEY (ROW_ID) - ) ; +) ; -------------------------------------------------------- -- DDL for Table OUTPUTEVENTS @@ -515,7 +513,8 @@ CREATE TABLE NOTEEVENTS DROP TABLE IF EXISTS OUTPUTEVENTS CASCADE; CREATE TABLE OUTPUTEVENTS - ( ROW_ID INT NOT NULL, +( + ROW_ID INT NOT NULL, SUBJECT_ID INT NOT NULL, HADM_ID INT, ICUSTAY_ID INT, @@ -529,7 +528,7 @@ CREATE TABLE OUTPUTEVENTS NEWBOTTLE CHAR(1), ISERROR INT, CONSTRAINT outputevents_cv_rowid_pk PRIMARY KEY (ROW_ID) - ) ; +) ; -------------------------------------------------------- -- DDL for Table PATIENTS @@ -537,7 +536,8 @@ CREATE TABLE OUTPUTEVENTS DROP TABLE IF EXISTS PATIENTS CASCADE; CREATE TABLE PATIENTS - ( ROW_ID INT NOT NULL, +( + ROW_ID INT NOT NULL, SUBJECT_ID INT NOT NULL, GENDER VARCHAR(5) NOT NULL, DOB TIMESTAMP(0) NOT NULL, @@ -545,9 +545,9 @@ CREATE TABLE PATIENTS DOD_HOSP TIMESTAMP(0), DOD_SSN TIMESTAMP(0), EXPIRE_FLAG INT NOT NULL, - CONSTRAINT pat_subid_unique UNIQUE (SUBJECT_ID), - CONSTRAINT pat_rowid_pk PRIMARY KEY (ROW_ID) - ) ; + CONSTRAINT pat_subid_unique UNIQUE (SUBJECT_ID), + CONSTRAINT pat_rowid_pk PRIMARY KEY (ROW_ID) +) ; -------------------------------------------------------- -- DDL for Table PRESCRIPTIONS @@ -555,7 +555,8 @@ CREATE TABLE PATIENTS DROP TABLE IF EXISTS PRESCRIPTIONS CASCADE; CREATE TABLE PRESCRIPTIONS - ( ROW_ID INT NOT NULL, +( + ROW_ID INT NOT NULL, SUBJECT_ID INT NOT NULL, HADM_ID INT NOT NULL, ICUSTAY_ID INT, @@ -575,7 +576,7 @@ CREATE TABLE PRESCRIPTIONS FORM_UNIT_DISP VARCHAR(120), ROUTE VARCHAR(120), CONSTRAINT prescription_rowid_pk PRIMARY KEY (ROW_ID) - ) ; +) ; -------------------------------------------------------- -- DDL for Table PROCEDUREEVENTS_MV @@ -583,7 +584,8 @@ CREATE TABLE PRESCRIPTIONS DROP TABLE IF EXISTS PROCEDUREEVENTS_MV CASCADE; CREATE TABLE PROCEDUREEVENTS_MV - ( ROW_ID INT NOT NULL, +( + ROW_ID INT NOT NULL, SUBJECT_ID INT NOT NULL, HADM_ID INT NOT NULL, ICUSTAY_ID INT, @@ -609,7 +611,7 @@ CREATE TABLE PROCEDUREEVENTS_MV COMMENTS_CANCELEDBY VARCHAR(30), COMMENTS_DATE TIMESTAMP(0), CONSTRAINT procedureevents_mv_rowid_pk PRIMARY KEY (ROW_ID) - ) ; +) ; -------------------------------------------------------- -- DDL for Table PROCEDURES_ICD @@ -617,13 +619,14 @@ CREATE TABLE PROCEDUREEVENTS_MV DROP TABLE IF EXISTS PROCEDURES_ICD CASCADE; CREATE TABLE PROCEDURES_ICD - ( ROW_ID INT NOT NULL, +( + ROW_ID INT NOT NULL, SUBJECT_ID INT NOT NULL, HADM_ID INT NOT NULL, SEQ_NUM INT NOT NULL, - ICD9_CODE VARCHAR(20) NOT NULL, + ICD9_CODE VARCHAR(10) NOT NULL, CONSTRAINT proceduresicd_rowid_pk PRIMARY KEY (ROW_ID) - ) ; +) ; -------------------------------------------------------- -- DDL for Table SERVICES @@ -631,14 +634,15 @@ CREATE TABLE PROCEDURES_ICD DROP TABLE IF EXISTS SERVICES CASCADE; CREATE TABLE SERVICES - ( ROW_ID INT NOT NULL, +( + ROW_ID INT NOT NULL, SUBJECT_ID INT NOT NULL, HADM_ID INT NOT NULL, TRANSFERTIME TIMESTAMP(0) NOT NULL, PREV_SERVICE VARCHAR(20), CURR_SERVICE VARCHAR(20), CONSTRAINT services_rowid_pk PRIMARY KEY (ROW_ID) - ) ; +) ; -------------------------------------------------------- -- DDL for Table TRANSFERS @@ -646,7 +650,8 @@ CREATE TABLE SERVICES DROP TABLE IF EXISTS TRANSFERS CASCADE; CREATE TABLE TRANSFERS - ( ROW_ID INT NOT NULL, +( + ROW_ID INT NOT NULL, SUBJECT_ID INT NOT NULL, HADM_ID INT NOT NULL, ICUSTAY_ID INT, @@ -660,4 +665,4 @@ CREATE TABLE TRANSFERS OUTTIME TIMESTAMP(0), LOS DOUBLE PRECISION, CONSTRAINT transfers_rowid_pk PRIMARY KEY (ROW_ID) - ) ; +) ; diff --git a/buildmimic/postgres/postgres_load_data.sql b/buildmimic/postgres/postgres_load_data.sql index c821253..8130f93 100644 --- a/buildmimic/postgres/postgres_load_data.sql +++ b/buildmimic/postgres/postgres_load_data.sql @@ -13,8 +13,8 @@ -- Change to the directory containing the data files \cd :mimic_data_dir --- Define the schema where all tables are created -SET search_path TO mimiciii; +-- If running scripts individually, you can set the schema where all tables are created as follows: +-- SET search_path TO mimiciii; -- Restoring the search path to its default value can be accomplished as follows: -- SET search_path TO "$user",public; diff --git a/buildmimic/postgres/postgres_load_data_7zip.sql b/buildmimic/postgres/postgres_load_data_7zip.sql new file mode 100644 index 0000000..1ea1624 --- /dev/null +++ b/buildmimic/postgres/postgres_load_data_7zip.sql @@ -0,0 +1,183 @@ +-- ------------------------------------------------------------------------------- +-- +-- Load data into the MIMIC-III schema +-- +-- ------------------------------------------------------------------------------- + +-------------------------------------------------------- +-- File created - Thursday-August-27-2015 +-------------------------------------------------------- + +\set ON_ERROR_STOP 1 + +-- Change to the directory containing the data files +\cd :mimic_data_dir + +-- If running scripts individually, you can set the schema where all tables are created as follows: +-- SET search_path TO mimiciii; + +-- Restoring the search path to its default value can be accomplished as follows: +-- SET search_path TO "$user",public; + +/* Set the mimic_data_dir variable to point to directory containing + all .csv files. If using Docker, this should not be changed here. + Rather, when running the docker container, use the -v option + to have Docker mount a host volume to the container path /mimic_data + as explained in the README file +*/ + +-------------------------------------------------------- +-- Load Data for Table ADMISSIONS +-------------------------------------------------------- + +\copy ADMISSIONS FROM PROGRAM '7z e -so ADMISSIONS.csv.gz' DELIMITER ',' CSV HEADER NULL '' + +-------------------------------------------------------- +-- Load Data for Table CALLOUT +-------------------------------------------------------- + +\copy CALLOUT from PROGRAM '7z e -so CALLOUT.csv.gz' delimiter ',' csv header NULL '' + +-------------------------------------------------------- +-- Load Data for Table CAREGIVERS +-------------------------------------------------------- + +\copy CAREGIVERS from PROGRAM '7z e -so CAREGIVERS.csv.gz' delimiter ',' csv header NULL '' + +-------------------------------------------------------- +-- Load Data for Table CHARTEVENTS +-------------------------------------------------------- + +\copy CHARTEVENTS from PROGRAM '7z e -so CHARTEVENTS.csv.gz' delimiter ',' csv header NULL '' + +-------------------------------------------------------- +-- Load Data for Table CPTEVENTS +-------------------------------------------------------- + +\copy CPTEVENTS from PROGRAM '7z e -so CPTEVENTS.csv.gz' delimiter ',' csv header NULL '' + +-------------------------------------------------------- +-- Load Data for Table DATETIMEEVENTS +-------------------------------------------------------- + +\copy DATETIMEEVENTS from PROGRAM '7z e -so DATETIMEEVENTS.csv.gz' delimiter ',' csv header NULL '' + +-------------------------------------------------------- +-- Load Data for Table DIAGNOSES_ICD +-------------------------------------------------------- + +\copy DIAGNOSES_ICD from PROGRAM '7z e -so DIAGNOSES_ICD.csv.gz' delimiter ',' csv header NULL '' + +-------------------------------------------------------- +-- Load Data for Table DRGCODES +-------------------------------------------------------- + +\copy DRGCODES from PROGRAM '7z e -so DRGCODES.csv.gz' delimiter ',' csv header NULL '' + +-------------------------------------------------------- +-- Load Data for Table D_CPT +-------------------------------------------------------- + +\copy D_CPT from PROGRAM '7z e -so D_CPT.csv.gz' delimiter ',' csv header NULL '' + +-------------------------------------------------------- +-- Load Data for Table D_ICD_DIAGNOSES +-------------------------------------------------------- + +\copy D_ICD_DIAGNOSES from PROGRAM '7z e -so D_ICD_DIAGNOSES.csv.gz' delimiter ',' csv header NULL '' + +-------------------------------------------------------- +-- Load Data for Table D_ICD_PROCEDURES +-------------------------------------------------------- + +\copy D_ICD_PROCEDURES from PROGRAM '7z e -so D_ICD_PROCEDURES.csv.gz' delimiter ',' csv header NULL '' + +-------------------------------------------------------- +-- Load Data for Table D_ITEMS +-------------------------------------------------------- + +\copy D_ITEMS from PROGRAM '7z e -so D_ITEMS.csv.gz' delimiter ',' csv header NULL '' + +-------------------------------------------------------- +-- Load Data for Table D_LABITEMS +-------------------------------------------------------- + +\copy D_LABITEMS from PROGRAM '7z e -so D_LABITEMS.csv.gz' delimiter ',' csv header NULL '' + +-------------------------------------------------------- +-- Load Data for Table ICUSTAYS +-------------------------------------------------------- + +\copy ICUSTAYS from PROGRAM '7z e -so ICUSTAYS.csv.gz' delimiter ',' csv header NULL '' + +-------------------------------------------------------- +-- Load Data for Table INPUTEVENTS_CV +-------------------------------------------------------- + +\copy INPUTEVENTS_CV from PROGRAM '7z e -so INPUTEVENTS_CV.csv.gz' delimiter ',' csv header NULL '' + +-------------------------------------------------------- +-- Load Data for Table INPUTEVENTS_MV +-------------------------------------------------------- + +\copy INPUTEVENTS_MV from PROGRAM '7z e -so INPUTEVENTS_MV.csv.gz' delimiter ',' csv header NULL '' + +-------------------------------------------------------- +-- Load Data for Table LABEVENTS +-------------------------------------------------------- + +\copy LABEVENTS from PROGRAM '7z e -so LABEVENTS.csv.gz' delimiter ',' csv header NULL '' + +-------------------------------------------------------- +-- Load Data for Table MICROBIOLOGYEVENTS +-------------------------------------------------------- + +\copy MICROBIOLOGYEVENTS from PROGRAM '7z e -so MICROBIOLOGYEVENTS.csv.gz' delimiter ',' csv header NULL '' + +-------------------------------------------------------- +-- Load Data for Table NOTEEVENTS +-------------------------------------------------------- + +\copy NOTEEVENTS from PROGRAM '7z e -so NOTEEVENTS.csv.gz' delimiter ',' csv header NULL '' + +-------------------------------------------------------- +-- Load Data for Table OUTPUTEVENTS +-------------------------------------------------------- + +\copy OUTPUTEVENTS from PROGRAM '7z e -so OUTPUTEVENTS.csv.gz' delimiter ',' csv header NULL '' + +-------------------------------------------------------- +-- Load Data for Table PATIENTS +-------------------------------------------------------- + +\copy PATIENTS from PROGRAM '7z e -so PATIENTS.csv.gz' delimiter ',' csv header NULL '' + +-------------------------------------------------------- +-- Load Data for Table PRESCRIPTIONS +-------------------------------------------------------- + +\copy PRESCRIPTIONS from PROGRAM '7z e -so PRESCRIPTIONS.csv.gz' delimiter ',' csv header NULL '' + +-------------------------------------------------------- +-- Load Data for Table PROCEDUREEVENTS_MV +-------------------------------------------------------- + +\copy PROCEDUREEVENTS_MV from PROGRAM '7z e -so PROCEDUREEVENTS_MV.csv.gz' delimiter ',' csv header NULL '' + +-------------------------------------------------------- +-- Load Data for Table PROCEDURES_ICD +-------------------------------------------------------- + +\copy PROCEDURES_ICD from PROGRAM '7z e -so PROCEDURES_ICD.csv.gz' delimiter ',' csv header NULL '' + +-------------------------------------------------------- +-- Load Data for Table SERVICES +-------------------------------------------------------- + +\copy SERVICES from PROGRAM '7z e -so SERVICES.csv.gz' delimiter ',' csv header NULL '' + +-------------------------------------------------------- +-- Load Data for Table TRANSFERS +-------------------------------------------------------- + +\copy TRANSFERS from PROGRAM '7z e -so TRANSFERS.csv.gz' delimiter ',' csv header NULL '' diff --git a/buildmimic/postgres/postgres_load_data_gz.sql b/buildmimic/postgres/postgres_load_data_gz.sql index d8c6c06..81bc99e 100644 --- a/buildmimic/postgres/postgres_load_data_gz.sql +++ b/buildmimic/postgres/postgres_load_data_gz.sql @@ -13,8 +13,8 @@ -- Change to the directory containing the data files \cd :mimic_data_dir --- Define the schema where all tables are created -SET search_path TO mimiciii; +-- If running scripts individually, you can set the schema where all tables are created as follows: +-- SET search_path TO mimiciii; -- Restoring the search path to its default value can be accomplished as follows: -- SET search_path TO "$user",public; diff --git a/concepts/README.md b/concepts/README.md new file mode 100644 index 0000000..0e82d88 --- /dev/null +++ b/concepts/README.md @@ -0,0 +1,43 @@ +# Contents of this folder + +This folder contains scripts to generate materialized views in a PostgreSQL database with MIMIC installed. If you do not have access to a PostgreSQL database with MIMIC, you can read more about it in the [buildmimic](https://github.com/MIT-LCP/mimic-code/tree/master/buildmimic/postgres) folder. + +Concepts are categorized into folders if possible, otherwise they remain in the top-level directory. + +# Subfolders + +## comorbidity + +These scripts derive binary flags indicating the presence of various comorbidities using billing codes (ICD-9) assigned to the patient at hospital discharge. + +## cookbook + +This is an asortment of scripts intended to give the user more familiarity with the MIMIC-III database. None of these scripts generate materialized views. + +## demographics + +Summary of patient/admission level information such as age, height, weight, etc. + +## firstday + +The first day subfolder contains scripts used to calculate various clinical concepts on the first day of a patient's admission to the ICU, such as the highest blood pressure, lowest temperature, etc. This folder contains many useful scripts which can be adapted to capture data outside the first day. + +## functions + +Useful snippets of SQL implementing common functions. For example, the `auroc.sql` file calculates the area under the receiver operator characteristic curve (AUROC) for a set of predictions, `PRED`, given a set of targets, `TAR`. The AUROC is a useful measure of the discrimination of a set of predictions. + +## other-languages + +Scripts in flavours of SQL which are not necessarily compatible with PostgreSQL. + +## sepsis + +Definitions of sepsis, a common cause of mortality for intensive care unit patients. + +## severityscores + +Severity of illness scores which summarize the acuity of a patient's illness on admission to the intensive care unit (usually in the first 24 hours). + +## durations + +Start and stop times for administration of various treatments or durations of various phenomena, including: medical agents which have a vasoactive effect on a patient's circulatory system, continuous renal replacement therapy (CRRT), and mechanical ventilation. diff --git a/concepts/code-status.sql b/concepts/code-status.sql new file mode 100644 index 0000000..2618d63 --- /dev/null +++ b/concepts/code-status.sql @@ -0,0 +1,119 @@ +-- This query extracts: +-- i) a patient's first code status +-- ii) a patient's last code status +-- iii) the time of the first entry of DNR or CMO + +DROP MATERIALIZED VIEW IF EXISTS CODE_STATUS; +CREATE MATERIALIZED VIEW CODE_STATUS AS +with t1 as +( + select icustay_id, charttime, value + -- use row number to identify first and last code status + , ROW_NUMBER() over (PARTITION BY icustay_id order by charttime) as rnFirst + , ROW_NUMBER() over (PARTITION BY icustay_id order by charttime desc) as rnLast + + -- coalesce the values + , case + when value in ('Full Code','Full code') then 1 + else 0 end as FullCode + , case + when value in ('Comfort Measures','Comfort measures only') then 1 + else 0 end as CMO + , case + when value = 'CPR Not Indicate' then 1 + else 0 end as DNCPR -- only in CareVue, i.e. only possible for ~60-70% of patients + , case + when value in ('Do Not Intubate','DNI (do not intubate)','DNR / DNI') then 1 + else 0 end as DNI + , case + when value in ('Do Not Resuscita','DNR (do not resuscitate)','DNR / DNI') then 1 + else 0 end as DNR + from chartevents + where itemid in (128, 223758) + and value is not null + and value != 'Other/Remarks' + -- exclude rows marked as error + AND error IS DISTINCT FROM 1 +) +-- examine the discharge summaries to determine if they were ever made cmo +, disch as +( + select + ne.hadm_id + , max(case + when substring(substring(text from '[^E]CMO') from 2 for 3) = 'CMO' + then 1 + else 0 + end) as CMO + -- + -- , case + -- when substring(text from '^[E]CMO') as CMO + from mimiciii.noteevents ne + where category = 'Discharge summary' + and text like '%CMO%' + group by hadm_id +) +-- examine the notes to determine if they were ever made cmo +, nnote as +( + select + hadm_id, charttime + , max(case + when substring(text from 'made CMO') != '' then 1 + when substring(lower(text) from 'cmo ordered') != '' then 1 + when substring(lower(text) from 'pt. is cmo') != '' then 1 + when substring(text from 'Code status:([ \r\n]+)Comfort measures only') != '' then 1 + --when substring(text from 'made CMO') != '' then 1 + --when substring(substring(text from '[^E]CMO') from 2 for 3) = 'CMO' + -- then 1 + else 0 + end) as CMO + from mimiciii.noteevents ne + where category in ('Nursing/other','Nursing','Physician') + and lower(text) like '%cmo%' + group by hadm_id, charttime +) +select ie.subject_id, ie.hadm_id, ie.icustay_id + -- first recorded code status + , max(case when rnFirst = 1 then t1.FullCode else null end) as FullCode_first + , max(case when rnFirst = 1 then t1.CMO else null end) as CMO_first + , max(case when rnFirst = 1 then t1.DNR else null end) as DNR_first + , max(case when rnFirst = 1 then t1.DNI else null end) as DNI_first + , max(case when rnFirst = 1 then t1.DNCPR else null end) as DNCPR_first + + -- last recorded code status + , max(case when rnLast = 1 then t1.FullCode else null end) as FullCode_last + , max(case when rnLast = 1 then t1.CMO else null end) as CMO_last + , max(case when rnLast = 1 then t1.DNR else null end) as DNR_last + , max(case when rnLast = 1 then t1.DNI else null end) as DNI_last + , max(case when rnLast = 1 then t1.DNCPR else null end) as DNCPR_last + + -- were they *at any time* given a certain code status + , max(t1.FullCode) as FullCode + , max(t1.CMO) as CMO + , max(t1.DNR) as DNR + , max(t1.DNI) as DNI + , max(t1.DNCPR) as DNCPR + + -- discharge summary mentions CMO + -- *** not totally robust, the note could say "NOT CMO", which would be flagged as 1 + , max(case when disch.cmo = 1 then 1 else 0 end) as CMO_ds + + -- time until their first DNR + , min(case when t1.DNR = 1 then t1.charttime else null end) + as TimeDNR_chart + + -- first code status of CMO + , min(case when t1.CMO = 1 then t1.charttime else null end) + as TimeCMO_chart + , min(case when t1.CMO = 1 then nn.charttime else null end) + as TimeCMO_NursingNote + +from icustays ie +left join t1 + on ie.icustay_id = t1.icustay_id +left join nnote nn + on ie.hadm_id = nn.hadm_id and nn.charttime between ie.intime and ie.outtime +left join disch + on ie.hadm_id = disch.hadm_id +group by ie.subject_id, ie.hadm_id, ie.icustay_id, ie.intime; diff --git a/comorbidity/postgres/elixhauser-ahrq-v37-with-drg.sql b/concepts/comorbidity/elixhauser-ahrq-v37-with-drg.sql similarity index 99% rename from comorbidity/postgres/elixhauser-ahrq-v37-with-drg.sql rename to concepts/comorbidity/elixhauser-ahrq-v37-with-drg.sql index ac6a29a..97c30d5 100644 --- a/comorbidity/postgres/elixhauser-ahrq-v37-with-drg.sql +++ b/concepts/comorbidity/elixhauser-ahrq-v37-with-drg.sql @@ -12,7 +12,8 @@ -- and we would like the latter behavior -- it's possible removing the whitespaces would fix this - but I didn't test it. -- I prefer consistency with AHRQ. -DROP MATERIALIZED VIEW IF EXISTS ELIXHAUSER_AHRQ; + +DROP MATERIALIZED VIEW IF EXISTS ELIXHAUSER_AHRQ CASCADE; CREATE MATERIALIZED VIEW ELIXHAUSER_AHRQ as with icd as diff --git a/concepts/comorbidity/elixhauser-quan.sql b/concepts/comorbidity/elixhauser-quan.sql new file mode 100644 index 0000000..3d289e5 --- /dev/null +++ b/concepts/comorbidity/elixhauser-quan.sql @@ -0,0 +1,278 @@ +-- This code calculates the Elixhauser comorbidities as defined in Quan et. al 2009: +-- Quan, Hude, et al. "Coding algorithms for defining comorbidities in +-- ICD-9-CM and ICD-10 administrative data." Medical care (2005): 1130-1139. +-- https://www.ncbi.nlm.nih.gov/pubmed/16224307 + +-- Quan defined an "Enhanced ICD-9" coding scheme for deriving Elixhauser +-- comorbidities from ICD-9 billing codes. This script implements that calculation. + +-- The logic of the code is roughly that, if the comorbidity lists a length 3 +-- ICD-9 code (e.g. 585), then we only require a match on the first 3 characters. + +-- This code derives each comorbidity as follows: +-- 1) ICD9_CODE is directly compared to 5 character codes +-- 2) The first 4 characters of ICD9_CODE are compared to 4 character codes +-- 3) The first 3 characters of ICD9_CODE are compared to 3 character codes + +DROP MATERIALIZED VIEW IF EXISTS ELIXHAUSER_QUAN CASCADE; +CREATE MATERIALIZED VIEW ELIXHAUSER_QUAN AS +with icd as +( + select hadm_id, seq_num, icd9_code + from mimiciii.diagnoses_icd + where seq_num != 1 -- we do not include the primary icd-9 code +) +, eliflg as +( +select hadm_id, seq_num, icd9_code +, CASE + when icd9_code in ('39891','40201','40211','40291','40401','40403','40411','40413','40491','40493') then 1 + when SUBSTRING(icd9_code FROM 1 for 4) in ('4254','4255','4257','4258','4259') then 1 + when SUBSTRING(icd9_code FROM 1 for 3) in ('428') then 1 + else 0 end as CHF /* Congestive heart failure */ + +, CASE + when icd9_code in ('42613','42610','42612','99601','99604') then 1 + when SUBSTRING(icd9_code FROM 1 for 4) in ('4260','4267','4269','4270','4271','4272','4273','4274','4276','4278','4279','7850','V450','V533') then 1 + else 0 end as ARRHY + +, CASE + when SUBSTRING(icd9_code FROM 1 for 4) in ('0932','7463','7464','7465','7466','V422','V433') then 1 + when SUBSTRING(icd9_code FROM 1 for 3) in ('394','395','396','397','424') then 1 + else 0 end as VALVE /* Valvular disease */ + +, CASE + when SUBSTRING(icd9_code FROM 1 for 4) in ('4150','4151','4170','4178','4179') then 1 + when SUBSTRING(icd9_code FROM 1 for 3) in ('416') then 1 + else 0 end as PULMCIRC /* Pulmonary circulation disorder */ + +, CASE + when SUBSTRING(icd9_code FROM 1 for 4) in ('0930','4373','4431','4432','4438','4439','4471','5571','5579','V434') then 1 + when SUBSTRING(icd9_code FROM 1 for 3) in ('440','441') then 1 + else 0 end as PERIVASC /* Peripheral vascular disorder */ + +, CASE + when SUBSTRING(icd9_code FROM 1 for 3) in ('401') then 1 + else 0 end as HTN /* Hypertension, uncomplicated */ + +, CASE + when SUBSTRING(icd9_code FROM 1 for 3) in ('402','403','404','405') then 1 + else 0 end as HTNCX /* Hypertension, complicated */ + +, CASE + when SUBSTRING(icd9_code FROM 1 for 4) in ('3341','3440','3441','3442','3443','3444','3445','3446','3449') then 1 + when SUBSTRING(icd9_code FROM 1 for 3) in ('342','343') then 1 + else 0 end as PARA /* Paralysis */ + +, CASE + when icd9_code in ('33392') then 1 + when SUBSTRING(icd9_code FROM 1 for 4) in ('3319','3320','3321','3334','3335','3362','3481','3483','7803','7843') then 1 + when SUBSTRING(icd9_code FROM 1 for 3) in ('334','335','340','341','345') then 1 + else 0 end as NEURO /* Other neurological */ + +, CASE + when SUBSTRING(icd9_code FROM 1 for 4) in ('4168','4169','5064','5081','5088') then 1 + when SUBSTRING(icd9_code FROM 1 for 3) in ('490','491','492','493','494','495','496','500','501','502','503','504','505') then 1 + else 0 end as CHRNLUNG /* Chronic pulmonary disease */ + +, CASE + when SUBSTRING(icd9_code FROM 1 for 4) in ('2500','2501','2502','2503') then 1 + else 0 end as DM /* Diabetes w/o chronic complications*/ + +, CASE + when SUBSTRING(icd9_code FROM 1 for 4) in ('2504','2505','2506','2507','2508','2509') then 1 + else 0 end as DMCX /* Diabetes w/ chronic complications */ + +, CASE + when SUBSTRING(icd9_code FROM 1 for 4) in ('2409','2461','2468') then 1 + when SUBSTRING(icd9_code FROM 1 for 3) in ('243','244') then 1 + else 0 end as HYPOTHY /* Hypothyroidism */ + +, CASE + when icd9_code in ('40301','40311','40391','40402','40403','40412','40413','40492','40493') then 1 + when SUBSTRING(icd9_code FROM 1 for 4) in ('5880','V420','V451') then 1 + when SUBSTRING(icd9_code FROM 1 for 3) in ('585','586','V56') then 1 + else 0 end as RENLFAIL /* Renal failure */ + +, CASE + when icd9_code in ('07022','07023','07032','07033','07044','07054') then 1 + when SUBSTRING(icd9_code FROM 1 for 4) in ('0706','0709','4560','4561','4562','5722','5723','5724','5728','5733','5734','5738','5739','V427') then 1 + when SUBSTRING(icd9_code FROM 1 for 3) in ('570','571') then 1 + else 0 end as LIVER /* Liver disease */ + +, CASE + when SUBSTRING(icd9_code FROM 1 for 4) in ('5317','5319','5327','5329','5337','5339','5347','5349') then 1 + else 0 end as ULCER /* Chronic Peptic ulcer disease (includes bleeding only if obstruction is also present) */ + +, CASE + when SUBSTRING(icd9_code FROM 1 for 3) in ('042','043','044') then 1 + else 0 end as AIDS /* HIV and AIDS */ + +, CASE + when SUBSTRING(icd9_code FROM 1 for 4) in ('2030','2386') then 1 + when SUBSTRING(icd9_code FROM 1 for 3) in ('200','201','202') then 1 + else 0 end as LYMPH /* Lymphoma */ + +, CASE + when SUBSTRING(icd9_code FROM 1 for 3) in ('196','197','198','199') then 1 + else 0 end as METS /* Metastatic cancer */ + +, CASE + when SUBSTRING(icd9_code FROM 1 for 3) in + ( + '140','141','142','143','144','145','146','147','148','149','150','151','152' + ,'153','154','155','156','157','158','159','160','161','162','163','164','165' + ,'166','167','168','169','170','171','172','174','175','176','177','178','179' + ,'180','181','182','183','184','185','186','187','188','189','190','191','192' + ,'193','194','195' + ) then 1 + else 0 end as TUMOR /* Solid tumor without metastasis */ + +, CASE + when icd9_code in ('72889','72930') then 1 + when SUBSTRING(icd9_code FROM 1 for 4) in ('7010','7100','7101','7102','7103','7104','7108','7109','7112','7193','7285') then 1 + when SUBSTRING(icd9_code FROM 1 for 3) in ('446','714','720','725') then 1 + else 0 end as ARTH /* Rheumatoid arthritis/collagen vascular diseases */ + +, CASE + when SUBSTRING(icd9_code FROM 1 for 4) in ('2871','2873','2874','2875') then 1 + when SUBSTRING(icd9_code FROM 1 for 3) in ('286') then 1 + else 0 end as COAG /* Coagulation deficiency */ + +, CASE + when SUBSTRING(icd9_code FROM 1 for 4) in ('2780') then 1 + else 0 end as OBESE /* Obesity */ + +, CASE + when SUBSTRING(icd9_code FROM 1 for 4) in ('7832','7994') then 1 + when SUBSTRING(icd9_code FROM 1 for 3) in ('260','261','262','263') then 1 + else 0 end as WGHTLOSS /* Weight loss */ + +, CASE + when SUBSTRING(icd9_code FROM 1 for 4) in ('2536') then 1 + when SUBSTRING(icd9_code FROM 1 for 3) in ('276') then 1 + else 0 end as LYTES /* Fluid and electrolyte disorders */ + +, CASE + when SUBSTRING(icd9_code FROM 1 for 4) in ('2800') then 1 + else 0 end as BLDLOSS /* Blood loss anemia */ + +, CASE + when SUBSTRING(icd9_code FROM 1 for 4) in ('2801','2808','2809') then 1 + when SUBSTRING(icd9_code FROM 1 for 3) in ('281') then 1 + else 0 end as ANEMDEF /* Deficiency anemias */ + +, CASE + when SUBSTRING(icd9_code FROM 1 for 4) in ('2652','2911','2912','2913','2915','2918','2919','3030','3039','3050','3575','4255','5353','5710','5711','5712','5713','V113') then 1 + when SUBSTRING(icd9_code FROM 1 for 3) in ('980') then 1 + else 0 end as ALCOHOL /* Alcohol abuse */ + +, CASE + when icd9_code in ('V6542') then 1 + when SUBSTRING(icd9_code FROM 1 for 4) in ('3052','3053','3054','3055','3056','3057','3058','3059') then 1 + when SUBSTRING(icd9_code FROM 1 for 3) in ('292','304') then 1 + else 0 end as DRUG /* Drug abuse */ + +, CASE + when icd9_code in ('29604','29614','29644','29654') then 1 + when SUBSTRING(icd9_code FROM 1 for 4) in ('2938') then 1 + when SUBSTRING(icd9_code FROM 1 for 3) in ('295','297','298') then 1 + else 0 end as PSYCH /* Psychoses */ + +, CASE + when SUBSTRING(icd9_code FROM 1 for 4) in ('2962','2963','2965','3004') then 1 + when SUBSTRING(icd9_code FROM 1 for 3) in ('309','311') then 1 + else 0 end as DEPRESS /* Depression */ +from icd +) +-- collapse the icd9_code specific flags into hadm_id specific flags +-- this groups comorbidities together for a single patient admission +, eligrp as +( + select hadm_id + , max(chf) as chf + , max(arrhy) as arrhy + , max(valve) as valve + , max(pulmcirc) as pulmcirc + , max(perivasc) as perivasc + , max(htn) as htn + , max(htncx) as htncx + , max(para) as para + , max(neuro) as neuro + , max(chrnlung) as chrnlung + , max(dm) as dm + , max(dmcx) as dmcx + , max(hypothy) as hypothy + , max(renlfail) as renlfail + , max(liver) as liver + , max(ulcer) as ulcer + , max(aids) as aids + , max(lymph) as lymph + , max(mets) as mets + , max(tumor) as tumor + , max(arth) as arth + , max(coag) as coag + , max(obese) as obese + , max(wghtloss) as wghtloss + , max(lytes) as lytes + , max(bldloss) as bldloss + , max(anemdef) as anemdef + , max(alcohol) as alcohol + , max(drug) as drug + , max(psych) as psych + , max(depress) as depress +from eliflg +group by hadm_id +) +-- now merge these flags together to define elixhauser +-- most are straightforward.. but hypertension flags are a bit more complicated + + +select adm.hadm_id +, chf as CONGESTIVE_HEART_FAILURE +, arrhy as CARDIAC_ARRHYTHMIAS +, valve as VALVULAR_DISEASE +, pulmcirc as PULMONARY_CIRCULATION +, perivasc as PERIPHERAL_VASCULAR +-- we combine "htn" and "htncx" into "HYPERTENSION" +, case + when htn = 1 then 1 + when htncx = 1 then 1 + else 0 end as HYPERTENSION +, para as PARALYSIS +, neuro as OTHER_NEUROLOGICAL +, chrnlung as CHRONIC_PULMONARY +-- only the more severe comorbidity (complicated diabetes) is kept +, case + when dmcx = 1 then 0 + when dm = 1 then 1 + else 0 end as DIABETES_UNCOMPLICATED +, dmcx as DIABETES_COMPLICATED +, hypothy as HYPOTHYROIDISM +, renlfail as RENAL_FAILURE +, liver as LIVER_DISEASE +, ulcer as PEPTIC_ULCER +, aids as AIDS +, lymph as LYMPHOMA +, mets as METASTATIC_CANCER +-- only the more severe comorbidity (metastatic cancer) is kept +, case + when mets = 1 then 0 + when tumor = 1 then 1 + else 0 end as SOLID_TUMOR +, arth as RHEUMATOID_ARTHRITIS +, coag as COAGULOPATHY +, obese as OBESITY +, wghtloss as WEIGHT_LOSS +, lytes as FLUID_ELECTROLYTE +, bldloss as BLOOD_LOSS_ANEMIA +, anemdef as DEFICIENCY_ANEMIAS +, alcohol as ALCOHOL_ABUSE +, drug as DRUG_ABUSE +, psych as PSYCHOSES +, depress as DEPRESSION + +from admissions adm +left join eligrp eli + on adm.hadm_id = eli.hadm_id +order by adm.hadm_id; diff --git a/concepts/comorbidity/elixhauser-score-ahrq.sql b/concepts/comorbidity/elixhauser-score-ahrq.sql new file mode 100644 index 0000000..6a12828 --- /dev/null +++ b/concepts/comorbidity/elixhauser-score-ahrq.sql @@ -0,0 +1,109 @@ +-- This query provides various methods of combining the Elixhauser components into a single score +-- The methods are called "vanWalRaven" and "SID30", and "SID29" + +DROP MATERIALIZED VIEW IF EXISTS ELIXHAUSER_AHRQ_SCORE; +CREATE MATERIALIZED VIEW ELIXHAUSER_AHRQ_SCORE AS +select subject_id, hadm_id +, -- Below is the van Walraven score + 0 * AIDS + + 0 * ALCOHOL_ABUSE + + -2 * BLOOD_LOSS_ANEMIA + + 7 * CONGESTIVE_HEART_FAILURE + + -- Cardiac arrhythmias are not included in van Walraven based on Quan 2007 + 3 * CHRONIC_PULMONARY + + 3 * COAGULOPATHY + + -2 * DEFICIENCY_ANEMIAS + + -3 * DEPRESSION + + 0 * DIABETES_COMPLICATED + + 0 * DIABETES_UNCOMPLICATED + + -7 * DRUG_ABUSE + + 5 * FLUID_ELECTROLYTE + + 0 * HYPERTENSION + + 0 * HYPOTHYROIDISM + + 11 * LIVER_DISEASE + + 9 * LYMPHOMA + + 12 * METASTATIC_CANCER + + 6 * OTHER_NEUROLOGICAL + + -4 * OBESITY + + 7 * PARALYSIS + + 2 * PERIPHERAL_VASCULAR + + 0 * PEPTIC_ULCER + + 0 * PSYCHOSES + + 4 * PULMONARY_CIRCULATION + + 0 * RHEUMATOID_ARTHRITIS + + 5 * RENAL_FAILURE + + 4 * SOLID_TUMOR + + -1 * VALVULAR_DISEASE + + 6 * WEIGHT_LOSS +as elixhauser_vanwalraven + + + +, -- Below is the 29 component SID score + 0 * AIDS + + -2 * ALCOHOL_ABUSE + + -2 * BLOOD_LOSS_ANEMIA + + -- Cardiac arrhythmias are not included in SID-29 + 9 * CONGESTIVE_HEART_FAILURE + + 3 * CHRONIC_PULMONARY + + 9 * COAGULOPATHY + + 0 * DEFICIENCY_ANEMIAS + + -4 * DEPRESSION + + 0 * DIABETES_COMPLICATED + + -1 * DIABETES_UNCOMPLICATED + + -8 * DRUG_ABUSE + + 9 * FLUID_ELECTROLYTE + + -1 * HYPERTENSION + + 0 * HYPOTHYROIDISM + + 5 * LIVER_DISEASE + + 6 * LYMPHOMA + + 13 * METASTATIC_CANCER + + 4 * OTHER_NEUROLOGICAL + + -4 * OBESITY + + 3 * PARALYSIS + + 0 * PEPTIC_ULCER + + 4 * PERIPHERAL_VASCULAR + + -4 * PSYCHOSES + + 5 * PULMONARY_CIRCULATION + + 6 * RENAL_FAILURE + + 0 * RHEUMATOID_ARTHRITIS + + 8 * SOLID_TUMOR + + 0 * VALVULAR_DISEASE + + 8 * WEIGHT_LOSS +as elixhauser_SID29 + + +, -- Below is the 30 component SID score + 0 * AIDS + + 0 * ALCOHOL_ABUSE + + -3 * BLOOD_LOSS_ANEMIA + + 8 * CARDIAC_ARRHYTHMIAS + + 9 * CONGESTIVE_HEART_FAILURE + + 3 * CHRONIC_PULMONARY + + 12 * COAGULOPATHY + + 0 * DEFICIENCY_ANEMIAS + + -5 * DEPRESSION + + 1 * DIABETES_COMPLICATED + + 0 * DIABETES_UNCOMPLICATED + + -11 * DRUG_ABUSE + + 11 * FLUID_ELECTROLYTE + + -2 * HYPERTENSION + + 0 * HYPOTHYROIDISM + + 7 * LIVER_DISEASE + + 8 * LYMPHOMA + + 17 * METASTATIC_CANCER + + 5 * OTHER_NEUROLOGICAL + + -5 * OBESITY + + 4 * PARALYSIS + + 0 * PEPTIC_ULCER + + 4 * PERIPHERAL_VASCULAR + + -6 * PSYCHOSES + + 5 * PULMONARY_CIRCULATION + + 7 * RENAL_FAILURE + + 0 * RHEUMATOID_ARTHRITIS + + 10 * SOLID_TUMOR + + 0 * VALVULAR_DISEASE + + 10 * WEIGHT_LOSS +as elixhauser_SID30 + +from ELIXHAUSER_AHRQ; diff --git a/concepts/comorbidity/elixhauser-score-quan.sql b/concepts/comorbidity/elixhauser-score-quan.sql new file mode 100644 index 0000000..b071f16 --- /dev/null +++ b/concepts/comorbidity/elixhauser-score-quan.sql @@ -0,0 +1,110 @@ +-- This query provides various methods of combining the Elixhauser components into a single score +-- The methods are called "vanWalRaven" and "SID30", and "SID29" +-- References: + +DROP MATERIALIZED VIEW IF EXISTS ELIXHAUSER_QUAN_SCORE; +CREATE MATERIALIZED VIEW ELIXHAUSER_QUAN_SCORE AS +select hadm_id +, -- Below is the van Walraven score + 0 * AIDS + + 0 * ALCOHOL_ABUSE + + -2 * BLOOD_LOSS_ANEMIA + + 7 * CONGESTIVE_HEART_FAILURE + + -- Cardiac arrhythmias are not included in van Walraven based on Quan 2007 + 3 * CHRONIC_PULMONARY + + 3 * COAGULOPATHY + + -2 * DEFICIENCY_ANEMIAS + + -3 * DEPRESSION + + 0 * DIABETES_COMPLICATED + + 0 * DIABETES_UNCOMPLICATED + + -7 * DRUG_ABUSE + + 5 * FLUID_ELECTROLYTE + + 0 * HYPERTENSION + + 0 * HYPOTHYROIDISM + + 11 * LIVER_DISEASE + + 9 * LYMPHOMA + + 12 * METASTATIC_CANCER + + 6 * OTHER_NEUROLOGICAL + + -4 * OBESITY + + 7 * PARALYSIS + + 2 * PERIPHERAL_VASCULAR + + 0 * PEPTIC_ULCER + + 0 * PSYCHOSES + + 4 * PULMONARY_CIRCULATION + + 0 * RHEUMATOID_ARTHRITIS + + 5 * RENAL_FAILURE + + 4 * SOLID_TUMOR + + -1 * VALVULAR_DISEASE + + 6 * WEIGHT_LOSS +as elixhauser_vanwalraven + + + +, -- Below is the 29 component SID score + 0 * AIDS + + -2 * ALCOHOL_ABUSE + + -2 * BLOOD_LOSS_ANEMIA + + -- Cardiac arrhythmias are not included in SID-29 + 9 * CONGESTIVE_HEART_FAILURE + + 3 * CHRONIC_PULMONARY + + 9 * COAGULOPATHY + + 0 * DEFICIENCY_ANEMIAS + + -4 * DEPRESSION + + 0 * DIABETES_COMPLICATED + + -1 * DIABETES_UNCOMPLICATED + + -8 * DRUG_ABUSE + + 9 * FLUID_ELECTROLYTE + + -1 * HYPERTENSION + + 0 * HYPOTHYROIDISM + + 5 * LIVER_DISEASE + + 6 * LYMPHOMA + + 13 * METASTATIC_CANCER + + 4 * OTHER_NEUROLOGICAL + + -4 * OBESITY + + 3 * PARALYSIS + + 0 * PEPTIC_ULCER + + 4 * PERIPHERAL_VASCULAR + + -4 * PSYCHOSES + + 5 * PULMONARY_CIRCULATION + + 6 * RENAL_FAILURE + + 0 * RHEUMATOID_ARTHRITIS + + 8 * SOLID_TUMOR + + 0 * VALVULAR_DISEASE + + 8 * WEIGHT_LOSS +as elixhauser_SID29 + + +, -- Below is the 30 component SID score + 0 * AIDS + + 0 * ALCOHOL_ABUSE + + -3 * BLOOD_LOSS_ANEMIA + + 8 * CARDIAC_ARRHYTHMIAS + + 9 * CONGESTIVE_HEART_FAILURE + + 3 * CHRONIC_PULMONARY + + 12 * COAGULOPATHY + + 0 * DEFICIENCY_ANEMIAS + + -5 * DEPRESSION + + 1 * DIABETES_COMPLICATED + + 0 * DIABETES_UNCOMPLICATED + + -11 * DRUG_ABUSE + + 11 * FLUID_ELECTROLYTE + + -2 * HYPERTENSION + + 0 * HYPOTHYROIDISM + + 7 * LIVER_DISEASE + + 8 * LYMPHOMA + + 17 * METASTATIC_CANCER + + 5 * OTHER_NEUROLOGICAL + + -5 * OBESITY + + 4 * PARALYSIS + + 0 * PEPTIC_ULCER + + 4 * PERIPHERAL_VASCULAR + + -6 * PSYCHOSES + + 5 * PULMONARY_CIRCULATION + + 7 * RENAL_FAILURE + + 0 * RHEUMATOID_ARTHRITIS + + 10 * SOLID_TUMOR + + 0 * VALVULAR_DISEASE + + 10 * WEIGHT_LOSS +as elixhauser_SID30 + +from ELIXHAUSER_QUAN; diff --git a/cookbook/postgres/age_hist.sql b/concepts/cookbook/age-histogram.sql similarity index 93% rename from cookbook/postgres/age_hist.sql rename to concepts/cookbook/age-histogram.sql index dc4dca7..78a8e5f 100644 --- a/cookbook/postgres/age_hist.sql +++ b/concepts/cookbook/age-histogram.sql @@ -1,6 +1,5 @@ -- ------------------------------------------------------------------ -- Title: Count the number of hospital admissions in equally sized bins of age --- MIMIC version: MIMIC-III v1.3 -- Notes: this query does not specify a schema. To run it on your local -- MIMIC schema, run the following command: -- SET SEARCH_PATH TO mimiciii; @@ -23,7 +22,7 @@ WITH agetbl AS SELECT age, width_bucket(age, 15, 100, 85) AS bucket FROM agetbl ) -SELECT bucket+15, count(*) +SELECT bucket+15 as age, count(*) FROM agebin GROUP BY bucket ORDER BY bucket; diff --git a/cookbook/postgres/basic_patient_info.sql b/concepts/cookbook/basic_patient_info.sql similarity index 93% rename from cookbook/postgres/basic_patient_info.sql rename to concepts/cookbook/basic_patient_info.sql index 7fe5838..ff7564b 100644 --- a/cookbook/postgres/basic_patient_info.sql +++ b/concepts/cookbook/basic_patient_info.sql @@ -1,6 +1,5 @@ -- ------------------------------------------------------------------ -- Title: Retrieves basic patient information from the patients table --- MIMIC version: MIMIC-III v1.3 -- Notes: this query does not specify a schema. To run it on your local -- MIMIC schema, run the following command: -- SET SEARCH_PATH TO mimiciii; diff --git a/cookbook/postgres/bun.sql b/concepts/cookbook/bun.sql similarity index 76% rename from cookbook/postgres/bun.sql rename to concepts/cookbook/bun.sql index 8715317..68ec283 100644 --- a/cookbook/postgres/bun.sql +++ b/concepts/cookbook/bun.sql @@ -1,6 +1,5 @@ -- -------------------------------------------------------- -- Title: Create a distribution of BUN values for adult hospital admissions --- MIMIC version: MIMIC-III v1.3 -- Notes: this query does not specify a schema. To run it on your local -- MIMIC schema, run the following command: -- SET SEARCH_PATH TO mimiciii; @@ -19,11 +18,15 @@ WITH agetbl AS -- group by subject_id to ensure there is only 1 subject_id per row group by ad.subject_id ) -SELECT bucket, count(*) FROM ( - SELECT width_bucket(valuenum, 0, 280, 280) AS bucket - FROM labevents le - INNER JOIN agetbl - ON le.subject_id = agetbl.subject_id - WHERE itemid IN (51006)) AS bun +, bun as +( + SELECT width_bucket(valuenum, 0, 280, 280) AS bucket + FROM labevents le + INNER JOIN agetbl + ON le.subject_id = agetbl.subject_id + WHERE itemid IN (51006) +) +SELECT bucket as blood_urea_nitrogen, count(*) +FROM bun GROUP BY bucket ORDER BY bucket; diff --git a/cookbook/postgres/gcs.sql b/concepts/cookbook/gcs.sql similarity index 78% rename from cookbook/postgres/gcs.sql rename to concepts/cookbook/gcs.sql index f6f0ad0..18d6c50 100644 --- a/cookbook/postgres/gcs.sql +++ b/concepts/cookbook/gcs.sql @@ -1,6 +1,5 @@ -- -------------------------------------------------------- --- Title: Find the glasgow coma score for each adult patient --- MIMIC version: MIMIC-III v1.3 +-- Title: Find the glasgow coma *MOTOR* score for each adult patient -- Notes: this query does not specify a schema. To run it on your local -- MIMIC schema, run the following command: -- SET SEARCH_PATH TO mimiciii; @@ -19,11 +18,19 @@ WITH agetbl AS -- group by subject_id to ensure there is only 1 subject_id per row group by ad.subject_id ) -SELECT bucket, count(*) FROM ( +, gcs as +( SELECT width_bucket(valuenum, 1, 30, 30) AS bucket FROM chartevents ce INNER JOIN agetbl ON ce.subject_id = agetbl.subject_id - WHERE itemid IN (198,223900)) AS gcs + WHERE itemid IN + ( + 454 -- "Motor Response" + , 223900 -- "GCS - Motor Response" + ) +) +SELECT bucket as GCS_Motor_Response, count(*) +FROM gcs GROUP BY bucket ORDER BY bucket; diff --git a/cookbook/postgres/glucose.sql b/concepts/cookbook/glucose.sql similarity index 71% rename from cookbook/postgres/glucose.sql rename to concepts/cookbook/glucose.sql index dc25485..9952f0e 100644 --- a/cookbook/postgres/glucose.sql +++ b/concepts/cookbook/glucose.sql @@ -1,6 +1,5 @@ -- -------------------------------------------------------- -- Title: Retrieves a glucose histogram of adult patients --- MIMIC version: MIMIC-III v1.3 -- Notes: this query does not specify a schema. To run it on your local -- MIMIC schema, run the following command: -- SET SEARCH_PATH TO mimiciii; @@ -19,13 +18,16 @@ WITH agetbl AS -- group by subject_id to ensure there is only 1 subject_id per row group by ad.subject_id ) -SELECT bucket, count(*) -FROM (SELECT width_bucket(valuenum, 0.5, 1000, 1000) AS bucket - FROM labevents le - INNER JOIN agetbl - ON le.subject_id = agetbl.subject_id - WHERE itemid IN (50809,50931) - AND valuenum IS NOT NULL - ) AS glucose +, glc as +( + SELECT width_bucket(valuenum, 0.5, 1000, 1000) AS bucket + FROM labevents le + INNER JOIN agetbl + ON le.subject_id = agetbl.subject_id + WHERE itemid IN (50809,50931) + AND valuenum IS NOT NULL +) +SELECT bucket as glucose, count(*) +FROM glc GROUP BY bucket ORDER BY bucket; diff --git a/cookbook/postgres/hco.sql b/concepts/cookbook/hco.sql similarity index 72% rename from cookbook/postgres/hco.sql rename to concepts/cookbook/hco.sql index 568cfea..8483414 100644 --- a/cookbook/postgres/hco.sql +++ b/concepts/cookbook/hco.sql @@ -1,6 +1,5 @@ -- -------------------------------------------------------- -- Title: Create a histogram bicarbonate levels for all patients (adults and neonates) --- MIMIC version: MIMIC-III v1.3 -- Notes: this query does not specify a schema. To run it on your local -- MIMIC schema, run the following command: -- SET SEARCH_PATH TO mimiciii; @@ -19,13 +18,16 @@ WITH agetbl AS -- group by subject_id to ensure there is only 1 subject_id per row group by ad.subject_id ) -SELECT bucket, count(*) -FROM (SELECT width_bucket(valuenum, 0, 231, 231) AS bucket - FROM labevents - INNER JOIN agetbl - ON le.subject_id = agetbl.subject_id - WHERE itemid IN (50803, 50804, 50882) - AND valuenum IS NOT NULL - ) AS hco +, hco as +( + SELECT width_bucket(valuenum, 0, 231, 231) AS bucket + FROM labevents le + INNER JOIN agetbl + ON le.subject_id = agetbl.subject_id + WHERE itemid IN (50803, 50804, 50882) + AND valuenum IS NOT NULL +) +SELECT bucket as bicarbonate, count(*) +FROM hco GROUP BY bucket ORDER BY bucket; diff --git a/cookbook/postgres/heart_rate.sql b/concepts/cookbook/heart_rate.sql similarity index 74% rename from cookbook/postgres/heart_rate.sql rename to concepts/cookbook/heart_rate.sql index 08aea2d..3fb95c7 100644 --- a/cookbook/postgres/heart_rate.sql +++ b/concepts/cookbook/heart_rate.sql @@ -1,6 +1,5 @@ -- -------------------------------------------------------- -- Title: Create a histogram of heart rates for adult patients --- MIMIC version: MIMIC-III v1.3 -- Notes: this query does not specify a schema. To run it on your local -- MIMIC schema, run the following command: -- SET SEARCH_PATH TO mimiciii; @@ -19,12 +18,15 @@ WITH agetbl AS -- group by subject_id to ensure there is only 1 subject_id per row group by ad.subject_id ) - -SELECT bucket, count(*) -FROM (SELECT width_bucket(valuenum, 0, 300, 301) AS bucket - FROM chartevents ce - INNER JOIN agetbl - ON ce.subject_id = agetbl.subject_id - WHERE itemid in (211,220045)) AS heart_rate +, hr as +( + SELECT width_bucket(valuenum, 0, 300, 301) AS bucket + FROM chartevents ce + INNER JOIN agetbl + ON ce.subject_id = agetbl.subject_id + WHERE itemid in (211,220045) +) +SELECT bucket as heart_rate, count(*) +FROM hr GROUP BY bucket ORDER BY bucket; diff --git a/cookbook/postgres/height.sql b/concepts/cookbook/height.sql similarity index 66% rename from cookbook/postgres/height.sql rename to concepts/cookbook/height.sql index 3383f4f..9bbdffa 100644 --- a/cookbook/postgres/height.sql +++ b/concepts/cookbook/height.sql @@ -2,21 +2,22 @@ -- Title: Create a histogram of heights for all patients -- note: some height ITEMIDs were not included, which may implicitly exclude -- some neonates from this calculation --- MIMIC version: MIMIC-III v1.3 -- Notes: this query does not specify a schema. To run it on your local -- MIMIC schema, run the following command: -- SET SEARCH_PATH TO mimiciii; -- Where "mimiciii" is the name of your schema, and may be different. -- -------------------------------------------------------- -SELECT bucket, count(*) -FROM ( - SELECT valuenum, width_bucket(valuenum, 1, 200, 200) AS bucket - FROM chartevents - WHERE itemid in (920,226730) - AND valuenum IS NOT NULL - AND valuenum > 0 - AND valuenum < 500 - ) AS height +WITH ht AS +( + SELECT valuenum, width_bucket(valuenum, 1, 200, 200) AS bucket + FROM chartevents + WHERE itemid in (920,226730) + AND valuenum IS NOT NULL + AND valuenum > 0 + AND valuenum < 500 +) +SELECT bucket as height, count(*) +FROM ht GROUP BY bucket ORDER BY bucket; diff --git a/concepts/cookbook/icd9agelimited.sql b/concepts/cookbook/icd9agelimited.sql new file mode 100644 index 0000000..5e5bd2b --- /dev/null +++ b/concepts/cookbook/icd9agelimited.sql @@ -0,0 +1,31 @@ +-- ------------------------------------------------------------------ +-- Title: Count the number of patients with a specific icd9 code above a certain age +-- MIMIC version: MIMIC-III v1.3 +-- Notes: this query does not specify a schema. To run it on your local +-- MIMIC schema, run the following command: +-- SET SEARCH_PATH TO mimiciii; +-- Where "mimiciii" is the name of your schema, and may be different. +-- Reference: tompollard, alistairewj, erinhong for code taken +-- from sodium.sql on the MIMIC III github repository +-- ------------------------------------------------------------------ + +WITH agetbl AS + ( + SELECT ad.subject_id + FROM admissions ad + INNER JOIN patients p + ON ad.subject_id = p.subject_id + WHERE + -- filter to only adults above 30 + EXTRACT(EPOCH FROM (ad.admittime - p.dob))/60.0/60.0/24.0/365.242 > 30 + -- group by subject_id to ensure there is only 1 subject_id per row + GROUP BY ad.subject_id + ) +SELECT COUNT(DISTINCT dia.subject_id) +AS "Hypertension Age 30+" +FROM diagnoses_icd dia +INNER JOIN agetbl +ON dia.subject_id = agetbl.subject_id +WHERE dia.icd9_code +-- 401% relates to Hypertension +LIKE '401%'; \ No newline at end of file diff --git a/concepts/cookbook/icd9count.sql b/concepts/cookbook/icd9count.sql new file mode 100644 index 0000000..a8c6e14 --- /dev/null +++ b/concepts/cookbook/icd9count.sql @@ -0,0 +1,16 @@ +-- ------------------------------------------------------------------ +-- Title: Count the number of patients with a specific icd9 code +-- MIMIC version: MIMIC-III v1.3 +-- Notes: this query does not specify a schema. To run it on your local +-- MIMIC schema, run the following command: +-- SET SEARCH_PATH TO mimiciii; +-- Where "mimiciii" is the name of your schema, and may be different. +-- Acknowledgement: Credit goes to Kris Kindle +-- ------------------------------------------------------------------ + +SELECT COUNT(DISTINCT subject_id) +AS "Hypertension" +FROM diagnoses_icd +WHERE icd9_code +-- 401% will search for all icd9 codes relating to hypertension +LIKE '401%'; \ No newline at end of file diff --git a/concepts/cookbook/icd9vagehistogram.sql b/concepts/cookbook/icd9vagehistogram.sql new file mode 100644 index 0000000..1b446ec --- /dev/null +++ b/concepts/cookbook/icd9vagehistogram.sql @@ -0,0 +1,53 @@ +-- ------------------------------------------------------------------ +-- Title: Count the number of patients with a specific icd9 code and shows the output as a histogram with groups of age +-- MIMIC version: MIMIC-III v1.3 +-- Notes: this query does not specify a schema. To run it on your local +-- MIMIC schema, run the following command: +-- SET SEARCH_PATH TO mimiciii; +-- Where "mimiciii" is the name of your schema, and may be different. +-- Acknowledgements: Made with help from Kris Kindle +-- Reference: tompollard, alistairewj for code taken +-- from age_hist.sql on the MIMIC III github repository +-- ------------------------------------------------------------------ + +WITH diatbl AS + ( + SELECT DISTINCT ON (dia.subject_id) dia.subject_id, ad.admittime + FROM diagnoses_icd dia + INNER JOIN admissions ad + ON dia.subject_id = ad.subject_id + WHERE dia.icd9_code + -- 401% relates to hypertension + LIKE '401%' + ), +agetbl AS + ( + SELECT dt.subject_id, + (extract(DAY FROM dt.admittime - p.dob) + + extract(HOUR FROM dt.admittime - p.dob)/24 + + extract(MINUTE FROM dt.admittime - p.dob)/24/60)/365.25 + AS age + FROM diatbl dt + INNER JOIN patients p + ON dt.subject_id = p.subject_id + ) +SELECT + COUNT(*) AS TOTAL, + COUNT(CASE WHEN age >= 0 AND age < 16 THEN '0 - 15' END) AS "0-15", + COUNT(CASE WHEN age >= 16 AND age < 21 THEN '16 - 20' END) AS "16-20", + COUNT(CASE WHEN age >= 21 AND age < 26 THEN '21 - 25' END) AS "21-25", + COUNT(CASE WHEN age >= 26 AND age < 31 THEN '26 - 30' END) AS "26-30", + COUNT(CASE WHEN age >= 31 AND age < 36 THEN '31 - 35' END) AS "31-35", + COUNT(CASE WHEN age >= 36 AND age < 41 THEN '36 - 40' END) AS "36-40", + COUNT(CASE WHEN age >= 41 AND age < 46 THEN '41 - 45' END) AS "41-45", + COUNT(CASE WHEN age >= 46 AND age < 51 THEN '46 - 50' END) AS "46-50", + COUNT(CASE WHEN age >= 51 AND age < 56 THEN '51 - 55' END) AS "51-55", + COUNT(CASE WHEN age >= 56 AND age < 61 THEN '56 - 60' END) AS "56-60", + COUNT(CASE WHEN age >= 61 AND age < 66 THEN '61 - 65' END) AS "61-65", + COUNT(CASE WHEN age >= 66 AND age < 71 THEN '66 - 70' END) AS "66-70", + COUNT(CASE WHEN age >= 71 AND age < 76 THEN '71 - 75' END) AS "71-75", + COUNT(CASE WHEN age >= 76 AND age < 81 THEN '76 - 80' END) AS "76-80", + COUNT(CASE WHEN age >= 81 AND age < 86 THEN '81 - 85' END) AS "81-85", + COUNT(CASE WHEN age >= 86 AND age < 91 THEN '86 - 90' END) AS "86-91", + COUNT(CASE WHEN age >= 91 THEN 'Over 91' END) AS ">91" +FROM agetbl; diff --git a/concepts/cookbook/icd9vicd9agelimited.sql b/concepts/cookbook/icd9vicd9agelimited.sql new file mode 100644 index 0000000..2059184 --- /dev/null +++ b/concepts/cookbook/icd9vicd9agelimited.sql @@ -0,0 +1,34 @@ +-- ------------------------------------------------------------------ +-- Title: Count the number of patients with two specific icd9 codes above a certain age +-- MIMIC version: MIMIC-III v1.3 +-- Notes: this query does not specify a schema. To run it on your local +-- MIMIC schema, run the following command: +-- SET SEARCH_PATH TO mimiciii; +-- Where "mimiciii" is the name of your schema, and may be different. +-- Reference: tompollard, alistairewj, erinhong for code taken +-- from sodium.sql on the MIMIC III github repository +-- ------------------------------------------------------------------ + +WITH agetbl AS + ( + SELECT ad.subject_id + FROM admissions ad + INNER JOIN patients p + ON ad.subject_id = p.subject_id + WHERE + EXTRACT(EPOCH FROM (ad.admittime - p.dob))/60.0/60.0/24.0/365.242 > 40 + GROUP BY ad.subject_id + ) +SELECT COUNT(DISTINCT dia.subject_id) +AS "Obesity vs Hypertension Age 40+" +FROM diagnoses_icd dia +INNER JOIN agetbl +ON dia.subject_id = agetbl.subject_id +INNER JOIN diagnoses_icd dib +ON dia.subject_id = dib.subject_id +WHERE dia.icd9_code +-- 278% relates to obesity +LIKE '278%' +AND dib.icd9_code +-- 401% relates to hypertension +LIKE '401%'; \ No newline at end of file diff --git a/concepts/cookbook/icd9vicd9count.sql b/concepts/cookbook/icd9vicd9count.sql new file mode 100644 index 0000000..0956bba --- /dev/null +++ b/concepts/cookbook/icd9vicd9count.sql @@ -0,0 +1,21 @@ +-- ------------------------------------------------------------------ +-- Title: Count the number of patients with two specific icd9 codes +-- MIMIC version: MIMIC-III v1.3 +-- Notes: this query does not specify a schema. To run it on your local +-- MIMIC schema, run the following command: +-- SET SEARCH_PATH TO mimiciii; +-- Where "mimiciii" is the name of your schema, and may be different. +-- Acknowledgement: Credit goes to Kris Kindle +-- ------------------------------------------------------------------ + +SELECT COUNT(DISTINCT a.subject_id) +AS "Obesity and Dyslipidemia" +FROM diagnoses_icd a +INNER JOIN diagnoses_icd b +ON a.subject_id = b.subject_id +WHERE a.icd9_code +-- 278% relates to obesity +LIKE '278%' +AND b.icd9_code +-- 272 relates to Dyslipidemia +LIKE '272%'; \ No newline at end of file diff --git a/concepts/cookbook/icustay-days.sql b/concepts/cookbook/icustay-days.sql new file mode 100644 index 0000000..1008187 --- /dev/null +++ b/concepts/cookbook/icustay-days.sql @@ -0,0 +1,39 @@ + +-- ---------------------------------------------------------- +-- Create a table that counts each day spent in the ICU -- +-- and assign a timestamp to the start and end of each day -- +-- ---------------------------------------------------------- + +-- ---------- +-- Columns: +-- ---------- +-- icustay_id +-- intime +-- outime +-- icudayseq_asc: Counting days since arrival in the ICU +-- 0 = day of arrival in the ICU +-- 1 = day 1 after arrival +-- 2 = day 2 after arrival etc +-- icudayseq_desc: Counting down to the day of discharge from the ICU +-- 2 = day 2 before discharge etc +-- 1 = day 1 before discharge +-- 0 = day of discharge from the ICU +-- startday: if day of arrival then intime, else midnight at start of day +-- endday: if day of discharge then outtime, else midnight at end of day +-- ---------- + +DROP MATERIALIZED VIEW icustay_days; +CREATE MATERIALIZED VIEW icustay_days AS +WITH dayseq AS ( + SELECT icustay_id, intime, outtime, + GENERATE_SERIES(0,CEIL(los)::INT-1,1) AS icudayseq_asc, + GENERATE_SERIES(CEIL(los)::INT-1,0,-1) AS icudayseq_desc + FROM icustays) +SELECT icustay_id, intime, outtime, + icudayseq_asc, icudayseq_desc, + CASE WHEN icudayseq_asc = 0 THEN intime + ELSE date_trunc('day', intime) + (INTERVAL '1 day' * icudayseq_asc) + END AS startday, + CASE WHEN icudayseq_desc = 0 THEN OUTTIME + ELSE date_trunc('day', intime) + INTERVAL '1 day' + (INTERVAL '1 day' * icudayseq_asc) END AS endday +FROM dayseq; diff --git a/cookbook/postgres/min_surviving_bp.sql b/concepts/cookbook/min_surviving_bp.sql similarity index 57% rename from cookbook/postgres/min_surviving_bp.sql rename to concepts/cookbook/min_surviving_bp.sql index 7bdb255..866756a 100644 --- a/cookbook/postgres/min_surviving_bp.sql +++ b/concepts/cookbook/min_surviving_bp.sql @@ -1,6 +1,5 @@ -- -------------------------------------------------------- -- Title: Retrieves the systolic blood pressure of hospital survivors --- MIMIC version: MIMIC-III v1.3 -- Notes: this query does not specify a schema. To run it on your local -- MIMIC schema, run the following command: -- SET SEARCH_PATH TO mimiciii; @@ -19,19 +18,24 @@ WITH agetbl AS -- group by subject_id to ensure there is only 1 subject_id per row group by ad.subject_id ) - -SELECT bucket, count(*) -FROM (SELECT width_bucket(min_sbp, 0, 300, 300) AS bucket FROM ( - SELECT p.subject_id, ce.icustay_id, min(valuenum) AS min_sbp - FROM chartevents ce - INNER JOIN agetbl - ON ce.subject_id = agetbl.subject_id - -- here we filter down to only survivors - INNER JOIN patients p - ON p.expire_flag = 0 - WHERE itemid IN (6, 51, 455, 6701, 220179, 220050) - GROUP BY p.subject_id, ce.icustay_id - ) AS min_surviving_bp - ) AS min_surviving_bp_counted +, min_surviving_bp as +( + SELECT p.subject_id, ce.icustay_id, min(valuenum) AS min_sbp + FROM chartevents ce + INNER JOIN agetbl + ON ce.subject_id = agetbl.subject_id + -- here we filter down to only survivors + INNER JOIN patients p + ON ce.subject_id = p.subject_id and p.expire_flag = 0 + WHERE itemid IN (6, 51, 455, 6701, 220179, 220050) + GROUP BY p.subject_id, ce.icustay_id +) +, min_surviving_bp_counted as +( + SELECT width_bucket(min_sbp, 0, 300, 300) AS bucket + FROM min_surviving_bp +) +SELECT bucket as systolic_blood_pressure, count(*) +FROM min_surviving_bp_counted GROUP BY bucket ORDER BY bucket; diff --git a/cookbook/postgres/mortality.sql b/concepts/cookbook/mortality.sql similarity index 92% rename from cookbook/postgres/mortality.sql rename to concepts/cookbook/mortality.sql index 20fe110..88b169e 100644 --- a/cookbook/postgres/mortality.sql +++ b/concepts/cookbook/mortality.sql @@ -1,6 +1,5 @@ -- ------------------------------------------------------------------ -- Title: Calculate in-hospital, 30-day, and 1 year mortality (from hospital admission) --- MIMIC version: MIMIC-III v1.3 -- Notes: this query does not specify a schema. To run it on your local -- MIMIC schema, run the following command: -- SET SEARCH_PATH TO mimiciii; @@ -11,8 +10,8 @@ WITH tmp as ( SELECT adm.hadm_id, admittime, dischtime, adm.deathtime, pat.dod - -- integer which is 1 for the most recent hospital admission - , ROW_NUMBER() OVER (PARTITION BY hadm_id ORDER BY admittime DESC) AS mostrecent + -- integer which is 1 for the first hospital admission + , ROW_NUMBER() OVER (PARTITION BY hadm_id ORDER BY admittime) AS FirstAdmission FROM admissions adm INNER JOIN patients pat ON adm.subject_id = pat.subject_id diff --git a/cookbook/postgres/number_of_patients.sql b/concepts/cookbook/number_of_patients.sql similarity index 92% rename from cookbook/postgres/number_of_patients.sql rename to concepts/cookbook/number_of_patients.sql index 81b9a4a..47a1ab7 100644 --- a/cookbook/postgres/number_of_patients.sql +++ b/concepts/cookbook/number_of_patients.sql @@ -1,6 +1,5 @@ -- -------------------------------------------------------- -- Title: Counts the total number of patients --- MIMIC version: MIMIC-III v1.3 -- Notes: this query does not specify a schema. To run it on your local -- MIMIC schema, run the following command: -- SET SEARCH_PATH TO mimiciii; diff --git a/cookbook/postgres/potassium.sql b/concepts/cookbook/potassium.sql similarity index 74% rename from cookbook/postgres/potassium.sql rename to concepts/cookbook/potassium.sql index 818687d..1aeba05 100644 --- a/cookbook/postgres/potassium.sql +++ b/concepts/cookbook/potassium.sql @@ -1,6 +1,5 @@ -- -------------------------------------------------------- -- Title: Creates a histogram of serum potassium for adult patients --- MIMIC version: MIMIC-III v1.3 -- Notes: this query does not specify a schema. To run it on your local -- MIMIC schema, run the following command: -- SET SEARCH_PATH TO mimiciii; @@ -19,13 +18,15 @@ WITH agetbl AS -- group by subject_id to ensure there is only 1 subject_id per row group by ad.subject_id ) -SELECT bucket / 10, count(*) -FROM ( - SELECT width_bucket(valuenum, 0, 10, 100) AS bucket - FROM labevents le - INNER JOIN agetbl - ON le.subject_id = agetbl.subject_id - WHERE itemid IN (50822, 50971) - ) AS potassium +, k as +( + SELECT width_bucket(valuenum, 0, 10, 100) AS bucket + FROM labevents le + INNER JOIN agetbl + ON le.subject_id = agetbl.subject_id + WHERE itemid IN (50822, 50971) +) +SELECT round(cast(bucket as numeric) / 10,2) as potassium_value, count(*) +FROM k GROUP BY bucket ORDER BY bucket; diff --git a/cookbook/postgres/rr.sql b/concepts/cookbook/rr.sql similarity index 81% rename from cookbook/postgres/rr.sql rename to concepts/cookbook/rr.sql index 22c72de..8ccef35 100644 --- a/cookbook/postgres/rr.sql +++ b/concepts/cookbook/rr.sql @@ -20,14 +20,15 @@ WITH agetbl AS -- group by subject_id to ensure there is only 1 subject_id per row group by ad.subject_id ) - -SELECT bucket/10, count(*) - FROM ( +, rr as +( SELECT valuenum, width_bucket(valuenum, 0, 130, 1400) AS bucket - FROM chartevents ce - INNER JOIN agetbl - ON ce.subject_id = agetbl.subject_id - WHERE itemid in (219, 615, 618) - ) AS respiration_rate + FROM chartevents ce + INNER JOIN agetbl + ON ce.subject_id = agetbl.subject_id + WHERE itemid in (219, 615, 618) +) +SELECT round(cast(bucket as numeric) / 10,2) as respiration_rate, count(*) +FROM rr GROUP BY bucket ORDER BY bucket; diff --git a/cookbook/postgres/sbp.sql b/concepts/cookbook/sbp.sql similarity index 61% rename from cookbook/postgres/sbp.sql rename to concepts/cookbook/sbp.sql index bc88f82..603e8d6 100644 --- a/cookbook/postgres/sbp.sql +++ b/concepts/cookbook/sbp.sql @@ -1,7 +1,5 @@ -- -------------------------------------------------------- -- Title: Retrieves the systolic blood pressure for adult patients --- only for patients recorded with carevue --- MIMIC version: MIMIC-III v1.3 -- Notes: this query does not specify a schema. To run it on your local -- MIMIC schema, run the following command: -- SET SEARCH_PATH TO mimiciii; @@ -20,14 +18,23 @@ WITH agetbl AS -- group by subject_id to ensure there is only 1 subject_id per row group by ad.subject_id ) - -SELECT bucket, count(*) -FROM ( - SELECT width_bucket(valuenum, 0, 300, 300) AS bucket - FROM chartevents ce - INNER JOIN agetbl - ON ce.subject_id = agetbl.subject_id - WHERE itemid IN (6, 51, 455, 6701) - ) AS systolic_blood_pressure +, sysbp as +( + SELECT width_bucket(valuenum, 0, 300, 300) AS bucket + FROM chartevents ce + INNER JOIN agetbl + ON ce.subject_id = agetbl.subject_id + WHERE itemid IN + ( + 6 -- ABP [Systolic] + , 51 -- Arterial BP [Systolic] + , 455 -- NBP [Systolic] + , 6701 -- Arterial BP #2 [Systolic] + , 220050 -- Arterial Blood Pressure systolic + , 220179 -- Non Invasive Blood Pressure systolic + ) +) +SELECT bucket as systolic_blood_pressure, count(*) +FROM sysbp GROUP BY bucket ORDER BY bucket; diff --git a/cookbook/postgres/sodium.sql b/concepts/cookbook/sodium.sql similarity index 76% rename from cookbook/postgres/sodium.sql rename to concepts/cookbook/sodium.sql index ac350fb..9ca5515 100644 --- a/cookbook/postgres/sodium.sql +++ b/concepts/cookbook/sodium.sql @@ -1,6 +1,5 @@ -- -------------------------------------------------------- -- Title: Retrieves the blood serum sodium levels for adult patients --- MIMIC version: MIMIC-III v1.3 -- Notes: this query does not specify a schema. To run it on your local -- MIMIC schema, run the following command: -- SET SEARCH_PATH TO mimiciii; @@ -19,12 +18,15 @@ WITH agetbl AS -- group by subject_id to ensure there is only 1 subject_id per row group by ad.subject_id ) - SELECT bucket, count(*) from ( +, sodium as +( SELECT width_bucket(valuenum, 0, 180, 180) AS bucket - FROM labevents le - INNER JOIN agetbl - ON le.subject_id = agetbl.subject_id - WHERE itemid IN (50824, 50983) - ) AS sodium - GROUP BY bucket - ORDER BY bucket; + FROM labevents le + INNER JOIN agetbl + ON le.subject_id = agetbl.subject_id + WHERE itemid IN (50824, 50983) +) +SELECT bucket as sodium, count(*) +FROM sodium +GROUP BY bucket +ORDER BY bucket; diff --git a/cookbook/postgres/temp.sql b/concepts/cookbook/temp.sql similarity index 53% rename from cookbook/postgres/temp.sql rename to concepts/cookbook/temp.sql index adbf1f8..5b8cc8d 100644 --- a/cookbook/postgres/temp.sql +++ b/concepts/cookbook/temp.sql @@ -1,7 +1,5 @@ -- -------------------------------------------------------- -- Title: Retrieves the temperature of adult patients --- only for patients recorded with carevue --- MIMIC version: MIMIC-III v1.3 -- Notes: this query does not specify a schema. To run it on your local -- MIMIC schema, run the following command: -- SET SEARCH_PATH TO mimiciii; @@ -20,16 +18,28 @@ WITH agetbl AS -- group by subject_id to ensure there is only 1 subject_id per row group by ad.subject_id ) - -SELECT (bucket/10) + 30, count(*) FROM ( +, temp as +( SELECT width_bucket( - CASE WHEN itemid IN (223762, 676) THEN valuenum -- celsius - WHEN itemid IN (223761, 678) THEN (valuenum - 32) * 5 / 9 --fahrenheit - END, 30, 45, 160) AS bucket - FROM chartevents ce - INNER JOIN agetbl - ON ce.subject_id = agetbl.subject_id - WHERE itemid IN (676, 677, 678, 679) - ) AS temperature - GROUP BY bucket - ORDER BY bucket; + CASE + WHEN itemid IN (223762, 676, 677) THEN valuenum -- celsius + WHEN itemid IN (223761, 678, 679) THEN (valuenum - 32) * 5 / 9 --fahrenheit + END + , 30, 45, 160) AS bucket + FROM chartevents ce + INNER JOIN agetbl + ON ce.subject_id = agetbl.subject_id + WHERE itemid IN + ( + 676 -- Temperature C + , 677 -- Temperature C (calc) + , 678 -- Temperature F + , 679 -- Temperature F (calc) + , 223761 -- Temperature Fahrenheit + , 223762 -- Temperature Celsius + ) +) +SELECT round((cast(bucket as numeric)/10) + 30,2) as temperature, count(*) +FROM temp +GROUP BY bucket +ORDER BY bucket; diff --git a/concepts/cookbook/uo.sql b/concepts/cookbook/uo.sql new file mode 100644 index 0000000..c889233 --- /dev/null +++ b/concepts/cookbook/uo.sql @@ -0,0 +1,71 @@ +-- -------------------------------------------------------- +-- Title: Retrieves the urine output of adult patients +-- Notes: this query does not specify a schema. To run it on your local +-- MIMIC schema, run the following command: +-- SET SEARCH_PATH TO mimiciii; +-- Where "mimiciii" is the name of your schema, and may be different. +-- -------------------------------------------------------- + +WITH agetbl AS +( + SELECT ie.icustay_id, ie.intime + FROM icustays ie + INNER JOIN patients p + ON ie.subject_id = p.subject_id + WHERE + -- filter to only adults + EXTRACT(EPOCH FROM (ie.intime - p.dob))/60.0/60.0/24.0/365.242 > 15 +) +-- Urine output is measured hourly, but the individual values are not of interest +-- Usually, you want an overall picture of patient output +-- This query sums the data over the first 24 hours +, uo_sum as +( + select oe.icustay_id, sum(oe.VALUE) as urineoutput + FROM outputevents oe + INNER JOIN agetbl + ON oe.icustay_id = agetbl.icustay_id + -- and ensure the data occurs during the first day + and oe.charttime between agetbl.intime and (agetbl.intime + interval '1' day) -- first ICU day + WHERE itemid IN + ( + -- these are the most frequently occurring urine output observations in CareVue + 40055, -- "Urine Out Foley" + 43175, -- "Urine ." + 40069, -- "Urine Out Void" + 40094, -- "Urine Out Condom Cath" + 40715, -- "Urine Out Suprapubic" + 40473, -- "Urine Out IleoConduit" + 40085, -- "Urine Out Incontinent" + 40057, -- "Urine Out Rt Nephrostomy" + 40056, -- "Urine Out Lt Nephrostomy" + 40405, -- "Urine Out Other" + 40428, -- "Urine Out Straight Cath" + 40086,-- Urine Out Incontinent + 40096, -- "Urine Out Ureteral Stent #1" + 40651, -- "Urine Out Ureteral Stent #2" + + -- these are the most frequently occurring urine output observations in Metavision + 226559, -- "Foley" + 226560, -- "Void" + 227510, -- "TF Residual" + 226561, -- "Condom Cath" + 226584, -- "Ileoconduit" + 226563, -- "Suprapubic" + 226564, -- "R Nephrostomy" + 226565, -- "L Nephrostomy" + 226567, -- Straight Cath + 226557, -- "R Ureteral Stent" + 226558 -- "L Ureteral Stent" + ) + group by oe.icustay_id +) +, uo as +( + SELECT width_bucket(urineoutput, 0, 5000, 50) AS bucket + FROM uo_sum +) +SELECT bucket*100 as UrineOutput, COUNT(*) +FROM uo +GROUP BY bucket +ORDER BY bucket; diff --git a/cookbook/postgres/wbc.sql b/concepts/cookbook/wbc.sql similarity index 71% rename from cookbook/postgres/wbc.sql rename to concepts/cookbook/wbc.sql index 0fee97f..7ab37cb 100644 --- a/cookbook/postgres/wbc.sql +++ b/concepts/cookbook/wbc.sql @@ -1,6 +1,5 @@ -- -------------------------------------------------------- -- Title: Retrieves the white blood cell count for adult patients --- MIMIC version: MIMIC-III v1.3 -- Notes: this query does not specify a schema. To run it on your local -- MIMIC schema, run the following command: -- SET SEARCH_PATH TO mimiciii; @@ -19,13 +18,16 @@ WITH agetbl AS -- group by subject_id to ensure there is only 1 subject_id per row group by ad.subject_id ) - -SELECT bucket/10, count(*) -FROM (SELECT width_bucket(valuenum, 0, 100, 1001) AS bucket - FROM labevents le - INNER JOIN agetbl - ON le.subject_id = agetbl.subject_id - WHERE itemid in (51300, 51301) - AND valuenum IS NOT NULL) AS white_blood_cell_count +, wbc as +( + SELECT width_bucket(valuenum, 0, 100, 1001) AS bucket + FROM labevents le + INNER JOIN agetbl + ON le.subject_id = agetbl.subject_id + WHERE itemid in (51300, 51301) + AND valuenum IS NOT NULL +) +SELECT round((cast(bucket as numeric)/10),2) as white_blood_cell_count, count(*) +FROM wbc GROUP BY bucket ORDER BY bucket; diff --git a/demographics/postgres/HeightWeightQuery.sql b/concepts/demographics/HeightWeightQuery.sql similarity index 97% rename from demographics/postgres/HeightWeightQuery.sql rename to concepts/demographics/HeightWeightQuery.sql index 15c0798..1b3f4c5 100644 --- a/demographics/postgres/HeightWeightQuery.sql +++ b/concepts/demographics/HeightWeightQuery.sql @@ -6,10 +6,7 @@ -- Created by: Erin Hong, Alistair Johnson -- ------------------------------------------------------------------ --- Define which schema to work on -SET search_path TO mimiciii; - -DROP MATERIALIZED VIEW IF EXISTS heightweight; +DROP MATERIALIZED VIEW IF EXISTS heightweight CASCADE; CREATE MATERIALIZED VIEW heightweight AS WITH FirstVRawData AS @@ -33,6 +30,8 @@ WITH FirstVRawData AS END AS valuenum FROM chartevents c WHERE c.valuenum IS NOT NULL + -- exclude rows marked as error + AND c.error IS DISTINCT FROM 1 AND ( ( c.itemid IN (762, 763, 3723, 3580, -- Weight Kg 3581, -- Weight lb 3582, -- Weight oz diff --git a/demographics/README.md b/concepts/demographics/README.md similarity index 100% rename from demographics/README.md rename to concepts/demographics/README.md diff --git a/demographics/postgres/icustay_detail.sql b/concepts/demographics/icustay_detail.sql similarity index 81% rename from demographics/postgres/icustay_detail.sql rename to concepts/demographics/icustay_detail.sql index 9698ce4..ca2358a 100644 --- a/demographics/postgres/icustay_detail.sql +++ b/concepts/demographics/icustay_detail.sql @@ -10,7 +10,7 @@ -- SET search_path TO mimiciii; -- This query extracts useful demographic/administrative information for patient ICU stays -DROP MATERIALIZED VIEW IF EXISTS icustay_detail; +DROP MATERIALIZED VIEW IF EXISTS icustay_detail CASCADE; CREATE MATERIALIZED VIEW icustay_detail as SELECT ie.subject_id, ie.hadm_id, ie.icustay_id @@ -20,8 +20,8 @@ SELECT ie.subject_id, ie.hadm_id, ie.icustay_id -- hospital level factors , adm.admittime, adm.dischtime -, ROUND( (CAST(adm.dischtime AS DATE) - CAST(adm.admittime AS DATE)) , 4) AS los_hospital -, ROUND( (CAST(adm.admittime AS DATE) - CAST(pat.dob AS DATE)) / 365.242, 4) AS age +, ROUND( (CAST(EXTRACT(epoch FROM adm.dischtime - adm.admittime)/(60*60*24) AS numeric)), 4) AS los_hospital +, ROUND( (CAST(EXTRACT(epoch FROM adm.admittime - pat.dob)/(60*60*24*365.242) AS numeric)), 4) AS age , adm.ethnicity, adm.ADMISSION_TYPE , adm.hospital_expire_flag , DENSE_RANK() OVER (PARTITION BY adm.subject_id ORDER BY adm.admittime) AS hospstay_seq @@ -31,7 +31,7 @@ SELECT ie.subject_id, ie.hadm_id, ie.icustay_id -- icu level factors , ie.intime, ie.outtime -, ROUND( (CAST(ie.outtime AS DATE) - CAST(ie.intime AS DATE)) , 4) AS los_icu +, ROUND( (CAST(EXTRACT(epoch FROM ie.outtime - ie.intime)/(60*60*24) AS numeric)), 4) AS los_icu , DENSE_RANK() OVER (PARTITION BY ie.hadm_id ORDER BY ie.intime) AS icustay_seq -- first ICU stay *for the current hospitalization* diff --git a/concepts/diagnosis/README.md b/concepts/diagnosis/README.md new file mode 100644 index 0000000..71cf589 --- /dev/null +++ b/concepts/diagnosis/README.md @@ -0,0 +1,5 @@ +The Clinical Classification Software (CCS) categorizes ICD-9 coded diagnoses into clinically meaningful groups. The categorization was developed by the Agency for Healthcare Research and Quality (AHRQ). More detail can be found on the AHRQ website: https://www.hcup-us.ahrq.gov/tools_software.jsp + +This folder contains: + +* `ccs_diagnosis_table.sql` - Creates two tables: `ccs_single_level_dx` and `ccs_multi_level_dx`. These two tables are loaded from `ccs_single_level_dx.csv.gz` and `ccs_multi_level_dx.csv.gz`. Note that the script assumes you are using PostgreSQL v9.4 or later, and you must execute the script from this directory. diff --git a/concepts/diagnosis/ccs_diagnosis_table.sql b/concepts/diagnosis/ccs_diagnosis_table.sql new file mode 100644 index 0000000..28d1343 --- /dev/null +++ b/concepts/diagnosis/ccs_diagnosis_table.sql @@ -0,0 +1,22 @@ +DROP TABLE IF EXISTS ccs_single_level_dx; +CREATE TABLE ccs_single_level_dx +( + ccs_id INT NOT NULL, + ccs_name VARCHAR(150) NOT NULL, + icd9_code CHAR(5) NOT NULL +); + +\copy ccs_single_level_dx from program 'gzip -dc ccs_single_level.csv.gz' CSV HEADER; + +DROP TABLE IF EXISTS ccs_multi_level_dx; +CREATE TABLE ccs_multi_level_dx +( + ccs_mid VARCHAR(10) NOT NULL, + ccs_name VARCHAR(100) NOT NULL, + ccs_group1 VARCHAR(100), + ccs_group2 VARCHAR(100), + ccs_group3 VARCHAR(100), + icd9_code CHAR(5) NOT NULL +); + +\copy ccs_multi_level_dx from program 'gzip -dc ccs_multi_level.csv.gz' CSV HEADER; diff --git a/concepts/diagnosis/ccs_multi_level.csv.gz b/concepts/diagnosis/ccs_multi_level.csv.gz new file mode 100644 index 0000000..2f54013 Binary files /dev/null and b/concepts/diagnosis/ccs_multi_level.csv.gz differ diff --git a/concepts/diagnosis/ccs_single_level.csv.gz b/concepts/diagnosis/ccs_single_level.csv.gz new file mode 100644 index 0000000..f3c0e02 Binary files /dev/null and b/concepts/diagnosis/ccs_single_level.csv.gz differ diff --git a/concepts/durations/adenosine-durations.sql b/concepts/durations/adenosine-durations.sql new file mode 100644 index 0000000..11ec870 --- /dev/null +++ b/concepts/durations/adenosine-durations.sql @@ -0,0 +1,227 @@ +-- This query extracts durations of adenosine administration +-- Consecutive administrations are numbered 1, 2, ... +-- Total time on the drug can be calculated from this table by grouping using ICUSTAY_ID + +-- *** COULD NOT FIND ADENOSINE IN THE INPUTEVENTS_MV TABLE *** +-- This drug is rarely used - it could just be that it was never used in MetaVision. +-- If using this code, ensure the durations make sense for carevue patients first + +DROP MATERIALIZED VIEW IF EXISTS ADENOSINEDURATIONS; +CREATE MATERIALIZED VIEW ADENOSINEDURATIONS as +with vasocv1 as +( + select + icustay_id, charttime + -- case statement determining whether the ITEMID is an instance of vasopressor usage + , max(case when itemid = 4649 then 1 else 0 end) as vaso -- adenosine + + -- the 'stopped' column indicates if a vasopressor has been disconnected + , 0 as vaso_stopped + , max(case when itemid = 4649 and valuenum is not null then 1 else 0 end) as vaso_null + , max(case when itemid = 4649 then valuenum else null end) as vaso_rate + , max(case when itemid = 4649 then valuenum else null end) as vaso_amount + + from mimiciii.chartevents + where itemid = 4649 -- adenosine + -- exclude rows marked as error + AND error IS DISTINCT FROM 1 + group by icustay_id, charttime +) +, vasocv2 as +( + select v.* + , sum(vaso_null) over (partition by icustay_id order by charttime) as vaso_partition + from + vasocv1 v +) +, vasocv3 as +( + select v.* + , first_value(vaso_rate) over (partition by icustay_id, vaso_partition order by charttime) as vaso_prevrate_ifnull + from + vasocv2 v +) +, vasocv4 as +( +select + icustay_id + , charttime + -- , (CHARTTIME - (LAG(CHARTTIME, 1) OVER (partition by icustay_id, vaso order by charttime))) AS delta + + , vaso + , vaso_rate + , vaso_amount + , vaso_stopped + , vaso_prevrate_ifnull + + -- We define start time here + , case + when vaso = 0 then null + + -- if this is the first instance of the vasoactive drug + when vaso_rate > 0 and + LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso, vaso_null + order by charttime + ) + is null + then 1 + + -- you often get a string of 0s + -- we decide not to set these as 1, just because it makes vasonum sequential + when vaso_rate = 0 and + LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) + = 0 + then 0 + + -- sometimes you get a string of NULL, associated with 0 volumes + -- same reason as before, we decide not to set these as 1 + -- vaso_prevrate_ifnull is equal to the previous value *iff* the current value is null + when vaso_prevrate_ifnull = 0 and + LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) + = 0 + then 0 + + -- If the last recorded rate was 0, newvaso = 1 + when LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) = 0 + then 1 + + -- If the last recorded vaso was D/C'd, newvaso = 1 + when + LAG(vaso_stopped,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) + = 1 then 1 + + -- ** not sure if the below is needed + --when (CHARTTIME - (LAG(CHARTTIME, 1) OVER (partition by icustay_id, vaso order by charttime))) > (interval '4 hours') then 1 + else null + end as vaso_start + +FROM + vasocv3 +) +-- propagate start/stop flags forward in time +, vasocv5 as +( + select v.* + , SUM(vaso_start) OVER (partition by icustay_id, vaso order by charttime) as vaso_first +FROM + vasocv4 v +) +, vasocv6 as +( + select v.* + -- We define end time here + , case + when vaso = 0 + then null + + -- If the recorded vaso was D/C'd, this is an end time + when vaso_stopped = 1 + then vaso_first + + -- If the rate is zero, this is the end time + when vaso_rate = 0 + then vaso_first + + -- the last row in the table is always a potential end time + -- this captures patients who die/are discharged while on vasopressors + -- in principle, this could add an extra end time for the vasopressor + -- however, since we later group on vaso_start, any extra end times are ignored + when LEAD(CHARTTIME,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) is null + then vaso_first + + else null + end as vaso_stop + from vasocv5 v +) + +-- -- if you want to look at the results of the table before grouping: +-- select +-- icustay_id, charttime, vaso, vaso_rate, vaso_amount +-- , case when vaso_stopped = 1 then 'Y' else '' end as stopped +-- , vaso_start +-- , vaso_first +-- , vaso_stop +-- from vasocv6 order by charttime; + + +, vasocv as +( +-- below groups together vasopressor administrations into groups +select + icustay_id + -- the first non-null rate is considered the starttime + , min(case when vaso_rate is not null then charttime else null end) as starttime + -- the *first* time the first/last flags agree is the stop time for this duration + , min(case when vaso_first = vaso_stop then charttime else null end) as endtime +from vasocv6 +where + vaso_first is not null -- bogus data +and + vaso_first != 0 -- sometimes *only* a rate of 0 appears, i.e. the drug is never actually delivered +and + icustay_id is not null -- there are data for "floating" admissions, we don't worry about these +group by icustay_id, vaso_first +having -- ensure start time is not the same as end time + min(charttime) != min(case when vaso_first = vaso_stop then charttime else null end) +and + max(vaso_rate) > 0 -- if the rate was always 0 or null, we consider it not a real drug delivery +) + +-- now we extract the associated data for metavision patients +, vasomv as +( + select + icustay_id, linkorderid + , min(starttime) as starttime, max(endtime) as endtime + from mimiciii.inputevents_mv + where itemid = 221282 -- adenosine + and statusdescription != 'Rewritten' -- only valid orders + group by icustay_id, linkorderid +) + +select + icustay_id + -- generate a sequential integer for convenience + , ROW_NUMBER() over (partition by icustay_id order by starttime) as vasonum + , starttime, endtime +from + vasocv + +UNION + +select + icustay_id + , ROW_NUMBER() over (partition by icustay_id order by starttime) as vasonum + , starttime, endtime +from + vasomv + +order by icustay_id, vasonum; diff --git a/concepts/durations/crrt-durations.sql b/concepts/durations/crrt-durations.sql new file mode 100644 index 0000000..da9cbb3 --- /dev/null +++ b/concepts/durations/crrt-durations.sql @@ -0,0 +1,203 @@ +DROP MATERIALIZED VIEW IF EXISTS crrtdurations; +CREATE MATERIALIZED VIEW crrtdurations as +with crrt_settings as +( + select ce.icustay_id, ce.charttime + , max( + case + when ce.itemid in + ( + 224149, -- Access Pressure + 224144, -- Blood Flow (ml/min) + 228004, -- Citrate (ACD-A) + 225183, -- Current Goal + 225977, -- Dialysate Fluid + 224154, -- Dialysate Rate + 224151, -- Effluent Pressure + 224150, -- Filter Pressure + 225958, -- Heparin Concentration (units/mL) + 224145, -- Heparin Dose (per hour) + 224191, -- Hourly Patient Fluid Removal + 228005, -- PBP (Prefilter) Replacement Rate + 228006, -- Post Filter Replacement Rate + 225976, -- Replacement Fluid + 224153, -- Replacement Rate + 224152, -- Return Pressure + 226457 -- Ultrafiltrate Output + ) then 1 + when ce.itemid in + ( + 29, -- Access mmHg + 173, -- Effluent Press mmHg + 192, -- Filter Pressure mmHg + 624, -- Return Pressure mmHg + 79, -- Blood Flow ml/min + 142, -- Current Goal + 146, -- Dialysate Flow ml/hr + 611, -- Replace Rate ml/hr + 5683 -- Hourly PFR + ) then 1 + when ce.itemid = 665 and value in ('Active','Clot Increasing','Clots Present','No Clot Present') + then 1 + when ce.itemid = 147 and value = 'Yes' + then 1 + when ce.itemid = 665 and value = 'Yes' + then 1 + else 0 end) + as RRT + -- Below indicates that a new instance of CRRT has started + , max( + case + -- System Integrity + when ce.itemid = 224146 and value in ('New Filter','Reinitiated') + then 1 + when ce.itemid = 665 and value in ('Initiated') + then 1 + else 0 + end ) as RRT_start + -- Below indicates that the current instance of CRRT has ended + , max( + case + -- System Integrity + when ce.itemid = 224146 and value in ('Discontinued','Recirculating') + then 1 + when ce.itemid = 665 and value in ('Clotted','DC' || CHR(39) || 'D') + then 1 + -- Reason for CRRT filter change + when ce.itemid = 225956 + then 1 + else 0 + end ) as RRT_end + from chartevents ce + where ce.itemid in + ( + -- MetaVision ITEMIDs + -- Below require special handling + 224146, -- System Integrity + 225956, -- Reason for CRRT Filter Change + -- Below are settings which indicate CRRT is started/continuing + 224149, -- Access Pressure + 224144, -- Blood Flow (ml/min) + 228004, -- Citrate (ACD-A) + 225183, -- Current Goal + 225977, -- Dialysate Fluid + 224154, -- Dialysate Rate + 224151, -- Effluent Pressure + 224150, -- Filter Pressure + 225958, -- Heparin Concentration (units/mL) + 224145, -- Heparin Dose (per hour) + 224191, -- Hourly Patient Fluid Removal + 228005, -- PBP (Prefilter) Replacement Rate + 228006, -- Post Filter Replacement Rate + 225976, -- Replacement Fluid + 224153, -- Replacement Rate + 224152, -- Return Pressure + 226457, -- Ultrafiltrate Output + -- CareVue ITEMIDs + -- Below require special handling + 665, -- System integrity + 147, -- Dialysate Infusing + 612, -- Replace.Fluid Infuse + -- Below are settings which indicate CRRT is started/continuing + 29, -- Access mmHg + 173, -- Effluent Press mmHg + 192, -- Filter Pressure mmHg + 624, -- Return Pressure mmHg + 142, -- Current Goal + 79, -- Blood Flow ml/min + 146, -- Dialysate Flow ml/hr + 611, -- Replace Rate ml/hr + 5683 -- Hourly PFR + ) + and ce.value is not null + and coalesce(ce.valuenum,1) != 0 -- non-zero rates/values + group by icustay_id, charttime +) +, vd1 as +( + select + icustay_id + -- this carries over the previous charttime + , case + when RRT=1 then + LAG(CHARTTIME, 1) OVER (partition by icustay_id, RRT order by charttime) + else null + end as charttime_lag + , charttime + , RRT + , RRT_start + , RRT_end + -- calculate the time since the last event + , case + -- non-null iff the current observation indicates settings are present + when RRT=1 then + CHARTTIME - + ( + LAG(CHARTTIME, 1) OVER + ( + partition by icustay_id, RRT + order by charttime + ) + ) + else null + end as CRRT_duration + + -- now we determine if the current event is a new instantiation + , case + when RRT_start = 1 + then 1 + -- if there is an end flag, we mark any subsequent event as new + when RRT_end = 1 + -- note the end is *not* a new event, the *subsequent* row is + -- so here we output 0 + then 0 + when + LAG(RRT_end,1) + OVER + ( + partition by icustay_id, case when RRT=1 or RRT_end=1 then 1 else 0 end + order by charttime + ) = 1 + then 1 + -- if there is less than 2 hours between CRRT settings, we do not treat this as a new CRRT event + when (CHARTTIME - (LAG(CHARTTIME, 1) + OVER + ( + partition by icustay_id, case when RRT=1 or RRT_end=1 then 1 else 0 end + order by charttime + ))) <= interval '2' hour + then 0 + else 1 + end as NewCRRT + -- use the temp table with only settings from chartevents + FROM crrt_settings +) +, vd2 as +( + select vd1.* + -- create a cumulative sum of the instances of new CRRT + -- this results in a monotonically increasing integer assigned to each CRRT + , case when RRT_start = 1 or RRT=1 or RRT_end = 1 then + SUM( NewCRRT ) + OVER ( partition by icustay_id order by charttime ) + else null end + as num + --- now we convert CHARTTIME of CRRT settings into durations + from vd1 + -- now we can isolate to just rows with settings + -- (before we had rows with start/end flags) + -- this removes any null values for NewCRRT + where + RRT_start = 1 or RRT = 1 or RRT_end = 1 +) +-- create the durations for each CRRT instance +select icustay_id + , ROW_NUMBER() over (partition by icustay_id order by num) as num + , min(charttime) as starttime + , max(charttime) as endtime + , extract(epoch from max(charttime)-min(charttime))/60/60 AS duration_hours + -- add durations +from vd2 +group by icustay_id, num +having min(charttime) != max(charttime) +order by icustay_id, num diff --git a/concepts/durations/dobutamine-durations.sql b/concepts/durations/dobutamine-durations.sql new file mode 100644 index 0000000..1c06a67 --- /dev/null +++ b/concepts/durations/dobutamine-durations.sql @@ -0,0 +1,225 @@ +-- This query extracts durations of dobutamine administration +-- Consecutive administrations are numbered 1, 2, ... +-- Total time on the drug can be calculated from this table by grouping using ICUSTAY_ID + +DROP MATERIALIZED VIEW IF EXISTS DOBUTAMINEDURATIONS; +CREATE MATERIALIZED VIEW DOBUTAMINEDURATIONS as +-- Get drug administration data from CareVue first +with vasocv1 as +( + select + icustay_id, charttime + -- case statement determining whether the ITEMID is an instance of vasopressor usage + , max(case when itemid in (30042,30306) then 1 else 0 end) as vaso -- dobutamine + + -- the 'stopped' column indicates if a vasopressor has been disconnected + , max(case when itemid in (30042,30306) and stopped in ('Stopped','D/C''d') then 1 + else 0 end) as vaso_stopped + + , max(case when itemid in (30042,30306) and rate is not null then 1 else 0 end) as vaso_null + , max(case when itemid in (30042,30306) then rate else null end) as vaso_rate + , max(case when itemid in (30042,30306) then amount else null end) as vaso_amount + + from mimiciii.inputevents_cv + where itemid in (30042,30306) -- dobutamine + and icustay_id = 203992 + group by icustay_id, charttime +) +, vasocv2 as +( + select v.* + , sum(vaso_null) over (partition by icustay_id order by charttime) as vaso_partition + from + vasocv1 v +) +, vasocv3 as +( + select v.* + , first_value(vaso_rate) over (partition by icustay_id, vaso_partition order by charttime) as vaso_prevrate_ifnull + from + vasocv2 v +) +, vasocv4 as +( +select + icustay_id + , charttime + -- , (CHARTTIME - (LAG(CHARTTIME, 1) OVER (partition by icustay_id, vaso order by charttime))) AS delta + + , vaso + , vaso_rate + , vaso_amount + , vaso_stopped + , vaso_prevrate_ifnull + + -- We define start time here + , case + when vaso = 0 then null + + -- if this is the first instance of the vasoactive drug + when vaso_rate > 0 and + LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso, vaso_null + order by charttime + ) + is null + then 1 + + -- you often get a string of 0s + -- we decide not to set these as 1, just because it makes vasonum sequential + when vaso_rate = 0 and + LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) + = 0 + then 0 + + -- sometimes you get a string of NULL, associated with 0 volumes + -- same reason as before, we decide not to set these as 1 + -- vaso_prevrate_ifnull is equal to the previous value *iff* the current value is null + when vaso_prevrate_ifnull = 0 and + LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) + = 0 + then 0 + + -- If the last recorded rate was 0, newvaso = 1 + when LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) = 0 + then 1 + + -- If the last recorded vaso was D/C'd, newvaso = 1 + when + LAG(vaso_stopped,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) + = 1 then 1 + + -- ** not sure if the below is needed + --when (CHARTTIME - (LAG(CHARTTIME, 1) OVER (partition by icustay_id, vaso order by charttime))) > (interval '4 hours') then 1 + else null + end as vaso_start + +FROM + vasocv3 +) +-- propagate start/stop flags forward in time +, vasocv5 as +( + select v.* + , SUM(vaso_start) OVER (partition by icustay_id, vaso order by charttime) as vaso_first +FROM + vasocv4 v +) +, vasocv6 as +( + select v.* + -- We define end time here + , case + when vaso = 0 + then null + + -- If the recorded vaso was D/C'd, this is an end time + when vaso_stopped = 1 + then vaso_first + + -- If the rate is zero, this is the end time + when vaso_rate = 0 + then vaso_first + + -- the last row in the table is always a potential end time + -- this captures patients who die/are discharged while on vasopressors + -- in principle, this could add an extra end time for the vasopressor + -- however, since we later group on vaso_start, any extra end times are ignored + when LEAD(CHARTTIME,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) is null + then vaso_first + + else null + end as vaso_stop + from vasocv5 v +) + +-- -- if you want to look at the results of the table before grouping: +-- select +-- icustay_id, charttime, vaso, vaso_rate, vaso_amount +-- , case when vaso_stopped = 1 then 'Y' else '' end as stopped +-- , vaso_start +-- , vaso_first +-- , vaso_stop +-- from vasocv6 order by charttime; + + +, vasocv as +( +-- below groups together vasopressor administrations into groups +select + icustay_id + -- the first non-null rate is considered the starttime + , min(case when vaso_rate is not null then charttime else null end) as starttime + -- the *first* time the first/last flags agree is the stop time for this duration + , min(case when vaso_first = vaso_stop then charttime else null end) as endtime +from vasocv6 +where + vaso_first is not null -- bogus data +and + vaso_first != 0 -- sometimes *only* a rate of 0 appears, i.e. the drug is never actually delivered +and + icustay_id is not null -- there are data for "floating" admissions, we don't worry about these +group by icustay_id, vaso_first +having -- ensure start time is not the same as end time + min(charttime) != min(case when vaso_first = vaso_stop then charttime else null end) +and + max(vaso_rate) > 0 -- if the rate was always 0 or null, we consider it not a real drug delivery +) + +-- now we extract the associated data for metavision patients +, vasomv as +( + select + icustay_id, linkorderid + , min(starttime) as starttime, max(endtime) as endtime + from mimiciii.inputevents_mv + where itemid = 221653 -- dobutamine + and statusdescription != 'Rewritten' -- only valid orders + group by icustay_id, linkorderid +) + +select + icustay_id + -- generate a sequential integer for convenience + , ROW_NUMBER() over (partition by icustay_id order by starttime) as vasonum + , starttime, endtime +from + vasocv + +UNION + +select + icustay_id + , ROW_NUMBER() over (partition by icustay_id order by starttime) as vasonum + , starttime, endtime +from + vasomv + +order by icustay_id, vasonum; diff --git a/concepts/durations/dopamine-durations.sql b/concepts/durations/dopamine-durations.sql new file mode 100644 index 0000000..bdf6b8e --- /dev/null +++ b/concepts/durations/dopamine-durations.sql @@ -0,0 +1,227 @@ +-- This query extracts durations of dopamine administration +-- Consecutive administrations are numbered 1, 2, ... +-- Total time on the drug can be calculated from this table by grouping using ICUSTAY_ID + +DROP MATERIALIZED VIEW IF EXISTS DOPAMINEDURATIONS; +CREATE MATERIALIZED VIEW DOPAMINEDURATIONS as +-- Get drug administration data from CareVue first +with vasocv1 as +( + select + icustay_id, charttime + -- case statement determining whether the ITEMID is an instance of vasopressor usage + , max(case when itemid in (30043,30307) then 1 else 0 end) as vaso -- dopamine + + -- the 'stopped' column indicates if a vasopressor has been disconnected + , max(case when itemid in (30043,30307) and stopped in ('Stopped','D/C''d') then 1 + else 0 end) as vaso_stopped + + , max(case when itemid in (30043,30307) and rate is not null then 1 else 0 end) as vaso_null + , max(case when itemid in (30043,30307) then rate else null end) as vaso_rate + , max(case when itemid in (30043,30307) then amount else null end) as vaso_amount + + from mimiciii.inputevents_cv + where itemid in + ( + 30043,30307 -- dopamine + ) + group by icustay_id, charttime +) +, vasocv2 as +( + select v.* + , sum(vaso_null) over (partition by icustay_id order by charttime) as vaso_partition + from + vasocv1 v +) +, vasocv3 as +( + select v.* + , first_value(vaso_rate) over (partition by icustay_id, vaso_partition order by charttime) as vaso_prevrate_ifnull + from + vasocv2 v +) +, vasocv4 as +( +select + icustay_id + , charttime + -- , (CHARTTIME - (LAG(CHARTTIME, 1) OVER (partition by icustay_id, vaso order by charttime))) AS delta + + , vaso + , vaso_rate + , vaso_amount + , vaso_stopped + , vaso_prevrate_ifnull + + -- We define start time here + , case + when vaso = 0 then null + + -- if this is the first instance of the vasoactive drug + when vaso_rate > 0 and + LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso, vaso_null + order by charttime + ) + is null + then 1 + + -- you often get a string of 0s + -- we decide not to set these as 1, just because it makes vasonum sequential + when vaso_rate = 0 and + LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) + = 0 + then 0 + + -- sometimes you get a string of NULL, associated with 0 volumes + -- same reason as before, we decide not to set these as 1 + -- vaso_prevrate_ifnull is equal to the previous value *iff* the current value is null + when vaso_prevrate_ifnull = 0 and + LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) + = 0 + then 0 + + -- If the last recorded rate was 0, newvaso = 1 + when LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) = 0 + then 1 + + -- If the last recorded vaso was D/C'd, newvaso = 1 + when + LAG(vaso_stopped,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) + = 1 then 1 + + -- ** not sure if the below is needed + --when (CHARTTIME - (LAG(CHARTTIME, 1) OVER (partition by icustay_id, vaso order by charttime))) > (interval '4 hours') then 1 + else null + end as vaso_start + +FROM + vasocv3 +) +-- propagate start/stop flags forward in time +, vasocv5 as +( + select v.* + , SUM(vaso_start) OVER (partition by icustay_id, vaso order by charttime) as vaso_first +FROM + vasocv4 v +) +, vasocv6 as +( + select v.* + -- We define end time here + , case + when vaso = 0 + then null + + -- If the recorded vaso was D/C'd, this is an end time + when vaso_stopped = 1 + then vaso_first + + -- If the rate is zero, this is the end time + when vaso_rate = 0 + then vaso_first + + -- the last row in the table is always a potential end time + -- this captures patients who die/are discharged while on vasopressors + -- in principle, this could add an extra end time for the vasopressor + -- however, since we later group on vaso_start, any extra end times are ignored + when LEAD(CHARTTIME,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) is null + then vaso_first + + else null + end as vaso_stop + from vasocv5 v +) + +-- -- if you want to look at the results of the table before grouping: +-- select +-- icustay_id, charttime, vaso, vaso_rate, vaso_amount +-- , case when vaso_stopped = 1 then 'Y' else '' end as stopped +-- , vaso_start +-- , vaso_first +-- , vaso_stop +-- from vasocv6 order by charttime; + + +, vasocv as +( +-- below groups together vasopressor administrations into groups +select + icustay_id + -- the first non-null rate is considered the starttime + , min(case when vaso_rate is not null then charttime else null end) as starttime + -- the *first* time the first/last flags agree is the stop time for this duration + , min(case when vaso_first = vaso_stop then charttime else null end) as endtime +from vasocv6 +where + vaso_first is not null -- bogus data +and + vaso_first != 0 -- sometimes *only* a rate of 0 appears, i.e. the drug is never actually delivered +and + icustay_id is not null -- there are data for "floating" admissions, we don't worry about these +group by icustay_id, vaso_first +having -- ensure start time is not the same as end time + min(charttime) != min(case when vaso_first = vaso_stop then charttime else null end) +and + max(vaso_rate) > 0 -- if the rate was always 0 or null, we consider it not a real drug delivery +) + +-- now we extract the associated data for metavision patients +, vasomv as +( + select + icustay_id, linkorderid + , min(starttime) as starttime, max(endtime) as endtime + from mimiciii.inputevents_mv + where itemid = 221662 -- dopamine + and statusdescription != 'Rewritten' -- only valid orders + group by icustay_id, linkorderid +) + +select + icustay_id + -- generate a sequential integer for convenience + , ROW_NUMBER() over (partition by icustay_id order by starttime) as vasonum + , starttime, endtime +from + vasocv + +UNION + +select + icustay_id + , ROW_NUMBER() over (partition by icustay_id order by starttime) as vasonum + , starttime, endtime +from + vasomv + +order by icustay_id, vasonum; diff --git a/concepts/durations/epinephrine-durations.sql b/concepts/durations/epinephrine-durations.sql new file mode 100644 index 0000000..7ad917b --- /dev/null +++ b/concepts/durations/epinephrine-durations.sql @@ -0,0 +1,227 @@ +-- This query extracts durations of epinephrine administration +-- Consecutive administrations are numbered 1, 2, ... +-- Total time on the drug can be calculated from this table by grouping using ICUSTAY_ID + +DROP MATERIALIZED VIEW IF EXISTS EPINEPHRINEDURATIONS; +CREATE MATERIALIZED VIEW EPINEPHRINEDURATIONS as +-- Get drug administration data from CareVue first +with vasocv1 as +( + select + icustay_id, charttime + -- case statement determining whether the ITEMID is an instance of vasopressor usage + , max(case when itemid in (30044,30119,30309) then 1 else 0 end) as vaso -- epinephrine + + -- the 'stopped' column indicates if a vasopressor has been disconnected + , max(case when itemid in (30044,30119,30309) and stopped in ('Stopped','D/C''d') then 1 + else 0 end) as vaso_stopped + + , max(case when itemid in (30044,30119,30309) and rate is not null then 1 else 0 end) as vaso_null + , max(case when itemid in (30044,30119,30309) then rate else null end) as vaso_rate + , max(case when itemid in (30044,30119,30309) then amount else null end) as vaso_amount + + from mimiciii.inputevents_cv + where itemid in + ( + 30044,30119,30309 -- epinephrine + ) + group by icustay_id, charttime +) +, vasocv2 as +( + select v.* + , sum(vaso_null) over (partition by icustay_id order by charttime) as vaso_partition + from + vasocv1 v +) +, vasocv3 as +( + select v.* + , first_value(vaso_rate) over (partition by icustay_id, vaso_partition order by charttime) as vaso_prevrate_ifnull + from + vasocv2 v +) +, vasocv4 as +( +select + icustay_id + , charttime + -- , (CHARTTIME - (LAG(CHARTTIME, 1) OVER (partition by icustay_id, vaso order by charttime))) AS delta + + , vaso + , vaso_rate + , vaso_amount + , vaso_stopped + , vaso_prevrate_ifnull + + -- We define start time here + , case + when vaso = 0 then null + + -- if this is the first instance of the vasoactive drug + when vaso_rate > 0 and + LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso, vaso_null + order by charttime + ) + is null + then 1 + + -- you often get a string of 0s + -- we decide not to set these as 1, just because it makes vasonum sequential + when vaso_rate = 0 and + LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) + = 0 + then 0 + + -- sometimes you get a string of NULL, associated with 0 volumes + -- same reason as before, we decide not to set these as 1 + -- vaso_prevrate_ifnull is equal to the previous value *iff* the current value is null + when vaso_prevrate_ifnull = 0 and + LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) + = 0 + then 0 + + -- If the last recorded rate was 0, newvaso = 1 + when LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) = 0 + then 1 + + -- If the last recorded vaso was D/C'd, newvaso = 1 + when + LAG(vaso_stopped,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) + = 1 then 1 + + -- ** not sure if the below is needed + --when (CHARTTIME - (LAG(CHARTTIME, 1) OVER (partition by icustay_id, vaso order by charttime))) > (interval '4 hours') then 1 + else null + end as vaso_start + +FROM + vasocv3 +) +-- propagate start/stop flags forward in time +, vasocv5 as +( + select v.* + , SUM(vaso_start) OVER (partition by icustay_id, vaso order by charttime) as vaso_first +FROM + vasocv4 v +) +, vasocv6 as +( + select v.* + -- We define end time here + , case + when vaso = 0 + then null + + -- If the recorded vaso was D/C'd, this is an end time + when vaso_stopped = 1 + then vaso_first + + -- If the rate is zero, this is the end time + when vaso_rate = 0 + then vaso_first + + -- the last row in the table is always a potential end time + -- this captures patients who die/are discharged while on vasopressors + -- in principle, this could add an extra end time for the vasopressor + -- however, since we later group on vaso_start, any extra end times are ignored + when LEAD(CHARTTIME,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) is null + then vaso_first + + else null + end as vaso_stop + from vasocv5 v +) + +-- -- if you want to look at the results of the table before grouping: +-- select +-- icustay_id, charttime, vaso, vaso_rate, vaso_amount +-- , case when vaso_stopped = 1 then 'Y' else '' end as stopped +-- , vaso_start +-- , vaso_first +-- , vaso_stop +-- from vasocv6 order by charttime; + + +, vasocv as +( +-- below groups together vasopressor administrations into groups +select + icustay_id + -- the first non-null rate is considered the starttime + , min(case when vaso_rate is not null then charttime else null end) as starttime + -- the *first* time the first/last flags agree is the stop time for this duration + , min(case when vaso_first = vaso_stop then charttime else null end) as endtime +from vasocv6 +where + vaso_first is not null -- bogus data +and + vaso_first != 0 -- sometimes *only* a rate of 0 appears, i.e. the drug is never actually delivered +and + icustay_id is not null -- there are data for "floating" admissions, we don't worry about these +group by icustay_id, vaso_first +having -- ensure start time is not the same as end time + min(charttime) != min(case when vaso_first = vaso_stop then charttime else null end) +and + max(vaso_rate) > 0 -- if the rate was always 0 or null, we consider it not a real drug delivery +) + +-- now we extract the associated data for metavision patients +, vasomv as +( + select + icustay_id, linkorderid + , min(starttime) as starttime, max(endtime) as endtime + from mimiciii.inputevents_mv + where itemid = 221289 -- epinephrine + and statusdescription != 'Rewritten' -- only valid orders + group by icustay_id, linkorderid +) + +select + icustay_id + -- generate a sequential integer for convenience + , ROW_NUMBER() over (partition by icustay_id order by starttime) as vasonum + , starttime, endtime +from + vasocv + +UNION + +select + icustay_id + , ROW_NUMBER() over (partition by icustay_id order by starttime) as vasonum + , starttime, endtime +from + vasomv + +order by icustay_id, vasonum; diff --git a/concepts/durations/isuprel-durations.sql b/concepts/durations/isuprel-durations.sql new file mode 100644 index 0000000..33c5f5f --- /dev/null +++ b/concepts/durations/isuprel-durations.sql @@ -0,0 +1,225 @@ +-- This query extracts durations of isuprel administration +-- Consecutive administrations are numbered 1, 2, ... +-- Total time on the drug can be calculated from this table by grouping using ICUSTAY_ID + +DROP MATERIALIZED VIEW IF EXISTS ISUPRELDURATIONS; +CREATE MATERIALIZED VIEW ISUPRELDURATIONS as +-- Get drug administration data from CareVue first +with vasocv1 as +( + select + icustay_id, charttime + -- case statement determining whether the ITEMID is an instance of vasopressor usage + , max(case when itemid = 30046 then 1 else 0 end) as vaso -- Isuprel + + -- the 'stopped' column indicates if a vasopressor has been disconnected + , max(case when itemid = 30046 and stopped in ('Stopped','D/C''d') then 1 + else 0 end) as vaso_stopped + + , max(case when itemid = 30046 and rate is not null then 1 else 0 end) as vaso_null + , max(case when itemid = 30046 then rate else null end) as vaso_rate + , max(case when itemid = 30046 then amount else null end) as vaso_amount + + from mimiciii.inputevents_cv + where itemid = 30046 -- Isuprel + and icustay_id = 203992 + group by icustay_id, charttime +) +, vasocv2 as +( + select v.* + , sum(vaso_null) over (partition by icustay_id order by charttime) as vaso_partition + from + vasocv1 v +) +, vasocv3 as +( + select v.* + , first_value(vaso_rate) over (partition by icustay_id, vaso_partition order by charttime) as vaso_prevrate_ifnull + from + vasocv2 v +) +, vasocv4 as +( +select + icustay_id + , charttime + -- , (CHARTTIME - (LAG(CHARTTIME, 1) OVER (partition by icustay_id, vaso order by charttime))) AS delta + + , vaso + , vaso_rate + , vaso_amount + , vaso_stopped + , vaso_prevrate_ifnull + + -- We define start time here + , case + when vaso = 0 then null + + -- if this is the first instance of the vasoactive drug + when vaso_rate > 0 and + LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso, vaso_null + order by charttime + ) + is null + then 1 + + -- you often get a string of 0s + -- we decide not to set these as 1, just because it makes vasonum sequential + when vaso_rate = 0 and + LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) + = 0 + then 0 + + -- sometimes you get a string of NULL, associated with 0 volumes + -- same reason as before, we decide not to set these as 1 + -- vaso_prevrate_ifnull is equal to the previous value *iff* the current value is null + when vaso_prevrate_ifnull = 0 and + LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) + = 0 + then 0 + + -- If the last recorded rate was 0, newvaso = 1 + when LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) = 0 + then 1 + + -- If the last recorded vaso was D/C'd, newvaso = 1 + when + LAG(vaso_stopped,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) + = 1 then 1 + + -- ** not sure if the below is needed + --when (CHARTTIME - (LAG(CHARTTIME, 1) OVER (partition by icustay_id, vaso order by charttime))) > (interval '4 hours') then 1 + else null + end as vaso_start + +FROM + vasocv3 +) +-- propagate start/stop flags forward in time +, vasocv5 as +( + select v.* + , SUM(vaso_start) OVER (partition by icustay_id, vaso order by charttime) as vaso_first +FROM + vasocv4 v +) +, vasocv6 as +( + select v.* + -- We define end time here + , case + when vaso = 0 + then null + + -- If the recorded vaso was D/C'd, this is an end time + when vaso_stopped = 1 + then vaso_first + + -- If the rate is zero, this is the end time + when vaso_rate = 0 + then vaso_first + + -- the last row in the table is always a potential end time + -- this captures patients who die/are discharged while on vasopressors + -- in principle, this could add an extra end time for the vasopressor + -- however, since we later group on vaso_start, any extra end times are ignored + when LEAD(CHARTTIME,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) is null + then vaso_first + + else null + end as vaso_stop + from vasocv5 v +) + +-- -- if you want to look at the results of the table before grouping: +-- select +-- icustay_id, charttime, vaso, vaso_rate, vaso_amount +-- , case when vaso_stopped = 1 then 'Y' else '' end as stopped +-- , vaso_start +-- , vaso_first +-- , vaso_stop +-- from vasocv6 order by charttime; + + +, vasocv as +( +-- below groups together vasopressor administrations into groups +select + icustay_id + -- the first non-null rate is considered the starttime + , min(case when vaso_rate is not null then charttime else null end) as starttime + -- the *first* time the first/last flags agree is the stop time for this duration + , min(case when vaso_first = vaso_stop then charttime else null end) as endtime +from vasocv6 +where + vaso_first is not null -- bogus data +and + vaso_first != 0 -- sometimes *only* a rate of 0 appears, i.e. the drug is never actually delivered +and + icustay_id is not null -- there are data for "floating" admissions, we don't worry about these +group by icustay_id, vaso_first +having -- ensure start time is not the same as end time + min(charttime) != min(case when vaso_first = vaso_stop then charttime else null end) +and + max(vaso_rate) > 0 -- if the rate was always 0 or null, we consider it not a real drug delivery +) + +-- now we extract the associated data for metavision patients +, vasomv as +( + select + icustay_id, linkorderid + , min(starttime) as starttime, max(endtime) as endtime + from mimiciii.inputevents_mv + where itemid = 227692 -- Isuprel + and statusdescription != 'Rewritten' -- only valid orders + group by icustay_id, linkorderid +) + +select + icustay_id + -- generate a sequential integer for convenience + , ROW_NUMBER() over (partition by icustay_id order by starttime) as vasonum + , starttime, endtime +from + vasocv + +UNION + +select + icustay_id + , ROW_NUMBER() over (partition by icustay_id order by starttime) as vasonum + , starttime, endtime +from + vasomv + +order by icustay_id, vasonum; diff --git a/concepts/durations/milrinone-durations.sql b/concepts/durations/milrinone-durations.sql new file mode 100644 index 0000000..f8d393b --- /dev/null +++ b/concepts/durations/milrinone-durations.sql @@ -0,0 +1,224 @@ +-- This query extracts durations of milrinone administration +-- Consecutive administrations are numbered 1, 2, ... +-- Total time on the drug can be calculated from this table by grouping using ICUSTAY_ID + +DROP MATERIALIZED VIEW IF EXISTS MILRINONEDURATIONS; +CREATE MATERIALIZED VIEW MILRINONEDURATIONS as +-- Get drug administration data from CareVue first +with vasocv1 as +( + select + icustay_id, charttime + -- case statement determining whether the ITEMID is an instance of vasopressor usage + , max(case when itemid = 30125 then 1 else 0 end) as vaso -- milrinone + + -- the 'stopped' column indicates if a vasopressor has been disconnected + , max(case when itemid = 30125 and stopped in ('Stopped','D/C''d') then 1 + else 0 end) as vaso_stopped + + , max(case when itemid = 30125 and rate is not null then 1 else 0 end) as vaso_null + , max(case when itemid = 30125 then rate else null end) as vaso_rate + , max(case when itemid = 30125 then amount else null end) as vaso_amount + + from mimiciii.inputevents_cv + where itemid = 30125 -- milrinone + group by icustay_id, charttime +) +, vasocv2 as +( + select v.* + , sum(vaso_null) over (partition by icustay_id order by charttime) as vaso_partition + from + vasocv1 v +) +, vasocv3 as +( + select v.* + , first_value(vaso_rate) over (partition by icustay_id, vaso_partition order by charttime) as vaso_prevrate_ifnull + from + vasocv2 v +) +, vasocv4 as +( +select + icustay_id + , charttime + -- , (CHARTTIME - (LAG(CHARTTIME, 1) OVER (partition by icustay_id, vaso order by charttime))) AS delta + + , vaso + , vaso_rate + , vaso_amount + , vaso_stopped + , vaso_prevrate_ifnull + + -- We define start time here + , case + when vaso = 0 then null + + -- if this is the first instance of the vasoactive drug + when vaso_rate > 0 and + LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso, vaso_null + order by charttime + ) + is null + then 1 + + -- you often get a string of 0s + -- we decide not to set these as 1, just because it makes vasonum sequential + when vaso_rate = 0 and + LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) + = 0 + then 0 + + -- sometimes you get a string of NULL, associated with 0 volumes + -- same reason as before, we decide not to set these as 1 + -- vaso_prevrate_ifnull is equal to the previous value *iff* the current value is null + when vaso_prevrate_ifnull = 0 and + LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) + = 0 + then 0 + + -- If the last recorded rate was 0, newvaso = 1 + when LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) = 0 + then 1 + + -- If the last recorded vaso was D/C'd, newvaso = 1 + when + LAG(vaso_stopped,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) + = 1 then 1 + + -- ** not sure if the below is needed + --when (CHARTTIME - (LAG(CHARTTIME, 1) OVER (partition by icustay_id, vaso order by charttime))) > (interval '4 hours') then 1 + else null + end as vaso_start + +FROM + vasocv3 +) +-- propagate start/stop flags forward in time +, vasocv5 as +( + select v.* + , SUM(vaso_start) OVER (partition by icustay_id, vaso order by charttime) as vaso_first +FROM + vasocv4 v +) +, vasocv6 as +( + select v.* + -- We define end time here + , case + when vaso = 0 + then null + + -- If the recorded vaso was D/C'd, this is an end time + when vaso_stopped = 1 + then vaso_first + + -- If the rate is zero, this is the end time + when vaso_rate = 0 + then vaso_first + + -- the last row in the table is always a potential end time + -- this captures patients who die/are discharged while on vasopressors + -- in principle, this could add an extra end time for the vasopressor + -- however, since we later group on vaso_start, any extra end times are ignored + when LEAD(CHARTTIME,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) is null + then vaso_first + + else null + end as vaso_stop + from vasocv5 v +) + +-- -- if you want to look at the results of the table before grouping: +-- select +-- icustay_id, charttime, vaso, vaso_rate, vaso_amount +-- , case when vaso_stopped = 1 then 'Y' else '' end as stopped +-- , vaso_start +-- , vaso_first +-- , vaso_stop +-- from vasocv6 order by charttime; + + +, vasocv as +( +-- below groups together vasopressor administrations into groups +select + icustay_id + -- the first non-null rate is considered the starttime + , min(case when vaso_rate is not null then charttime else null end) as starttime + -- the *first* time the first/last flags agree is the stop time for this duration + , min(case when vaso_first = vaso_stop then charttime else null end) as endtime +from vasocv6 +where + vaso_first is not null -- bogus data +and + vaso_first != 0 -- sometimes *only* a rate of 0 appears, i.e. the drug is never actually delivered +and + icustay_id is not null -- there are data for "floating" admissions, we don't worry about these +group by icustay_id, vaso_first +having -- ensure start time is not the same as end time + min(charttime) != min(case when vaso_first = vaso_stop then charttime else null end) +and + max(vaso_rate) > 0 -- if the rate was always 0 or null, we consider it not a real drug delivery +) + +-- now we extract the associated data for metavision patients +, vasomv as +( + select + icustay_id, linkorderid + , min(starttime) as starttime, max(endtime) as endtime + from mimiciii.inputevents_mv + where itemid = 221986 -- milrinone + and statusdescription != 'Rewritten' -- only valid orders + group by icustay_id, linkorderid +) + +select + icustay_id + -- generate a sequential integer for convenience + , ROW_NUMBER() over (partition by icustay_id order by starttime) as vasonum + , starttime, endtime +from + vasocv + +UNION + +select + icustay_id + , ROW_NUMBER() over (partition by icustay_id order by starttime) as vasonum + , starttime, endtime +from + vasomv + +order by icustay_id, vasonum; diff --git a/concepts/durations/norepinephrine-durations.sql b/concepts/durations/norepinephrine-durations.sql new file mode 100644 index 0000000..a1ca96f --- /dev/null +++ b/concepts/durations/norepinephrine-durations.sql @@ -0,0 +1,223 @@ +-- This query extracts durations of norepinephrine administration +-- Consecutive administrations are numbered 1, 2, ... +-- Total time on the drug can be calculated from this table by grouping using ICUSTAY_ID + +DROP MATERIALIZED VIEW IF EXISTS NOREPINEPHRINEDURATIONS; +CREATE MATERIALIZED VIEW NOREPINEPHRINEDURATIONS as +-- Get drug administration data from CareVue first +with vasocv1 as +( + select + icustay_id, charttime + -- case statement determining whether the ITEMID is an instance of vasopressor usage + , max(case when itemid in (30047,30120) then 1 else 0 end) as vaso -- norepinephrine + + -- the 'stopped' column indicates if a vasopressor has been disconnected + , max(case when itemid in (30047,30120) and stopped in ('Stopped','D/C''d') then 1 + else 0 end) as vaso_stopped + + , max(case when itemid in (30047,30120) and rate is not null then 1 else 0 end) as vaso_null + , max(case when itemid in (30047,30120) then rate else null end) as vaso_rate + , max(case when itemid in (30047,30120) then amount else null end) as vaso_amount + + from mimiciii.inputevents_cv + where itemid in (30047,30120) -- norepinephrine + group by icustay_id, charttime +) +, vasocv2 as +( + select v.* + , sum(vaso_null) over (partition by icustay_id order by charttime) as vaso_partition + from + vasocv1 v +) +, vasocv3 as +( + select v.* + , first_value(vaso_rate) over (partition by icustay_id, vaso_partition order by charttime) as vaso_prevrate_ifnull + from + vasocv2 v +) +, vasocv4 as +( +select + icustay_id + , charttime + -- , (CHARTTIME - (LAG(CHARTTIME, 1) OVER (partition by icustay_id, vaso order by charttime))) AS delta + + , vaso + , vaso_rate + , vaso_amount + , vaso_stopped + , vaso_prevrate_ifnull + + -- We define start time here + , case + when vaso = 0 then null + + -- if this is the first instance of the vasoactive drug + when vaso_rate > 0 and + LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso, vaso_null + order by charttime + ) + is null + then 1 + + -- you often get a string of 0s + -- we decide not to set these as 1, just because it makes vasonum sequential + when vaso_rate = 0 and + LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) + = 0 + then 0 + + -- sometimes you get a string of NULL, associated with 0 volumes + -- same reason as before, we decide not to set these as 1 + -- vaso_prevrate_ifnull is equal to the previous value *iff* the current value is null + when vaso_prevrate_ifnull = 0 and + LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) + = 0 + then 0 + + -- If the last recorded rate was 0, newvaso = 1 + when LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) = 0 + then 1 + + -- If the last recorded vaso was D/C'd, newvaso = 1 + when + LAG(vaso_stopped,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) + = 1 then 1 + + -- ** not sure if the below is needed + --when (CHARTTIME - (LAG(CHARTTIME, 1) OVER (partition by icustay_id, vaso order by charttime))) > (interval '4 hours') then 1 + else null + end as vaso_start + +FROM + vasocv3 +) +-- propagate start/stop flags forward in time +, vasocv5 as +( + select v.* + , SUM(vaso_start) OVER (partition by icustay_id, vaso order by charttime) as vaso_first +FROM + vasocv4 v +) +, vasocv6 as +( + select v.* + -- We define end time here + , case + when vaso = 0 + then null + + -- If the recorded vaso was D/C'd, this is an end time + when vaso_stopped = 1 + then vaso_first + + -- If the rate is zero, this is the end time + when vaso_rate = 0 + then vaso_first + + -- the last row in the table is always a potential end time + -- this captures patients who die/are discharged while on vasopressors + -- in principle, this could add an extra end time for the vasopressor + -- however, since we later group on vaso_start, any extra end times are ignored + when LEAD(CHARTTIME,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) is null + then vaso_first + + else null + end as vaso_stop + from vasocv5 v +) + +-- -- if you want to look at the results of the carevue data before grouping: +-- select +-- icustay_id, charttime, vaso, vaso_rate, vaso_amount +-- , case when vaso_stopped = 1 then 'Y' else '' end as stopped +-- , vaso_start +-- , vaso_first +-- , vaso_stop +-- from vasocv6 order by charttime; + +, vasocv as +( +-- below groups together vasopressor administrations into groups +select + icustay_id + -- the first non-null rate is considered the starttime + , min(case when vaso_rate is not null then charttime else null end) as starttime + -- the *first* time the first/last flags agree is the stop time for this duration + , min(case when vaso_first = vaso_stop then charttime else null end) as endtime +from vasocv6 +where + vaso_first is not null -- bogus data +and + vaso_first != 0 -- sometimes *only* a rate of 0 appears, i.e. the drug is never actually delivered +and + icustay_id is not null -- there are data for "floating" admissions, we don't worry about these +group by icustay_id, vaso_first +having -- ensure start time is not the same as end time + min(charttime) != min(case when vaso_first = vaso_stop then charttime else null end) +and + max(vaso_rate) > 0 -- if the rate was always 0 or null, we consider it not a real drug delivery +) + +-- now we extract the associated data for metavision patients +, vasomv as +( + select + icustay_id, linkorderid + , min(starttime) as starttime, max(endtime) as endtime + from mimiciii.inputevents_mv + where itemid = 221906 -- norepinephrine + and statusdescription != 'Rewritten' -- only valid orders + group by icustay_id, linkorderid +) + +select + icustay_id + -- generate a sequential integer for convenience + , ROW_NUMBER() over (partition by icustay_id order by starttime) as vasonum + , starttime, endtime +from + vasocv + +UNION + +select + icustay_id + , ROW_NUMBER() over (partition by icustay_id order by starttime) as vasonum + , starttime, endtime +from + vasomv + +order by icustay_id, vasonum; diff --git a/concepts/durations/phenylephrine-durations.sql b/concepts/durations/phenylephrine-durations.sql new file mode 100644 index 0000000..c6845ed --- /dev/null +++ b/concepts/durations/phenylephrine-durations.sql @@ -0,0 +1,227 @@ +-- This query extracts durations of phenylephrine administration +-- Consecutive administrations are numbered 1, 2, ... +-- Total time on the drug can be calculated from this table by grouping using ICUSTAY_ID + +DROP MATERIALIZED VIEW IF EXISTS PHENYLEPHRINEDURATIONS; +CREATE MATERIALIZED VIEW PHENYLEPHRINEDURATIONS as +-- Get drug administration data from CareVue first +with vasocv1 as +( + select + icustay_id, charttime + -- case statement determining whether the ITEMID is an instance of vasopressor usage + , max(case when itemid in (30127,30128) then 1 else 0 end) as vaso -- phenylephrine + + -- the 'stopped' column indicates if a vasopressor has been disconnected + , max(case when itemid in (30127,30128) and stopped in ('Stopped','D/C''d') then 1 + else 0 end) as vaso_stopped + + , max(case when itemid in (30127,30128) and rate is not null then 1 else 0 end) as vaso_null + , max(case when itemid in (30127,30128) then rate else null end) as vaso_rate + , max(case when itemid in (30127,30128) then amount else null end) as vaso_amount + + from mimiciii.inputevents_cv + where itemid in + ( + 30127,30128 -- phenylephrine + ) + group by icustay_id, charttime +) +, vasocv2 as +( + select v.* + , sum(vaso_null) over (partition by icustay_id order by charttime) as vaso_partition + from + vasocv1 v +) +, vasocv3 as +( + select v.* + , first_value(vaso_rate) over (partition by icustay_id, vaso_partition order by charttime) as vaso_prevrate_ifnull + from + vasocv2 v +) +, vasocv4 as +( +select + icustay_id + , charttime + -- , (CHARTTIME - (LAG(CHARTTIME, 1) OVER (partition by icustay_id, vaso order by charttime))) AS delta + + , vaso + , vaso_rate + , vaso_amount + , vaso_stopped + , vaso_prevrate_ifnull + + -- We define start time here + , case + when vaso = 0 then null + + -- if this is the first instance of the vasoactive drug + when vaso_rate > 0 and + LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso, vaso_null + order by charttime + ) + is null + then 1 + + -- you often get a string of 0s + -- we decide not to set these as 1, just because it makes vasonum sequential + when vaso_rate = 0 and + LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) + = 0 + then 0 + + -- sometimes you get a string of NULL, associated with 0 volumes + -- same reason as before, we decide not to set these as 1 + -- vaso_prevrate_ifnull is equal to the previous value *iff* the current value is null + when vaso_prevrate_ifnull = 0 and + LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) + = 0 + then 0 + + -- If the last recorded rate was 0, newvaso = 1 + when LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) = 0 + then 1 + + -- If the last recorded vaso was D/C'd, newvaso = 1 + when + LAG(vaso_stopped,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) + = 1 then 1 + + -- ** not sure if the below is needed + --when (CHARTTIME - (LAG(CHARTTIME, 1) OVER (partition by icustay_id, vaso order by charttime))) > (interval '4 hours') then 1 + else null + end as vaso_start + +FROM + vasocv3 +) +-- propagate start/stop flags forward in time +, vasocv5 as +( + select v.* + , SUM(vaso_start) OVER (partition by icustay_id, vaso order by charttime) as vaso_first +FROM + vasocv4 v +) +, vasocv6 as +( + select v.* + -- We define end time here + , case + when vaso = 0 + then null + + -- If the recorded vaso was D/C'd, this is an end time + when vaso_stopped = 1 + then vaso_first + + -- If the rate is zero, this is the end time + when vaso_rate = 0 + then vaso_first + + -- the last row in the table is always a potential end time + -- this captures patients who die/are discharged while on vasopressors + -- in principle, this could add an extra end time for the vasopressor + -- however, since we later group on vaso_start, any extra end times are ignored + when LEAD(CHARTTIME,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) is null + then vaso_first + + else null + end as vaso_stop + from vasocv5 v +) + +-- -- if you want to look at the results of the table before grouping: +-- select +-- icustay_id, charttime, vaso, vaso_rate, vaso_amount +-- , case when vaso_stopped = 1 then 'Y' else '' end as stopped +-- , vaso_start +-- , vaso_first +-- , vaso_stop +-- from vasocv6 order by charttime; + + +, vasocv as +( +-- below groups together vasopressor administrations into groups +select + icustay_id + -- the first non-null rate is considered the starttime + , min(case when vaso_rate is not null then charttime else null end) as starttime + -- the *first* time the first/last flags agree is the stop time for this duration + , min(case when vaso_first = vaso_stop then charttime else null end) as endtime +from vasocv6 +where + vaso_first is not null -- bogus data +and + vaso_first != 0 -- sometimes *only* a rate of 0 appears, i.e. the drug is never actually delivered +and + icustay_id is not null -- there are data for "floating" admissions, we don't worry about these +group by icustay_id, vaso_first +having -- ensure start time is not the same as end time + min(charttime) != min(case when vaso_first = vaso_stop then charttime else null end) +and + max(vaso_rate) > 0 -- if the rate was always 0 or null, we consider it not a real drug delivery +) + +-- now we extract the associated data for metavision patients +, vasomv as +( + select + icustay_id, linkorderid + , min(starttime) as starttime, max(endtime) as endtime + from mimiciii.inputevents_mv + where itemid = 221749 -- phenylephrine + and statusdescription != 'Rewritten' -- only valid orders + group by icustay_id, linkorderid +) + +select + icustay_id + -- generate a sequential integer for convenience + , ROW_NUMBER() over (partition by icustay_id order by starttime) as vasonum + , starttime, endtime +from + vasocv + +UNION + +select + icustay_id + , ROW_NUMBER() over (partition by icustay_id order by starttime) as vasonum + , starttime, endtime +from + vasomv + +order by icustay_id, vasonum; diff --git a/concepts/durations/vasopressin-durations.sql b/concepts/durations/vasopressin-durations.sql new file mode 100644 index 0000000..0fa038d --- /dev/null +++ b/concepts/durations/vasopressin-durations.sql @@ -0,0 +1,225 @@ +-- This query extracts durations of vasopressin administration +-- Consecutive administrations are numbered 1, 2, ... +-- Total time on the drug can be calculated from this table by grouping using ICUSTAY_ID + +DROP MATERIALIZED VIEW IF EXISTS VASOPRESSINDURATIONS; +CREATE MATERIALIZED VIEW VASOPRESSINDURATIONS as +-- Get drug administration data from CareVue first +with vasocv1 as +( + select + icustay_id, charttime + -- case statement determining whether the ITEMID is an instance of vasopressor usage + , max(case when itemid = 30051 then 1 else 0 end) as vaso -- vasopressin + + -- the 'stopped' column indicates if a vasopressor has been disconnected + , max(case when itemid = 30051 and stopped in ('Stopped','D/C''d') then 1 + else 0 end) as vaso_stopped + + , max(case when itemid = 30051 and rate is not null then 1 else 0 end) as vaso_null + , max(case when itemid = 30051 then rate else null end) as vaso_rate + , max(case when itemid = 30051 then amount else null end) as vaso_amount + + from mimiciii.inputevents_cv + where itemid = 30051 -- vasopressin + -- and icustay_id = 259409 + group by icustay_id, charttime +) +, vasocv2 as +( + select v.* + , sum(vaso_null) over (partition by icustay_id order by charttime) as vaso_partition + from + vasocv1 v +) +, vasocv3 as +( + select v.* + , first_value(vaso_rate) over (partition by icustay_id, vaso_partition order by charttime) as vaso_prevrate_ifnull + from + vasocv2 v +) +, vasocv4 as +( +select + icustay_id + , charttime + -- , (CHARTTIME - (LAG(CHARTTIME, 1) OVER (partition by icustay_id, vaso order by charttime))) AS delta + + , vaso + , vaso_rate + , vaso_amount + , vaso_stopped + , vaso_prevrate_ifnull + + -- We define start time here + , case + when vaso = 0 then null + + -- if this is the first instance of the vasoactive drug + when vaso_rate > 0 and + LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso, vaso_null + order by charttime + ) + is null + then 1 + + -- you often get a string of 0s + -- we decide not to set these as 1, just because it makes vasonum sequential + when vaso_rate = 0 and + LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) + = 0 + then 0 + + -- sometimes you get a string of NULL, associated with 0 volumes + -- same reason as before, we decide not to set these as 1 + -- vaso_prevrate_ifnull is equal to the previous value *iff* the current value is null + when vaso_prevrate_ifnull = 0 and + LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) + = 0 + then 0 + + -- If the last recorded rate was 0, newvaso = 1 + when LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) = 0 + then 1 + + -- If the last recorded vaso was D/C'd, newvaso = 1 + when + LAG(vaso_stopped,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) + = 1 then 1 + + -- ** not sure if the below is needed + --when (CHARTTIME - (LAG(CHARTTIME, 1) OVER (partition by icustay_id, vaso order by charttime))) > (interval '4 hours') then 1 + else null + end as vaso_start + +FROM + vasocv3 +) +-- propagate start/stop flags forward in time +, vasocv5 as +( + select v.* + , SUM(vaso_start) OVER (partition by icustay_id, vaso order by charttime) as vaso_first +FROM + vasocv4 v +) +, vasocv6 as +( + select v.* + -- We define end time here + , case + when vaso = 0 + then null + + -- If the recorded vaso was D/C'd, this is an end time + when vaso_stopped = 1 + then vaso_first + + -- If the rate is zero, this is the end time + when vaso_rate = 0 + then vaso_first + + -- the last row in the table is always a potential end time + -- this captures patients who die/are discharged while on vasopressors + -- in principle, this could add an extra end time for the vasopressor + -- however, since we later group on vaso_start, any extra end times are ignored + when LEAD(CHARTTIME,1) + OVER + ( + partition by icustay_id, vaso + order by charttime + ) is null + then vaso_first + + else null + end as vaso_stop + from vasocv5 v +) + +-- -- if you want to look at the results of the table before grouping: +-- select +-- icustay_id, charttime, vaso, vaso_rate, vaso_amount +-- , case when vaso_stopped = 1 then 'Y' else '' end as stopped +-- , vaso_start +-- , vaso_first +-- , vaso_stop +-- from vasocv6 order by charttime; + + +, vasocv as +( +-- below groups together vasopressor administrations into groups +select + icustay_id + -- the first non-null rate is considered the starttime + , min(case when vaso_rate is not null then charttime else null end) as starttime + -- the *first* time the first/last flags agree is the stop time for this duration + , min(case when vaso_first = vaso_stop then charttime else null end) as endtime +from vasocv6 +where + vaso_first is not null -- bogus data +and + vaso_first != 0 -- sometimes *only* a rate of 0 appears, i.e. the drug is never actually delivered +and + icustay_id is not null -- there are data for "floating" admissions, we don't worry about these +group by icustay_id, vaso_first +having -- ensure start time is not the same as end time + min(charttime) != min(case when vaso_first = vaso_stop then charttime else null end) +and + max(vaso_rate) > 0 -- if the rate was always 0 or null, we consider it not a real drug delivery +) + +-- now we extract the associated data for metavision patients +, vasomv as +( + select + icustay_id, linkorderid + , min(starttime) as starttime, max(endtime) as endtime + from mimiciii.inputevents_mv + where itemid = 222315 -- vasopressin + and statusdescription != 'Rewritten' -- only valid orders + group by icustay_id, linkorderid +) + +select + icustay_id + -- generate a sequential integer for convenience + , ROW_NUMBER() over (partition by icustay_id order by starttime) as vasonum + , starttime, endtime +from + vasocv + +UNION + +select + icustay_id + , ROW_NUMBER() over (partition by icustay_id order by starttime) as vasonum + , starttime, endtime +from + vasomv + +order by icustay_id, vasonum; diff --git a/concepts/durations/vasopressor-durations.sql b/concepts/durations/vasopressor-durations.sql new file mode 100644 index 0000000..f9baa40 --- /dev/null +++ b/concepts/durations/vasopressor-durations.sql @@ -0,0 +1,312 @@ +-- This query extracts durations of vasopressor administration +-- It groups together any administration of the below list of drugs: +-- norepinephrine - 30047,30120,221906 +-- epinephrine - 30044,30119,30309,221289 +-- phenylephrine - 30127,30128,221749 +-- vasopressin - 30051,222315 (42273, 42802 also for 2 patients) +-- dopamine - 30043,30307,221662 +-- dobutamine - 30042,30306,221653 +-- milrinone - 30125,221986 + +-- Consecutive administrations are numbered 1, 2, ... +-- Total time on the drug can be calculated from this table +-- by grouping using ICUSTAY_ID + +-- select only the ITEMIDs from the inputevents_cv table related to vasopressors +DROP MATERIALIZED VIEW IF EXISTS VASOPRESSORDURATIONS; +CREATE MATERIALIZED VIEW VASOPRESSORDURATIONS as +with io_cv as +( + select + icustay_id, charttime, itemid, stopped + -- ITEMIDs (42273, 42802) accidentally store rate in amount column + , case + when itemid in (42273, 42802) + then amount + else rate + end as rate + , case + when itemid in (42273, 42802) + then rate + else amount + end as amount + from mimiciii.inputevents_cv + where itemid in + ( + 30047,30120,30044,30119,30309,30127 + , 30128,30051,30043,30307,30042,30306,30125 + , 42273, 42802 + ) +) +-- select only the ITEMIDs from the inputevents_mv table related to vasopressors +, io_mv as +( + select + icustay_id, linkorderid, starttime, endtime + from mimiciii.inputevents_mv io + -- Subselect the vasopressor ITEMIDs + where itemid in + ( + 221906,221289,221749,222315,221662,221653,221986 + ) + and statusdescription != 'Rewritten' -- only valid orders +) +, vasocv1 as +( + select + icustay_id, charttime, itemid + -- case statement determining whether the ITEMID is an instance of vasopressor usage + , 1 as vaso + + -- the 'stopped' column indicates if a vasopressor has been disconnected + , max(case when stopped in ('Stopped','D/C''d') then 1 + else 0 end) as vaso_stopped + + , max(case when rate is not null then 1 else 0 end) as vaso_null + , max(rate) as vaso_rate + , max(amount) as vaso_amount + + from io_cv + group by icustay_id, charttime, itemid +) +, vasocv2 as +( + select v.* + , sum(vaso_null) over (partition by icustay_id, itemid order by charttime) as vaso_partition + from + vasocv1 v +) +, vasocv3 as +( + select v.* + , first_value(vaso_rate) over (partition by icustay_id, itemid, vaso_partition order by charttime) as vaso_prevrate_ifnull + from + vasocv2 v +) +, vasocv4 as +( +select + icustay_id + , charttime + , itemid + -- , (CHARTTIME - (LAG(CHARTTIME, 1) OVER (partition by icustay_id, vaso order by charttime))) AS delta + + , vaso + , vaso_rate + , vaso_amount + , vaso_stopped + , vaso_prevrate_ifnull + + -- We define start time here + , case + when vaso = 0 then null + + -- if this is the first instance of the vasoactive drug + when vaso_rate > 0 and + LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, itemid, vaso, vaso_null + order by charttime + ) + is null + then 1 + + -- you often get a string of 0s + -- we decide not to set these as 1, just because it makes vasonum sequential + when vaso_rate = 0 and + LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, itemid, vaso + order by charttime + ) + = 0 + then 0 + + -- sometimes you get a string of NULL, associated with 0 volumes + -- same reason as before, we decide not to set these as 1 + -- vaso_prevrate_ifnull is equal to the previous value *iff* the current value is null + when vaso_prevrate_ifnull = 0 and + LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, itemid, vaso + order by charttime + ) + = 0 + then 0 + + -- If the last recorded rate was 0, newvaso = 1 + when LAG(vaso_prevrate_ifnull,1) + OVER + ( + partition by icustay_id, itemid, vaso + order by charttime + ) = 0 + then 1 + + -- If the last recorded vaso was D/C'd, newvaso = 1 + when + LAG(vaso_stopped,1) + OVER + ( + partition by icustay_id, itemid, vaso + order by charttime + ) + = 1 then 1 + + -- ** not sure if the below is needed + --when (CHARTTIME - (LAG(CHARTTIME, 1) OVER (partition by icustay_id, vaso order by charttime))) > (interval '4 hours') then 1 + else null + end as vaso_start + +FROM + vasocv3 +) +-- propagate start/stop flags forward in time +, vasocv5 as +( + select v.* + , SUM(vaso_start) OVER (partition by icustay_id, itemid, vaso order by charttime) as vaso_first +FROM + vasocv4 v +) +, vasocv6 as +( + select v.* + -- We define end time here + , case + when vaso = 0 + then null + + -- If the recorded vaso was D/C'd, this is an end time + when vaso_stopped = 1 + then vaso_first + + -- If the rate is zero, this is the end time + when vaso_rate = 0 + then vaso_first + + -- the last row in the table is always a potential end time + -- this captures patients who die/are discharged while on vasopressors + -- in principle, this could add an extra end time for the vasopressor + -- however, since we later group on vaso_start, any extra end times are ignored + when LEAD(CHARTTIME,1) + OVER + ( + partition by icustay_id, itemid, vaso + order by charttime + ) is null + then vaso_first + + else null + end as vaso_stop + from vasocv5 v +) + +-- -- if you want to look at the results of the table before grouping: +-- select +-- icustay_id, charttime, vaso, vaso_rate, vaso_amount +-- , case when vaso_stopped = 1 then 'Y' else '' end as stopped +-- , vaso_start +-- , vaso_first +-- , vaso_stop +-- from vasocv6 order by charttime; + + +, vasocv as +( +-- below groups together vasopressor administrations into groups +select + icustay_id + , itemid + -- the first non-null rate is considered the starttime + , min(case when vaso_rate is not null then charttime else null end) as starttime + -- the *first* time the first/last flags agree is the stop time for this duration + , min(case when vaso_first = vaso_stop then charttime else null end) as endtime +from vasocv6 +where + vaso_first is not null -- bogus data +and + vaso_first != 0 -- sometimes *only* a rate of 0 appears, i.e. the drug is never actually delivered +and + icustay_id is not null -- there are data for "floating" admissions, we don't worry about these +group by icustay_id, itemid, vaso_first +having -- ensure start time is not the same as end time + min(charttime) != min(case when vaso_first = vaso_stop then charttime else null end) +and + max(vaso_rate) > 0 -- if the rate was always 0 or null, we consider it not a real drug delivery +) +-- we do not group by ITEMID in below query +-- this is because we want to collapse all vasopressors together +, vasocv_grp as +( +SELECT + s1.icustay_id, + s1.starttime, + MIN(t1.endtime) AS endtime +FROM vasocv s1 +INNER JOIN vasocv t1 + ON s1.icustay_id = t1.icustay_id + AND s1.starttime <= t1.endtime + AND NOT EXISTS(SELECT * FROM vasocv t2 + WHERE t1.icustay_id = t2.icustay_id + AND t1.endtime >= t2.starttime + AND t1.endtime < t2.endtime) +WHERE NOT EXISTS(SELECT * FROM vasocv s2 + WHERE s1.icustay_id = s2.icustay_id + AND s1.starttime > s2.starttime + AND s1.starttime <= s2.endtime) +GROUP BY s1.icustay_id, s1.starttime +ORDER BY s1.icustay_id, s1.starttime +) +-- now we extract the associated data for metavision patients +-- do not need to group by itemid because we group by linkorderid +, vasomv as +( + select + icustay_id, linkorderid + , min(starttime) as starttime, max(endtime) as endtime + from io_mv + group by icustay_id, linkorderid +) +, vasomv_grp as +( +SELECT + s1.icustay_id, + s1.starttime, + MIN(t1.endtime) AS endtime +FROM vasomv s1 +INNER JOIN vasomv t1 + ON s1.icustay_id = t1.icustay_id + AND s1.starttime <= t1.endtime + AND NOT EXISTS(SELECT * FROM vasomv t2 + WHERE t1.icustay_id = t2.icustay_id + AND t1.endtime >= t2.starttime + AND t1.endtime < t2.endtime) +WHERE NOT EXISTS(SELECT * FROM vasomv s2 + WHERE s1.icustay_id = s2.icustay_id + AND s1.starttime > s2.starttime + AND s1.starttime <= s2.endtime) +GROUP BY s1.icustay_id, s1.starttime +ORDER BY s1.icustay_id, s1.starttime +) +select + icustay_id + -- generate a sequential integer for convenience + , ROW_NUMBER() over (partition by icustay_id order by starttime) as vasonum + , starttime, endtime +from + vasocv_grp + +UNION + +select + icustay_id + , ROW_NUMBER() over (partition by icustay_id order by starttime) as vasonum + , starttime, endtime +from + vasomv_grp + +order by icustay_id, vasonum; diff --git a/concepts/durations/ventilation-durations.sql b/concepts/durations/ventilation-durations.sql new file mode 100644 index 0000000..1d336fe --- /dev/null +++ b/concepts/durations/ventilation-durations.sql @@ -0,0 +1,257 @@ +-- This query extracts the duration of mechanical ventilation +-- The main goal of the query is to aggregate sequential ventilator settings +-- into single mechanical ventilation "events". The start and end time of these +-- events can then be used for various purposes: calculating the total duration +-- of mechanical ventilation, cross-checking values (e.g. PaO2:FiO2 on vent), etc + +-- The query's logic is roughly: +-- 1) The presence of a mechanical ventilation setting starts a new ventilation event +-- 2) Any instance of a setting in the next 8 hours continues the event +-- 3) Certain elements end the current ventilation event +-- a) documented extubation ends the current ventilation +-- b) initiation of non-invasive vent and/or oxygen ends the current vent +-- The ventilation events are numbered consecutively by the `num` column. + + +-- First, create a temporary table to store relevant data from CHARTEVENTS. +DROP MATERIALIZED VIEW IF EXISTS ventsettings CASCADE; +CREATE MATERIALIZED VIEW ventsettings AS +select + icustay_id, charttime + -- case statement determining whether it is an instance of mech vent + , max( + case + when itemid is null or value is null then 0 -- can't have null values + when itemid = 720 and value != 'Other/Remarks' THEN 1 -- VentTypeRecorded + when itemid = 223848 and value != 'Other' THEN 1 + when itemid = 223849 then 1 -- ventilator mode + when itemid = 467 and value = 'Ventilator' THEN 1 -- O2 delivery device == ventilator + when itemid in + ( + 445, 448, 449, 450, 1340, 1486, 1600, 224687 -- minute volume + , 639, 654, 681, 682, 683, 684,224685,224684,224686 -- tidal volume + , 218,436,535,444,459,224697,224695,224696,224746,224747 -- High/Low/Peak/Mean/Neg insp force ("RespPressure") + , 221,1,1211,1655,2000,226873,224738,224419,224750,227187 -- Insp pressure + , 543 -- PlateauPressure + , 5865,5866,224707,224709,224705,224706 -- APRV pressure + , 60,437,505,506,686,220339,224700 -- PEEP + , 3459 -- high pressure relief + , 501,502,503,224702 -- PCV + , 223,667,668,669,670,671,672 -- TCPCV + , 224701 -- PSVlevel + ) + THEN 1 + else 0 + end + ) as MechVent + , max( + case + -- initiation of oxygen therapy indicates the ventilation has ended + when itemid = 226732 and value in + ( + 'Nasal cannula', -- 153714 observations + 'Face tent', -- 24601 observations + 'Aerosol-cool', -- 24560 observations + 'Trach mask ', -- 16435 observations + 'High flow neb', -- 10785 observations + 'Non-rebreather', -- 5182 observations + 'Venti mask ', -- 1947 observations + 'Medium conc mask ', -- 1888 observations + 'T-piece', -- 1135 observations + 'High flow nasal cannula', -- 925 observations + 'Ultrasonic neb', -- 9 observations + 'Vapomist' -- 3 observations + ) then 1 + when itemid = 467 and value in + ( + 'Cannula', -- 278252 observations + 'Nasal Cannula', -- 248299 observations + 'None', -- 95498 observations + 'Face Tent', -- 35766 observations + 'Aerosol-Cool', -- 33919 observations + 'Trach Mask', -- 32655 observations + 'Hi Flow Neb', -- 14070 observations + 'Non-Rebreather', -- 10856 observations + 'Venti Mask', -- 4279 observations + 'Medium Conc Mask', -- 2114 observations + 'Vapotherm', -- 1655 observations + 'T-Piece', -- 779 observations + 'Hood', -- 670 observations + 'Hut', -- 150 observations + 'TranstrachealCat', -- 78 observations + 'Heated Neb', -- 37 observations + 'Ultrasonic Neb' -- 2 observations + ) then 1 + else 0 + end + ) as OxygenTherapy + , max( + case when itemid is null or value is null then 0 + -- extubated indicates ventilation event has ended + when itemid = 640 and value = 'Extubated' then 1 + when itemid = 640 and value = 'Self Extubation' then 1 + else 0 + end + ) + as Extubated + , max( + case when itemid is null or value is null then 0 + when itemid = 640 and value = 'Self Extubation' then 1 + else 0 + end + ) + as SelfExtubated +from chartevents ce +where ce.value is not null +-- exclude rows marked as error +and ce.error IS DISTINCT FROM 1 +and itemid in +( + -- the below are settings used to indicate ventilation + 720, 223849 -- vent mode + , 223848 -- vent type + , 445, 448, 449, 450, 1340, 1486, 1600, 224687 -- minute volume + , 639, 654, 681, 682, 683, 684,224685,224684,224686 -- tidal volume + , 218,436,535,444,224697,224695,224696,224746,224747 -- High/Low/Peak/Mean ("RespPressure") + , 221,1,1211,1655,2000,226873,224738,224419,224750,227187 -- Insp pressure + , 543 -- PlateauPressure + , 5865,5866,224707,224709,224705,224706 -- APRV pressure + , 60,437,505,506,686,220339,224700 -- PEEP + , 3459 -- high pressure relief + , 501,502,503,224702 -- PCV + , 223,667,668,669,670,671,672 -- TCPCV + , 224701 -- PSVlevel + + -- the below are settings used to indicate extubation + , 640 -- extubated + + -- the below indicate oxygen/NIV, i.e. the end of a mechanical vent event + , 468 -- O2 Delivery Device#2 + , 469 -- O2 Delivery Mode + , 470 -- O2 Flow (lpm) + , 471 -- O2 Flow (lpm) #2 + , 227287 -- O2 Flow (additional cannula) + , 226732 -- O2 Delivery Device(s) + , 223834 -- O2 Flow + + -- used in both oxygen + vent calculation + , 467 -- O2 Delivery Device +) +group by icustay_id, charttime +UNION +-- add in the extubation flags from procedureevents_mv +-- note that we only need the start time for the extubation +-- (extubation is always charted as ending 1 minute after it started) +select + icustay_id, starttime as charttime + , 0 as MechVent + , 0 as OxygenTherapy + , 1 as Extubated + , case when itemid = 225468 then 1 else 0 end as SelfExtubated +from procedureevents_mv +where itemid in +( + 227194 -- "Extubation" +, 225468 -- "Unplanned Extubation (patient-initiated)" +, 225477 -- "Unplanned Extubation (non-patient initiated)" +); + + +--DROP MATERIALIZED VIEW IF EXISTS VENTDURATIONS CASCADE; +DROP MATERIALIZED VIEW IF EXISTS VENTDURATIONS CASCADE; +create MATERIALIZED VIEW ventdurations as +with vd0 as +( + select + icustay_id + -- this carries over the previous charttime which had a mechanical ventilation event + , case + when MechVent=1 then + LAG(CHARTTIME, 1) OVER (partition by icustay_id, MechVent order by charttime) + else null + end as charttime_lag + , charttime + , MechVent + , OxygenTherapy + , Extubated + , SelfExtubated + from ventsettings +) +, vd1 as +( + select + icustay_id + , charttime_lag + , charttime + , MechVent + , OxygenTherapy + , Extubated + , SelfExtubated + + -- if this is a mechanical ventilation event, we calculate the time since the last event + , case + -- if the current observation indicates mechanical ventilation is present + -- calculate the time since the last vent event + when MechVent=1 then + CHARTTIME - charttime_lag + else null + end as ventduration + + , LAG(Extubated,1) + OVER + ( + partition by icustay_id, case when MechVent=1 or Extubated=1 then 1 else 0 end + order by charttime + ) as ExtubatedLag + + -- now we determine if the current mech vent event is a "new", i.e. they've just been intubated + , case + -- if there is an extubation flag, we mark any subsequent ventilation as a new ventilation event + --when Extubated = 1 then 0 -- extubation is *not* a new ventilation event, the *subsequent* row is + when + LAG(Extubated,1) + OVER + ( + partition by icustay_id, case when MechVent=1 or Extubated=1 then 1 else 0 end + order by charttime + ) + = 1 then 1 + -- if patient has initiated oxygen therapy, and is not currently vented, start a newvent + when MechVent = 0 and OxygenTherapy = 1 then 1 + -- if there is less than 8 hours between vent settings, we do not treat this as a new ventilation event + when (CHARTTIME - charttime_lag) > interval '8' hour + then 1 + else 0 + end as newvent + -- use the staging table with only vent settings from chart events + FROM vd0 ventsettings +) +, vd2 as +( + select vd1.* + -- create a cumulative sum of the instances of new ventilation + -- this results in a monotonic integer assigned to each instance of ventilation + , case when MechVent=1 or Extubated = 1 then + SUM( newvent ) + OVER ( partition by icustay_id order by charttime ) + else null end + as ventnum + --- now we convert CHARTTIME of ventilator settings into durations + from vd1 +) +-- create the durations for each mechanical ventilation instance +select icustay_id + -- regenerate ventnum so it's sequential + , ROW_NUMBER() over (partition by icustay_id order by ventnum) as ventnum + , min(charttime) as starttime + , max(charttime) as endtime + , extract(epoch from max(charttime)-min(charttime))/60/60 AS duration_hours +from vd2 +group by icustay_id, ventnum +having min(charttime) != max(charttime) +-- patient had to be mechanically ventilated at least once +-- i.e. max(mechvent) should be 1 +-- this excludes a frequent situation of NIV/oxygen before intub +-- in these cases, ventnum=0 and max(mechvent)=0, so they are ignored +and max(mechvent) = 1 +order by icustay_id, ventnum; diff --git a/etc/echo-data.sql b/concepts/echo-data.sql similarity index 98% rename from etc/echo-data.sql rename to concepts/echo-data.sql index 56e0b38..b235c83 100644 --- a/etc/echo-data.sql +++ b/concepts/echo-data.sql @@ -2,8 +2,7 @@ -- You can join it to the text notes using ROW_ID -- Just note that ROW_ID will differ across versions of MIMIC-III. -DROP MATERIALIZED VIEW IF EXISTS ECHODATA; - +DROP MATERIALIZED VIEW IF EXISTS ECHODATA CASCADE; CREATE MATERIALIZED VIEW ECHODATA AS select ROW_ID , subject_id, hadm_id diff --git a/etc/firstday/blood-gas-first-day-arterial.sql b/concepts/firstday/blood-gas-first-day-arterial.sql similarity index 98% rename from etc/firstday/blood-gas-first-day-arterial.sql rename to concepts/firstday/blood-gas-first-day-arterial.sql index 854c89d..8e257e5 100644 --- a/etc/firstday/blood-gas-first-day-arterial.sql +++ b/concepts/firstday/blood-gas-first-day-arterial.sql @@ -1,5 +1,5 @@ -DROP MATERIALIZED VIEW IF EXISTS bloodgasfirstdayarterial CASCADE; +DROP MATERIALIZED VIEW IF EXISTS bloodgasfirstdayarterial CASCADE; CREATE MATERIALIZED VIEW bloodgasfirstdayarterial AS with stg_spo2 as ( @@ -47,6 +47,8 @@ with stg_spo2 as , 223835 -- Inspired O2 Fraction (FiO2) , 3422 -- FiO2 [measured] ) + -- exclude rows marked as error + and error IS DISTINCT FROM 1 group by SUBJECT_ID, HADM_ID, ICUSTAY_ID, CHARTTIME ) , stg2 as diff --git a/etc/firstday/blood-gas-first-day.sql b/concepts/firstday/blood-gas-first-day.sql similarity index 97% rename from etc/firstday/blood-gas-first-day.sql rename to concepts/firstday/blood-gas-first-day.sql index 6414e00..ce6deec 100644 --- a/etc/firstday/blood-gas-first-day.sql +++ b/concepts/firstday/blood-gas-first-day.sql @@ -1,44 +1,12 @@ --- This query is designed for MIMIC-III v1.3 - -- The aim of this query is to pivot entries related to blood gases and -- chemistry values which were found in LABEVENTS -drop materialized view IF EXISTS bloodgasfirstday; -- things to check: -- when a mixed venous/arterial blood sample are taken at the same time, is the store time different? +DROP MATERIALIZED VIEW IF EXISTS bloodgasfirstday CASCADE; create materialized view bloodgasfirstday as -select pvt.SUBJECT_ID, pvt.HADM_ID, pvt.ICUSTAY_ID, pvt.CHARTTIME - -, max(case when label = 'SPECIMEN' then value else null end) as SPECIMEN -, max(case when label = 'AADO2' then valuenum else null end) as AADO2 -, max(case when label = 'BASEEXCESS' then valuenum else null end) as BASEEXCESS -, max(case when label = 'BICARBONATE' then valuenum else null end) as BICARBONATE -, max(case when label = 'TOTALCO2' then valuenum else null end) as TOTALCO2 -, max(case when label = 'CARBOXYHEMOGLOBIN' then valuenum else null end) as CARBOXYHEMOGLOBIN -, max(case when label = 'CHLORIDE' then valuenum else null end) as CHLORIDE -, max(case when label = 'CALCIUM' then valuenum else null end) as CALCIUM -, max(case when label = 'GLUCOSE' then valuenum else null end) as GLUCOSE -, max(case when label = 'HEMATOCRIT' then valuenum else null end) as HEMATOCRIT -, max(case when label = 'HEMOGLOBIN' then valuenum else null end) as HEMOGLOBIN -, max(case when label = 'INTUBATED' then valuenum else null end) as INTUBATED -, max(case when label = 'LACTATE' then valuenum else null end) as LACTATE -, max(case when label = 'METHEMOGLOBIN' then valuenum else null end) as METHEMOGLOBIN -, max(case when label = 'O2FLOW' then valuenum else null end) as O2FLOW -, max(case when label = 'FIO2' then valuenum else null end) as FIO2 -, max(case when label = 'SO2' then valuenum else null end) as SO2 -- OXYGENSATURATION -, max(case when label = 'PCO2' then valuenum else null end) as PCO2 -, max(case when label = 'PEEP' then valuenum else null end) as PEEP -, max(case when label = 'PH' then valuenum else null end) as PH -, max(case when label = 'PO2' then valuenum else null end) as PO2 -, max(case when label = 'POTASSIUM' then valuenum else null end) as POTASSIUM -, max(case when label = 'REQUIREDO2' then valuenum else null end) as REQUIREDO2 -, max(case when label = 'SODIUM' then valuenum else null end) as SODIUM -, max(case when label = 'TEMPERATURE' then valuenum else null end) as TEMPERATURE -, max(case when label = 'TIDALVOLUME' then valuenum else null end) as TIDALVOLUME -, max(case when label = 'VENTILATIONRATE' then valuenum else null end) as VENTILATIONRATE -, max(case when label = 'VENTILATOR' then valuenum else null end) as VENTILATOR -from +with pvt as ( -- begin query that extracts the data select ie.subject_id, ie.hadm_id, ie.icustay_id -- here we assign labels to ITEMIDs @@ -100,6 +68,36 @@ from , 50820, 50821, 50822, 50823, 50824, 50825, 50826, 50827, 50828 , 51545 ) -) pvt +) +select pvt.SUBJECT_ID, pvt.HADM_ID, pvt.ICUSTAY_ID, pvt.CHARTTIME +, max(case when label = 'SPECIMEN' then value else null end) as SPECIMEN +, max(case when label = 'AADO2' then valuenum else null end) as AADO2 +, max(case when label = 'BASEEXCESS' then valuenum else null end) as BASEEXCESS +, max(case when label = 'BICARBONATE' then valuenum else null end) as BICARBONATE +, max(case when label = 'TOTALCO2' then valuenum else null end) as TOTALCO2 +, max(case when label = 'CARBOXYHEMOGLOBIN' then valuenum else null end) as CARBOXYHEMOGLOBIN +, max(case when label = 'CHLORIDE' then valuenum else null end) as CHLORIDE +, max(case when label = 'CALCIUM' then valuenum else null end) as CALCIUM +, max(case when label = 'GLUCOSE' then valuenum else null end) as GLUCOSE +, max(case when label = 'HEMATOCRIT' then valuenum else null end) as HEMATOCRIT +, max(case when label = 'HEMOGLOBIN' then valuenum else null end) as HEMOGLOBIN +, max(case when label = 'INTUBATED' then valuenum else null end) as INTUBATED +, max(case when label = 'LACTATE' then valuenum else null end) as LACTATE +, max(case when label = 'METHEMOGLOBIN' then valuenum else null end) as METHEMOGLOBIN +, max(case when label = 'O2FLOW' then valuenum else null end) as O2FLOW +, max(case when label = 'FIO2' then valuenum else null end) as FIO2 +, max(case when label = 'SO2' then valuenum else null end) as SO2 -- OXYGENSATURATION +, max(case when label = 'PCO2' then valuenum else null end) as PCO2 +, max(case when label = 'PEEP' then valuenum else null end) as PEEP +, max(case when label = 'PH' then valuenum else null end) as PH +, max(case when label = 'PO2' then valuenum else null end) as PO2 +, max(case when label = 'POTASSIUM' then valuenum else null end) as POTASSIUM +, max(case when label = 'REQUIREDO2' then valuenum else null end) as REQUIREDO2 +, max(case when label = 'SODIUM' then valuenum else null end) as SODIUM +, max(case when label = 'TEMPERATURE' then valuenum else null end) as TEMPERATURE +, max(case when label = 'TIDALVOLUME' then valuenum else null end) as TIDALVOLUME +, max(case when label = 'VENTILATIONRATE' then valuenum else null end) as VENTILATIONRATE +, max(case when label = 'VENTILATOR' then valuenum else null end) as VENTILATOR +from pvt group by pvt.subject_id, pvt.hadm_id, pvt.icustay_id, pvt.CHARTTIME order by pvt.subject_id, pvt.hadm_id, pvt.icustay_id, pvt.CHARTTIME; diff --git a/etc/firstday/gcs-first-day.sql b/concepts/firstday/gcs-first-day.sql similarity index 97% rename from etc/firstday/gcs-first-day.sql rename to concepts/firstday/gcs-first-day.sql index 65c628d..3a74866 100644 --- a/etc/firstday/gcs-first-day.sql +++ b/concepts/firstday/gcs-first-day.sql @@ -21,8 +21,7 @@ -- This was ascertained either from interviewing the physician who ordered the sedation, -- or by reviewing the patient's medical record. -drop materialized view IF EXISTS gcsfirstday CASCADE; - +DROP MATERIALIZED VIEW IF EXISTS gcsfirstday CASCADE; create materialized view gcsfirstday as with base as ( @@ -81,6 +80,8 @@ with base as ) -- Only get data for the first 24 hours and l.charttime between b.intime and b.intime + interval '1' day + -- exclude rows marked as error + and l.error IS DISTINCT FROM 1 ) pvt group by pvt.ICUSTAY_ID, pvt.charttime ) diff --git a/etc/firstday/height-first-day.sql b/concepts/firstday/height-first-day.sql similarity index 94% rename from etc/firstday/height-first-day.sql rename to concepts/firstday/height-first-day.sql index 5205255..a473aa2 100644 --- a/etc/firstday/height-first-day.sql +++ b/concepts/firstday/height-first-day.sql @@ -5,7 +5,7 @@ -- ** Requires the echodata view, generated by etc/echo-data.sql -DROP MATERIALIZED VIEW IF EXISTS heightfirstday; +DROP MATERIALIZED VIEW IF EXISTS heightfirstday CASCADE; CREATE MATERIALIZED VIEW heightfirstday as -- staging table to ensure all heights are in centimeters with ce0 as @@ -26,6 +26,8 @@ with ce0 as WHERE c.valuenum IS NOT NULL AND c.itemid in (226730,920, 1394, 4187, 3486,3485,4188) -- height AND c.valuenum != 0 + -- exclude rows marked as error + AND c.error IS DISTINCT FROM 1 ) , ce as ( diff --git a/concepts/firstday/labs-first-day.sql b/concepts/firstday/labs-first-day.sql new file mode 100644 index 0000000..a50ca72 --- /dev/null +++ b/concepts/firstday/labs-first-day.sql @@ -0,0 +1,155 @@ +-- This query pivots lab values taken in the first 24 hours of a patient's stay + +-- Have already confirmed that the unit of measurement is always the same: null or the correct unit + +DROP MATERIALIZED VIEW IF EXISTS labsfirstday CASCADE; +CREATE materialized VIEW labsfirstday AS +SELECT + pvt.subject_id, pvt.hadm_id, pvt.icustay_id + + , min(CASE WHEN label = 'ANION GAP' THEN valuenum ELSE null END) as ANIONGAP_min + , max(CASE WHEN label = 'ANION GAP' THEN valuenum ELSE null END) as ANIONGAP_max + , min(CASE WHEN label = 'ALBUMIN' THEN valuenum ELSE null END) as ALBUMIN_min + , max(CASE WHEN label = 'ALBUMIN' THEN valuenum ELSE null END) as ALBUMIN_max + , min(CASE WHEN label = 'BANDS' THEN valuenum ELSE null END) as BANDS_min + , max(CASE WHEN label = 'BANDS' THEN valuenum ELSE null END) as BANDS_max + , min(CASE WHEN label = 'BICARBONATE' THEN valuenum ELSE null END) as BICARBONATE_min + , max(CASE WHEN label = 'BICARBONATE' THEN valuenum ELSE null END) as BICARBONATE_max + , min(CASE WHEN label = 'BILIRUBIN' THEN valuenum ELSE null END) as BILIRUBIN_min + , max(CASE WHEN label = 'BILIRUBIN' THEN valuenum ELSE null END) as BILIRUBIN_max + , min(CASE WHEN label = 'CREATININE' THEN valuenum ELSE null END) as CREATININE_min + , max(CASE WHEN label = 'CREATININE' THEN valuenum ELSE null END) as CREATININE_max + , min(CASE WHEN label = 'CHLORIDE' THEN valuenum ELSE null END) as CHLORIDE_min + , max(CASE WHEN label = 'CHLORIDE' THEN valuenum ELSE null END) as CHLORIDE_max + , min(CASE WHEN label = 'GLUCOSE' THEN valuenum ELSE null END) as GLUCOSE_min + , max(CASE WHEN label = 'GLUCOSE' THEN valuenum ELSE null END) as GLUCOSE_max + , min(CASE WHEN label = 'HEMATOCRIT' THEN valuenum ELSE null END) as HEMATOCRIT_min + , max(CASE WHEN label = 'HEMATOCRIT' THEN valuenum ELSE null END) as HEMATOCRIT_max + , min(CASE WHEN label = 'HEMOGLOBIN' THEN valuenum ELSE null END) as HEMOGLOBIN_min + , max(CASE WHEN label = 'HEMOGLOBIN' THEN valuenum ELSE null END) as HEMOGLOBIN_max + , min(CASE WHEN label = 'LACTATE' THEN valuenum ELSE null END) as LACTATE_min + , max(CASE WHEN label = 'LACTATE' THEN valuenum ELSE null END) as LACTATE_max + , min(CASE WHEN label = 'PLATELET' THEN valuenum ELSE null END) as PLATELET_min + , max(CASE WHEN label = 'PLATELET' THEN valuenum ELSE null END) as PLATELET_max + , min(CASE WHEN label = 'POTASSIUM' THEN valuenum ELSE null END) as POTASSIUM_min + , max(CASE WHEN label = 'POTASSIUM' THEN valuenum ELSE null END) as POTASSIUM_max + , min(CASE WHEN label = 'PTT' THEN valuenum ELSE null END) as PTT_min + , max(CASE WHEN label = 'PTT' THEN valuenum ELSE null END) as PTT_max + , min(CASE WHEN label = 'INR' THEN valuenum ELSE null END) as INR_min + , max(CASE WHEN label = 'INR' THEN valuenum ELSE null END) as INR_max + , min(CASE WHEN label = 'PT' THEN valuenum ELSE null END) as PT_min + , max(CASE WHEN label = 'PT' THEN valuenum ELSE null END) as PT_max + , min(CASE WHEN label = 'SODIUM' THEN valuenum ELSE null END) as SODIUM_min + , max(CASE WHEN label = 'SODIUM' THEN valuenum ELSE null end) as SODIUM_max + , min(CASE WHEN label = 'BUN' THEN valuenum ELSE null end) as BUN_min + , max(CASE WHEN label = 'BUN' THEN valuenum ELSE null end) as BUN_max + , min(CASE WHEN label = 'WBC' THEN valuenum ELSE null end) as WBC_min + , max(CASE WHEN label = 'WBC' THEN valuenum ELSE null end) as WBC_max + + +FROM +( -- begin query that extracts the data + SELECT ie.subject_id, ie.hadm_id, ie.icustay_id + -- here we assign labels to ITEMIDs + -- this also fuses together multiple ITEMIDs containing the same data + , CASE + WHEN itemid = 50868 THEN 'ANION GAP' + WHEN itemid = 50862 THEN 'ALBUMIN' + WHEN itemid = 51144 THEN 'BANDS' + WHEN itemid = 50882 THEN 'BICARBONATE' + WHEN itemid = 50885 THEN 'BILIRUBIN' + WHEN itemid = 50912 THEN 'CREATININE' + WHEN itemid = 50806 THEN 'CHLORIDE' + WHEN itemid = 50902 THEN 'CHLORIDE' + WHEN itemid = 50809 THEN 'GLUCOSE' + WHEN itemid = 50931 THEN 'GLUCOSE' + WHEN itemid = 50810 THEN 'HEMATOCRIT' + WHEN itemid = 51221 THEN 'HEMATOCRIT' + WHEN itemid = 50811 THEN 'HEMOGLOBIN' + WHEN itemid = 51222 THEN 'HEMOGLOBIN' + WHEN itemid = 50813 THEN 'LACTATE' + WHEN itemid = 51265 THEN 'PLATELET' + WHEN itemid = 50822 THEN 'POTASSIUM' + WHEN itemid = 50971 THEN 'POTASSIUM' + WHEN itemid = 51275 THEN 'PTT' + WHEN itemid = 51237 THEN 'INR' + WHEN itemid = 51274 THEN 'PT' + WHEN itemid = 50824 THEN 'SODIUM' + WHEN itemid = 50983 THEN 'SODIUM' + WHEN itemid = 51006 THEN 'BUN' + WHEN itemid = 51300 THEN 'WBC' + WHEN itemid = 51301 THEN 'WBC' + ELSE null + END AS label + , -- add in some sanity checks on the values + -- the where clause below requires all valuenum to be > 0, so these are only upper limit checks + CASE + WHEN itemid = 50862 and valuenum > 10 THEN null -- g/dL 'ALBUMIN' + WHEN itemid = 50868 and valuenum > 10000 THEN null -- mEq/L 'ANION GAP' + WHEN itemid = 51144 and valuenum < 0 THEN null -- immature band forms, % + WHEN itemid = 51144 and valuenum > 100 THEN null -- immature band forms, % + WHEN itemid = 50882 and valuenum > 10000 THEN null -- mEq/L 'BICARBONATE' + WHEN itemid = 50885 and valuenum > 150 THEN null -- mg/dL 'BILIRUBIN' + WHEN itemid = 50806 and valuenum > 10000 THEN null -- mEq/L 'CHLORIDE' + WHEN itemid = 50902 and valuenum > 10000 THEN null -- mEq/L 'CHLORIDE' + WHEN itemid = 50912 and valuenum > 150 THEN null -- mg/dL 'CREATININE' + WHEN itemid = 50809 and valuenum > 10000 THEN null -- mg/dL 'GLUCOSE' + WHEN itemid = 50931 and valuenum > 10000 THEN null -- mg/dL 'GLUCOSE' + WHEN itemid = 50810 and valuenum > 100 THEN null -- % 'HEMATOCRIT' + WHEN itemid = 51221 and valuenum > 100 THEN null -- % 'HEMATOCRIT' + WHEN itemid = 50811 and valuenum > 50 THEN null -- g/dL 'HEMOGLOBIN' + WHEN itemid = 51222 and valuenum > 50 THEN null -- g/dL 'HEMOGLOBIN' + WHEN itemid = 50813 and valuenum > 50 THEN null -- mmol/L 'LACTATE' + WHEN itemid = 51265 and valuenum > 10000 THEN null -- K/uL 'PLATELET' + WHEN itemid = 50822 and valuenum > 30 THEN null -- mEq/L 'POTASSIUM' + WHEN itemid = 50971 and valuenum > 30 THEN null -- mEq/L 'POTASSIUM' + WHEN itemid = 51275 and valuenum > 150 THEN null -- sec 'PTT' + WHEN itemid = 51237 and valuenum > 50 THEN null -- 'INR' + WHEN itemid = 51274 and valuenum > 150 THEN null -- sec 'PT' + WHEN itemid = 50824 and valuenum > 200 THEN null -- mEq/L == mmol/L 'SODIUM' + WHEN itemid = 50983 and valuenum > 200 THEN null -- mEq/L == mmol/L 'SODIUM' + WHEN itemid = 51006 and valuenum > 300 THEN null -- 'BUN' + WHEN itemid = 51300 and valuenum > 1000 THEN null -- 'WBC' + WHEN itemid = 51301 and valuenum > 1000 THEN null -- 'WBC' + ELSE le.valuenum + END AS valuenum + + FROM icustays ie + + LEFT JOIN labevents le + ON le.subject_id = ie.subject_id AND le.hadm_id = ie.hadm_id + AND le.charttime BETWEEN (ie.intime - interval '6' hour) AND (ie.intime + interval '1' day) + AND le.ITEMID in + ( + -- comment is: LABEL | CATEGORY | FLUID | NUMBER OF ROWS IN LABEVENTS + 50868, -- ANION GAP | CHEMISTRY | BLOOD | 769895 + 50862, -- ALBUMIN | CHEMISTRY | BLOOD | 146697 + 51144, -- BANDS - hematology + 50882, -- BICARBONATE | CHEMISTRY | BLOOD | 780733 + 50885, -- BILIRUBIN, TOTAL | CHEMISTRY | BLOOD | 238277 + 50912, -- CREATININE | CHEMISTRY | BLOOD | 797476 + 50902, -- CHLORIDE | CHEMISTRY | BLOOD | 795568 + 50806, -- CHLORIDE, WHOLE BLOOD | BLOOD GAS | BLOOD | 48187 + 50931, -- GLUCOSE | CHEMISTRY | BLOOD | 748981 + 50809, -- GLUCOSE | BLOOD GAS | BLOOD | 196734 + 51221, -- HEMATOCRIT | HEMATOLOGY | BLOOD | 881846 + 50810, -- HEMATOCRIT, CALCULATED | BLOOD GAS | BLOOD | 89715 + 51222, -- HEMOGLOBIN | HEMATOLOGY | BLOOD | 752523 + 50811, -- HEMOGLOBIN | BLOOD GAS | BLOOD | 89712 + 50813, -- LACTATE | BLOOD GAS | BLOOD | 187124 + 51265, -- PLATELET COUNT | HEMATOLOGY | BLOOD | 778444 + 50971, -- POTASSIUM | CHEMISTRY | BLOOD | 845825 + 50822, -- POTASSIUM, WHOLE BLOOD | BLOOD GAS | BLOOD | 192946 + 51275, -- PTT | HEMATOLOGY | BLOOD | 474937 + 51237, -- INR(PT) | HEMATOLOGY | BLOOD | 471183 + 51274, -- PT | HEMATOLOGY | BLOOD | 469090 + 50983, -- SODIUM | CHEMISTRY | BLOOD | 808489 + 50824, -- SODIUM, WHOLE BLOOD | BLOOD GAS | BLOOD | 71503 + 51006, -- UREA NITROGEN | CHEMISTRY | BLOOD | 791925 + 51301, -- WHITE BLOOD CELLS | HEMATOLOGY | BLOOD | 753301 + 51300 -- WBC COUNT | HEMATOLOGY | BLOOD | 2371 + ) + AND valuenum IS NOT null AND valuenum > 0 -- lab values cannot be 0 and cannot be negative +) pvt +GROUP BY pvt.subject_id, pvt.hadm_id, pvt.icustay_id +ORDER BY pvt.subject_id, pvt.hadm_id, pvt.icustay_id; diff --git a/etc/firstday/rrt-first-day.sql b/concepts/firstday/rrt-first-day.sql similarity index 97% rename from etc/firstday/rrt-first-day.sql rename to concepts/firstday/rrt-first-day.sql index 3357211..6fff001 100644 --- a/etc/firstday/rrt-first-day.sql +++ b/concepts/firstday/rrt-first-day.sql @@ -35,7 +35,7 @@ -- ) rrt -- where rn = 1; -DROP MATERIALIZED VIEW IF EXISTS rrtfirstday; +DROP MATERIALIZED VIEW IF EXISTS rrtfirstday CASCADE; CREATE MATERIALIZED VIEW rrtfirstday as with cv as ( @@ -85,8 +85,7 @@ with cv as and itemid in ( -- Checkboxes - 225126 -- | Dialysis patient | Adm History/FHPA | chartevents | Checkbox - , 226118 -- | Dialysis Catheter placed in outside facility | Access Lines - Invasive | chartevents | Checkbox + 226118 -- | Dialysis Catheter placed in outside facility | Access Lines - Invasive | chartevents | Checkbox , 227357 -- | Dialysis Catheter Dressing Occlusive | Access Lines - Invasive | chartevents | Checkbox , 225725 -- | Dialysis Catheter Tip Cultured | Access Lines - Invasive | chartevents | Checkbox -- Numeric values diff --git a/etc/firstday/urine-output-first-day.sql b/concepts/firstday/urine-output-first-day.sql similarity index 81% rename from etc/firstday/urine-output-first-day.sql rename to concepts/firstday/urine-output-first-day.sql index 2ebb932..dd52360 100644 --- a/etc/firstday/urine-output-first-day.sql +++ b/concepts/firstday/urine-output-first-day.sql @@ -1,19 +1,19 @@ -- ------------------------------------------------------------------ -- Purpose: Create a view of the urine output for each ICUSTAY_ID over the first 24 hours. --- Tested on MIMIC-III v1.2 --- Written by: Alistair E. W. Johnson --- References: TBA. -- ------------------------------------------------------------------ -drop materialized view IF EXISTS uofirstday CASCADE; +DROP MATERIALIZED VIEW IF EXISTS uofirstday CASCADE; create materialized view uofirstday as select -- patient identifiers ie.subject_id, ie.hadm_id, ie.icustay_id -- volumes associated with urine output ITEMIDs - , sum(VALUE) as UrineOutput - + , sum( + -- we consider input of GU irrigant as a negative volume + case when oe.itemid = 227488 then -1*VALUE + else VALUE end + ) as UrineOutput from icustays ie -- Join to the outputevents table to get urine output left join outputevents oe @@ -39,21 +39,19 @@ where itemid in 40096, -- "Urine Out Ureteral Stent #1" 40651, -- "Urine Out Ureteral Stent #2" --- these are the most frequently occurring urine output observations in CareVue +-- these are the most frequently occurring urine output observations in MetaVision 226559, -- "Foley" 226560, -- "Void" -227510, -- "TF Residual" 226561, -- "Condom Cath" 226584, -- "Ileoconduit" 226563, -- "Suprapubic" 226564, -- "R Nephrostomy" 226565, -- "L Nephrostomy" 226567, -- Straight Cath -226557, -- "R Ureteral Stent" -226558 -- "L Ureteral Stent" +226557, -- R Ureteral Stent +226558, -- L Ureteral Stent +227488, -- GU Irrigant Volume In +227489 -- GU Irrigant/Urine Volume Out ) group by ie.subject_id, ie.hadm_id, ie.icustay_id order by ie.subject_id, ie.hadm_id, ie.icustay_id; - --- TODO: subtract GU irrigant volume from the below ITEMID --- 227489, -- "GU Irrigant/Urine Volume Out" diff --git a/concepts/firstday/ventilation-first-day.sql b/concepts/firstday/ventilation-first-day.sql new file mode 100644 index 0000000..6fdd47f --- /dev/null +++ b/concepts/firstday/ventilation-first-day.sql @@ -0,0 +1,26 @@ +-- Determines if a patient is ventilated on the first day of their ICU stay. +-- Creates a table with the result. +-- Requires the `ventdurations` table, generated by ../ventilation-durations.sql + +DROP MATERIALIZED VIEW IF EXISTS ventfirstday CASCADE; +CREATE MATERIALIZED VIEW ventfirstday AS +select + ie.subject_id, ie.hadm_id, ie.icustay_id + -- if vd.icustay_id is not null, then they have a valid ventilation event + -- in this case, we say they are ventilated + -- otherwise, they are not + , max(case + when vd.icustay_id is not null then 1 + else 0 end) as vent +from icustays ie +left join ventdurations vd + on ie.icustay_id = vd.icustay_id + and + ( + -- ventilation duration overlaps with ICU admission -> vented on admission + (vd.starttime <= ie.intime and vd.endtime >= ie.intime) + -- ventilation started during the first day + OR (vd.starttime >= ie.intime and vd.starttime <= ie.intime + interval '1' day) + ) +group by ie.subject_id, ie.hadm_id, ie.icustay_id +order by ie.subject_id, ie.hadm_id, ie.icustay_id; diff --git a/etc/firstday/vitals-first-day.sql b/concepts/firstday/vitals-first-day.sql similarity index 97% rename from etc/firstday/vitals-first-day.sql rename to concepts/firstday/vitals-first-day.sql index 5d3cd1e..933fe28 100644 --- a/etc/firstday/vitals-first-day.sql +++ b/concepts/firstday/vitals-first-day.sql @@ -1,8 +1,7 @@ -- This query pivots the vital signs for the first 24 hours of a patient's stay -- Vital signs include heart rate, blood pressure, respiration rate, and temperature -drop materialized view IF EXISTS vitalsfirstday; - +DROP MATERIALIZED VIEW IF EXISTS vitalsfirstday CASCADE; create materialized view vitalsfirstday as SELECT pvt.subject_id, pvt.hadm_id, pvt.icustay_id @@ -53,6 +52,8 @@ FROM ( left join chartevents ce on ie.subject_id = ce.subject_id and ie.hadm_id = ce.hadm_id and ie.icustay_id = ce.icustay_id and ce.charttime between ie.intime and ie.intime + interval '1' day + -- exclude rows marked as error + and ce.error IS DISTINCT FROM 1 where ce.itemid in ( -- HEART RATE @@ -115,5 +116,3 @@ FROM ( ) pvt group by pvt.subject_id, pvt.hadm_id, pvt.icustay_id order by pvt.subject_id, pvt.hadm_id, pvt.icustay_id; - -commit; diff --git a/etc/firstday/weight-first-day.sql b/concepts/firstday/weight-first-day.sql similarity index 94% rename from etc/firstday/weight-first-day.sql rename to concepts/firstday/weight-first-day.sql index 4499e80..112e6a4 100644 --- a/etc/firstday/weight-first-day.sql +++ b/concepts/firstday/weight-first-day.sql @@ -4,7 +4,7 @@ -- ** Requires the echodata view, generated by etc/echo-data.sql -DROP MATERIALIZED VIEW IF EXISTS weightfirstday; +DROP MATERIALIZED VIEW IF EXISTS weightfirstday CASCADE; CREATE MATERIALIZED VIEW weightfirstday as with ce as ( @@ -22,6 +22,8 @@ with ce as WHERE c.valuenum IS NOT NULL AND c.itemid in (762,226512) -- Admit Wt AND c.valuenum != 0 + -- exclude rows marked as error + AND c.error IS DISTINCT FROM 1 group by c.icustay_id ) , dwt as @@ -37,6 +39,8 @@ with ce as WHERE c.valuenum IS NOT NULL AND c.itemid in (763,224639) -- Daily Weight AND c.valuenum != 0 + -- exclude rows marked as error + AND c.error IS DISTINCT FROM 1 group by c.icustay_id ) -- we split in-hospital/out of hospital echoes as we would like to prioritize in-hospital data diff --git a/etc/auroc.sql b/concepts/functions/auroc.sql similarity index 100% rename from etc/auroc.sql rename to concepts/functions/auroc.sql diff --git a/concepts/make-concepts.sql b/concepts/make-concepts.sql new file mode 100644 index 0000000..2e72f27 --- /dev/null +++ b/concepts/make-concepts.sql @@ -0,0 +1,72 @@ +-- This file makes all materialized views in this subfolder +-- Note that this may take a large amount of time and hard drive space + +\echo '' +\echo '===' +\echo 'Beginning to create materialized views for MIMIC database.' +\echo 'Any notices of the form "NOTICE: materialized view "XXXXXX" does not exist" can be ignored.' +\echo 'The scripts drop views before creating them, and these notices indicate nothing existed prior to creating the view.' +\echo '===' +\echo '' + +\echo 'Top level files..' +\i code-status.sql +\i echo-data.sql + +-- Durations (usually of treatments) +\echo 'Directory 1 of 7: durations' +\i durations/ventilation-durations.sql +\i durations/crrt-durations.sql +\i durations/adenosine-durations.sql +\i durations/dobutamine-durations.sql +\i durations/dopamine-durations.sql +\i durations/epinephrine-durations.sql +\i durations/isuprel-durations.sql +\i durations/milrinone-durations.sql +\i durations/norepinephrine-durations.sql +\i durations/phenylephrine-durations.sql +\i durations/vasopressin-durations.sql +\i durations/vasopressor-durations.sql + +\echo 'Directory 2 of 7: comorbidity' +\i comorbidity/elixhauser-ahrq-v37-with-drg.sql +\i comorbidity/elixhauser-quan.sql +\i comorbidity/elixhauser-score-ahrq.sql +\i comorbidity/elixhauser-score-quan.sql + +\echo 'Directory 3 of 7: demographics' +\i demographics/HeightWeightQuery.sql +\i demographics/icustay_detail.sql + +\echo 'Directory 4 of 7: firstday' +-- data which is extracted from a patient's first ICU stay +\i firstday/blood-gas-first-day.sql +\i firstday/blood-gas-first-day-arterial.sql +\i firstday/gcs-first-day.sql +\i firstday/height-first-day.sql +\i firstday/labs-first-day.sql +\i firstday/rrt-first-day.sql +\i firstday/urine-output-first-day.sql +\i firstday/ventilation-first-day.sql +\i firstday/vitals-first-day.sql +\i firstday/weight-first-day.sql + +\echo 'Directory 5 of 7: sepsis' +\i sepsis/angus.sql + +-- diagnosis mapping using CCS +\echo 'Directory 6 of 7: diagnosis' +\cd diagnosis +\i ccs_diagnosis_table.sql +\cd .. + +-- Severity of illness scores (requires many views from above) +\echo 'Directory 7 of 7: severityscores' +\i severityscores/oasis.sql +\i severityscores/sofa.sql +\i severityscores/saps.sql +\i severityscores/sapsii.sql +\i severityscores/apsiii.sql +\i severityscores/lods.sql + +\echo 'Finished loading materialized views.' diff --git a/comorbidity/mysql/4-elixhauser.sql b/concepts/other-languages/4-elixhauser.sql similarity index 100% rename from comorbidity/mysql/4-elixhauser.sql rename to concepts/other-languages/4-elixhauser.sql diff --git a/etc/rrt.sql b/concepts/rrt.sql similarity index 96% rename from etc/rrt.sql rename to concepts/rrt.sql index 7fb6377..2a45d3e 100644 --- a/etc/rrt.sql +++ b/concepts/rrt.sql @@ -35,7 +35,7 @@ -- ) rrt -- where rn = 1; -DROP MATERIALIZED VIEW IF EXISTS rrt; +DROP MATERIALIZED VIEW IF EXISTS rrt CASCADE; CREATE MATERIALIZED VIEW rrt as with cv as ( @@ -71,6 +71,8 @@ with cv as ) and ce.value is not null where ie.dbsource = 'carevue' + -- exclude rows marked as error + and ce.error IS DISTINCT FROM 1 group by ie.icustay_id ) , mv_ce as @@ -81,8 +83,7 @@ with cv as where itemid in ( -- Checkboxes - 225126 -- | Dialysis patient | Adm History/FHPA | chartevents | Checkbox - , 226118 -- | Dialysis Catheter placed in outside facility | Access Lines - Invasive | chartevents | Checkbox + 226118 -- | Dialysis Catheter placed in outside facility | Access Lines - Invasive | chartevents | Checkbox , 227357 -- | Dialysis Catheter Dressing Occlusive | Access Lines - Invasive | chartevents | Checkbox , 225725 -- | Dialysis Catheter Tip Cultured | Access Lines - Invasive | chartevents | Checkbox -- Numeric values @@ -109,7 +110,9 @@ with cv as , 224406 -- | VEN Lumen Volume | Dialysis | chartevents | Numeric , 226457 -- | Ultrafiltrate Output | Dialysis | chartevents | Numeric ) - and valuenum > 0 -- also ensures it's not null + and ce.valuenum > 0 -- also ensures it's not null + -- exclude rows marked as error + and ce.error IS DISTINCT FROM 1 group by icustay_id ) , mv_ie as diff --git a/sepsis/angus.sql b/concepts/sepsis/angus.sql similarity index 64% rename from sepsis/angus.sql rename to concepts/sepsis/angus.sql index 955506b..797d95c 100644 --- a/sepsis/angus.sql +++ b/concepts/sepsis/angus.sql @@ -4,20 +4,19 @@ -- http://www.ncbi.nlm.nih.gov/pubmed/11445675 -- Case selection and definitions --- To identify cases with severe sepsis, we selected all acute care +-- To identify cases with severe sepsis, we selected all acute care -- hospitalizations with ICD-9-CM codes for both: --- (a) a bacterial or fungal infectious process AND --- (b) a diagnosis of acute organ dysfunction (Appendix 2). +-- (a) a bacterial or fungal infectious process AND +-- (b) a diagnosis of acute organ dysfunction (Appendix 2). --- By Sharukh Lokhandwala and Tom Pollard 2015 - --- Appendix 1: ICD9-codes (infection) -DROP MATERIALIZED VIEW IF EXISTS angus_sepsis; +DROP MATERIALIZED VIEW IF EXISTS angus_sepsis CASCADE; CREATE MATERIALIZED VIEW angus_sepsis as -WITH infection_group AS ( +-- ICD-9 codes for infection - as sourced from Appendix 1 of above paper +WITH infection_group AS +( SELECT subject_id, hadm_id, - CASE + CASE WHEN substring(icd9_code,1,3) IN ('001','002','003','004','005','008', '009','010','011','012','013','014','015','016','017','018', '020','021','022','023','024','025','026','027','030','031', @@ -33,61 +32,74 @@ WITH infection_group AS ( WHEN substring(icd9_code,1,5) IN ('49121','56201','56203','56211','56213', '56983') THEN 1 ELSE 0 END AS infection - FROM MIMICIII.DIAGNOSES_ICD), --- Appendix 2: ICD9-codes (organ dysfunction) - organ_diag_group as ( + FROM diagnoses_icd +), +-- ICD-9 codes for organ dysfunction - as sourced from Appendix 2 of above paper +organ_diag_group as +( SELECT subject_id, hadm_id, - CASE + CASE -- Acute Organ Dysfunction Diagnosis Codes WHEN substring(icd9_code,1,3) IN ('458','293','570','584') THEN 1 WHEN substring(icd9_code,1,4) IN ('7855','3483','3481', - '2874','2875','2869','2866','5734') THEN 1 + '2874','2875','2869','2866','5734') THEN 1 ELSE 0 END AS organ_dysfunction, -- Explicit diagnosis of severe sepsis or septic shock - CASE + CASE WHEN substring(icd9_code,1,5) IN ('99592','78552') THEN 1 ELSE 0 END AS explicit_sepsis - FROM MIMICIII.DIAGNOSES_ICD), - + FROM diagnoses_icd +), -- Mechanical ventilation - organ_proc_group as ( +organ_proc_group as +( SELECT subject_id, hadm_id, - CASE + CASE WHEN substring(icd9_code,1,4) IN ('9670','9671','9672') THEN 1 ELSE 0 END AS mech_vent - FROM MIMICIII.PROCEDURES_ICD), - --- Aggregate - aggregate as ( + FROM procedures_icd +), +-- Aggregate above views together +aggregate as +( SELECT subject_id, hadm_id, CASE - WHEN hadm_id in (SELECT DISTINCT hadm_id - FROM infection_group - WHERE infection = 1) THEN 1 + WHEN hadm_id in + (SELECT DISTINCT hadm_id + FROM infection_group + WHERE infection = 1) + THEN 1 ELSE 0 END AS infection, - CASE - WHEN hadm_id in (SELECT DISTINCT hadm_id - FROM organ_diag_group - WHERE explicit_sepsis = 1) THEN 1 + CASE + WHEN hadm_id in + (SELECT DISTINCT hadm_id + FROM organ_diag_group + WHERE explicit_sepsis = 1) + THEN 1 ELSE 0 END AS explicit_sepsis, CASE - WHEN hadm_id in (SELECT DISTINCT hadm_id - FROM organ_diag_group - WHERE organ_dysfunction = 1) THEN 1 + WHEN hadm_id in + (SELECT DISTINCT hadm_id + FROM organ_diag_group + WHERE organ_dysfunction = 1) + THEN 1 ELSE 0 END AS organ_dysfunction, CASE - WHEN hadm_id in (SELECT DISTINCT hadm_id + WHEN hadm_id in + (SELECT DISTINCT hadm_id FROM organ_proc_group - WHERE mech_vent = 1) THEN 1 - ELSE 0 END AS mech_vent - FROM MIMICIII.ADMISSIONS) --- List angus score for each admission + WHERE mech_vent = 1) + THEN 1 + ELSE 0 END AS mech_vent + FROM admissions +) +-- Output component flags (explicit sepsis, organ dysfunction) and final flag (angus) SELECT subject_id, hadm_id, infection, - explicit_sepsis, organ_dysfunction, mech_vent, - CASE + explicit_sepsis, organ_dysfunction, mech_vent, +CASE WHEN explicit_sepsis = 1 THEN 1 WHEN infection = 1 AND organ_dysfunction = 1 THEN 1 WHEN infection = 1 AND mech_vent = 1 THEN 1 - ELSE 0 END AS Angus + ELSE 0 END +AS angus FROM aggregate; - diff --git a/sepsis/angus2001.pdf b/concepts/sepsis/angus2001.pdf similarity index 100% rename from sepsis/angus2001.pdf rename to concepts/sepsis/angus2001.pdf diff --git a/severityscores/README.md b/concepts/severityscores/README.md similarity index 90% rename from severityscores/README.md rename to concepts/severityscores/README.md index 0da4c12..f74a7db 100644 --- a/severityscores/README.md +++ b/concepts/severityscores/README.md @@ -4,15 +4,15 @@ This folder generates materialized views for a variety of severity of illness sc The queries make use of materialized views which aggregate data from the first day of a patient's ICU stay. To run the code, it is necessary to: 1. Run the scripts which generate the constituent materialized views - * uofirstday - generated by urine-output-first-day.sql - * ventfirstday - generated by ventilated-first-day.sql - * vitalsfirstday - generated by vitals-first-day.sql - * gcsfirstday - generated by gcs-first-day.sql - * labsfirstday - generated by labs-first-day.sql - * bloodgasfirstday - generated by blood-gas-first-day.sql (needed for subsequent view) - * bloodgasfirstdayarterial - generated by blood-gas-first-day-arterial.sql - * echodata - generated by echo-data.sql - * ventdurations - generated by ventilation-durations.sql + * echodata - generated by /concepts/echo-data.sql + * ventdurations - generated by /concepts/ventilation-durations.sql - (needed for subsequent view) + * vitalsfirstday - generated by /concepts/firstday/vitals-first-day.sql + * uofirstday - generated by /concepts/firstday/urine-output-first-day.sql + * ventfirstday - generated by /concepts/firstday/ventilated-first-day.sql + * gcsfirstday - generated by /concepts/firstday/gcs-first-day.sql + * labsfirstday - generated by /concepts/firstday/labs-first-day.sql + * bloodgasfirstday - generated by /concepts/firstday/blood-gas-first-day.sql - (needed for subsequent view) + * bloodgasfirstdayarterial - generated by /concepts/firstday/blood-gas-first-day-arterial.sql 2. Run the script for the severity of illness score you are interested in * OASIS - oasis.sql * SAPS - saps.sql diff --git a/severityscores/apsiii.sql b/concepts/severityscores/apsiii.sql similarity index 98% rename from severityscores/apsiii.sql rename to concepts/severityscores/apsiii.sql index 4ccfd88..2f6d99f 100644 --- a/severityscores/apsiii.sql +++ b/concepts/severityscores/apsiii.sql @@ -1,15 +1,9 @@ -- ------------------------------------------------------------------ -- Title: Acute Physiology Score III (APS III) --- MIMIC version: MIMIC-III v1.4 --- Originally written by: Alistair Johnson --- Contact: aewj [at] mit [dot] edu --- ------------------------------------------------------------------ - -- This query extracts the acute physiology score III. -- This score is a measure of patient severity of illness. -- The score is calculated on the first day of each ICU patients' stay. --- The score is calculated for *all* ICU patients, with the assumption that the user will subselect appropriate ICUSTAY_IDs. --- For example, the score is calculated for neonates, but it is likely inappropriate to actually use the score values for these patients. +-- ------------------------------------------------------------------ -- Reference for APS III: -- Knaus WA, Wagner DP, Draper EA, Zimmerman JE, Bergner M, Bastos PG, Sirio CA, Murphy DJ, Lotring T, Damiano A. @@ -35,6 +29,9 @@ -- 4) gcsfirstday - generated by gcs-first-day.sql -- 5) labsfirstday - generated by labs-first-day.sql +-- Note: +-- The score is calculated for *all* ICU patients, with the assumption that the user will subselect appropriate ICUSTAY_IDs. +-- For example, the score is calculated for neonates, but it is likely inappropriate to actually use the score values for these patients. -- List of TODO: -- The site of temperature is not incorporated. Axillary measurements should be increased by 1 degree. @@ -42,9 +39,7 @@ -- 674 | Temp. Site -- 224642 | Temperature Site - -DROP MATERIALIZED VIEW IF EXISTS APSIII; - +DROP MATERIALIZED VIEW IF EXISTS APSIII CASCADE; CREATE MATERIALIZED VIEW APSIII as with bg as ( @@ -233,7 +228,7 @@ select ie.subject_id, ie.hadm_id, ie.icustay_id -- , labs.bicarbonate_min -- , labs.bicarbonate_max - , vent.mechvent as vent + , vent.vent , uo.urineoutput -- gcs and its components , gcs.mingcs diff --git a/concepts/severityscores/lods-paper.pdf b/concepts/severityscores/lods-paper.pdf new file mode 100644 index 0000000..27e7444 Binary files /dev/null and b/concepts/severityscores/lods-paper.pdf differ diff --git a/severityscores/lods.sql b/concepts/severityscores/lods.sql similarity index 90% rename from severityscores/lods.sql rename to concepts/severityscores/lods.sql index 42bd215..76bbfdb 100644 --- a/severityscores/lods.sql +++ b/concepts/severityscores/lods.sql @@ -1,17 +1,9 @@ -- ------------------------------------------------------------------ -- Title: Logistic Organ Dysfunction Score (LODS) --- MIMIC version: MIMIC-III v1.4 --- Originally written as sofa.sql by: Alistair Johnson --- Edited as lods.sql by: Josh Gieringer --- Contact: aewj [at] mit [dot] edu --- Contact: joshua [dot] gieringer [at] digitalreasoning [dot] com --- ------------------------------------------------------------------ - -- This query extracts the logistic organ dysfunction system. -- This score is a measure of organ failure in a patient. -- The score is calculated on the first day of each ICU patients' stay. --- The score is calculated for *all* ICU patients, with the assumption that the user will subselect appropriate ICUSTAY_IDs. --- For example, the score is calculated for neonates, but it is likely inappropriate to actually use the score values for these patients. +-- ------------------------------------------------------------------ -- Reference for LODS: -- Le Gall, J. R., Klar, J., Lemeshow, S., Saulnier, F., Alberti, C., Artigas, A., & Teres, D. @@ -34,7 +26,11 @@ -- 5) labsfirstday - generated by labs-first-day.sql -- 5) bloodgasfirstdayarterial - generated by blood-gas-first-day-arterial.sql -DROP MATERIALIZED VIEW IF EXISTS LODS; +-- Note: +-- The score is calculated for *all* ICU patients, with the assumption that the user will subselect appropriate ICUSTAY_IDs. +-- For example, the score is calculated for neonates, but it is likely inappropriate to actually use the score values for these patients. + +DROP MATERIALIZED VIEW IF EXISTS LODS CASCADE; CREATE MATERIALIZED VIEW LODS as -- extract CPAP from the "Oxygen Delivery Device" fields with cpap as @@ -42,7 +38,7 @@ with cpap as select ie.icustay_id , min(charttime - interval '1' hour) as starttime , max(charttime + interval '4' hour) as endtime - , max(case when value in ('CPAP Mask','Bipap Mask') then 1 else 0 end) as cpap + , max(case when lower(ce.value) similar to '%(cpap mask|bipap mask)%' then 1 else 0 end) as cpap from icustays ie inner join chartevents ce on ie.icustay_id = ce.icustay_id @@ -52,7 +48,9 @@ with cpap as -- TODO: when metavision data import fixed, check the values in 226732 match the value clause below 467, 469, 226732 ) - and value in ('CPAP Mask','Bipap Mask') + and lower(ce.value) similar to '%(cpap mask|bipap mask)%' + -- exclude rows marked as error + AND ce.error IS DISTINCT FROM 1 group by ie.icustay_id ) , pafi1 as diff --git a/concepts/severityscores/make-severity-scores.sql b/concepts/severityscores/make-severity-scores.sql new file mode 100644 index 0000000..f66047a --- /dev/null +++ b/concepts/severityscores/make-severity-scores.sql @@ -0,0 +1,40 @@ +-- This function runs all the scripts necessary to generate the following severity scores: +-- OASIS +-- SAPS +-- SAPS II +-- APS III +-- SOFA + +-- As the script is generating many materialized views, it may take some time. +-- Note: you should run this script from the 'severityscores' subfolder, as it makes use of relative pathnames. + +BEGIN; +-- ----------------------------- -- +-- ---------- STAGE 1 ---------- -- +-- ----------------------------- -- + +-- Generate the views which the severity scores are based on +\i ../firstday/urine-output-first-day.sql +\i ../firstday/vitals-first-day.sql +\i ../firstday/gcs-first-day.sql +\i ../firstday/labs-first-day.sql +\i ../firstday/blood-gas-first-day.sql +\i ../firstday/blood-gas-first-day-arterial.sql +\i ../echo-data.sql +\i ../ventilation-durations.sql +-- note vent first day relies on vent durations +\i ../firstday/ventilation-first-day.sql + +-- ----------------------------- -- +-- ---------- STAGE 2 ---------- -- +-- ----------------------------- -- + +-- Generate the severity of illness scores +\i oasis.sql +\i sofa.sql +\i saps.sql +\i sapsii.sql +\i apsiii.sql +\i lods.sql + +COMMIT; diff --git a/severityscores/mlods.sql b/concepts/severityscores/mlods.sql similarity index 91% rename from severityscores/mlods.sql rename to concepts/severityscores/mlods.sql index ff3425c..81053ba 100644 --- a/severityscores/mlods.sql +++ b/concepts/severityscores/mlods.sql @@ -1,12 +1,9 @@ -- ------------------------------------------------------------------ -- Title: Modified Logistic organ dysfunction system (mLODS) --- Originally written by: Alistair Johnson --- Contact: aewj [at] mit [dot] edu --- ------------------------------------------------------------------ - -- This query extracts a modified version of the logistic organ dysfunction system. -- This score was used in the third international definition of sepsis: Sepsis-3. -- This score is a measure of organ failure in a patient. +-- ------------------------------------------------------------------ -- Reference for LODS: -- Le Gall, J. R., Klar, J., Lemeshow, S., Saulnier, F., Alberti, C., Artigas, A., & Teres, D. @@ -28,7 +25,11 @@ -- Variables *excluded*, that are used in the original LODS: -- prothrombin time (PT), blood urea nitrogen, urine output -DROP MATERIALIZED VIEW IF EXISTS MLODS; +-- Note: +-- The score is calculated for *all* ICU patients, with the assumption that the user will subselect appropriate ICUSTAY_IDs. +-- For example, the score is calculated for neonates, but it is likely inappropriate to actually use the score values for these patients. + +DROP MATERIALIZED VIEW IF EXISTS MLODS CASCADE; CREATE MATERIALIZED VIEW MLODS as -- extract CPAP from the "Oxygen Delivery Device" fields with cpap as @@ -36,7 +37,7 @@ with cpap as select ie.icustay_id , min(charttime - interval '1' hour) as starttime , max(charttime + interval '4' hour) as endtime - , max(case when value in ('CPAP Mask','Bipap Mask') then 1 else 0 end) as cpap + , max(case when lower(ce.value) similar to '%(cpap mask|bipap mask)%' then 1 else 0 end) as cpap from icustays ie inner join chartevents ce on ie.icustay_id = ce.icustay_id @@ -46,7 +47,9 @@ with cpap as -- TODO: when metavision data import fixed, check the values in 226732 match the value clause below 467, 469, 226732 ) - and value in ('CPAP Mask','Bipap Mask') + and lower(ce.value) similar to '%(cpap mask|bipap mask)%' + -- exclude rows marked as error + AND ce.error IS DISTINCT FROM 1 group by ie.icustay_id ) , pafi1 as diff --git a/severityscores/oasis.sql b/concepts/severityscores/oasis.sql similarity index 95% rename from severityscores/oasis.sql rename to concepts/severityscores/oasis.sql index c153053..0ffd538 100644 --- a/severityscores/oasis.sql +++ b/concepts/severityscores/oasis.sql @@ -1,15 +1,9 @@ -- ------------------------------------------------------------------ -- Title: Oxford Acute Severity of Illness Score (OASIS) --- MIMIC version: MIMIC-III v1.4 --- Originally written by: Alistair Johnson --- Contact: aewj [at] mit [dot] edu --- ------------------------------------------------------------------ - -- This query extracts the Oxford acute severity of illness score. -- This score is a measure of severity of illness for patients in the ICU. -- The score is calculated on the first day of each ICU patients' stay. --- The score is calculated for *all* ICU patients, with the assumption that the user will subselect appropriate ICUSTAY_IDs. --- For example, the score is calculated for neonates, but it is likely inappropriate to actually use the score values for these patients. +-- ------------------------------------------------------------------ -- Reference for OASIS: -- Johnson, Alistair EW, Andrew A. Kramer, and Gari D. Clifford. @@ -33,7 +27,11 @@ -- Regarding missing values: -- The ventilation flag is always 0/1. It cannot be missing, since VENT=0 if no data is found for vent settings. -DROP MATERIALIZED VIEW IF EXISTS OASIS; +-- Note: +-- The score is calculated for *all* ICU patients, with the assumption that the user will subselect appropriate ICUSTAY_IDs. +-- For example, the score is calculated for neonates, but it is likely inappropriate to actually use the score values for these patients. + +DROP MATERIALIZED VIEW IF EXISTS OASIS CASCADE; CREATE MATERIALIZED VIEW OASIS as with surgflag as @@ -66,7 +64,7 @@ select ie.subject_id, ie.hadm_id, ie.icustay_id , vital.resprate_min , vital.tempc_max , vital.tempc_min - , vent.mechvent + , vent.vent as mechvent , uo.urineoutput , case diff --git a/severityscores/qsofa.sql b/concepts/severityscores/qsofa.sql similarity index 83% rename from severityscores/qsofa.sql rename to concepts/severityscores/qsofa.sql index 93afa1b..2ed8aaf 100644 --- a/severityscores/qsofa.sql +++ b/concepts/severityscores/qsofa.sql @@ -1,15 +1,9 @@ -- ------------------------------------------------------------------ -- Title: Quick Sequential Organ Failure Assessment (qSOFA) --- MIMIC version: MIMIC-III v1.3 --- Originally written by: Alistair Johnson --- Contact: aewj [at] mit [dot] edu --- ------------------------------------------------------------------ - --- This query extracts the quick sequential organ failure assessment (formally: sepsis-related organ failure assessment). +-- This query extracts the quick sequential organ failure assessment. -- This score was a recent revision of SOFA, aiming to detect patients at risk of sepsis. -- The score is calculated on the first day of each ICU patients' stay - though needn't be. --- The score is calculated for *all* ICU patients, with the assumption that the user will subselect appropriate ICUSTAY_IDs. --- For example, the score is calculated for neonates, but it is likely inappropriate to actually use the score values for these patients. +-- ------------------------------------------------------------------ -- Reference for qSOFA: -- Singer M, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) @@ -22,8 +16,11 @@ -- 1) gcsfirstday - generated by gcs-first-day.sql -- 2) vitalsfirstday - generated by vitals-first-day.sql -DROP MATERIALIZED VIEW IF EXISTS QSOFA CASCADE; +-- Note: +-- The score is calculated for *all* ICU patients, with the assumption that the user will subselect appropriate ICUSTAY_IDs. +-- For example, the score is calculated for neonates, but it is likely inappropriate to actually use the score values for these patients. +DROP MATERIALIZED VIEW IF EXISTS QSOFA CASCADE; CREATE MATERIALIZED VIEW QSOFA AS with scorecomp as ( diff --git a/severityscores/saps.sql b/concepts/severityscores/saps.sql similarity index 94% rename from severityscores/saps.sql rename to concepts/severityscores/saps.sql index 465a8c5..af6de85 100644 --- a/severityscores/saps.sql +++ b/concepts/severityscores/saps.sql @@ -1,18 +1,12 @@ -- ------------------------------------------------------------------ -- Title: Simplified Acute Physiology Score (SAPS) --- MIMIC version: MIMIC-III v1.4 --- Originally written by: Alistair Johnson --- Contact: aewj [at] mit [dot] edu --- ------------------------------------------------------------------ - -- This query extracts the simplified acute physiology score. -- This score is a measure of patient severity of illness. -- The score is calculated on the first day of each ICU patients' stay. --- The score is calculated for *all* ICU patients, with the assumption that the user will subselect appropriate ICUSTAY_IDs. --- For example, the score is calculated for neonates, but it is likely inappropriate to actually use the score values for these patients. +-- ------------------------------------------------------------------ -- Reference for SAPS: --- Jean-Roger Le Gall, Philippe Loirat, Annick Alperovitch, Paul Glaser, Claude Granthil, +-- Jean-Roger Le Gall, Philippe Loirat, Annick Alperovitch, Paul Glaser, Claude Granthil, -- Daniel Mathieu, Philippe Mercier, Remi Thomas, and Daniel Villers. -- "A simplified acute physiology score for ICU patients." -- Critical care medicine 12, no. 11 (1984): 975-977. @@ -31,14 +25,17 @@ -- 4) gcsfirstday - generated by gcs-first-day.sql -- 5) labsfirstday - generated by labs-first-day.sql -DROP MATERIALIZED VIEW IF EXISTS SAPS; +-- Note: +-- The score is calculated for *all* ICU patients, with the assumption that the user will subselect appropriate ICUSTAY_IDs. +-- For example, the score is calculated for neonates, but it is likely inappropriate to actually use the score values for these patients. +DROP MATERIALIZED VIEW IF EXISTS SAPS CASCADE; CREATE MATERIALIZED VIEW SAPS as -- extract CPAP from the "Oxygen Delivery Device" fields with cpap as ( select ie.icustay_id - , max(case when value in ('CPAP Mask','Bipap Mask') then 1 else 0 end) as cpap + , max(case when lower(value) similar to '%(cpap mask|bipap mask)%' then 1 else 0 end) as cpap from icustays ie inner join chartevents ce on ie.icustay_id = ce.icustay_id @@ -48,7 +45,9 @@ with cpap as -- TODO: when metavision data import fixed, check the values in 226732 match the value clause below 467, 469, 226732 ) - and value in ('CPAP Mask','Bipap Mask') + and lower(ce.value) similar to '%(cpap mask|bipap mask)%' + -- exclude rows marked as error + AND ce.error IS DISTINCT FROM 1 group by ie.icustay_id ) , cohort as @@ -86,7 +85,7 @@ select ie.subject_id, ie.hadm_id, ie.icustay_id , labs.bicarbonate_max , labs.bicarbonate_min - , vent.mechvent + , vent.vent as mechvent , uo.urineoutput , cp.cpap diff --git a/severityscores/sapsii.sql b/concepts/severityscores/sapsii.sql similarity index 94% rename from severityscores/sapsii.sql rename to concepts/severityscores/sapsii.sql index c3f022f..ebc7ad1 100644 --- a/severityscores/sapsii.sql +++ b/concepts/severityscores/sapsii.sql @@ -1,15 +1,9 @@ -- ------------------------------------------------------------------ -- Title: Simplified Acute Physiology Score II (SAPS II) --- MIMIC version: MIMIC-III v1.4 --- Originally written by: Alistair Johnson --- Contact: aewj [at] mit [dot] edu --- ------------------------------------------------------------------ - -- This query extracts the simplified acute physiology score II. -- This score is a measure of patient severity of illness. -- The score is calculated on the first day of each ICU patients' stay. --- The score is calculated for *all* ICU patients, with the assumption that the user will subselect appropriate ICUSTAY_IDs. --- For example, the score is calculated for neonates, but it is likely inappropriate to actually use the score values for these patients. +-- ------------------------------------------------------------------ -- Reference for SAPS II: -- Le Gall, Jean-Roger, Stanley Lemeshow, and Fabienne Saulnier. @@ -25,13 +19,16 @@ -- The following views are required to run this query: -- 1) uofirstday - generated by urine-output-first-day.sql --- 2) ventfirstday - generated by ventilated-first-day.sql +-- 2) ventdurations - generated by ventilation-durations.sql -- 3) vitalsfirstday - generated by vitals-first-day.sql -- 4) gcsfirstday - generated by gcs-first-day.sql -- 5) labsfirstday - generated by labs-first-day.sql -DROP MATERIALIZED VIEW IF EXISTS SAPSII; +-- Note: +-- The score is calculated for *all* ICU patients, with the assumption that the user will subselect appropriate ICUSTAY_IDs. +-- For example, the score is calculated for neonates, but it is likely inappropriate to actually use the score values for these patients. +DROP MATERIALIZED VIEW IF EXISTS SAPSII CASCADE; CREATE MATERIALIZED VIEW SAPSII as -- extract CPAP from the "Oxygen Delivery Device" fields with cpap as @@ -39,7 +36,7 @@ with cpap as select ie.icustay_id , min(charttime - interval '1' hour) as starttime , max(charttime + interval '4' hour) as endtime - , max(case when value in ('CPAP Mask','Bipap Mask') then 1 else 0 end) as cpap + , max(case when lower(ce.value) similar to '%(cpap mask|bipap mask)%' then 1 else 0 end) as cpap from icustays ie inner join chartevents ce on ie.icustay_id = ce.icustay_id @@ -49,7 +46,9 @@ with cpap as -- TODO: when metavision data import fixed, check the values in 226732 match the value clause below 467, 469, 226732 ) - and value in ('CPAP Mask','Bipap Mask') + and lower(ce.value) similar to '%(cpap mask|bipap mask)%' + -- exclude rows marked as error + AND ce.error IS DISTINCT FROM 1 group by ie.icustay_id ) -- extract a flag for surgical service diff --git a/severityscores/sirs.sql b/concepts/severityscores/sirs.sql similarity index 88% rename from severityscores/sirs.sql rename to concepts/severityscores/sirs.sql index f387e61..2e3abbe 100644 --- a/severityscores/sirs.sql +++ b/concepts/severityscores/sirs.sql @@ -1,15 +1,9 @@ -- ------------------------------------------------------------------ -- Title: Systemic inflammatory response syndrome (SIRS) criteria --- MIMIC version: MIMIC-III v1.3 --- Originally written by: Alistair Johnson --- Contact: aewj [at] mit [dot] edu --- ------------------------------------------------------------------ - -- This query extracts the Systemic inflammatory response syndrome (SIRS) criteria -- The criteria quantify the level of inflammatory response of the body -- The score is calculated on the first day of each ICU patients' stay. --- The score is calculated for *all* ICU patients, with the assumption that the user will subselect appropriate ICUSTAY_IDs. --- For example, the score is calculated for neonates, but it is likely inappropriate to actually use the score values for these patients. +-- ------------------------------------------------------------------ -- Reference for SIRS: -- American College of Chest Physicians/Society of Critical Care Medicine Consensus Conference: @@ -30,8 +24,11 @@ -- 2) labsfirstday - generated by labs-first-day.sql -- 3) bloodgasfirstdayarterial - generated by blood-gas-first-day-arterial.sql -DROP MATERIALIZED VIEW IF EXISTS SIRS; +-- Note: +-- The score is calculated for *all* ICU patients, with the assumption that the user will subselect appropriate ICUSTAY_IDs. +-- For example, the score is calculated for neonates, but it is likely inappropriate to actually use the score values for these patients. +DROP MATERIALIZED VIEW IF EXISTS SIRS CASCADE; CREATE MATERIALIZED VIEW SIRS AS with bg as ( diff --git a/severityscores/sofa.sql b/concepts/severityscores/sofa.sql similarity index 91% rename from severityscores/sofa.sql rename to concepts/severityscores/sofa.sql index d657676..64965a7 100644 --- a/severityscores/sofa.sql +++ b/concepts/severityscores/sofa.sql @@ -1,15 +1,9 @@ -- ------------------------------------------------------------------ -- Title: Sequential Organ Failure Assessment (SOFA) --- MIMIC version: MIMIC-III v1.4 --- Originally written by: Alistair Johnson --- Contact: aewj [at] mit [dot] edu --- ------------------------------------------------------------------ - -- This query extracts the sequential organ failure assessment (formally: sepsis-related organ failure assessment). -- This score is a measure of organ failure for patients in the ICU. -- The score is calculated on the first day of each ICU patients' stay. --- The score is calculated for *all* ICU patients, with the assumption that the user will subselect appropriate ICUSTAY_IDs. --- For example, the score is calculated for neonates, but it is likely inappropriate to actually use the score values for these patients. +-- ------------------------------------------------------------------ -- Reference for SOFA: -- Jean-Louis Vincent, Rui Moreno, Jukka Takala, Sheila Willatts, Arnaldo De Mendonça, @@ -25,16 +19,18 @@ -- The following views required to run this query: -- 1) uofirstday - generated by urine-output-first-day.sql --- 2) ventfirstday - generated by ventilated-first-day.sql --- 3) vitalsfirstday - generated by vitals-first-day.sql --- 4) gcsfirstday - generated by gcs-first-day.sql --- 5) labsfirstday - generated by labs-first-day.sql --- 6) bloodgasfirstdayarterial - generated by blood-gas-first-day-arterial.sql --- 7) echodata - generated by echo-data.sql --- 8) ventdurations - generated by ventilation-durations.sql +-- 2) vitalsfirstday - generated by vitals-first-day.sql +-- 3) gcsfirstday - generated by gcs-first-day.sql +-- 4) labsfirstday - generated by labs-first-day.sql +-- 5) bloodgasfirstdayarterial - generated by blood-gas-first-day-arterial.sql +-- 6) echodata - generated by echo-data.sql +-- 7) ventdurations - generated by ventilation-durations.sql -DROP MATERIALIZED VIEW IF EXISTS SOFA; +-- Note: +-- The score is calculated for *all* ICU patients, with the assumption that the user will subselect appropriate ICUSTAY_IDs. +-- For example, the score is calculated for neonates, but it is likely inappropriate to actually use the score values for these patients. +DROP MATERIALIZED VIEW IF EXISTS SOFA CASCADE; CREATE MATERIALIZED VIEW SOFA AS with wt AS ( @@ -64,6 +60,8 @@ with wt AS ) AND valuenum != 0 and charttime between ie.intime - interval '1' day and ie.intime + interval '1' day + -- exclude rows marked as error + AND c.error IS DISTINCT FROM 1 group by ie.icustay_id ) -- 5% of patients are missing a weight, but we can impute weight using their echo notes diff --git a/cookbook/postgres/uo.sql b/cookbook/postgres/uo.sql deleted file mode 100644 index ccf3d57..0000000 --- a/cookbook/postgres/uo.sql +++ /dev/null @@ -1,35 +0,0 @@ --- -------------------------------------------------------- --- Title: Retrieves the urine output of adult patients --- only for patients recorded on carevue --- MIMIC version: MIMIC-III v1.3 --- Notes: this query does not specify a schema. To run it on your local --- MIMIC schema, run the following command: --- SET SEARCH_PATH TO mimiciii; --- Where "mimiciii" is the name of your schema, and may be different. --- -------------------------------------------------------- - -WITH agetbl AS -( - SELECT ad.subject_id - FROM admissions ad - INNER JOIN patients p - ON ad.subject_id = p.subject_id - WHERE - -- filter to only adults - EXTRACT(EPOCH FROM (ad.admittime - p.dob))/60.0/60.0/24.0/365.242 > 15 - -- group by subject_id to ensure there is only 1 subject_id per row - group by ad.subject_id -) - -SELECT bucket*5, COUNT(*) FROM ( - SELECT width_bucket(volume, 0, 1000, 200) AS bucket - FROM ioevents ie - INNER JOIN agetbl - ON ie.subject_id = agetbl.subject_id - WHERE itemid IN (55, 56, 57, 61, 65, 69, 85, 94, 96, 288, 405, 428, - 473, 651, 715, 1922, 2042, 2068, 2111, 2119, 2130, 2366, 2463, 2507, - 2510, 2592, 2676, 2810, 2859, 3053, 3175, 3462, 3519, 3966, 3987, - 4132, 4253, 5927) - ) AS urine_output - GROUP BY bucket - ORDER BY bucket; diff --git a/etc/README.md b/etc/README.md deleted file mode 100644 index d12e436..0000000 --- a/etc/README.md +++ /dev/null @@ -1,18 +0,0 @@ -# Contents of this folder - -This folder contains miscellaneous scripts used as staging tables for useful clinical concepts, e.g. severity scores. Each script generates a materialized view of the data. - -## auroc.sql - -This file calculates the area under the receiver operator characteristic curve (AUROC) for a set of predictions, `PRED`, given a set of targets, `TAR`. The AUROC is a useful measure of the discrimination of a set of predictions. - -# Subfolders - -## firstday - -The first day subfolder contains scripts used to calculate various clinical concepts on the first day of a patient's admission to the ICU. These values are usually used in the calculation of severity scores. - - -# Last updated - -This folder was last updated January 26th, 2016. diff --git a/etc/firstday/lab-first-day.sql b/etc/firstday/lab-first-day.sql deleted file mode 100644 index 9141791..0000000 --- a/etc/firstday/lab-first-day.sql +++ /dev/null @@ -1,152 +0,0 @@ --- This query pivots lab values taken in the first 24 hours of a patient's stay - --- Have already confirmed that the unit of measurement is always the same: null or the correct unit - -drop materialized view IF EXISTS labsfirstday; - -create materialized view labsfirstday as -select - pvt.subject_id, pvt.hadm_id, pvt.icustay_id - - , min(case when label = 'ANION GAP' then valuenum else null end) as ANIONGAP_min - , max(case when label = 'ANION GAP' then valuenum else null end) as ANIONGAP_max - , min(case when label = 'ALBUMIN' then valuenum else null end) as ALBUMIN_min - , max(case when label = 'ALBUMIN' then valuenum else null end) as ALBUMIN_max - , min(case when label = 'BICARBONATE' then valuenum else null end) as BICARBONATE_min - , max(case when label = 'BICARBONATE' then valuenum else null end) as BICARBONATE_max - , min(case when label = 'BILIRUBIN' then valuenum else null end) as BILIRUBIN_min - , max(case when label = 'BILIRUBIN' then valuenum else null end) as BILIRUBIN_max - , min(case when label = 'CREATININE' then valuenum else null end) as CREATININE_min - , max(case when label = 'CREATININE' then valuenum else null end) as CREATININE_max - , min(case when label = 'CHLORIDE' then valuenum else null end) as CHLORIDE_min - , max(case when label = 'CHLORIDE' then valuenum else null end) as CHLORIDE_max - , min(case when label = 'GLUCOSE' then valuenum else null end) as GLUCOSE_min - , max(case when label = 'GLUCOSE' then valuenum else null end) as GLUCOSE_max - , min(case when label = 'HEMATOCRIT' then valuenum else null end) as HEMATOCRIT_min - , max(case when label = 'HEMATOCRIT' then valuenum else null end) as HEMATOCRIT_max - , min(case when label = 'HEMOGLOBIN' then valuenum else null end) as HEMOGLOBIN_min - , max(case when label = 'HEMOGLOBIN' then valuenum else null end) as HEMOGLOBIN_max - , min(case when label = 'LACTATE' then valuenum else null end) as LACTATE_min - , max(case when label = 'LACTATE' then valuenum else null end) as LACTATE_max - , min(case when label = 'PLATELET' then valuenum else null end) as PLATELET_min - , max(case when label = 'PLATELET' then valuenum else null end) as PLATELET_max - , min(case when label = 'POTASSIUM' then valuenum else null end) as POTASSIUM_min - , max(case when label = 'POTASSIUM' then valuenum else null end) as POTASSIUM_max - , min(case when label = 'PTT' then valuenum else null end) as PTT_min - , max(case when label = 'PTT' then valuenum else null end) as PTT_max - , min(case when label = 'INR' then valuenum else null end) as INR_min - , max(case when label = 'INR' then valuenum else null end) as INR_max - , min(case when label = 'PT' then valuenum else null end) as PT_min - , max(case when label = 'PT' then valuenum else null end) as PT_max - , min(case when label = 'SODIUM' then valuenum else null end) as SODIUM_min - , max(case when label = 'SODIUM' then valuenum else null end) as SODIUM_max - , min(case when label = 'BUN' then valuenum else null end) as BUN_min - , max(case when label = 'BUN' then valuenum else null end) as BUN_max - , min(case when label = 'WBC' then valuenum else null end) as WBC_min - , max(case when label = 'WBC' then valuenum else null end) as WBC_max - - -from -( -- begin query that extracts the data - select ie.subject_id, ie.hadm_id, ie.icustay_id - -- here we assign labels to ITEMIDs - -- this also fuses together multiple ITEMIDs containing the same data - , case - when itemid = 50868 then 'ANION GAP' - when itemid = 50862 then 'ALBUMIN' - when itemid = 50882 then 'BICARBONATE' - when itemid = 50885 then 'BILIRUBIN' - when itemid = 50912 then 'CREATININE' - when itemid = 50806 then 'CHLORIDE' - when itemid = 50902 then 'CHLORIDE' - when itemid = 50809 then 'GLUCOSE' - when itemid = 50931 then 'GLUCOSE' - when itemid = 50810 then 'HEMATOCRIT' - when itemid = 51221 then 'HEMATOCRIT' - when itemid = 50811 then 'HEMOGLOBIN' - when itemid = 51222 then 'HEMOGLOBIN' - when itemid = 50813 then 'LACTATE' - when itemid = 51265 then 'PLATELET' - when itemid = 50822 then 'POTASSIUM' - when itemid = 50971 then 'POTASSIUM' - when itemid = 51275 then 'PTT' - when itemid = 51237 then 'INR' - when itemid = 51274 then 'PT' - when itemid = 50824 then 'SODIUM' - when itemid = 50983 then 'SODIUM' - when itemid = 51006 then 'BUN' - when itemid = 51300 then 'WBC' - when itemid = 51301 then 'WBC' - else null - end as label - , -- add in some sanity checks on the values - -- the where clause below requires all valuenum to be > 0, so these are only upper limit checks - case - when itemid = 50862 and valuenum > 10 then null -- g/dL 'ALBUMIN' - when itemid = 50868 and valuenum > 10000 then null -- mEq/L 'ANION GAP' - when itemid = 50882 and valuenum > 10000 then null -- mEq/L 'BICARBONATE' - when itemid = 50885 and valuenum > 150 then null -- mg/dL 'BILIRUBIN' - when itemid = 50806 and valuenum > 10000 then null -- mEq/L 'CHLORIDE' - when itemid = 50902 and valuenum > 10000 then null -- mEq/L 'CHLORIDE' - when itemid = 50912 and valuenum > 150 then null -- mg/dL 'CREATININE' - when itemid = 50809 and valuenum > 10000 then null -- mg/dL 'GLUCOSE' - when itemid = 50931 and valuenum > 10000 then null -- mg/dL 'GLUCOSE' - when itemid = 50810 and valuenum > 100 then null -- % 'HEMATOCRIT' - when itemid = 51221 and valuenum > 100 then null -- % 'HEMATOCRIT' - when itemid = 50811 and valuenum > 50 then null -- g/dL 'HEMOGLOBIN' - when itemid = 51222 and valuenum > 50 then null -- g/dL 'HEMOGLOBIN' - when itemid = 50813 and valuenum > 50 then null -- mmol/L 'LACTATE' - when itemid = 51265 and valuenum > 10000 then null -- K/uL 'PLATELET' - when itemid = 50822 and valuenum > 30 then null -- mEq/L 'POTASSIUM' - when itemid = 50971 and valuenum > 30 then null -- mEq/L 'POTASSIUM' - when itemid = 51275 and valuenum > 150 then null -- sec 'PTT' - when itemid = 51237 and valuenum > 50 then null -- 'INR' - when itemid = 51274 and valuenum > 150 then null -- sec 'PT' - when itemid = 50824 and valuenum > 200 then null -- mEq/L == mmol/L 'SODIUM' - when itemid = 50983 and valuenum > 200 then null -- mEq/L == mmol/L 'SODIUM' - when itemid = 51006 and valuenum > 300 then null -- 'BUN' - when itemid = 51300 and valuenum > 1000 then null -- 'WBC' - when itemid = 51301 and valuenum > 1000 then null -- 'WBC' - else le.valuenum - end as valuenum - - from icustays ie - - left join labevents le - on le.subject_id = ie.subject_id and le.hadm_id = ie.hadm_id - and le.charttime between (ie.intime - interval '6' hour) and (ie.intime + interval '1' day) - and le.ITEMID in - ( - -- comment is: LABEL | CATEGORY | FLUID | NUMBER OF ROWS IN LABEVENTS - 50868, -- ANION GAP | CHEMISTRY | BLOOD | 769895 - 50862, -- ALBUMIN | CHEMISTRY | BLOOD | 146697 - 50882, -- BICARBONATE | CHEMISTRY | BLOOD | 780733 - 50885, -- BILIRUBIN, TOTAL | CHEMISTRY | BLOOD | 238277 - 50912, -- CREATININE | CHEMISTRY | BLOOD | 797476 - 50902, -- CHLORIDE | CHEMISTRY | BLOOD | 795568 - 50806, -- CHLORIDE, WHOLE BLOOD | BLOOD GAS | BLOOD | 48187 - 50931, -- GLUCOSE | CHEMISTRY | BLOOD | 748981 - 50809, -- GLUCOSE | BLOOD GAS | BLOOD | 196734 - 51221, -- HEMATOCRIT | HEMATOLOGY | BLOOD | 881846 - 50810, -- HEMATOCRIT, CALCULATED | BLOOD GAS | BLOOD | 89715 - 51222, -- HEMOGLOBIN | HEMATOLOGY | BLOOD | 752523 - 50811, -- HEMOGLOBIN | BLOOD GAS | BLOOD | 89712 - 50813, -- LACTATE | BLOOD GAS | BLOOD | 187124 - 51265, -- PLATELET COUNT | HEMATOLOGY | BLOOD | 778444 - 50971, -- POTASSIUM | CHEMISTRY | BLOOD | 845825 - 50822, -- POTASSIUM, WHOLE BLOOD | BLOOD GAS | BLOOD | 192946 - 51275, -- PTT | HEMATOLOGY | BLOOD | 474937 - 51237, -- INR(PT) | HEMATOLOGY | BLOOD | 471183 - 51274, -- PT | HEMATOLOGY | BLOOD | 469090 - 50983, -- SODIUM | CHEMISTRY | BLOOD | 808489 - 50824, -- SODIUM, WHOLE BLOOD | BLOOD GAS | BLOOD | 71503 - 51006, -- UREA NITROGEN | CHEMISTRY | BLOOD | 791925 - 51301, -- WHITE BLOOD CELLS | HEMATOLOGY | BLOOD | 753301 - 51300 -- WBC COUNT | HEMATOLOGY | BLOOD | 2371 - ) - and valuenum is not null and valuenum > 0 -- lab values cannot be 0 and cannot be negative -) pvt -group by pvt.subject_id, pvt.hadm_id, pvt.icustay_id -order by pvt.subject_id, pvt.hadm_id, pvt.icustay_id; - -commit; diff --git a/etc/firstday/ventilation-first-day.sql b/etc/firstday/ventilation-first-day.sql deleted file mode 100644 index b3004fc..0000000 --- a/etc/firstday/ventilation-first-day.sql +++ /dev/null @@ -1,126 +0,0 @@ --- Determines if a patient is ventilated on the first day of their ICU stay. --- Creates a table with the result. --- Contact: aewj@mit.edu --- Copyright 2015, Alistair Johnson - -DROP MATERIALIZED VIEW IF EXISTS ventfirstday; -CREATE MATERIALIZED VIEW ventfirstday AS --- group together the flags based on icustay_id -select - subject_id, hadm_id, icustay_id - , max(case - -- if no chart data matched, they are not ventilated - when value is null - then 0 - when (VentTypeRecorded + MinuteVolume + TV + InspPressure - + PlateauPressure + APRVPressure + PEEP + HighPressureRelease - + PCV + TCPCV + RespPressure + psvlevel + ett + O2FromVentilator) > 0 - then 1 - else 0 - end) as MechVent -from -( -select - ie.subject_id, ie.icustay_id, ie.hadm_id, ce.charttime - , ce.itemid, di.label, ce.value - , case - when ce.itemid = 720 and value != 'Other/Remarks' then 1 - else 0 end as VentTypeRecorded -- VentType - - , case - when ce.itemid in (445, 448, 449, 450, 1340, 1486, 1600, 224687) then 1 -- minute volume - else 0 end as MinuteVolume -- MinuteVolume - - , case - when ce.itemid in (639, 654, 681, 682, 683, 684,224685,224684,224686) then 1 -- tidal volume - else 0 end as TV -- TidalVolume - - , case - when ce.itemid in (218,436,535,444,459,224697,224695,224696,224746,224747) then 1 -- High/Low/Peak/Mean/Neg insp force - else 0 end as RespPressure - , case - when ce.itemid in (221,1,1211,1655,2000,226873,224738,224419,224750,227187) then 1 -- Insp pressure - else 0 end as InspPressure - - , case - when ce.itemid in (543) then 1 -- PlateauPressure - else 0 end as PlateauPressure - - , case - when ce.itemid in (5865,5866,224707,224709,224705,224706) then 1 -- APRV pressure - else 0 end as APRVPressure - - , case - when ce.itemid in (60,437,505,506,686,220339,224700) then 1 -- peep - else 0 end as PEEP - - , case - when ce.itemid in (141,224417,224418) then 1 -- cuff volume/pressure - else 0 end as CuffPressure - - , case - when ce.itemid in (3459) then 1 -- high pressure relief - else 0 end as HighPressureRelease - - , case - when ce.itemid in (501,502,503,224702) then 1 -- PCV - else 0 end as PCV - - , case - when ce.itemid in (223,667,668,669,670,671,672) then 1 -- TCPCV - else 0 end as TCPCV -- TCPCV - - , case - when ce.itemid = 467 and ce.value = 'Ventilator' then 1 -- O2 delivery device == ventilator - else 0 end as O2FromVentilator - - , case - when ce.itemid in (578,3605) then 1 -- Pressure/Respiratory - else 0 end as PressureSupport -- Pressure/Respiratory - - , case - when ce.itemid in (3688,3689) then 1 -- Vt [Ventilator] and Vt [Spontaneous] - measured in inches??? :S - else 0 end as VtInches - - , case - when ce.itemid in (63,64,65,66,67,68 - ,227579,227580,227582,227581) then 1 -- BIPAP - else 0 end as BIPAP -- - - , case - when ce.itemid in (157,158,1852,3398,3399,3400,3401,3402,3403,3404,8382 - ,227809,227810) then 1 -- ETT - else 0 end as ETT -- - - , case - when ce.itemid in (224701) then 1 else 0 end PSVlevel - - , case - when ce.itemid in (223835) then 1 -- FiO2 - else 0 end as FiO2 - - , case - when ce.itemid in (614,615,619,653,224688,224690,224689) then 1 - else 0 end as RespRate -- RespRate - , case - when ce.itemid in (194, 195, 224691) then 1 -- flow by - else 0 end as FlowBy - - , case - when ce.itemid in (470,471,223834,227287) then 1 -- O2 FLOW - else 0 end as O2Flow - - , case - when ce.itemid = 648 and value = 'Intubated/trach' THEN 1 -- Speech = intubated - else 0 end as SpeechIntubated - -from icustays ie -left join chartevents ce - on ie.icustay_id = ce.icustay_id and ce.value is not null - -- only first day of their ICU stay - and ce.charttime between ie.intime and ie.intime + interval '1' day -left join d_items di - on ce.itemid = di.itemid -) AS tt -group by subject_id, hadm_id, icustay_id -order by subject_id, hadm_id, icustay_id; diff --git a/etc/ventilation-durations.sql b/etc/ventilation-durations.sql deleted file mode 100644 index 636d6ce..0000000 --- a/etc/ventilation-durations.sql +++ /dev/null @@ -1,137 +0,0 @@ --- This query extracts the duration of mechanical ventilation -drop materialized view if exists ventdurations cascade; -drop table if exists ventdurations cascade; -create materialized view ventdurations as --- create the durations for each mechanical ventilation instance -select icustay_id, ventnum - , min(charttime) as starttime - , max(charttime) as endtime -from - ( - select vd1.* - -- create a cumulative sum of the instances of new ventilation - -- this results in a monotonic integer assigned to each instance of ventilation - , case when MechVent=1 or Extubated = 1 then - SUM( newvent ) - OVER ( partition by icustay_id order by charttime ) - else null end - as ventnum - --- now we convert CHARTTIME of ventilator settings into durations - from ( -- vd1 - select - icustay_id - -- this carries over the previous charttime which had a mechanical ventilation event - , case - when MechVent=1 then - LAG(CHARTTIME, 1) OVER (partition by icustay_id, MechVent order by charttime) - else null - end as charttime_lag - , charttime - , MechVent - , Extubated - , SelfExtubated - - -- if this is a mechanical ventilation event, we calculate the time since the last event - , case - -- if the current observation indicates mechanical ventilation is present - when MechVent=1 then - -- copy over the previous charttime where mechanical ventilation was present - CHARTTIME - (LAG(CHARTTIME, 1) OVER (partition by icustay_id, MechVent order by charttime)) - else null - end as ventduration - - -- now we determine if the current mech vent event is a "new", i.e. they've just been intubated - , case - -- if there is an extubation flag, we mark any subsequent ventilation as a new ventilation event - when Extubated = 1 then 0 -- extubation is *not* a new ventilation event, the *subsequent* row is - when - LAG(Extubated,1) - OVER - ( - partition by icustay_id, case when MechVent=1 or Extubated=1 then 1 else 0 end - order by charttime - ) - = 1 then 1 - -- if there is less than 8 hours between vent settings, we do not treat this as a new ventilation event - when (CHARTTIME - (LAG(CHARTTIME, 1) OVER (partition by icustay_id, MechVent order by charttime))) <= interval '8' hour - then 0 - else 1 - end as newvent - -- First create a staging table with only vent settings from chart events - FROM ( -- ventsettings - select - icustay_id, charttime - -- case statement determining whether it is an instance of mech vent - , max( - case - when itemid is null or value is null then 0 -- can't have null values - when itemid = 720 and value != 'Other/Remarks' THEN 1 -- VentTypeRecorded - when itemid = 467 and value = 'Ventilator' THEN 1 -- O2 delivery device == ventilator - when itemid = 648 and value = 'Intubated/trach' THEN 1 -- Speech = intubated - when itemid in - ( - 445, 448, 449, 450, 1340, 1486, 1600, 224687 -- minute volume - , 639, 654, 681, 682, 683, 684,224685,224684,224686 -- tidal volume - , 218,436,535,444,459,224697,224695,224696,224746,224747 -- High/Low/Peak/Mean/Neg insp force ("RespPressure") - , 221,1,1211,1655,2000,226873,224738,224419,224750,227187 -- Insp pressure - , 543 -- PlateauPressure - , 5865,5866,224707,224709,224705,224706 -- APRV pressure - , 60,437,505,506,686,220339,224700 -- PEEP - , 3459 -- high pressure relief - , 501,502,503,224702 -- PCV - , 223,667,668,669,670,671,672 -- TCPCV - , 157,158,1852,3398,3399,3400,3401,3402,3403,3404,8382,227809,227810 -- ETT - , 224701 -- PSVlevel - ) - THEN 1 - else 0 - end - ) as MechVent - , max( - case when itemid is null or value is null then 0 - when itemid = 640 and value = 'Extubated' then 1 - when itemid = 640 and value = 'Self Extubation' then 1 - else 0 - end - ) - as Extubated - , max( - case when itemid is null or value is null then 0 - when itemid = 640 and value = 'Self Extubation' then 1 - else 0 - end - ) - as SelfExtubated - - from chartevents ce - where value is not null - and itemid in - ( - 640 -- extubated - , 648 -- speech - , 720 -- vent type - , 467 -- O2 delivery device - , 445, 448, 449, 450, 1340, 1486, 1600, 224687 -- minute volume - , 639, 654, 681, 682, 683, 684,224685,224684,224686 -- tidal volume - , 218,436,535,444,459,224697,224695,224696,224746,224747 -- High/Low/Peak/Mean/Neg insp force ("RespPressure") - , 221,1,1211,1655,2000,226873,224738,224419,224750,227187 -- Insp pressure - , 543 -- PlateauPressure - , 5865,5866,224707,224709,224705,224706 -- APRV pressure - , 60,437,505,506,686,220339,224700 -- PEEP - , 3459 -- high pressure relief - , 501,502,503,224702 -- PCV - , 223,667,668,669,670,671,672 -- TCPCV - , 157,158,1852,3398,3399,3400,3401,3402,3403,3404,8382,227809,227810 -- ETT - , 224701 -- PSVlevel - ) - group by icustay_id, charttime - ) AS ventsettings - ) AS vd1 - -- now we can isolate to just rows with ventilation settings/extubation settings - -- (before we had rows with extubation flags) - -- this removes any null values for newvent - where - MechVent = 1 or Extubated = 1 - ) AS vd2 -group by icustay_id, ventnum -order by icustay_id, ventnum; diff --git a/migrating/labid.csv b/migrating/labid.csv deleted file mode 100644 index 96977d4..0000000 --- a/migrating/labid.csv +++ /dev/null @@ -1,756 +0,0 @@ -MIMIC III ITEMID,MIMIC III LABEL,MIMIC III FLUID,MIMIC III CATEGORY,MIMIC III LOINC CODE,MIMIC II LOINC CODE,MIMIC II LOINC DESCRIPTION,MIMIC II ITEMID,COMMENTS -50852,% HEMOGLOBIN A1C,BLOOD,CHEMISTRY,4548-4,4548-4,Hemoglobin A1c (glycated HgB)/Hemoglobin.total [Moles] in Blood,50054,Duplicate v26 ITEMID with 50183 -51066,24 HR CALCIUM,URINE,CHEMISTRY,,6874-2,Calcium [Mass/time] in 24 hour Urine,50253, -51067,24 HR CREATININE,URINE,CHEMISTRY,,2162-6,Creatinine [Mass/time] in 24 hour Urine,50254, -51068,24 HR PROTEIN,URINE,CHEMISTRY,,2889-4,Protein [Mass/time] in 24 hour Urine,50255, -50853,25-OH VITAMIN D,BLOOD,CHEMISTRY,,,,, -51011,,CEREBROSPINAL FLUID (CSF),CHEMISTRY,,1746-7,Albumin [Mass/volume] in Cerebral spinal fluid,50199, -50854,ABSOLUTE A1C,BLOOD,CHEMISTRY,,4548-4,Hemoglobin A1c (glycated HgB)/Hemoglobin.total [Moles] in Blood,50183, -51130,ABSOLUTE CD3 COUNT,BLOOD,HEMATOLOGY,8124-0,8124-0,CD3 cells/100 cells in Blood,50352, -51131,ABSOLUTE CD4 COUNT,BLOOD,HEMATOLOGY,8128-1,8128-1,CD4 cells/100 cells in Blood,50356, -51132,ABSOLUTE CD8 COUNT,BLOOD,HEMATOLOGY,8138-0,8138-0,CD8 cells/100 cells in Blood,50367, -50855,ABSOLUTE HEMOGLOBIN,BLOOD,CHEMISTRY,,718-7,Hemoglobin [Mass/volume] in Blood,50184, -51133,ABSOLUTE LYMPHOCYTE COUNT,BLOOD,HEMATOLOGY,,26474-7,Lymphocytes [#/volume] in Blood,50320, -51134,ACANTHOCYTES,BLOOD,HEMATOLOGY,7789-1,7789-1,Acanthocytes [Presence] in Blood by Light microscopy,50324, -50856,ACETAMINOPHEN,BLOOD,CHEMISTRY,3297-9,3298-7,Acetaminophen [Mass/volume] in Serum or Plasma,50056, -50857,ACETONE,BLOOD,CHEMISTRY,5567-3,5567-3,Acetone [Presence] in Serum or Plasma,50057, -51521,ACID PHOS,BLOOD,CHEMISTRY,,1711-1,Acid phosphatase [Enzymatic activity/volume] in Blood,50058,"No match found in 01_2013 lab data dump, exists in mimic2v26, therefore the new LABEL was invented and an itemid was generated" -50858,ACID PHOSPHATASE,BLOOD,CHEMISTRY,1715-2,,,50693, -50859,"ACID PHOSPHATASE, NON-PROSTATIC",BLOOD,CHEMISTRY,,,,50143, -51135,ADP,BLOOD,HEMATOLOGY,,,,50325, -51530,AF-AFP,OTHER BODY FLUID,CHEMISTRY,,,,50700,"No match found in 01_2013 lab data dump, exists in mimic2v26, therefore the new LABEL was invented and an itemid was generated" -50860,"AFP, MATERNAL SCREEN",BLOOD,CHEMISTRY,,,,50687, -50861,ALANINE AMINOTRANSFERASE (ALT),BLOOD,CHEMISTRY,1742-6,1742-6,Alanine aminotransferase [Enzymatic activity/volume] in Serum or Plasma,50062, -50862,ALBUMIN,BLOOD,CHEMISTRY,1751-7,1751-7,Albumin [Mass/volume] in Serum or Plasma,50060, -50835,"ALBUMIN, ASCITES",ASCITES,CHEMISTRY,,1749-1,Albumin [Mass/volume] in Peritoneal fluid,50037, -51025,"ALBUMIN, BODY FLUID",OTHER BODY FLUID,CHEMISTRY,,1747-5,Albumin [Mass/volume] in Body fluid,50211, -51019,"ALBUMIN, JOINT FLUID",JOINT FLUID,CHEMISTRY,,,,50227, -51046,"ALBUMIN, PLEURAL",PLEURAL,CHEMISTRY,,1748-3,Albumin [Mass/volume] in Pleural fluid,50234, -51069,"ALBUMIN, URINE",URINE,CHEMISTRY,,1754-1,Albumin [Mass/volume] in Urine,50288, -51070,"ALBUMIN/CREATININE, URINE",URINE,CHEMISTRY,14958-3,9318-7,Albumin/Creatinine [Mass ratio] in Urine,50287, -50863,ALKALINE PHOSPHATASE,BLOOD,CHEMISTRY,6768-6,1783-0,Alkaline phosphatase [Enzymatic activity/volume] in Blood,50061, -51136,ALPHA ANTIPLASMIN,BLOOD,HEMATOLOGY,,,,50327, -50864,ALPHA-FETOPROTEIN,BLOOD,CHEMISTRY,1834-1,1834-1,Alpha-1-Fetoprotein [Mass/volume] in Serum or Plasma,50059, -50801,ALVEOLAR-ARTERIAL GRADIENT,BLOOD,BLOOD GAS,,19991-9,Oxygen.alveolar - arterial [Partial pressure] Respiratory system,50001, -50865,AMIKACIN,BLOOD,CHEMISTRY,,,,, -50866,AMMONIA,BLOOD,CHEMISTRY,,16362-6,Ammonia [Moles/volume] in Plasma,50064, -51461,AMMONIUM BIURATE,URINE,HEMATOLOGY,,12454-5,Urate crystals amorphous [Presence] in Urine sediment by Light microscopy,50621, -51462,AMORPHOUS CRYSTALS,URINE,HEMATOLOGY,8246-1,8246-1,Amorphous sediment [Presence] in Urine sediment by Light microscopy,50622, -51071,"AMPHETAMINE SCREEN, URINE",URINE,CHEMISTRY,,3349-8,Amphetamines [Presence] in Urine,50289, -50867,AMYLASE,BLOOD,CHEMISTRY,6697-7,1798-8,Amylase [Enzymatic activity/volume] in Serum or Plasma,50065, -50836,"AMYLASE, ASCITES",ASCITES,CHEMISTRY,,1797-0,Amylase [Enzymatic activity/volume] in Peritoneal fluid,50038, -51026,"AMYLASE, BODY FLUID",OTHER BODY FLUID,CHEMISTRY,,1795-4,Amylase [Enzymatic activity/volume] in Body fluid,50212, -51020,"AMYLASE, JOINT FLUID",JOINT FLUID,CHEMISTRY,,14388-3,Amylase [Enzymatic activity/volume] in Synovial fluid,50206, -51047,"AMYLASE, PLEURAL",PLEURAL,CHEMISTRY,,1796-2,Amylase [Enzymatic activity/volume] in Pleural fluid,50235, -51072,"AMYLASE, URINE",URINE,CHEMISTRY,,1799-6,Amylase [Enzymatic activity/volume] in Urine,50261, -51073,"AMYLASE/CREATININE RATIO, URINE",URINE,CHEMISTRY,,34235-2,Amylase/Creatinine [Ratio] in Urine,50260, -50868,ANION GAP,BLOOD,CHEMISTRY,1863-0,41276-7,Anion gap in Blood,50068, -51137,ANISOCYTOSIS,BLOOD,HEMATOLOGY,702-1,38892-6,Anisocytosis [Presence] in Blood,50326, -50869,ANTI-DGP (IGA/IGG),BLOOD,CHEMISTRY,,,,50696, -50870,"ANTI-GLIADIN ANTIBODY, IGA",BLOOD,CHEMISTRY,,7893-1,Gliadin Ab [Units/volume] in Serum,50110, -51520,ANTI-MC,BLOOD,CHEMISTRY,,32042-4,Thyroperoxidase Ab [Presence] in Serum,50069,"No match found in 01_2013 lab data dump, exists in mimic2v26, therefore the new LABEL was invented and an itemid was generated" -50871,ANTI-MITOCHONDRIAL ANTIBODY,BLOOD,CHEMISTRY,,14236-4,Mitochondria Ab [Presence] in Serum,50063,possible mismatch -50872,ANTI-NEUTROPHIL CYTOPLASMIC ANTIBODY,BLOOD,CHEMISTRY,,35279-9,Neutrophil cytoplasmic Ab [Presence] in Serum by Immunofluorescence,50067, -50873,ANTI-NUCLEAR ANTIBODY,BLOOD,CHEMISTRY,5047-6,8061-4,Nuclear Ab [Presence] in Serum,50066, -50874,"ANTI-NUCLEAR ANTIBODY, TITER",BLOOD,CHEMISTRY,8061-4,5048-4,Nuclear Ab [Titer] in Serum by Immunofluorescence,50168, -50875,ANTI-PARIETAL CELL ANTIBODY,BLOOD,CHEMISTRY,,26969-6,Parietal cell Ab [Presence] in Serum by Immunofluorescence,50155, -50876,ANTI-SMOOTH MUSCLE ANTIBODY,BLOOD,CHEMISTRY,,14252-1,Smooth muscle Ab [Presence] in Serum,50161, -50877,ANTI-THYROGLOBULIN ANTIBODIES,BLOOD,CHEMISTRY,5380-1,8098-6,Thyroglobulin Ab [Units/volume] in Serum,50070, -51138,ANTICARDIOLIPIN ANTIBODY IGG,BLOOD,HEMATOLOGY,,8065-5,Cardiolipin IgG Ab [Units/volume] in Serum,50322, -51139,ANTICARDIOLIPIN ANTIBODY IGM,BLOOD,HEMATOLOGY,,3182-3,Cardiolipin IgM Ab [Units/volume] in Serum by Immunoassay,50323, -51140,ANTITHROMBIN,BLOOD,HEMATOLOGY,,27811-9,Antithrombin actual/normal in Platelet poor plasma by Chromogenic method,50330, -51141,APT TEST,BLOOD,HEMATOLOGY,,,,50328, -51142,ARACHADONIC ACID,BLOOD,HEMATOLOGY,,,,50329, -50878,ASPARATE AMINOTRANSFERASE (AST),BLOOD,CHEMISTRY,1920-8,1920-8,Aspartate aminotransferase [Enzymatic activity/volume] in Serum or Plasma,50073, -51110,ATYPICAL LYMPHOCYTES,ASCITES,HEMATOLOGY,,33369-0,Lymphocytes Variant/100 leukocytes in Peritoneal fluid,50298, -51143,ATYPICAL LYMPHOCYTES,BLOOD,HEMATOLOGY,733-6,13046-8,Lymphocytes Variant/100 leukocytes in Blood,50331, -51343,ATYPICAL LYMPHOCYTES,CEREBROSPINAL FLUID (CSF),HEMATOLOGY,,30416-2,Lymphocytes Variant/100 leukocytes in Cerebral spinal fluid,50509, -51365,ATYPICAL LYMPHOCYTES,JOINT FLUID,HEMATOLOGY,,33371-6,Lymphocytes Variant/100 leukocytes in Synovial fluid,50529, -51385,ATYPICAL LYMPHOCYTES,OTHER BODY FLUID,HEMATOLOGY,,30417-0,Lymphocytes Variant/100 leukocytes in Body fluid,50549, -51440,ATYPICAL LYMPHOCYTES,PLEURAL,HEMATOLOGY,,33370-8,Lymphocytes Variant/100 leukocytes in Pleural fluid,50600, -51463,BACTERIA,URINE,HEMATOLOGY,5769-5,25145-4,Bacteria [Presence] in Urine sediment by Light microscopy,50624, -51111,BANDS,ASCITES,HEMATOLOGY,,,,50299, -51144,BANDS,BLOOD,HEMATOLOGY,763-3,26508-2,Neutrophils.band form/100 leukocytes in Blood,50332, -51344,BANDS,CEREBROSPINAL FLUID (CSF),HEMATOLOGY,,26509-0,Neutrophils.band form/100 leukocytes in Cerebral spinal fluid,50510, -51366,BANDS,JOINT FLUID,HEMATOLOGY,,33361-7,Neutrophils.band form/100 leukocytes in Synovial fluid,50530, -51386,BANDS,OTHER BODY FLUID,HEMATOLOGY,,26510-8,Neutrophils.band form/100 leukocytes in Body fluid,50550, -51441,BANDS,PLEURAL,HEMATOLOGY,,,,50601, -50879,BARBITURATE SCREEN,BLOOD,CHEMISTRY,,3376-1,"Barbiturates [Presence] in Serum, Plasma or Blood",50186, -51074,"BARBITURATE SCREEN, URINE",URINE,CHEMISTRY,3377-9,3377-9,Barbiturates [Presence] in Urine,50290, -50802,BASE EXCESS,BLOOD,BLOOD GAS,,11555-0,Base excess in Blood,50002, -51145,BASOPHILIC STIPPLING,BLOOD,HEMATOLOGY,703-9,703-9,Basophilic stippling [Presence] in Blood by Light microscopy,50456, -51112,BASOPHILS,ASCITES,HEMATOLOGY,,35069-4,Basophils/100 leukocytes in Peritoneal fluid,50300, -51146,BASOPHILS,BLOOD,HEMATOLOGY,704-7,30180-4,Basophils/100 leukocytes in Blood,50333, -51345,BASOPHILS,CEREBROSPINAL FLUID (CSF),HEMATOLOGY,,30374-3,Basophils/100 leukocytes in Cerebral spinal fluid,50511, -51367,BASOPHILS,JOINT FLUID,HEMATOLOGY,,17833-5,Basophils/100 leukocytes in Synovial fluid,50531, -51387,BASOPHILS,OTHER BODY FLUID,HEMATOLOGY,,28543-7,Basophils/100 leukocytes in Body fluid,50551, -51442,BASOPHILS,PLEURAL,HEMATOLOGY,,35070-2,Basophils/100 leukocytes in Pleural fluid,50602, -50880,BENZODIAZEPINE SCREEN,BLOOD,CHEMISTRY,,42662-7,Benzodiazepines [Presence] in Blood,50187, -51075,"BENZODIAZEPINE SCREEN, URINE",URINE,CHEMISTRY,,3390-2,Benzodiazepines [Presence] in Urine,50291, -50881,BETA-2 MICROGLOBULIN,BLOOD,CHEMISTRY,,32731-2,Beta 2 globulin [Mass/volume] in Serum or Plasma by Electrophoresis,50185, -50882,BICARBONATE,BLOOD,CHEMISTRY,1963-8,1963-8,Bicarbonate [Moles/volume] in Serum,50172, -50837,"BICARBONATE, ASCITES",ASCITES,CHEMISTRY,,54360-3,Bicarbonate [Moles/volume] in Peritoneal fluid,50041, -51027,"BICARBONATE, OTHER FLUID",OTHER BODY FLUID,CHEMISTRY,,11211-0,"Carbon dioxide, total [Moles/volume] in Body fluid",50230,"LOINC code from old data probably wrong, should be bicarbonate" -51048,"BICARBONATE, PLEURAL",PLEURAL,CHEMISTRY,,54361-1,Bicarbonate [Moles/volume] in Pleural fluid,50238, -51061,"BICARBONATE, STOOL",STOOL,CHEMISTRY,,14040-0,"Carbon dioxide, total [Moles/volume] in Stool",50249,"LOINC code from old data probably wrong, should be bicarbonate" -51076,"BICARBONATE, URINE",URINE,CHEMISTRY,1964-6,13538-4,"Carbon dioxide, total [Moles/volume] in Urine",50279, -51464,BILIRUBIN,URINE,HEMATOLOGY,5770-3,1977-8,Bilirubin [Presence] in Urine,50626, -51465,BILIRUBIN CRYSTALS,URINE,HEMATOLOGY,5771-1,5771-1,Bilirubin crystals [Presence] in Urine sediment by Light microscopy,50625, -50883,"BILIRUBIN, DIRECT",BLOOD,CHEMISTRY,1968-7,1968-7,Direct bilirubin [Mass/volume] in Serum or Plasma,50096, -50884,"BILIRUBIN, INDIRECT",BLOOD,CHEMISTRY,1971-1,1971-1,Indirect bilirubin [Mass/volume] in Serum or Plasma,50127, -50885,"BILIRUBIN, TOTAL",BLOOD,CHEMISTRY,1975-2,1975-2,Bilirubin [Mass/volume] in Serum or Plasma,50170, -50838,"BILIRUBIN, TOTAL, ASCITES",ASCITES,CHEMISTRY,,14422-0,Bilirubin [Mass/volume] in Peritoneal fluid,50050, -51028,"BILIRUBIN, TOTAL, BODY FLUID",OTHER BODY FLUID,CHEMISTRY,,1974-5,Bilirubin [Mass/volume] in Body fluid,50228, -51012,"BILIRUBIN, TOTAL, CSF",CEREBROSPINAL FLUID (CSF),CHEMISTRY,,1973-7,Bilirubin [Mass/volume] in Cerebral spinal fluid,50205, -51049,"BILIRUBIN, TOTAL, PLEURAL",PLEURAL,CHEMISTRY,,14421-2,Bilirubin [Mass/volume] in Pleural fluid,50245, -51147,BITE CELLS,BLOOD,HEMATOLOGY,10371-3,10371-3,Bite cells [Presence] in Blood by Light microscopy,50334, -51113,BLASTS,ASCITES,HEMATOLOGY,,,,, -51148,BLASTS,BLOOD,HEMATOLOGY,708-8,26446-5,Blasts/100 leukocytes in Blood,50335, -51346,BLASTS,CEREBROSPINAL FLUID (CSF),HEMATOLOGY,,26447-3,Blasts/100 leukocytes in Cerebral spinal fluid,50512, -51443,BLASTS,PLEURAL,HEMATOLOGY,,,,50731, -51149,BLEEDING TIME,BLOOD,HEMATOLOGY,,11067-6,Bleeding time,50336, -51466,BLOOD,URINE,HEMATOLOGY,,5794-3,Hemoglobin [Presence] in Urine by Test strip,50627, -50886,BLOOD CULTURE HOLD,BLOOD,CHEMISTRY,,,,50074, -51150,BLOOD PARASITE SMEAR,BLOOD,HEMATOLOGY,,24429-3,Parasite identified in Blood,50425, -51460,"BLOOD, OCCULT",STOOL,HEMATOLOGY,,2335-8,Hemoglobin.gastrointestinal [Presence] in Stool,50619, -50887,BLUE TOP HOLD,BLOOD,CHEMISTRY,,,,50124, -50888,BLUE TOP HOLD FROZEN,BLOOD,CHEMISTRY,,,,50678, -51467,BROAD CASTS,URINE,HEMATOLOGY,,18487-9,Broad casts [#/area] in Urine sediment by Microscopy low power field,50628, -51151,BURR CELLS,BLOOD,HEMATOLOGY,7790-9,7790-9,Burr cells [Presence] in Blood by Light microscopy,50337, -51525,Billed,BLOOD,CHEMISTRY,,,,50679,"No match found in 01_2013 lab data dump, exists in mimic2v26, therefore the new LABEL was invented and an itemid was generated" -50889,C-REACTIVE PROTEIN,BLOOD,CHEMISTRY,1988-5,,,50091, -50890,C3,BLOOD,CHEMISTRY,,4485-9,Complement C3 [Mass/volume] in Serum or Plasma,50075, -50891,C4,BLOOD,CHEMISTRY,,4498-2,Complement C4 [Mass/volume] in Serum or Plasma,50076, -50892,CA-125,BLOOD,CHEMISTRY,,10334-1,Cancer Ag 125 [Units/volume] in Serum or Plasma,50077, -51468,CALCIUM CARBONATE CRYSTALS,URINE,HEMATOLOGY,5773-7,5773-7,Calcium carbonate crystals [Presence] in Urine sediment by Light microscopy,50629, -51469,CALCIUM OXALATE CRYSTALS,URINE,HEMATOLOGY,5774-5,5774-5,Calcium oxalate crystals [Presence] in Urine sediment by Light microscopy,50630, -51470,CALCIUM PHOSPHATE CRYSTALS,URINE,HEMATOLOGY,5775-2,5775-2,Calcium phosphate crystals [Presence] in Urine sediment by Light microscopy,50631, -51029,"CALCIUM, BODY FLUID",OTHER BODY FLUID,CHEMISTRY,,15155-5,Calcium [Mass/volume] in Body fluid,50213, -50893,"CALCIUM, TOTAL",BLOOD,CHEMISTRY,2000-8,17861-6,Calcium [Mass/volume] in Serum or Plasma,50079, -51077,"CALCIUM, URINE",URINE,CHEMISTRY,2004-0,17862-4,Calcium [Mass/volume] in Urine,50262, -50803,"CALCULATED BICARBONATE, WHOLE BLOOD",BLOOD,BLOOD GAS,,1959-6,Bicarbonate [Moles/volume] in Blood,50022, -50894,CALCULATED FREE TESTOSTERONE,BLOOD,CHEMISTRY,2991-8,2991-8,Testosterone Free [Mass/volume] in Serum or Plasma,50105, -50895,CALCULATED TBG,BLOOD,CHEMISTRY,3027-0,3027-0,Thyroxine/Thyroxine binding globulin [Mass ratio] in Serum or Plasma,50191, -50896,CALCULATED THYROXINE (T4) INDEX,BLOOD,CHEMISTRY,,32215-6,Thyroxine free index in Serum or Plasma,50164, -50804,CALCULATED TOTAL CO2,BLOOD,BLOOD GAS,,1959-6,Bicarbonate [Moles/volume] in Blood,50025, -50897,CALL,BLOOD,CHEMISTRY,,,,,"No ITEMID in mimic2v26, but exists in 2010 lab data dump" -50898,CANCER ANTIGEN 27.29,BLOOD,CHEMISTRY,,17842-6,Cancer Ag 27-29 [Units/volume] in Serum or Plasma,50078, -50899,CARBAMAZEPINE,BLOOD,CHEMISTRY,3432-2,3432-2,Carbamazepine [Mass/volume] in Serum or Plasma,50081, -50805,CARBOXYHEMOGLOBIN,BLOOD,BLOOD GAS,,20563-3,Carboxyhemoglobin/Hemoglobin.total in Blood,50003, -50900,CARCINOEMBYRONIC ANTIGEN (CEA),BLOOD,CHEMISTRY,2039-6,2039-6,Carcinoembryonic Ag [Mass/volume] in Serum or Plasma,50082, -51152,CD10,BLOOD,HEMATOLOGY,,8107-5,CD10 cells/100 cells in Blood,50338, -51303,CD10,BONE MARROW,HEMATOLOGY,,51216-0,CD10 cells/100 cells in Bone marrow,50474, -51388,CD10,OTHER BODY FLUID,HEMATOLOGY,,51217-8,CD10 cells/100 cells in Body fluid,50552, -51153,CD103,BLOOD,HEMATOLOGY,,,,50706, -51304,CD103,BONE MARROW,HEMATOLOGY,,51221-0,CD103 cells/100 cells in Bone marrow,50475, -51389,CD103,OTHER BODY FLUID,HEMATOLOGY,,,,50724, -51154,CD117,BLOOD,HEMATOLOGY,,17107-4,CD117 cells/100 cells in Blood,50339, -51305,CD117,BONE MARROW,HEMATOLOGY,,42866-4,CD117 cells/100 cells in Bone marrow,50476, -51390,CD117,OTHER BODY FLUID,HEMATOLOGY,,42867-2,CD117 cells/100 cells in Body fluid,50553, -51155,CD11C,BLOOD,HEMATOLOGY,,8109-1,CD11c cells/100 cells in Blood,50340, -51306,CD11C,BONE MARROW,HEMATOLOGY,,33202-3,CD11c cells/100 cells in Bone marrow,50477, -51391,CD11C,OTHER BODY FLUID,HEMATOLOGY,,51232-7,CD11c cells/100 cells in Body fluid,50554, -51156,CD13,BLOOD,HEMATOLOGY,,8110-9,CD13 cells/100 cells in Blood,50341, -51307,CD13,BONE MARROW,HEMATOLOGY,,51237-6,CD13 cells/100 cells in Bone marrow,50478, -51392,CD13,OTHER BODY FLUID,HEMATOLOGY,,51238-4,CD13 cells/100 cells in Body fluid,50555, -51157,CD138,BLOOD,HEMATOLOGY,,42869-8,CD138 cells/100 cells in Blood,50342, -51308,CD138,BONE MARROW,HEMATOLOGY,,,,50716, -51393,CD138,OTHER BODY FLUID,HEMATOLOGY,,42871-4,CD138 cells/100 cells in Body fluid,50556, -51158,CD14,BLOOD,HEMATOLOGY,,8111-7,CD14 cells/100 cells in Blood,50343, -51309,CD14,BONE MARROW,HEMATOLOGY,,32507-6,CD14 cells/100 cells in Bone marrow,50479, -51394,CD14,OTHER BODY FLUID,HEMATOLOGY,,51248-3,CD14 cells/100 cells in Body fluid,50557, -51159,CD15,BLOOD,HEMATOLOGY,,17117-3,CD15 cells/100 cells in Blood,50344, -51310,CD15,BONE MARROW,HEMATOLOGY,,51251-7,CD15 cells/100 cells in Bone marrow,50480, -51395,CD15,OTHER BODY FLUID,HEMATOLOGY,,51252-5,CD15 cells/100 cells in Body fluid,50558, -51160,CD15/56,BLOOD,HEMATOLOGY,,18267-5,CD16+CD56+ cells/100 cells in Blood,50346, -51161,CD16,BLOOD,HEMATOLOGY,,8115-8,CD16 cells/100 cells in Blood,50345, -51311,CD16,BONE MARROW,HEMATOLOGY,,,,50481, -51396,CD16,OTHER BODY FLUID,HEMATOLOGY,,,,50725, -51312,CD16/56,BONE MARROW,HEMATOLOGY,,51255-8,CD16+CD56+ cells/100 cells in Bone marrow,50482, -51397,CD16/56,OTHER BODY FLUID,HEMATOLOGY,,18268-3,CD16+CD56+ cells/100 cells in Body fluid,50559, -51162,CD16/56 ABSOLUTE COUNT,BLOOD,HEMATOLOGY,,,,,look for CD16+CD56 [#/volume] LOINC -51163,CD16/56%,BLOOD,HEMATOLOGY,,18267-5,CD16+CD56+ cells/100 cells in Blood,50346, -51164,CD19,BLOOD,HEMATOLOGY,,8117-4,CD19 cells/100 cells in Blood,50347, -51313,CD19,BONE MARROW,HEMATOLOGY,,32525-8,CD19 cells/100 cells in Bone marrow,50483, -51398,CD19,OTHER BODY FLUID,HEMATOLOGY,,17829-3,CD19 cells/100 cells in Body fluid,50560, -51165,CD19 %,BLOOD,HEMATOLOGY,,,,, -51166,CD19 ABSOLUTE COUNT,BLOOD,HEMATOLOGY,,,,, -51167,CD2,BLOOD,HEMATOLOGY,,8118-2,CD2 cells/100 cells in Blood,50348, -51314,CD2,BONE MARROW,HEMATOLOGY,,32527-4,CD2 cells/100 cells in Bone marrow,50484, -51399,CD2,OTHER BODY FLUID,HEMATOLOGY,,17827-7,CD2 cells/100 cells in Body fluid,50561, -51168,CD20,BLOOD,HEMATOLOGY,,8119-0,CD20 cells/100 cells in Blood,50349, -51315,CD20,BONE MARROW,HEMATOLOGY,,,,50485, -51400,CD20,OTHER BODY FLUID,HEMATOLOGY,,57418-6,CD20 cells/100 cells in Body fluid,50562, -51169,CD20 %,BLOOD,HEMATOLOGY,,,,, -51170,CD20 ABSOLUTE COUNT,BLOOD,HEMATOLOGY,,,,, -51171,CD22,BLOOD,HEMATOLOGY,,14017-8,CD22 cells/100 cells in Blood,50350, -51316,CD22,BONE MARROW,HEMATOLOGY,,,,50717, -51401,CD22,OTHER BODY FLUID,HEMATOLOGY,,42875-5,CD22 cells/100 cells in Body fluid,50563, -51172,CD23,BLOOD,HEMATOLOGY,,14018-6,CD23 cells/100 cells in Blood,50351, -51317,CD23,BONE MARROW,HEMATOLOGY,,51268-1,CD23 cells/100 cells in Bone marrow,50486, -51402,CD23,OTHER BODY FLUID,HEMATOLOGY,,51269-9,CD23 cells/100 cells in Body fluid,50564, -51173,CD25,BLOOD,HEMATOLOGY,,,,50707, -51318,CD25,BONE MARROW,HEMATOLOGY,,,,50718, -51403,CD25,OTHER BODY FLUID,HEMATOLOGY,,,,50726, -51319,CD3,BONE MARROW,HEMATOLOGY,,32529-0,CD3 cells/100 cells in Bone marrow,50487, -51404,CD3,OTHER BODY FLUID,HEMATOLOGY,,17826-9,CD3 cells/100 cells in Body fluid,50565, -51174,CD3 %,BLOOD,HEMATOLOGY,,,,, -51175,CD3 ABSOLUTE COUNT,BLOOD,HEMATOLOGY,,8122-4,CD3 cells [#/volume] in Blood,50317, -51176,"CD3 CELLS, PERCENT",BLOOD,HEMATOLOGY,8122-4,8124-0,CD3 cells/100 cells in Blood,50352, -51177,CD33,BLOOD,HEMATOLOGY,,8102-6,CD33 cells/100 cells in Blood,50353, -51320,CD33,BONE MARROW,HEMATOLOGY,,51293-9,CD33 cells/100 cells in Bone marrow,50488, -51405,CD33,OTHER BODY FLUID,HEMATOLOGY,,51294-7,CD33 cells/100 cells in Body fluid,50566, -51178,CD34,BLOOD,HEMATOLOGY,5465-0,8125-7,CD34 cells/100 cells in Blood,50354, -51321,CD34,BONE MARROW,HEMATOLOGY,,57400-4,CD34 cells/100 cells in Bone marrow,50489, -51406,CD34,OTHER BODY FLUID,HEMATOLOGY,,,,50567, -51179,CD38,BLOOD,HEMATOLOGY,,8126-5,CD38 cells/100 cells in Blood,50355, -51322,CD38,BONE MARROW,HEMATOLOGY,,,,50719, -51407,CD38,OTHER BODY FLUID,HEMATOLOGY,,51299-6,CD38 cells/100 cells in Body fluid,50568, -51323,CD4,BONE MARROW,HEMATOLOGY,,51301-0,CD4 cells/100 cells in Bone marrow,50490, -51408,CD4,OTHER BODY FLUID,HEMATOLOGY,,17822-8,CD4 cells/100 cells in Body fluid,50569, -51180,"CD4 CELLS, PERCENT",BLOOD,HEMATOLOGY,8127-3,8127-3,CD4 cells [#/volume] in Blood,50318, -51181,CD4/CD8 RATIO,BLOOD,HEMATOLOGY,,8129-9,CD4 cells/CD8 Cells [# Ratio] in Blood,50357, -51409,CD4/CD8 RATIO,OTHER BODY FLUID,HEMATOLOGY,,18266-7,CD4 cells/CD8 Cells [# Ratio] in Body fluid,50570, -51182,CD41,BLOOD,HEMATOLOGY,,17148-8,CD41 cells/100 cells in Blood,50358, -51324,CD41,BONE MARROW,HEMATOLOGY,,51319-2,CD41 cells/100 cells in Bone marrow,50491, -51410,CD41,OTHER BODY FLUID,HEMATOLOGY,,51320-0,CD41 cells/100 cells in Body fluid,50571, -51183,CD45,BLOOD,HEMATOLOGY,,8130-7,CD45 cells/100 cells in Blood,50359, -51325,CD45,BONE MARROW,HEMATOLOGY,,51340-8,CD45 cells/100 cells in Bone marrow,50492, -51411,CD45,OTHER BODY FLUID,HEMATOLOGY,,17823-6,CD45 cells/100 cells in Body fluid,50572, -51184,CD5,BLOOD,HEMATOLOGY,,8132-3,CD5 cells/100 cells in Blood,50360, -51326,CD5,BONE MARROW,HEMATOLOGY,,35640-2,CD5 cells/100 cells in Bone marrow,50493, -51412,CD5,OTHER BODY FLUID,HEMATOLOGY,,57423-6,CD5 cells/100 cells in Body fluid,50573, -51185,CD5 %,BLOOD,HEMATOLOGY,,,,, -51186,CD5 ABSOLUTE COUNT,BLOOD,HEMATOLOGY,,,,,look for CD5 [#/volume] LOINC -51187,CD55,BLOOD,HEMATOLOGY,,17175-1,CD55 cells/100 cells in Blood,50361, -51327,CD55,BONE MARROW,HEMATOLOGY,,51344-0,CD55 cells/100 cells in Bone marrow,50494, -51535,CD55,OTHER BODY FLUID,HEMATOLOGY,,,,50727,"No match found in 01_2013 lab data dump, exists in mimic2v26, therefore the new LABEL was invented and an itemid was generated" -51188,CD56,BLOOD,HEMATOLOGY,,8133-1,CD56 cells/100 cells in Blood,50362, -51328,CD56,BONE MARROW,HEMATOLOGY,,,,50495, -51413,CD56,OTHER BODY FLUID,HEMATOLOGY,,57424-4,CD56 cells/100 cells in Body fluid,50574, -51189,CD57,BLOOD,HEMATOLOGY,,,,50708, -51329,CD57,BONE MARROW,HEMATOLOGY,,,,50720, -51414,CD57,OTHER BODY FLUID,HEMATOLOGY,,,,50728, -51190,CD59,BLOOD,HEMATOLOGY,,17177-7,CD59 cells/100 cells in Blood,50363, -51330,CD59,BONE MARROW,HEMATOLOGY,,51358-0,CD59 cells/100 cells in Bone marrow,50496, -51536,CD59,OTHER BODY FLUID,HEMATOLOGY,,,,50729,"No match found in 01_2013 lab data dump, exists in mimic2v26, therefore the new LABEL was invented and an itemid was generated" -51191,CD64,BLOOD,HEMATOLOGY,,17183-5,CD64 cells/100 cells in Blood,50364, -51331,CD64,BONE MARROW,HEMATOLOGY,,51365-5,CD64 cells/100 cells in Bone marrow,50497, -51415,CD64,OTHER BODY FLUID,HEMATOLOGY,,51366-3,CD64 cells/100 cells in Body fluid,50575, -51192,CD7,BLOOD,HEMATOLOGY,,8135-6,CD7 cells/100 cells in Blood,50365, -51332,CD7,BONE MARROW,HEMATOLOGY,,35641-0,CD7 cells/100 cells in Bone marrow,50498, -51416,CD7,OTHER BODY FLUID,HEMATOLOGY,,57425-1,CD7 cells/100 cells in Body fluid,50576, -51193,CD71,BLOOD,HEMATOLOGY,,8136-4,CD71 cells/100 cells in Blood,50366, -51333,CD71,BONE MARROW,HEMATOLOGY,,,,50499, -51417,CD71,OTHER BODY FLUID,HEMATOLOGY,,57426-9,CD71 cells/100 cells in Body fluid,50577, -51334,CD8,BONE MARROW,HEMATOLOGY,,51372-1,CD8+HLA-DR+ cells/100 cells in Bone marrow,50500, -51418,CD8,OTHER BODY FLUID,HEMATOLOGY,,17824-4,CD8 cells/100 cells in Body fluid,50578, -51194,"CD8 CELLS, PERCENT",BLOOD,HEMATOLOGY,8137-2,8137-2,CD8 cells [#/volume] in Blood,50319, -51471,CELLULAR CAST,URINE,HEMATOLOGY,,,,50632, -50901,CENTROMERE,BLOOD,CHEMISTRY,,16570-4,Centromere Ab [Presence] in Serum by Immunofluorescence,50088, -50902,CHLORIDE,BLOOD,CHEMISTRY,2075-0,2069-3,Chloride [Moles/volume] in Blood,50083, -50839,"CHLORIDE, ASCITES",ASCITES,CHEMISTRY,,33366-6,Chloride [Moles/volume] in Peritoneal fluid,50039, -51030,"CHLORIDE, BODY FLUID",OTHER BODY FLUID,CHEMISTRY,,2072-7,Chloride [Moles/volume] in Body fluid,50214, -51013,"CHLORIDE, CSF",CEREBROSPINAL FLUID (CSF),CHEMISTRY,,,,, -51050,"CHLORIDE, PLEURAL",PLEURAL,CHEMISTRY,,53627-6,Chloride [Moles/volume] in Pleural fluid,50236, -51062,"CHLORIDE, STOOL",STOOL,CHEMISTRY,,15158-9,Chloride [Moles/volume] in Stool,50248, -51078,"CHLORIDE, URINE",URINE,CHEMISTRY,2078-4,2078-4,Chloride [Moles/volume] in Urine,50263, -50806,"CHLORIDE, WHOLE BLOOD",BLOOD,BLOOD GAS,,2069-3,Chloride [Moles/volume] in Blood,50004, -51472,CHOLESTEROL CRYSTALS,URINE,HEMATOLOGY,5777-8,,,50732, -50903,CHOLESTEROL RATIO (TOTAL/HDL),BLOOD,CHEMISTRY,9322-9,32309-7,Cholesterol.total/Cholesterol.in HDL [Molar ratio] in Serum or Plasma,50084, -50840,"CHOLESTEROL, ASCITES",ASCITES,CHEMISTRY,,14441-0,Cholesterol [Mass/volume] in Peritoneal fluid,50040, -51031,"CHOLESTEROL, BODY FLUID",OTHER BODY FLUID,CHEMISTRY,,12183-0,Cholesterol [Mass/volume] in Body fluid,50215, -50904,"CHOLESTEROL, HDL",BLOOD,CHEMISTRY,2086-7,2085-9,Cholesterol.in HDL [Mass/volume] in Serum or Plasma,50122, -50905,"CHOLESTEROL, LDL, CALCULATED",BLOOD,CHEMISTRY,2090-9,13457-7,Cholesterol.in LDL [Mass/volume] in Serum or Plasma by calculation,50135, -50906,"CHOLESTEROL, LDL, MEASURED",BLOOD,CHEMISTRY,,18262-6,Cholesterol.in LDL [Mass/volume] in Serum or Plasma by Direct assay,50136, -51051,"CHOLESTEROL, PLEURAL",PLEURAL,CHEMISTRY,,9618-0,Cholesterol [Mass/volume] in Pleural fluid,50237, -50907,"CHOLESTEROL, TOTAL",BLOOD,CHEMISTRY,2093-3,2093-3,Cholesterol [Mass/volume] in Serum or Plasma,50085, -50908,CK-MB INDEX,BLOOD,CHEMISTRY,,20569-0,Creatine kinase.MB/Creatine kinase.total in Serum or Plasma,50141, -51079,"COCAINE, URINE",URINE,CHEMISTRY,,3397-7,Cocaine [Presence] in Urine,50292, -51195,COLLAGEN,BLOOD,HEMATOLOGY,,,,50369, -50807,COMMENTS,BLOOD,BLOOD GAS,,,,,"No ITEMID in mimic2v26, but exists in 2010 lab data dump" -50909,CORTISOL,BLOOD,CHEMISTRY,2143-6,2143-6,Cortisol [Mass/volume] in Serum or Plasma,50089, -50910,CREATINE KINASE (CK),BLOOD,CHEMISTRY,2157-6,2157-6,Creatine kinase [Enzymatic activity/volume] in Serum or Plasma,50086, -50911,"CREATINE KINASE, MB ISOENZYME",BLOOD,CHEMISTRY,6773-6,49551-5,Creatine kinase.MB [Mass/volume] in Blood,50087, -50912,CREATININE,BLOOD,CHEMISTRY,2160-0,2160-0,Creatinine [Mass/volume] in Serum or Plasma,50090, -51080,CREATININE CLEARANCE,URINE,CHEMISTRY,,33558-8,Creatinine renal clearance in unspecified time,50265, -50841,"CREATININE, ASCITES",ASCITES,CHEMISTRY,,12191-3,Creatinine [Mass/volume] in Peritoneal fluid,50042, -51032,"CREATININE, BODY FLUID",OTHER BODY FLUID,CHEMISTRY,,12190-5,Creatinine [Mass/volume] in Body fluid,50216, -51021,"CREATININE, JOINT FLUID",JOINT FLUID,CHEMISTRY,,14401-4,Creatinine [Mass/volume] in Synovial fluid,50207, -51052,"CREATININE, PLEURAL",PLEURAL,CHEMISTRY,,14399-0,Creatinine [Mass/volume] in Pleural fluid,50239, -51081,"CREATININE, SERUM",URINE,CHEMISTRY,,,,50257, -51082,"CREATININE, URINE",URINE,CHEMISTRY,2161-8,2161-8,Creatinine [Mass/volume] in Urine,50264, -50913,CRYOGLOBULIN,BLOOD,CHEMISTRY,,5117-7,Cryoglobulin [Presence] in Serum,50092, -50914,CYCLOSPORIN,BLOOD,CHEMISTRY,3521-2,3520-4,Cyclosporine [Mass/volume] in Blood,50093, -51473,CYSTEINE CRYSTALS,URINE,HEMATOLOGY,5782-8,34277-4,Cysteine [Moles/volume] in Urine,50635, -50915,D-DIMER,BLOOD,CHEMISTRY,,,,,"No ITEMID in mimic2v26, but exists in 2010 lab data dump" -51196,D-DIMER,BLOOD,HEMATOLOGY,,48065-7,Fibrin D-dimer FEU [Mass/volume] in Platelet poor plasma,50370, -50916,DHEA-SULFATE,BLOOD,CHEMISTRY,2191-5,2191-5,Dehydroepiandrosterone sulfate [Mass/volume] in Serum or Plasma,50094, -50917,DIGOXIN,BLOOD,CHEMISTRY,10535-3,10535-3,Digoxin [Mass/volume] in Serum or Plasma,50095, -50918,DOUBLE STRANDED DNA,BLOOD,CHEMISTRY,,5130-0,DNA double strand Ab [Units/volume] in Serum,50192, -50919,EDTA HOLD,BLOOD,CHEMISTRY,,,,50097, -51197,ELLIPTOCYTES,BLOOD,HEMATOLOGY,6681-1,11274-8,Elliptocytes [Presence] in Blood by Light microscopy,50371, -51198,ENVELOPE CELLS,BLOOD,HEMATOLOGY,681-7,,,50372, -51199,EOSINOPHIL COUNT,BLOOD,HEMATOLOGY,,26449-9,Eosinophils [#/volume] in Blood,50374, -51114,EOSINOPHILS,ASCITES,HEMATOLOGY,,30380-0,Eosinophils/100 leukocytes in Peritoneal fluid,50301, -51200,EOSINOPHILS,BLOOD,HEMATOLOGY,711-2,26450-7,Eosinophils/100 leukocytes in Blood,50373, -51347,EOSINOPHILS,CEREBROSPINAL FLUID (CSF),HEMATOLOGY,,26451-5,Eosinophils/100 leukocytes in Cerebral spinal fluid,50513, -51368,EOSINOPHILS,JOINT FLUID,HEMATOLOGY,,17834-3,Eosinophils/100 leukocytes in Synovial fluid,50534, -51419,EOSINOPHILS,OTHER BODY FLUID,HEMATOLOGY,,26452-3,Eosinophils/100 leukocytes in Body fluid,50579, -51444,EOSINOPHILS,PLEURAL,HEMATOLOGY,,30379-2,Eosinophils/100 leukocytes in Pleural fluid,50603, -51474,EOSINOPHILS,URINE,HEMATOLOGY,,25156-1,Eosinophils [Presence] in Urine sediment by Light microscopy,50636, -51201,EPINEPHERINE,BLOOD,HEMATOLOGY,,,,50375, -51475,EPITHELIAL CASTS,URINE,HEMATOLOGY,,,,50733, -51476,EPITHELIAL CELLS,URINE,HEMATOLOGY,5787-7,5787-7,Epithelial cells [#/area] in Urine sediment by Microscopy high power field,50637, -50920,ESTIMATED GFR (MDRD EQUATION),BLOOD,CHEMISTRY,,33914-3,Glomerular filtration rate/1.73 sq M.predicted by Creatinine-based formula (MDRD),50193, -50921,ESTRADIOL,BLOOD,CHEMISTRY,,2243-4,Estradiol [Mass/volume] in Serum or Plasma,50098, -50922,ETHANOL,BLOOD,CHEMISTRY,5642-4,5640-8,Ethanol [Mass/volume] in Blood,50099, -51083,"ETHANOL, URINE",URINE,CHEMISTRY,,5644-0,Ethanol [Presence] in Urine,50293, -51202,FACTOR II,BLOOD,HEMATOLOGY,3289-6,3289-6,Prothrombin activity actual/normal in Platelet poor plasma by Coagulation assay,50397,this loinc code seems completely wrong -51203,FACTOR IX,BLOOD,HEMATOLOGY,3188-0,3187-2,Coagulation factor IX activity actual/normal in Platelet poor plasma by Coagulation assay,50401, -51204,FACTOR V,BLOOD,HEMATOLOGY,,3193-0,Coagulation factor V activity actual/normal in Platelet poor plasma by Coagulation assay,50461, -51205,FACTOR VII,BLOOD,HEMATOLOGY,,3198-9,Coagulation factor VII activity actual/normal in Platelet poor plasma by Coagulation assay,50462, -51206,FACTOR VIII,BLOOD,HEMATOLOGY,,3209-4,Coagulation factor VIII activity actual/normal in Platelet poor plasma by Coagulation assay,50463, -51207,FACTOR VIII INHIBITOR,BLOOD,HEMATOLOGY,,3204-5,Coagulation factor VIII inhibitor [Units/volume] in Platelet poor plasma by Coagulation assay,50464, -51208,FACTOR X,BLOOD,HEMATOLOGY,3218-5,,,50469, -51209,FACTOR XI,BLOOD,HEMATOLOGY,3226-8,3226-8,Coagulation factor XI activity actual/normal in Platelet poor plasma by Coagulation assay,50470, -51210,FACTOR XII,BLOOD,HEMATOLOGY,,3232-6,Coagulation factor XII activity actual/normal in Platelet poor plasma by Coagulation assay,50471, -51211,FACTOR XIII,BLOOD,HEMATOLOGY,3240-9,27815-0,Coagulation factor XIII activity actual/normal in Platelet poor plasma by Chromogenic method,50472, -51524,FATTY,URINE,HEMATOLOGY,,5789-3,Fatty casts [#/area] in Urine sediment by Microscopy low power field,50639,"No match found in 01_2013 lab data dump, exists in mimic2v26, therefore the new LABEL was invented and an itemid was generated" -50923,FAX,BLOOD,CHEMISTRY,,,,,"No ITEMID in mimic2v26, but exists in 2010 lab data dump" -50924,FERRITIN,BLOOD,CHEMISTRY,2276-4,2276-4,Ferritin [Mass/volume] in Serum or Plasma,50101, -51212,FETAL HEMOGLOBIN,BLOOD,HEMATOLOGY,,4576-5,Hemoglobin F/Hemoglobin.total in Blood,50377, -51033,FETALFN,OTHER BODY FLUID,CHEMISTRY,,,,50701, -51213,FIBRIN DEGRADATION PRODUCTS,BLOOD,HEMATOLOGY,,30226-5,Fibrin+Fibrinogen fragments [Mass/volume] in Platelet poor plasma,50376, -51214,"FIBRINOGEN, FUNCTIONAL",BLOOD,HEMATOLOGY,,3255-7,Fibrinogen [Mass/volume] in Platelet poor plasma by Coagulation assay,50378, -50829,FLUID TYPE,OTHER BODY FLUID,BLOOD GAS,,14725-6,[Type] of Body fluid,50031, -51215,FMC-7,BLOOD,HEMATOLOGY,,17220-5,FMC7 cells/100 cells in Blood,50379, -51335,FMC-7,BONE MARROW,HEMATOLOGY,,,,50501, -51420,FMC-7,OTHER BODY FLUID,HEMATOLOGY,,57428-5,FMC7 cells/100 cells in Body fluid,50580, -50925,FOLATE,BLOOD,CHEMISTRY,2284-8,2284-8,Folate [Mass/volume] in Serum or Plasma,50103, -50926,FOLLICLE STIMULATING HORMONE,BLOOD,CHEMISTRY,2286-3,15067-2,Follitropin [Units/volume] in Serum or Plasma,50106, -51216,FRAGMENTED CELLS,BLOOD,HEMATOLOGY,10373-9,40746-0,Fragments [Presence] in Blood by Automated count,50380, -50808,FREE CALCIUM,BLOOD,BLOOD GAS,,1994-3,Calcium.ionized [Moles/volume] in Blood,50030, -51477,FREE FAT,URINE,HEMATOLOGY,,53359-6,Fat [#/area] in Urine by Computer assisted method,50640, -51526,FRUCAMN+,BLOOD,CHEMISTRY,,,,50683,"No match found in 01_2013 lab data dump, exists in mimic2v26, therefore the new LABEL was invented and an itemid was generated" -50927,GAMMA GLUTAMYLTRANSFERASE,BLOOD,CHEMISTRY,2324-2,2324-2,Gamma glutamyl transferase [Enzymatic activity/volume] in Serum or Plasma,50109, -50928,GASTRIN,BLOOD,CHEMISTRY,2333-3,2333-3,Gastrin [Mass/volume] in Serum or Plasma,50107, -50929,GENTAMICIN,BLOOD,CHEMISTRY,3664-0,35668-3,Gentamicin [Mass/volume] in Serum or Plasma,50108, -50930,GLOBULIN,BLOOD,CHEMISTRY,2336-6,2336-6,Globulin [Mass/volume] in Serum,50111, -50809,GLUCOSE,BLOOD,BLOOD GAS,,2339-0,Glucose [Mass/volume] in Blood,50006, -50931,GLUCOSE,BLOOD,CHEMISTRY,6777-7,2345-7,Glucose [Mass/volume] in Serum or Plasma,50112, -51478,GLUCOSE,URINE,HEMATOLOGY,5792-7,2350-7,Glucose [Mass/volume] in Urine,50641, -50842,"GLUCOSE, ASCITES",ASCITES,CHEMISTRY,,2347-3,Glucose [Mass/volume] in Peritoneal fluid,50043, -51034,"GLUCOSE, BODY FLUID",OTHER BODY FLUID,CHEMISTRY,,2344-0,Glucose [Mass/volume] in Body fluid,50217, -51014,"GLUCOSE, CSF",CEREBROSPINAL FLUID (CSF),CHEMISTRY,,2342-4,Glucose [Mass/volume] in Cerebral spinal fluid,50201, -51022,"GLUCOSE, JOINT FLUID",JOINT FLUID,CHEMISTRY,,2348-1,Glucose [Mass/volume] in Synovial fluid,50208, -51053,"GLUCOSE, PLEURAL",PLEURAL,CHEMISTRY,,2346-5,Glucose [Mass/volume] in Pleural fluid,50240, -51084,"GLUCOSE, URINE",URINE,CHEMISTRY,,2350-7,Glucose [Mass/volume] in Urine,50266, -51217,GLYCO A,BLOOD,HEMATOLOGY,,17221-3,CD235a cells/100 cells in Blood,50381, -51336,GLYCO A,BONE MARROW,HEMATOLOGY,,33208-0,CD235a cells/100 cells in Bone marrow,50502, -51421,GLYCO A,OTHER BODY FLUID,HEMATOLOGY,,57430-1,CD235a cells/100 cells in Body fluid,50581, -51523,GR HOLD,URINE,CHEMISTRY,,,,50267,"No match found in 01_2013 lab data dump, exists in mimic2v26, therefore the new LABEL was invented and an itemid was generated" -51479,GRANULAR CASTS,URINE,HEMATOLOGY,5793-5,5793-5,Granular casts [#/area] in Urine sediment by Microscopy low power field,50642, -51218,GRANULOCYTE COUNT,BLOOD,HEMATOLOGY,,30394-1,Granulocytes [#/volume] in Blood,50382, -50932,GRAY TOP HOLD (PLASMA),BLOOD,CHEMISTRY,,,,50114, -50933,GREEN TOP HOLD (PLASMA),BLOOD,CHEMISTRY,,,,50113, -50934,H,BLOOD,CHEMISTRY,,,,50684, -51219,H/O SMEAR,BLOOD,HEMATOLOGY,,38924-7,TBX5 gene mutations found [Identifier] in Blood or Tissue by Molecular genetics method Nominal,50393,this loinc code looks super suspicious -50935,HAPTOGLOBIN,BLOOD,CHEMISTRY,4542-7,4542-7,Haptoglobin [Mass/volume] in Serum or Plasma,50115, -50936,"HCG, MATERNAL SCREEN",BLOOD,CHEMISTRY,,,,50689, -51085,"HCG, URINE, QUALITATIVE",URINE,CHEMISTRY,,2106-3,Choriogonadotropin [Presence] in Urine,50281, -51220,HEINZ BODY PREP,BLOOD,HEMATOLOGY,,48068-1,Heinz bodies [Presence] in Blood,50384, -51221,HEMATOCRIT,BLOOD,HEMATOLOGY,4544-3,20570-8,Hematocrit [Volume Fraction] of Blood,50383, -51480,HEMATOCRIT,URINE,HEMATOLOGY,,17809-5,Hematocrit [Volume Fraction] of Urine,50643, -51115,"HEMATOCRIT, ASCITES",ASCITES,HEMATOLOGY,,,,50302, -50810,"HEMATOCRIT, CALCULATED",BLOOD,BLOOD GAS,,20570-8,Hematocrit [Volume Fraction] of Blood,50029, -51348,"HEMATOCRIT, CSF",CEREBROSPINAL FLUID (CSF),HEMATOLOGY,,30398-2,Hematocrit [Volume Fraction] of Cerebral spinal fluid,50514, -51369,"HEMATOCRIT, JOINT FLUID",JOINT FLUID,HEMATOLOGY,,,,50535, -51422,"HEMATOCRIT, OTHER FLUID",OTHER BODY FLUID,HEMATOLOGY,,11153-4,Hematocrit [Volume Fraction] of Body fluid,50582, -51445,"HEMATOCRIT, PLEURAL",PLEURAL,HEMATOLOGY,,,,50604, -50811,HEMOGLOBIN,BLOOD,BLOOD GAS,,718-7,Hemoglobin [Mass/volume] in Blood,50007, -51222,HEMOGLOBIN,BLOOD,HEMATOLOGY,718-7,718-7,Hemoglobin [Mass/volume] in Blood,50386, -51223,HEMOGLOBIN A2,BLOOD,HEMATOLOGY,4552-6,4551-8,Hemoglobin A2/Hemoglobin.total in Blood,50388, -51224,HEMOGLOBIN C,BLOOD,HEMATOLOGY,4561-7,4563-3,Hemoglobin C/Hemoglobin.total in Blood,50389, -51225,HEMOGLOBIN F,BLOOD,HEMATOLOGY,9749-3,4576-5,Hemoglobin F/Hemoglobin.total in Blood,50390, -51226,HEMOGLOBLIN A,BLOOD,HEMATOLOGY,10346-5,4546-8,Hemoglobin A/Hemoglobin.total in Blood,50387, -51227,HEMOGLOBLIN S,BLOOD,HEMATOLOGY,4622-7,4625-0,Hemoglobin S/Hemoglobin.total in Blood,50391, -51481,HEMOSIDERIN,URINE,HEMATOLOGY,,4644-1,Hemosiderin [Presence] in Urine,50644, -51228,HEPARIN,BLOOD,HEMATOLOGY,,13055-9,Heparin [Units/volume] in Platelet poor plasma,50385, -51229,"HEPARIN, LMW",BLOOD,HEMATOLOGY,,32684-3,LMW Heparin [Units/volume] in Platelet poor plasma,50405, -50937,HEPATITIS A VIRUS ANTIBODY,BLOOD,CHEMISTRY,5183-9,20575-7,Hepatitis A virus Ab [Presence] in Serum,50116, -50938,HEPATITIS A VIRUS IGM ANTIBODY,BLOOD,CHEMISTRY,,22314-9,Hepatitis A virus IgM Ab [Presence] in Serum,50132, -50939,"HEPATITIS B CORE ANTIBODY, IGM",BLOOD,CHEMISTRY,,31204-1,Hepatitis B virus core IgM Ab [Presence] in Serum,50133, -50940,HEPATITIS B SURFACE ANTIBODY,BLOOD,CHEMISTRY,5193-8,32019-2,Hepatitis B virus surface Ab [Titer] in Serum,50118, -50941,HEPATITIS B SURFACE ANTIGEN,BLOOD,CHEMISTRY,5196-1,5195-3,Hepatitis B virus surface Ag [Presence] in Serum,50119, -50942,HEPATITIS B VIRUS CORE ANTIBODY,BLOOD,CHEMISTRY,5187-0,16933-4,Hepatitis B virus core Ab [Presence] in Serum,50117, -50943,HEPATITIS C VIRUS ANTIBODY,BLOOD,CHEMISTRY,16128-1,16128-1,Hepatitis C virus Ab [Presence] in Serum,50121, -50944,HIV ANTIBODY,BLOOD,CHEMISTRY,5220-9,,,50123, -51230,HLA-DR,BLOOD,HEMATOLOGY,,32621-5,HLA-DR [Presence],50392, -51337,HLA-DR,BONE MARROW,HEMATOLOGY,,51380-4,HLA-DR+ cells/100 cells in Bone marrow,50503, -51423,HLA-DR,OTHER BODY FLUID,HEMATOLOGY,,51381-2,HLA-DR+ cells/100 cells in Body fluid,50583, -50945,HOMOCYSTEINE,BLOOD,CHEMISTRY,13965-9,20646-6,Homocystine [Moles/volume] in Serum or Plasma,50125, -51231,HOWELL-JOLLY BODIES,BLOOD,HEMATOLOGY,7793-3,7793-3,Howell-Jolly bodies [Presence] in Blood by Light microscopy,50394, -50946,HUMAN CHORIONIC GONADOTROPIN,BLOOD,CHEMISTRY,2119-6,19080-1,Choriogonadotropin [Units/volume] in Serum or Plasma,50120, -51482,HYALINE CASTS,URINE,HEMATOLOGY,5796-8,5796-8,Hyaline casts [#/area] in Urine sediment by Microscopy low power field,50645, -51232,HYPERSEGMENTED NEUTROPHILS,BLOOD,HEMATOLOGY,766-6,30450-1,Neutrophils.hypersegmented/100 leukocytes in Blood,50395, -51349,HYPERSEGMENTED NEUTROPHILS,CEREBROSPINAL FLUID (CSF),HEMATOLOGY,,26506-6,Neutrophils.segmented/100 leukocytes in Cerebral spinal fluid,50515, -51483,HYPHENATED YEAST,URINE,HEMATOLOGY,,41865-7,Yeast.hyphae [Presence] in Urine sediment by Light microscopy,50646, -51233,HYPOCHROMIA,BLOOD,HEMATOLOGY,728-6,728-6,Hypochromia [Presence] in Blood by Light microscopy,50396, -50947,I,BLOOD,CHEMISTRY,,,,50685, -50948,IMMUNOFIXATION,BLOOD,CHEMISTRY,,49275-1,Immunofixation [interpretation] for Serum or Plasma Narrative,50126, -51086,"IMMUNOFIXATION, URINE",URINE,CHEMISTRY,,49276-9,Immunofixation [interpretation] for Urine Narrative,50269, -50949,IMMUNOGLOBULIN A,BLOOD,CHEMISTRY,2458-8,2458-8,IgA [Mass/volume] in Serum,50129, -50950,IMMUNOGLOBULIN G,BLOOD,CHEMISTRY,2465-3,2465-3,IgG [Mass/volume] in Serum,50130, -50951,IMMUNOGLOBULIN M,BLOOD,CHEMISTRY,2472-9,2472-9,IgM [Mass/volume] in Serum,50131, -51234,IMMUNOPHENOTYPING,BLOOD,HEMATOLOGY,,,,50400, -51338,IMMUNOPHENOTYPING,BONE MARROW,HEMATOLOGY,,,,50504, -51350,IMMUNOPHENOTYPING,CEREBROSPINAL FLUID (CSF),HEMATOLOGY,,,,50721, -51424,IMMUNOPHENOTYPING,OTHER BODY FLUID,HEMATOLOGY,,,,50584, -51235,INHIBITOR SCREEN,BLOOD,HEMATOLOGY,,,,50398, -51236,INPATIENT HEMATOLOGY/ONCOLOGY SMEAR,BLOOD,HEMATOLOGY,,,,50709, -51237,INR(PT),BLOOD,HEMATOLOGY,5895-7,34714-6,INR in Blood by Coagulation assay,50399, -50812,INTUBATED,BLOOD,BLOOD GAS,,,,50008, -50952,IRON,BLOOD,CHEMISTRY,2498-4,2498-4,Iron [Mass/volume] in Serum or Plasma,50128, -50953,"IRON BINDING CAPACITY, TOTAL",BLOOD,CHEMISTRY,,2500-7,Iron binding capacity [Mass/volume] in Serum or Plasma,50190, -51339,IRON STAIN,BONE MARROW,HEMATOLOGY,,13513-7,Iron.microscopic observation [Identifier] in Bone marrow by Potassium ferrocyanide stain,50505, -51370,"JOINT CRYSTALS, BIREFRINGENCE",JOINT FLUID,HEMATOLOGY,,,,50532, -51371,"JOINT CRYSTALS, COMMENT",JOINT FLUID,HEMATOLOGY,,,,,"No ITEMID in mimic2v26, but exists in 2010 lab data dump" -51372,"JOINT CRYSTALS, LOCATION",JOINT FLUID,HEMATOLOGY,,,,50536, -51373,"JOINT CRYSTALS, NUMBER",JOINT FLUID,HEMATOLOGY,,,,50543, -51374,"JOINT CRYSTALS, SHAPE",JOINT FLUID,HEMATOLOGY,,,,50547, -51238,KAPPA,BLOOD,HEMATOLOGY,,,,50402, -51340,KAPPA,BONE MARROW,HEMATOLOGY,,,,50506, -51425,KAPPA,OTHER BODY FLUID,HEMATOLOGY,,,,50585, -51484,KETONE,URINE,HEMATOLOGY,5797-6,49779-2,Ketones [Mass/volume] in Urine,50647, -50813,LACTATE,BLOOD,BLOOD GAS,,32693-4,Lactate [Moles/volume] in Blood,50010, -50954,LACTATE DEHYDROGENASE (LD),BLOOD,CHEMISTRY,2532-0,2532-0,Lactate dehydrogenase [Enzymatic activity/volume] in Serum or Plasma,50134, -50843,"LACTATE DEHYDROGENASE, ASCITES",ASCITES,CHEMISTRY,,2531-2,Lactate dehydrogenase [Enzymatic activity/volume] in Peritoneal fluid,50044, -51015,"LACTATE DEHYDROGENASE, CSF",CEREBROSPINAL FLUID (CSF),CHEMISTRY,,2528-8,Lactate dehydrogenase [Enzymatic activity/volume] in Cerebral spinal fluid,50202, -51054,"LACTATE DEHYDROGENASE, PLEURAL",PLEURAL,CHEMISTRY,,2530-4,Lactate dehydrogenase [Enzymatic activity/volume] in Pleural fluid,50241, -51239,LAMBDA,BLOOD,HEMATOLOGY,,,,50403, -51341,LAMBDA,BONE MARROW,HEMATOLOGY,,,,50507, -51426,LAMBDA,OTHER BODY FLUID,HEMATOLOGY,,,,50586, -51240,LARGE PLATELETS,BLOOD,HEMATOLOGY,,34167-7,Platelets Large [Presence] in Blood by Automated count,50406, -51035,"LD, BODY FLUID",OTHER BODY FLUID,CHEMISTRY,,2529-6,Lactate dehydrogenase [Enzymatic activity/volume] in Body fluid,50218, -51023,"LD, JOINT FLUID",JOINT FLUID,CHEMISTRY,,2533-8,Lactate dehydrogenase [Enzymatic activity/volume] in Synovial fluid,50209, -51087,LENGTH OF URINE COLLECTION,URINE,CHEMISTRY,,13362-9,Collection duration of Urine,50268, -51485,LEUCINE CRYSTALS,URINE,HEMATOLOGY,5798-4,,,50734, -51241,LEUKOCYTE ALKALINE PHOSPHATASE,BLOOD,HEMATOLOGY,4659-9,15112-6,Leukocyte phosphatase [Units/volume] in Leukocytes,50404, -51486,LEUKOCYTES,URINE,HEMATOLOGY,5799-2,33052-2,Leukocytes [Presence] in Urine,50648, -50955,LIGHT GREEN TOP HOLD,BLOOD,CHEMISTRY,,,,50686, -50956,LIPASE,BLOOD,CHEMISTRY,,3040-3,Triacylglycerol lipase [Enzymatic activity/volume] in Serum or Plasma,50138, -50844,"LIPASE, ASCITES",ASCITES,CHEMISTRY,,32722-1,Triacylglycerol lipase [Enzymatic activity/volume] in Peritoneal fluid,50045, -51036,"LIPASE, BODY FLUID",OTHER BODY FLUID,CHEMISTRY,,15212-4,Triacylglycerol lipase [Enzymatic activity/volume] in Body fluid,50219, -51055,"LIPASE, PLEURAL",PLEURAL,CHEMISTRY,,,,50702, -50957,LITHIUM,BLOOD,CHEMISTRY,3719-2,14334-7,Lithium [Moles/volume] in Serum or Plasma,50139, -51242,LUC,BLOOD,HEMATOLOGY,,,,50710, -51243,LUPUS ANTICOAGULANT,BLOOD,HEMATOLOGY,,,,50407, -50958,LUTEINIZING HORMONE,BLOOD,CHEMISTRY,1599-0,10501-5,Lutropin [Units/volume] in Serum or Plasma,50137, -51116,LYMPHOCYTES,ASCITES,HEMATOLOGY,,26482-0,Lymphocytes/100 leukocytes in Peritoneal fluid,50303, -51244,LYMPHOCYTES,BLOOD,HEMATOLOGY,731-0,26478-8,Lymphocytes/100 leukocytes in Blood,50408, -51375,LYMPHOCYTES,JOINT FLUID,HEMATOLOGY,,26483-8,Lymphocytes/100 leukocytes in Synovial fluid,50537, -51427,LYMPHOCYTES,OTHER BODY FLUID,HEMATOLOGY,,11031-2,Lymphocytes/100 leukocytes in Body fluid,50587, -51446,LYMPHOCYTES,PLEURAL,HEMATOLOGY,,26481-2,Lymphocytes/100 leukocytes in Pleural fluid,50605, -51245,"LYMPHOCYTES, PERCENT",BLOOD,HEMATOLOGY,,26478-8,Lymphocytes/100 leukocytes in Blood,50315, -51351,LYMPHS,CEREBROSPINAL FLUID (CSF),HEMATOLOGY,,26479-6,Lymphocytes/100 leukocytes in Cerebral spinal fluid,50516, -50959,MACRO PROLACTIN,BLOOD,CHEMISTRY,,,,50691, -51246,MACROCYTES,BLOOD,HEMATOLOGY,738-5,30424-6,Macrocytes [Presence] in Blood,50409, -51247,MACROOVALOCYTES,BLOOD,HEMATOLOGY,10376-2,10376-2,Oval macrocytes [Presence] in Blood by Light microscopy,50410, -51117,MACROPHAGE,ASCITES,HEMATOLOGY,,40517-5,Macrophages/100 leukocytes in Peritoneal fluid by Manual count,50304, -51352,MACROPHAGE,CEREBROSPINAL FLUID (CSF),HEMATOLOGY,,30426-1,Macrophages/100 leukocytes in Cerebral spinal fluid,50517, -51376,MACROPHAGE,JOINT FLUID,HEMATOLOGY,,33376-5,Macrophages/100 leukocytes in Synovial fluid,50538, -51428,MACROPHAGE,OTHER BODY FLUID,HEMATOLOGY,,12230-9,Macrophages/100 leukocytes in Body fluid by Manual count,50588, -51447,MACROPHAGES,PLEURAL,HEMATOLOGY,,40520-9,Macrophages/100 leukocytes in Pleural fluid by Manual count,50606, -50960,MAGNESIUM,BLOOD,CHEMISTRY,2601-3,19123-9,Magnesium [Mass/volume] in Serum or Plasma,50140, -51037,"MAGNESIUM, BODY FLUID",OTHER BODY FLUID,CHEMISTRY,,29365-4,Magnesium [Mass/volume] in Body fluid,50220, -51088,"MAGNESIUM, URINE",URINE,CHEMISTRY,2598-1,19124-7,Magnesium [Mass/volume] in Urine,50270, -51089,MARIJUANA,URINE,CHEMISTRY,,3427-2,Cannabinoids [Presence] in Urine,50294, -51248,MCH,BLOOD,HEMATOLOGY,785-6,28539-5,Erythrocyte mean corpuscular hemoglobin [Entitic mass],50411, -51249,MCHC,BLOOD,HEMATOLOGY,786-4,28540-3,Erythrocyte mean corpuscular hemoglobin concentration [Mass/volume],50412, -51250,MCV,BLOOD,HEMATOLOGY,787-2,30428-7,Erythrocyte mean corpuscular volume [Entitic volume],50413, -51118,MESOTHELIAL CELL,ASCITES,HEMATOLOGY,,30432-9,Mesothelial cells/100 leukocytes in Peritoneal fluid,50305, -51353,MESOTHELIAL CELLS,CEREBROSPINAL FLUID (CSF),HEMATOLOGY,,30429-5,Mesothelial cells/100 leukocytes in Cerebral spinal fluid,50518, -51377,MESOTHELIAL CELLS,JOINT FLUID,HEMATOLOGY,,33365-8,Mesothelial cells/100 leukocytes in Synovial fluid,50539, -51429,MESOTHELIAL CELLS,OTHER BODY FLUID,HEMATOLOGY,,28544-5,Mesothelial cells/100 leukocytes in Body fluid,50589, -51448,MESOTHELIAL CELLS,PLEURAL,HEMATOLOGY,,30431-1,Mesothelial cells/100 leukocytes in Pleural fluid,50607, -51119,METAMYELOCYTES,ASCITES,HEMATOLOGY,,,,50306, -51251,METAMYELOCYTES,BLOOD,HEMATOLOGY,,28541-1,Metamyelocytes/100 leukocytes in Blood,50414, -51354,METAMYELOCYTES,CEREBROSPINAL FLUID (CSF),HEMATOLOGY,,30366-9,Metamyelocytes/100 leukocytes in Cerebral spinal fluid,50519, -51378,METAMYELOCYTES,JOINT FLUID,HEMATOLOGY,,,,50540, -51430,METAMYELOCYTES,OTHER BODY FLUID,HEMATOLOGY,,17801-2,Metamyelocytes/100 leukocytes in Body fluid,50590, -51449,METAMYELOCYTES,PLEURAL,HEMATOLOGY,,,,50608, -51090,"METHADONE, URINE",URINE,CHEMISTRY,,3773-9,Methadone [Presence] in Urine,50295, -50814,METHEMOGLOBIN,BLOOD,BLOOD GAS,,2614-6,Methemoglobin/Hemoglobin.total in Blood,50011, -50961,METHOTREXATE,BLOOD,CHEMISTRY,,,,50698, -51252,MICROCYTES,BLOOD,HEMATOLOGY,741-9,30434-5,Microcytes [Presence] in Blood,50415, -50845,"MISCELLANEOUS, ASCITES",ASCITES,CHEMISTRY,,,,50046, -51038,"MISCELLANEOUS, BODY FLUID",OTHER BODY FLUID,CHEMISTRY,,,,50221, -51016,"MISCELLANEOUS, CSF",CEREBROSPINAL FLUID (CSF),CHEMISTRY,,,,50203, -51056,"MISCELLANEOUS, PLEURAL",PLEURAL,CHEMISTRY,,,,50242, -51253,MONOCYTE COUNT,BLOOD,HEMATOLOGY,,26484-6,Monocytes [#/volume] in Blood,50416, -51120,MONOCYTES,ASCITES,HEMATOLOGY,,26488-7,Monocytes/100 leukocytes in Peritoneal fluid,50307, -51254,MONOCYTES,BLOOD,HEMATOLOGY,742-7,26485-3,Monocytes/100 leukocytes in Blood,50417, -51355,MONOCYTES,CEREBROSPINAL FLUID (CSF),HEMATOLOGY,,26486-1,Monocytes/100 leukocytes in Cerebral spinal fluid,50520, -51379,MONOCYTES,JOINT FLUID,HEMATOLOGY,,17835-0,Monocytes/100 leukocytes in Synovial fluid,50541, -51431,MONOS,OTHER BODY FLUID,HEMATOLOGY,,10330-9,Monocytes/100 leukocytes in Body fluid by Manual count,50591, -51450,MONOS,PLEURAL,HEMATOLOGY,,33362-5,Monocytes/100 leukocytes in Pleural fluid,50609, -51527,MS-DIA,BLOOD,CHEMISTRY,,,,50688,"No match found in 01_2013 lab data dump, exists in mimic2v26, therefore the new LABEL was invented and an itemid was generated" -51121,MYELOCYTES,ASCITES,HEMATOLOGY,,,,50308, -51255,MYELOCYTES,BLOOD,HEMATOLOGY,,26498-6,Myelocytes/100 leukocytes in Blood,50418, -51356,MYELOCYTES,CEREBROSPINAL FLUID (CSF),HEMATOLOGY,,30447-7,Myelocytes/100 leukocytes in Cerebral spinal fluid,50521, -51432,MYELOCYTES,OTHER BODY FLUID,HEMATOLOGY,,17800-4,Myelocytes/100 leukocytes in Body fluid,50592, -51451,MYELOCYTES,PLEURAL,HEMATOLOGY,,,,50610, -51534,MYELOS,JOINT FLUID,HEMATOLOGY,,,,50723,"No match found in 01_2013 lab data dump, exists in mimic2v26, therefore the new LABEL was invented and an itemid was generated" -51091,"MYOGLOBIN, URINE",URINE,CHEMISTRY,,2640-1,Myoglobin [Presence] in Urine,50271, -50962,N-ACETYLPROCAINAMIDE (NAPA),BLOOD,CHEMISTRY,3834-9,3834-9,N-acetylprocainamide [Mass/volume] in Serum or Plasma,50142, -51256,NEUTROPHILS,BLOOD,HEMATOLOGY,761-7,26505-8,Neutrophils.segmented/100 leukocytes in Blood,50419, -51487,NITRITE,URINE,HEMATOLOGY,5802-4,5802-4,Nitrite [Presence] in Urine by Test strip,50650, -51488,NON-SQUAMOUS EPITHELIAL CELLS,URINE,HEMATOLOGY,,41284-1,Epithelial cells.non-squamous [#/area] in Urine sediment by Microscopy high power field,50651, -51489,NONSQUAMOUS EPITHELIAL CELL,URINE,HEMATOLOGY,,41284-1,Epithelial cells.non-squamous [#/area] in Urine sediment by Microscopy high power field,50652, -51357,NRBC,CEREBROSPINAL FLUID (CSF),HEMATOLOGY,,48778-5,Nucleated Erythrocytes/100 cells in Cerebral spinal fluid,50522, -51380,NRBC,JOINT FLUID,HEMATOLOGY,,48040-0,Nucleated Erythrocytes/100 leukocytes [Ratio] in Synovial fluid by Manual count,50542, -51433,NRBC,OTHER BODY FLUID,HEMATOLOGY,,,,50593, -51452,NRBC,PLEURAL,HEMATOLOGY,,,,50611, -50963,NTPROBNP,BLOOD,CHEMISTRY,,33762-6,Natriuretic peptide.B prohormone [Mass/volume] in Serum or Plasma,50195, -51122,NUCLEATED RBC,ASCITES,HEMATOLOGY,,,,50309, -51257,NUCLEATED RED CELLS,BLOOD,HEMATOLOGY,772-4,26461-4,Nucleated Erythrocytes/100 erythrocytes in Blood,50420, -50815,O2 FLOW,BLOOD,BLOOD GAS,,3151-8,Inhaled oxygen flow rate,50014, -51092,"OPIATE SCREEN, URINE",URINE,CHEMISTRY,,3879-4,Opiates [Presence] in Urine,50296, -50846,"OSMOLALITY, ASCITES",ASCITES,CHEMISTRY,,2691-4,Osmolality of Peritoneal fluid,50047, -51039,"OSMOLALITY, BODY FLUID",OTHER BODY FLUID,CHEMISTRY,,15200-9,Osmolality of Body fluid,50222, -50964,"OSMOLALITY, MEASURED",BLOOD,CHEMISTRY,,2692-2,Osmolality of Serum or Plasma,50144, -51063,"OSMOLALITY, STOOL",STOOL,CHEMISTRY,,2693-0,Osmolality of Stool,50250, -51093,"OSMOLALITY, URINE",URINE,CHEMISTRY,,2695-5,Osmolality of Urine,50272, -51258,OSMOTIC FRAGILITY,BLOOD,HEMATOLOGY,,34964-7,Osmotic fragility [interpretation] of Red Blood Cells,50421, -51123,OTHER,ASCITES,HEMATOLOGY,,,,50310, -51358,OTHER,CEREBROSPINAL FLUID (CSF),HEMATOLOGY,,,,50523, -51381,OTHER,JOINT FLUID,HEMATOLOGY,,,,50544, -51453,OTHER,PLEURAL,HEMATOLOGY,,,,50612, -51434,OTHER CELL,OTHER BODY FLUID,HEMATOLOGY,,,,50594, -51259,OTHER CELLS,BLOOD,HEMATOLOGY,729-4,,,50422, -51490,OVAL FAT BODY,URINE,HEMATOLOGY,,,,50735, -51260,OVALOCYTES,BLOOD,HEMATOLOGY,774-0,774-0,Ovalocytes [Presence] in Blood by Light microscopy,50423, -50816,OXYGEN,BLOOD,BLOOD GAS,,19994-3,Oxygen/Inspired gas setting [Volume Fraction] Ventilator,50013, -50817,OXYGEN SATURATION,BLOOD,BLOOD GAS,,20564-1,Oxygen saturation in Blood,50015, -51261,PAPPENHEIMER BODIES,BLOOD,HEMATOLOGY,7795-8,7795-8,Pappenheimer bodies [Presence] in Blood by Light microscopy,50424, -50965,PARATHYROID HORMONE,BLOOD,CHEMISTRY,2731-8,2731-8,Parathyrin.intact [Mass/volume] in Serum or Plasma,50154, -50818,PCO2,BLOOD,BLOOD GAS,,11557-6,Carbon dioxide [Partial pressure] in Blood,50016, -50830,"PCO2, BODY FLUID",OTHER BODY FLUID,BLOOD GAS,,2023-0,Carbon dioxide [Partial pressure] in Body fluid,50034, -50819,PEEP,BLOOD,BLOOD GAS,,20077-4,Positive end expiratory pressure setting Ventilator,50017, -51262,PENCIL CELLS,BLOOD,HEMATOLOGY,10377-0,10377-0,Pencil cells [Presence] in Blood by Light microscopy,50426, -51017,"PEP, CSF",CEREBROSPINAL FLUID (CSF),CHEMISTRY,,24352-7,Protein electrophoresis panel in Cerebral spinal fluid,50200, -51522,PG,OTHER BODY FLUID,CHEMISTRY,,48785-0,Prostaglandin D2 [Mass/volume] in Body fluid,50223,"No match found in 01_2013 lab data dump, exists in mimic2v26, therefore the new LABEL was invented and an itemid was generated" -50820,PH,BLOOD,BLOOD GAS,,11558-4,pH of Blood,50018, -50831,PH,OTHER BODY FLUID,BLOOD GAS,,2748-2,pH of Body fluid,50035, -51094,PH,URINE,CHEMISTRY,,2756-5,pH of Urine,50297, -51491,PH,URINE,HEMATOLOGY,5803-2,2756-5,pH of Urine,50653, -50966,PHENOBARBITAL,BLOOD,CHEMISTRY,3947-9,3948-7,Phenobarbital [Mass/volume] in Serum or Plasma,50146, -50967,PHENYTOIN,BLOOD,CHEMISTRY,3967-7,3968-5,Phenytoin [Mass/volume] in Serum or Plasma,50147, -50968,"PHENYTOIN, FREE",BLOOD,CHEMISTRY,3969-3,3969-3,Phenytoin Free [Mass/volume] in Serum or Plasma,50194, -50969,"PHENYTOIN, PERCENT FREE",BLOOD,CHEMISTRY,,10548-6,Phenytoin Free/Phenytoin.total in Serum or Plasma,50055, -50970,PHOSPHATE,BLOOD,CHEMISTRY,2777-1,2777-1,Phosphate [Mass/volume] in Serum or Plasma,50148, -51040,"PHOSPHATE, BODY FLUID",OTHER BODY FLUID,CHEMISTRY,,12242-4,Phosphate [Mass/volume] in Body fluid,50224, -51095,"PHOSPHATE, URINE",URINE,CHEMISTRY,2778-9,2778-9,Phosphate [Mass/volume] in Urine,50273, -51124,PLASMA,ASCITES,HEMATOLOGY,,40518-3,Plasma cells/100 leukocytes in Peritoneal fluid by Manual count,50311, -51359,PLASMA,CEREBROSPINAL FLUID (CSF),HEMATOLOGY,,47413-0,Plasma cells/100 leukocytes in Cerebral spinal fluid,50524, -51435,PLASMA,OTHER BODY FLUID,HEMATOLOGY,,17803-8,Plasma cells/100 leukocytes in Body fluid,50595, -51263,PLASMA CELLS,BLOOD,HEMATOLOGY,,13047-6,Plasma cells/100 leukocytes in Blood,50427, -51454,PLASMA CELLS,PLEURAL,HEMATOLOGY,,40522-5,Plasma cells/100 leukocytes in Pleural fluid by Manual count,50613, -51532,PLASMGN,BLOOD,HEMATOLOGY,,,,50711,"No match found in 01_2013 lab data dump, exists in mimic2v26, therefore the new LABEL was invented and an itemid was generated" -51264,PLATELET CLUMPS,BLOOD,HEMATOLOGY,,40741-1,Platelet clump [Presence] in Blood by Automated count,50430, -51265,PLATELET COUNT,BLOOD,HEMATOLOGY,777-3,26515-7,Platelets [#/volume] in Blood,50428, -51266,PLATELET SMEAR,BLOOD,HEMATOLOGY,778-1,9317-9,Platelets [Presence] in Blood by Manual count,50429, -50821,PO2,BLOOD,BLOOD GAS,,11556-8,Oxygen [Partial pressure] in Blood,50019, -50832,"PO2, BODY FLUID",OTHER BODY FLUID,BLOOD GAS,,2706-0,Oxygen [Partial pressure] in Body fluid,50036, -51267,POIKILOCYTOSIS,BLOOD,HEMATOLOGY,779-9,779-9,Poikilocytosis [Presence] in Blood by Light microscopy,50431, -51268,POLYCHROMASIA,BLOOD,HEMATOLOGY,10378-8,10378-8,Polychromasia [Presence] in Blood by Light microscopy,50432, -51125,POLYS,ASCITES,HEMATOLOGY,,26520-7,Polymorphonuclear cells/100 leukocytes in Peritoneal fluid,50312, -51360,POLYS,CEREBROSPINAL FLUID (CSF),HEMATOLOGY,,26517-3,Polymorphonuclear cells/100 leukocytes in Cerebral spinal fluid,50525, -51382,POLYS,JOINT FLUID,HEMATOLOGY,,26522-3,Polymorphonuclear cells/100 leukocytes in Synovial fluid,50545, -51436,POLYS,OTHER BODY FLUID,HEMATOLOGY,,26518-1,Polymorphonuclear cells/100 leukocytes in Body fluid,50596, -51455,POLYS,PLEURAL,HEMATOLOGY,,26519-9,Polymorphonuclear cells/100 leukocytes in Pleural fluid,50614, -51096,PORPHOBILINOGEN SCREEN,URINE,CHEMISTRY,,2809-2,Porphobilinogen [Presence] in Urine,50274, -50971,POTASSIUM,BLOOD,CHEMISTRY,2823-3,2823-3,Potassium [Moles/volume] in Serum or Plasma,50149, -50833,POTASSIUM,OTHER BODY FLUID,BLOOD GAS,,2821-7,Potassium [Moles/volume] in Body fluid,50032, -50847,"POTASSIUM, ASCITES",ASCITES,CHEMISTRY,,49789-1,Potassium [Moles/volume] in Peritoneal fluid,50048, -51041,"POTASSIUM, BODY FLUID",OTHER BODY FLUID,CHEMISTRY,,2821-7,Potassium [Moles/volume] in Body fluid,50225, -51057,"POTASSIUM, PLEURAL",PLEURAL,CHEMISTRY,,,,50243, -51064,"POTASSIUM, STOOL",STOOL,CHEMISTRY,,15202-5,Potassium [Moles/volume] in Stool,50251, -51097,"POTASSIUM, URINE",URINE,CHEMISTRY,2828-2,2828-2,Potassium [Moles/volume] in Urine,50275, -50822,"POTASSIUM, WHOLE BLOOD",BLOOD,BLOOD GAS,,6298-4,Potassium [Moles/volume] in Blood,50009, -51540,PROBLEM SPECIMEN,BLOOD,CHEMISTRY,,,,,This data originally existed in the 2013 data dump but was deleted before creation of this table. Re-importing the original 2013 lab data dump will restore the rows -51541,PROBLEM SPECIMEN,BLOOD,HEMATOLOGY,,,,,This data originally existed in the 2013 data dump but was deleted before creation of this table. Re-importing the original 2013 lab data dump will restore the rows -51542,PROBLEM SPECIMEN,URINE,HEMATOLOGY,,,,,This data originally existed in the 2013 data dump but was deleted before creation of this table. Re-importing the original 2013 lab data dump will restore the rows -50972,PROCAINAMIDE,BLOOD,CHEMISTRY,3982-6,3982-6,Procainamide [Mass/volume] in Serum or Plasma,50151, -50973,PROLACTIN,BLOOD,CHEMISTRY,2842-3,2842-3,Prolactin [Mass/volume] in Serum or Plasma,50152, -51126,PROMYELOCYTES,ASCITES,HEMATOLOGY,,,,50704, -51269,PROMYELOCYTES,BLOOD,HEMATOLOGY,781-5,26524-9,Promyelocytes/100 leukocytes in Blood,50434, -51361,PROMYELOCYTES,CEREBROSPINAL FLUID (CSF),HEMATOLOGY,,,,50722, -51437,PROMYELOCYTES,OTHER BODY FLUID,HEMATOLOGY,,17799-8,Promyelocytes/100 leukocytes in Body fluid,50597, -51456,PROMYELOCYTES,PLEURAL,HEMATOLOGY,,,,50615, -50974,PROSTATE SPECIFIC ANTIGEN,BLOOD,CHEMISTRY,2857-1,2857-1,Prostate specific Ag [Mass/volume] in Serum or Plasma,50153, -51098,"PROT. ELECTROPHORESIS, URINE",URINE,CHEMISTRY,,13438-7,Protein Fractions [interpretation] in Urine by Electrophoresis,50280, -51492,PROTEIN,URINE,HEMATOLOGY,5804-0,5804-0,Protein [Mass/volume] in Urine by Test strip,50655, -51270,"PROTEIN C, ANTIGEN",BLOOD,HEMATOLOGY,,27820-0,Protein C Ag actual/normal in Platelet poor plasma by Immunologic method,50435, -51271,"PROTEIN C, FUNCTIONAL",BLOOD,HEMATOLOGY,,27818-4,Protein C actual/normal in Platelet poor plasma by Chromogenic method,50436, -50975,PROTEIN ELECTROPHORESIS,BLOOD,CHEMISTRY,,24351-9,Protein electrophoresis panel in Serum or Plasma,50145, -51272,"PROTEIN S, ANTIGEN",BLOOD,HEMATOLOGY,,27823-4,Protein S Ag actual/normal in Platelet poor plasma by Immunologic method,50437, -51273,"PROTEIN S, FUNCTIONAL",BLOOD,HEMATOLOGY,,31102-7,Protein S actual/normal in Platelet poor plasma by Chromogenic method,50438, -50976,"PROTEIN, TOTAL",BLOOD,CHEMISTRY,2885-2,2885-2,Protein [Mass/volume] in Serum or Plasma,50171, -51099,PROTEIN/CREATININE RATIO,URINE,CHEMISTRY,2890-2,2890-2,Protein/Creatinine [Mass ratio] in Urine,50276, -51274,PT,BLOOD,HEMATOLOGY,5902-2,5964-2,Prothrombin time (PT) in Blood by Coagulation assay,50439, -51275,PTT,BLOOD,HEMATOLOGY,5898-2,3173-2,Activated partial thrombplastin time (aPTT) in Blood by Coagulation assay,50440, -51276,QUANTITATIVE G6PD,BLOOD,HEMATOLOGY,,32546-4,Glucose-6-Phosphate dehydrogenase [Enzymatic activity/mass] in Red Blood Cells,50441, -50977,QUINIDINE,BLOOD,CHEMISTRY,6694-4,4014-7,Quinidine [Mass/volume] in Blood,50156, -50978,RAPAMYCIN,BLOOD,CHEMISTRY,,29247-4,Sirolimus [Mass/volume] in Blood,50196, -51493,RBC,URINE,HEMATOLOGY,5808-1,13945-1,Erythrocytes [#/area] in Urine sediment by Microscopy high power field,50656, -51494,RBC CASTS,URINE,HEMATOLOGY,5807-3,5807-3,RBC casts [#/area] in Urine sediment by Microscopy low power field,50657, -51495,RBC CLUMPS,URINE,HEMATOLOGY,,,,50658, -51127,"RBC, ASCITES",ASCITES,HEMATOLOGY,,26457-2,Erythrocytes [#/volume] in Peritoneal fluid,50313, -51362,"RBC, CSF",CEREBROSPINAL FLUID (CSF),HEMATOLOGY,,26454-9,Erythrocytes [#/volume] in Cerebral spinal fluid,50526, -51383,"RBC, JOINT FLUID",JOINT FLUID,HEMATOLOGY,,26458-0,Erythrocytes [#/volume] in Synovial fluid,50546, -51438,"RBC, OTHER FLUID",OTHER BODY FLUID,HEMATOLOGY,,26455-6,Erythrocytes [#/volume] in Body fluid,50598, -51457,"RBC, PLEURAL",PLEURAL,HEMATOLOGY,,26456-4,Erythrocytes [#/volume] in Pleural fluid,50616, -51277,RDW,BLOOD,HEMATOLOGY,788-0,30385-9,Erythrocyte distribution width [Ratio],50444, -51278,RED BLOOD CELL FRAGMENTS,BLOOD,HEMATOLOGY,,,,50443, -51279,RED BLOOD CELLS,BLOOD,HEMATOLOGY,789-8,26453-1,Erythrocytes [#/volume] in Blood,50442, -50979,RED TOP HOLD,BLOOD,CHEMISTRY,,,,50157, -51496,"REDUCING SUBSTANCES, URINE",URINE,HEMATOLOGY,,,,50660, -51497,RENAL EPITHELIAL CELLS,URINE,HEMATOLOGY,,26052-1,Epithelial cells.renal [#/area] in Urine sediment by Microscopy high power field,50659, -51280,REPTILASE TIME,BLOOD,HEMATOLOGY,,6683-7,Reptilase time in Platelet poor plasma by Coagulation assay,50446, -51281,REPTILASE TIME CONTROL,BLOOD,HEMATOLOGY,,,,50713, -50823,REQUIRED O2,BLOOD,BLOOD GAS,,,,50021, -51282,"RETICULOCYTE COUNT, ABSOLUTE",BLOOD,HEMATOLOGY,,14196-0,Reticulocytes [#/volume] in Red Blood Cells,50321, -51283,"RETICULOCYTE COUNT, AUTOMATED",BLOOD,HEMATOLOGY,,17849-1,Reticulocytes/100 erythrocytes in Red Blood Cells by Automated count,50447, -51284,"RETICULOCYTE COUNT, MANUAL",BLOOD,HEMATOLOGY,,31112-6,Reticulocytes/100 erythrocytes in Red Blood Cells by Manual,50448, -51285,"RETICULOCYTE, CELLULAR HEMOGLOBIN",BLOOD,HEMATOLOGY,,,,50368, -50980,RHEUMATOID FACTOR,BLOOD,CHEMISTRY,6928-6,,,50158, -51286,RISTOCETIN,BLOOD,HEMATOLOGY,,,,50449, -50981,SALICYLATE,BLOOD,CHEMISTRY,4023-8,3306-8,Acetylsalicylate [Mass/volume] in Serum or Plasma,50072, -51287,SCHISTOCYTES,BLOOD,HEMATOLOGY,800-3,800-3,Schistocytes [Presence] in Blood by Light microscopy,50450, -51288,SEDIMENTATION RATE,BLOOD,HEMATOLOGY,4537-7,30341-2,Erythrocyte sedimentation rate,50451, -51289,SERUM VISCOSITY,BLOOD,HEMATOLOGY,3128-6,3128-6,Viscosity of Serum,50452, -50982,SEX HORMONE BINDING GLOBULIN,BLOOD,CHEMISTRY,2942-1,,,, -51290,SICKLE CELL PREPARATION,BLOOD,HEMATOLOGY,,6864-3,Hemoglobin S [Presence] in Blood by Solubility test,50454,"old lab ITEMID is correct, but loinc seems wrong ??" -51291,SICKLE CELLS,BLOOD,HEMATOLOGY,801-1,801-1,Sickle cells [Presence] in Blood by Light microscopy,50453, -50983,SODIUM,BLOOD,CHEMISTRY,2951-2,2951-2,Sodium [Moles/volume] in Serum or Plasma,50159, -50848,"SODIUM, ASCITES",ASCITES,CHEMISTRY,,49790-9,Sodium [Moles/volume] in Peritoneal fluid,50049, -50834,"SODIUM, BODY FLUID",OTHER BODY FLUID,BLOOD GAS,,2950-4,Sodium [Moles/volume] in Body fluid,50033, -51042,"SODIUM, BODY FLUID",OTHER BODY FLUID,CHEMISTRY,,2950-4,Sodium [Moles/volume] in Body fluid,50226, -51058,"SODIUM, PLEURAL",PLEURAL,CHEMISTRY,,,,50244, -51065,"SODIUM, STOOL",STOOL,CHEMISTRY,,15207-4,Sodium [Moles/volume] in Stool,50252, -51100,"SODIUM, URINE",URINE,CHEMISTRY,2955-3,2955-3,Sodium [Moles/volume] in Urine,50277, -50824,"SODIUM, WHOLE BLOOD",BLOOD,BLOOD GAS,,2947-0,Sodium [Moles/volume] in Blood,50012, -51498,SPECIFIC GRAVITY,URINE,HEMATOLOGY,5811-5,5811-5,Specific gravity of Urine by Test strip,50661, -50800,SPECIMEN TYPE,BLOOD,BLOOD GAS,,,,50026,Not in v30 but will be next data dump -51499,SPERM,URINE,HEMATOLOGY,,8248-7,Spermatozoa [Presence] in Urine sediment by Light microscopy,50662, -51292,SPHEROCYTES,BLOOD,HEMATOLOGY,802-9,802-9,Spherocytes [Presence] in Blood by Light microscopy,50455, -50984,STAT,BLOOD,CHEMISTRY,,,,,"No ITEMID in mimic2v26, but exists in 2010 lab data dump" -51528,STDY HOLD,BLOOD,CHEMISTRY,,,,50695,"No match found in 01_2013 lab data dump, exists in mimic2v26, therefore the new LABEL was invented and an itemid was generated" -51531,STDYURINE,URINE,CHEMISTRY,,,,50703,"No match found in 01_2013 lab data dump, exists in mimic2v26, therefore the new LABEL was invented and an itemid was generated" -50985,STUDY TUBES,BLOOD,CHEMISTRY,,,,, -51293,SUGAR WATER TEST,BLOOD,HEMATOLOGY,,12260-6,Sucrose hemolysis [Presence] of Red Blood Cells,50457, -51500,SULFONAMIDES,URINE,HEMATOLOGY,,5812-3,Sulfonamide crystals [Presence] in Urine sediment by Light microscopy,50663, -51555,SURFACTANT ALBUMIN RATIO,OTHER BODY FLUID,CHEMISTRY,,,,,"This data was not found in the 2013 data dump, so an itemid with a descriptive name was created" -50986,TACROFK,BLOOD,CHEMISTRY,,11253-2,Tacrolimus [Mass/volume] in Blood,50102,"Duplicate v26 ITEMID exists (50699), contains same type of data" -51539,TACROFK_2,BLOOD,CHEMISTRY,,,,50699,"There are two ITEMIDs in mimic2v26 which represent TACROFK, but only one in the 2013 lab data dump, so this ITEMID/LABEL was created" -51294,TARGET CELLS,BLOOD,HEMATOLOGY,10381-2,10381-2,Target cells [Presence] in Blood by Light microscopy,50458, -51295,TDT,BLOOD,HEMATOLOGY,,,,50714, -51537,TDT,OTHER BODY FLUID,HEMATOLOGY,,,,50730,"No match found in 01_2013 lab data dump, exists in mimic2v26, therefore the new LABEL was invented and an itemid was generated" -51296,TEARDROP CELLS,BLOOD,HEMATOLOGY,7791-7,7791-7,Dacrocytes [Presence] in Blood by Light microscopy,50459, -50825,TEMPERATURE,BLOOD,BLOOD GAS,,,,50023, -50988,TESTOSTERONE,BLOOD,CHEMISTRY,2993-4,2986-8,Testosterone [Mass/volume] in Serum or Plasma,50165, -50989,"TESTOSTERONE, FREE",BLOOD,CHEMISTRY,2991-8,2991-8,Testosterone Free [Mass/volume] in Serum or Plasma,50105, -50990,THEOPHYLLINE,BLOOD,CHEMISTRY,4049-3,4049-3,Theophylline [Mass/volume] in Serum or Plasma,50166, -51297,THROMBIN,BLOOD,HEMATOLOGY,,3243-3,Thrombin time in Platelet poor plasma by Coagulation assay,50460, -50991,THYROGLOBULIN,BLOOD,CHEMISTRY,3013-0,3013-0,Thyroglobulin [Mass/volume] in Serum or Plasma,50167, -50992,THYROID PEROXIDASE ANTIBODIES,BLOOD,CHEMISTRY,,8099-4,Thyroperoxidase Ab [Units/volume] in Serum,50071, -50993,THYROID STIMULATING HORMONE,BLOOD,CHEMISTRY,3016-3,3015-5,Thyrotropin [Units/volume] in Blood,50175, -50994,THYROXINE (T4),BLOOD,CHEMISTRY,3026-2,3025-4,Thyroxine [Mass/volume] in Blood,50163, -50995,"THYROXINE (T4), FREE",BLOOD,CHEMISTRY,3024-7,3024-7,Thyroxine free [Mass/volume] in Serum or Plasma,50104, -50826,TIDAL VOLUME,BLOOD,BLOOD GAS,,20112-9,Tidal volume setting Ventilator,50024, -50996,"TISSUE TRANSGLUTAMINASE AB, IGA",BLOOD,CHEMISTRY,,31017-7,Tissue transglutaminase IgA Ab [Units/volume] in Serum,50197, -50997,TOBRAMYCIN,BLOOD,CHEMISTRY,4058-4,35670-9,Tobramycin [Mass/volume] in Serum or Plasma,50169, -51101,TOTAL COLLECTION TIME,URINE,CHEMISTRY,,13362-9,Collection duration of Urine,50256, -50849,"TOTAL PROTEIN, ASCITES",ASCITES,CHEMISTRY,,2883-7,Protein [Mass/volume] in Peritoneal fluid,50051, -51043,"TOTAL PROTEIN, BODY FLUID",OTHER BODY FLUID,CHEMISTRY,,2881-1,Protein [Mass/volume] in Body fluid,50229, -51018,"TOTAL PROTEIN, CSF",CEREBROSPINAL FLUID (CSF),CHEMISTRY,,2880-3,Protein [Mass/volume] in Cerebral spinal fluid,50204, -51024,"TOTAL PROTEIN, JOINT FLUID",JOINT FLUID,CHEMISTRY,,2886-0,Protein [Mass/volume] in Synovial fluid,50210, -51059,"TOTAL PROTEIN, PLEURAL",PLEURAL,CHEMISTRY,,2882-9,Protein [Mass/volume] in Pleural fluid,50246, -51102,"TOTAL PROTEIN, URINE",URINE,CHEMISTRY,2887-8,2888-6,Protein [Mass/volume] in Urine,50278, -50998,TRANSFERRIN,BLOOD,CHEMISTRY,2500-7,3034-6,Transferrin [Mass/volume] in Serum or Plasma,50173, -51501,TRANSITIONAL EPITHELIAL CELLS,URINE,HEMATOLOGY,,30089-7,Transitional cells [#/area] in Urine sediment by Microscopy high power field,50664, -51502,TRICHOMONAS,URINE,HEMATOLOGY,,32724-7,Trichomonas sp [Presence] in Urine by Light microscopy,50665, -50999,TRICYCLIC ANTIDEPRESSANT SCREEN,BLOOD,CHEMISTRY,,4073-3,Tricyclic antidepressants [Presence] in Serum or Plasma,50198, -51044,TRIGLYCER,OTHER BODY FLUID,CHEMISTRY,,12228-3,Triglyceride [Mass/volume] in Body fluid,50231, -51000,TRIGLYCERIDES,BLOOD,CHEMISTRY,1644-4,2571-8,Triglyceride [Mass/volume] in Serum or Plasma,50174, -50850,"TRIGLYCERIDES, ASCITES",ASCITES,CHEMISTRY,,14447-7,Triglyceride [Mass/volume] in Peritoneal fluid,50052, -51060,"TRIGLYCERIDES, PLEURAL",PLEURAL,CHEMISTRY,,9619-8,Triglyceride [Mass/volume] in Pleural fluid,50247, -51001,TRIIODOTHYRONINE (T3),BLOOD,CHEMISTRY,3053-6,3053-6,Triiodothyronine [Mass/volume] in Serum or Plasma,50162, -51503,TRIPLE PHOSPHATE CRYSTALS,URINE,HEMATOLOGY,5814-9,5814-9,Triple phosphate crystals [Presence] in Urine sediment by Light microscopy,50620, -51002,TROPONIN I,BLOOD,CHEMISTRY,10839-9,42757-5,Troponin I.cardiac [Mass/volume] in Blood,50188, -51003,TROPONIN T,BLOOD,CHEMISTRY,6598-7,48425-3,Troponin T.cardiac [Mass/volume] in Blood,50189, -51504,TYROSINE CRYSTALS,URINE,HEMATOLOGY,5815-6,5815-6,Tyrosine crystals [Presence] in Urine sediment by Light microscopy,50667, -51004,"UE3, MATERNAL SCREEN",BLOOD,CHEMISTRY,,,,50690, -51103,UHOLD,URINE,CHEMISTRY,,,,50282, -51005,UPTAKE RATIO,BLOOD,CHEMISTRY,3050-2,,,50176, -51006,UREA NITROGEN,BLOOD,CHEMISTRY,3094-0,3094-0,Urea nitrogen [Mass/volume] in Serum or Plasma,50177, -50851,"UREA NITROGEN, ASCITES",ASCITES,CHEMISTRY,,12265-5,Urea nitrogen [Mass/volume] in Peritoneal fluid,50053, -51045,"UREA NITROGEN, BODY FLUID",OTHER BODY FLUID,CHEMISTRY,,3093-2,Urea nitrogen [Mass/volume] in Body fluid,50232, -51104,"UREA NITROGEN, URINE",URINE,CHEMISTRY,,3095-7,Urea nitrogen [Mass/volume] in Urine,50283, -51007,URIC ACID,BLOOD,CHEMISTRY,3084-1,3084-1,Urate [Mass/volume] in Serum or Plasma,50178, -51505,URIC ACID CRYSTALS,URINE,HEMATOLOGY,5817-2,5817-2,Urate crystals [Presence] in Urine sediment by Light microscopy,50670, -51105,"URIC ACID, URINE",URINE,CHEMISTRY,3086-6,3086-6,Urate [Mass/volume] in Urine,50284, -51506,URINE APPEARANCE,URINE,HEMATOLOGY,5767-9,5767-9,Appearance of Urine,50623, -51507,"URINE CASTS, OTHER",URINE,HEMATOLOGY,9842-6,,,50668, -51508,URINE COLOR,URINE,HEMATOLOGY,5778-6,5778-6,Color of Urine,50633, -51509,URINE COMMENTS,URINE,HEMATOLOGY,,,,,"No ITEMID in mimic2v26, but exists in 2010 lab data dump" -51106,URINE CREATININE,URINE,CHEMISTRY,,,,50258, -51510,"URINE CRYSTALS, OTHER",URINE,HEMATOLOGY,,5783-6,Unidentified crystals [Presence] in Urine sediment by Light microscopy,50669, -51511,URINE FAT BODIES,URINE,HEMATOLOGY,,2272-3,Fat [Presence] in Urine,50638, -51512,URINE MUCOUS,URINE,HEMATOLOGY,8247-9,8247-9,Mucus [Presence] in Urine sediment by Light microscopy,50649, -51513,URINE SPECIMEN TYPE,URINE,HEMATOLOGY,,19159-3,Urinalysis specimen collection method,50666, -51107,"URINE TUBE, HELD",URINE,CHEMISTRY,,,,50282, -51108,URINE VOLUME,URINE,CHEMISTRY,,28009-9,Volume of Urine,50285, -51109,"URINE VOLUME, TOTAL",URINE,CHEMISTRY,,28009-9,Volume of Urine,50259, -51514,UROBILINOGEN,URINE,HEMATOLOGY,5818-0,3107-0,Urobilinogen [Mass/volume] in Urine,50671, -51008,VALPROIC ACID,BLOOD,CHEMISTRY,4086-5,4086-5,Valproate [Mass/volume] in Serum or Plasma,50179, -51009,VANCOMYCIN,BLOOD,CHEMISTRY,4091-5,20578-1,Vancomycin [Mass/volume] in Serum or Plasma,50180, -50827,VENTILATION RATE,BLOOD,BLOOD GAS,,,,50020, -50828,VENTILATOR,BLOOD,BLOOD GAS,,,,50027, -51010,VITAMIN B12,BLOOD,CHEMISTRY,2170-9,16695-9,Cobalamin [Mass/volume] in Blood,50181, -51549,VOIDED SPECIMEN,ASCITES,CHEMISTRY,,,,,This data originally existed in the 2013 data dump but was deleted before creation of this table. Re-importing the original 2013 lab data dump will restore the rows -51545,VOIDED SPECIMEN,BLOOD,BLOOD GAS,,,,,This data originally existed in the 2013 data dump but was deleted before creation of this table. Re-importing the original 2013 lab data dump will restore the rows -51543,VOIDED SPECIMEN,BLOOD,CHEMISTRY,,,,,This data originally existed in the 2013 data dump but was deleted before creation of this table. Re-importing the original 2013 lab data dump will restore the rows -51550,VOIDED SPECIMEN,BLOOD,HEMATOLOGY,,,,,This data originally existed in the 2013 data dump but was deleted before creation of this table. Re-importing the original 2013 lab data dump will restore the rows -51546,VOIDED SPECIMEN,CSF,CHEMISTRY,,,,,This data originally existed in the 2013 data dump but was deleted before creation of this table. Re-importing the original 2013 lab data dump will restore the rows -51554,VOIDED SPECIMEN,JOINT FLUID,HEMATOLOGY,,,,,This data originally existed in the 2013 data dump but was deleted before creation of this table. Re-importing the original 2013 lab data dump will restore the rows -51544,VOIDED SPECIMEN,OTHER BODY FLUID,BLOOD GAS,,,,,This data originally existed in the 2013 data dump but was deleted before creation of this table. Re-importing the original 2013 lab data dump will restore the rows -51547,VOIDED SPECIMEN,OTHER BODY FLUID,CHEMISTRY,,,,,This data originally existed in the 2013 data dump but was deleted before creation of this table. Re-importing the original 2013 lab data dump will restore the rows -51551,VOIDED SPECIMEN,OTHER BODY FLUID,HEMATOLOGY,,,,,This data originally existed in the 2013 data dump but was deleted before creation of this table. Re-importing the original 2013 lab data dump will restore the rows -51552,VOIDED SPECIMEN,STOOL,CHEMISTRY,,,,,This data originally existed in the 2013 data dump but was deleted before creation of this table. Re-importing the original 2013 lab data dump will restore the rows -51553,VOIDED SPECIMEN,URINE,CHEMISTRY,,,,,This data originally existed in the 2013 data dump but was deleted before creation of this table. Re-importing the original 2013 lab data dump will restore the rows -51548,VOIDED SPECIMEN,URINE,HEMATOLOGY,,,,,This data originally existed in the 2013 data dump but was deleted before creation of this table. Re-importing the original 2013 lab data dump will restore the rows -51298,VON WILLEBRAND FACTOR ACTIVITY,BLOOD,HEMATOLOGY,6014-5,6014-5,von Willebrand factor (vWf) ristocetin cofactor actual/normal in Platelet poor plasma by Aggregation,50466, -51299,VON WILLEBRAND FACTOR ANTIGEN,BLOOD,HEMATOLOGY,6012-9,27816-8,von Willebrand factor (vWf) Ag actual/normal in Platelet poor plasma by Immunologic method,50465, -51515,WAXY CASTS,URINE,HEMATOLOGY,5819-8,5819-8,Waxy casts [#/area] in Urine sediment by Microscopy low power field,50673, -51516,WBC,URINE,HEMATOLOGY,5821-4,5821-4,Leukocytes [#/area] in Urine sediment by Microscopy high power field,50674, -51517,WBC CASTS,URINE,HEMATOLOGY,5820-6,5820-6,WBC casts [#/area] in Urine sediment by Microscopy low power field,50675, -51518,WBC CLUMPS,URINE,HEMATOLOGY,,,,50676, -51300,WBC COUNT,BLOOD,HEMATOLOGY,,26464-8,Leukocytes [#/volume] in Blood,50316, -51128,"WBC, ASCITES",ASCITES,HEMATOLOGY,,26468-9,Leukocytes [#/volume] in Peritoneal fluid,50314, -51363,"WBC, CSF",CEREBROSPINAL FLUID (CSF),HEMATOLOGY,,26465-5,Leukocytes [#/volume] in Cerebral spinal fluid,50527, -51384,"WBC, JOINT FLUID",JOINT FLUID,HEMATOLOGY,,26469-7,Leukocytes [#/volume] in Synovial fluid,50548, -51439,"WBC, OTHER FLUID",OTHER BODY FLUID,HEMATOLOGY,,26466-3,Leukocytes [#/volume] in Body fluid,50599, -51458,"WBC, PLEURAL",PLEURAL,HEMATOLOGY,,26467-1,Leukocytes [#/volume] in Pleural fluid,50617, -51533,WBCP,BLOOD,HEMATOLOGY,,,,50715,"No match found in 01_2013 lab data dump, exists in mimic2v26, therefore the new LABEL was invented and an itemid was generated" -51301,WHITE BLOOD CELLS,BLOOD,HEMATOLOGY,804-5,26464-8,Leukocytes [#/volume] in Blood,50468, -51342,WRIGHT GIEMSA,BONE MARROW,HEMATOLOGY,,10355-6,Microscopic observation [Identifier] in Bone marrow by Wright Giemsa stain,50508, -51519,YEAST,URINE,HEMATOLOGY,5822-2,32356-8,Yeast [Presence] in Urine sediment by Light microscopy,50677, -51129,YOUNG,ASCITES,HEMATOLOGY,,,,50705, -51364,YOUNG,CEREBROSPINAL FLUID (CSF),HEMATOLOGY,,,,50528, -51302,YOUNG CELLS,BLOOD,HEMATOLOGY,,51633-6,Platelets reticulated/100 platelets in Blood,50473, -51459,YOUNG CELLS,PLEURAL,HEMATOLOGY,,,,50618, -51538,[A1c],BLOOD,CHEMISTRY,,4548-4,Hemoglobin A1c (glycated HgB)/Hemoglobin.total [Moles] in Blood,50183,"No match found in 01_2013 lab data dump, exists in mimic2v26, therefore the new LABEL was invented and an itemid was generated" -51529,eAG,BLOOD,CHEMISTRY,,,,50697,"No match found in 01_2013 lab data dump, exists in mimic2v26, therefore the new LABEL was invented and an itemid was generated" \ No newline at end of file diff --git a/notebooks/aline/README.md b/notebooks/aline/README.md new file mode 100644 index 0000000..1017a1e --- /dev/null +++ b/notebooks/aline/README.md @@ -0,0 +1,33 @@ +# Reproducing the indwelling arterial catheter study (aline study) + +This folder contains code for reproducing a study on indwelling arterial catheters: + +> Hsu DJ, Feng M, Kothari R, Zhou H, Chen KP, Celi LA. The association between indwelling arterial catheters and mortality in hemodynamically stable patients with respiratory failure: a propensity score analysis. CHEST Journal. 2015 Dec 1;148(6):1470-6. + +The study showed, in the MIMIC-II database, that after adjustment for various confounders, indwelling arterial catheters were not associated with a mortality benefit. + +The code here reproduces this study in the MIMIC-III database. This involved many technical changes due to schema differences and data differences between MIMIC-II and MIMIC-III. As MIMIC-III covers four additional years, the cohort extracted here is larger than that reported in the study. + +# Requirements + +There are a number of prerequesites to running this code: + +* an installation of MIMIC-III in a PostgreSQL database +* Python 2.7 with the numpy, pandas, matplotlib, and psycopg2 packages +* Jupyter with a Python 2 kernel +* R with the `Matching`, `pROC`, `MASS` libraries + +# Running the study + +The study can be reproduced by: + +1. Running the `aline.ipynb` file - this notebook generates the data (using the SQL files in this directory) and saves a single dataframe to CSV +2. Running the `aline_propensity_score.Rmd` file - this R markdown file uses the above CSV to create a propensity score and calculate the mortality difference between matched pairs + +# Slight modifications + +There are a few minor differences between our reproduction and the original study. + +* the original study subselected variables using a genetic algorithm, whereas we simply use the final set of variables they report +* we did not include PO2 and PCO2 in the propensity score +* we removed patients based on hospital service, not ICU service diff --git a/notebooks/aline/aline.ipynb b/notebooks/aline/aline.ipynb new file mode 100644 index 0000000..114e215 --- /dev/null +++ b/notebooks/aline/aline.ipynb @@ -0,0 +1,1743 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Arterial line study\n", + "\n", + "This notebook reproduces the arterial line study in MIMIC-III. The following is an outline of the notebook:\n", + "\n", + "1. Generate necessary materialized views in SQL\n", + "2. Combine materialized views and acquire a single dataframe\n", + "3. Write this data to file for use in R\n", + "\n", + "The R code then evaluates whether an arterial line is associated with mortality after propensity matching.\n", + "\n", + "Note that the original arterial line study used a genetic algorithm to select the covariates in the propensity score. We omit the genetic algorithm step, and instead use the final set of covariates described by the authors. For more detail, see:\n", + "\n", + "> Hsu DJ, Feng M, Kothari R, Zhou H, Chen KP, Celi LA. The association between indwelling arterial catheters and mortality in hemodynamically stable patients with respiratory failure: a propensity score analysis. CHEST Journal. 2015 Dec 1;148(6):1470-6." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import print_function\n", + "\n", + "# Import libraries\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import psycopg2\n", + "import os\n", + "\n", + "# below is used to print out pretty pandas dataframes\n", + "from IPython.display import display, HTML\n", + "\n", + "%matplotlib inline\n", + "\n", + "def execute_query_safely(sql, con):\n", + " cur = con.cursor()\n", + " \n", + " # try to execute the query\n", + " try:\n", + " cur.execute(sql)\n", + " except:\n", + " # if an exception, rollback, rethrow the exception - finally closes the connection\n", + " cur.execute('rollback;')\n", + " raise\n", + " finally:\n", + " cur.close()\n", + " \n", + " return\n", + " \n", + "\n", + "# location of the queries to generate aline specific materialized views\n", + "aline_path = './'\n", + "\n", + "# location of the queries to generate materialized views from the MIMIC code repository\n", + "concepts_path = '../../concepts/'\n", + "\n", + "# specify user/password/where the database is\n", + "sqluser = 'postgres'\n", + "sqlpass = 'postgres'\n", + "dbname = 'mimic'\n", + "schema_name = 'mimiciii'\n", + "host = 'localhost'" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# connect to the database\n", + "con = psycopg2.connect(dbname=dbname, user=sqluser, password=sqlpass, host=host)\n", + "\n", + "# all queries are prepended by this statement to ensure we use the correct schema\n", + "query_schema = 'SET SEARCH_PATH TO public,' + schema_name + ';'\n", + "# note that by placing 'public' first, we create materialized views on the public schema\n", + "# ... but can still access tables on the `schema_name` table (usually mimiciii)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1 - Generate materialized views\n", + "\n", + "Before generating the aline cohort, we require the following materialized views to be already generated:\n", + "\n", + "* angus - from `angus.sql`\n", + "* heightweight - from `HeightWeightQuery.sql`\n", + "* aline_vaso_flag - from `aline_vaso_flag.sql`\n", + "\n", + "You can generate the above by executing the below codeblock. If you haven't changed the directory structure, the below should work, otherwise you may need to modify the `concepts_path` variable above." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating materialized view using ../../concepts/sepsis/angus.sql ... done.\n", + "Generating materialized view using ../../concepts/demographics/HeightWeightQuery.sql ... done.\n", + "Generating materialized view using ./aline_vaso_flag.sql ... done.\n" + ] + } + ], + "source": [ + "# Load in the query from file\n", + "f = os.path.join(concepts_path,'sepsis/angus.sql')\n", + "with open(f) as fp:\n", + " query = ''.join(fp.readlines())\n", + " \n", + "# Execute the query\n", + "print('Generating materialized view using {} ...'.format(f),end=' ')\n", + "execute_query_safely(query_schema + query, con)\n", + "print('done.')\n", + "\n", + "# Load in the query from file\n", + "f = os.path.join(concepts_path,'demographics/HeightWeightQuery.sql')\n", + "with open(f) as fp:\n", + " query = ''.join(fp.readlines())\n", + " \n", + "# Execute the query\n", + "print('Generating materialized view using {} ...'.format(f),end=' ')\n", + "execute_query_safely(query_schema + query, con)\n", + "print('done.')\n", + "\n", + "\n", + "# Load in the query from file\n", + "f = os.path.join(aline_path,'aline_vaso_flag.sql')\n", + "with open(f) as fp:\n", + " query = ''.join(fp.readlines())\n", + " \n", + "# Execute the query\n", + "print('Generating materialized view using {} ...'.format(f),end=' ')\n", + "execute_query_safely(query_schema + query, con)\n", + "print('done.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we generate the *aline_cohort* table using the aline_cohort.sql file.\n", + "\n", + "Afterwards, we can generate the remaining 6 materialized views in any order, as they all depend on only *aline_cohort* and raw MIMIC-III data." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generating materialized view using ./aline_cohort.sql ... done.\n" + ] + } + ], + "source": [ + "# Load in the query from file\n", + "f = os.path.join(aline_path,'aline_cohort.sql')\n", + "with open(f) as fp:\n", + " query = ''.join(fp.readlines())\n", + " \n", + "# Execute the query\n", + "print('Generating materialized view using {} ...'.format(f),end=' ')\n", + "execute_query_safely(query_schema + query, con)\n", + "print('done.')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "14024 - exclusion_readmission\n", + " 8948 - exclusion_shortstay\n", + "17399 - exclusion_vasopressors\n", + "17007 - exclusion_septic\n", + "12961 - exclusion_aline_before_admission\n", + "29452 - exclusion_not_ventilated_first24hr\n", + "15499 - exclusion_service_surgical\n", + "Will remove 49908 of 52430 patients.\n", + "\n", + "\n", + "Reproducing the flow of the flowchart from Chest paper.\n", + "52430 - removing 20816 (39.70%) patients - short stay // readmission.\n", + "31614 - removing 15738 (49.78%) patients - not ventilated in first 24 hours.\n", + "15876\n", + " - removing 5716 (36.00%) patients - additional 5716 36.00% - exclusion_septic\n", + " - removing 9112 (57.39%) patients - additional 5753 36.24% - exclusion_vasopressors\n", + " - removing 7603 (47.89%) patients - additional 1598 10.07% - exclusion_aline_before_admission\n", + " - removing 6750 (42.52%) patients - additional 287 1.81% - exclusion_service_surgical\n", + "2522 - final cohort.\n" + ] + } + ], + "source": [ + "query = query_schema + \"\"\"\n", + "select\n", + "icustay_id\n", + ", exclusion_readmission\n", + ", exclusion_shortstay\n", + ", exclusion_vasopressors\n", + ", exclusion_septic\n", + ", exclusion_aline_before_admission\n", + ", exclusion_not_ventilated_first24hr\n", + ", exclusion_service_surgical\n", + "from aline_cohort_all\n", + "\"\"\"\n", + "\n", + "# Load the result of the query into a dataframe\n", + "df = pd.read_sql_query(query, con)\n", + "\n", + "# print out exclusions\n", + "idxRem = df['icustay_id'].isnull()\n", + "for c in df.columns:\n", + " if 'exclusion_' in c:\n", + " print('{:5d} - {}'.format(df[c].sum(), c))\n", + " idxRem[df[c]==1] = True \n", + " \n", + "# final exclusion (excl sepsis/something else)\n", + "print('Will remove {} of {} patients.'.format(np.sum(idxRem), df.shape[0]))\n", + "\n", + "\n", + "print('')\n", + "print('')\n", + "print('Reproducing the flow of the flowchart from Chest paper.')\n", + "\n", + "# first stay\n", + "idxRem = (df['exclusion_readmission']==1) | (df['exclusion_shortstay']==1)\n", + "print('{:5d} - removing {:5d} ({:2.2f}%) patients - short stay // readmission.'.format(\n", + " df.shape[0], np.sum(idxRem), 100.0*np.mean(idxRem)))\n", + "df = df.loc[~idxRem,:]\n", + "\n", + "idxRem = df['exclusion_not_ventilated_first24hr']==1\n", + "print('{:5d} - removing {:5d} ({:2.2f}%) patients - not ventilated in first 24 hours.'.format(\n", + " df.shape[0], np.sum(idxRem), 100.0*np.mean(idxRem)))\n", + "\n", + "df = df.loc[df['exclusion_not_ventilated_first24hr']==0,:]\n", + "\n", + "print('{:5d}'.format(df.shape[0]))\n", + "idxRem = df['icustay_id'].isnull()\n", + "for c in ['exclusion_septic', 'exclusion_vasopressors',\n", + " 'exclusion_aline_before_admission', 'exclusion_service_surgical']:\n", + " print('{:5s} - removing {:5d} ({:2.2f}%) patients - additional {:5d} {:2.2f}% - {}'.format(\n", + " '', df[c].sum(), 100.0*df[c].mean(),\n", + " np.sum((idxRem==0)&(df[c]==1)), 100.0*np.mean((idxRem==0)&(df[c]==1)),\n", + " c))\n", + " idxRem = idxRem | (df[c]==1)\n", + "\n", + "df = df.loc[~idxRem,:]\n", + "print('{} - final cohort.'.format(df.shape[0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following codeblock loads in the SQL from each file in the aline subfolder and executes the query to generate the materialized view. We specifically exclude the aline_cohort.sql file as we have already executed it above. Again, the order of query execution does not matter for these queries. Note also that the filenames are the same as the created materialized view names for convenience." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Executing aline_bmi.sql ... done.\n", + "Executing aline_vitals.sql ... done.\n", + "Executing aline_sedatives.sql ... done.\n", + "Executing aline_icd.sql ... done.\n", + "Executing aline_labs.sql ... done.\n", + "Executing aline_sofa.sql ... done.\n" + ] + } + ], + "source": [ + "# get a list of all files in the subfolder\n", + "aline_queries = [f for f in os.listdir(aline_path) \n", + " # only keep the filename if it is actually a file (and not a directory)\n", + " if os.path.isfile(os.path.join(aline_path,f))\n", + " # and only keep the filename if it is an SQL file\n", + " & f.endswith('.sql')\n", + " # and we do *not* want aline_cohort - it's generated above\n", + " & (f != 'aline_cohort.sql') & (f != 'aline_vaso_flag.sql')]\n", + "\n", + "for f in aline_queries:\n", + " print('Executing {} ...'.format(f), end=' ')\n", + " \n", + " with open(os.path.join(aline_path,f)) as fp:\n", + " query = ''.join(fp.readlines())\n", + " \n", + " execute_query_safely(query_schema + query, con)\n", + " \n", + " print('done.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Summarize the cohort exclusions before we pull all the data together." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2 - Extract all covariates and outcome measures\n", + "\n", + "We now aggregate all the data from the various views into a single dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python2.7/dist-packages/numpy/lib/function_base.py:4116: RuntimeWarning: Invalid value encountered in percentile\n", + " interpolation=interpolation)\n" + ] + }, + { + "data": { + "text/html": [ + "
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countmeanstdmin25%50%75%max
subject_id2522.041220.21054729718.43664622.00000015635.00000031280.00000066550.50000099881.000000
hadm_id2522.0149910.32712129250.749642100016.000000124288.500000150295.000000175301.000000199962.000000
icustay_id2522.0250851.05670128929.896901200019.000000226867.250000251784.500000275922.500000299995.000000
age2522.064.54866450.09517216.20318442.34849357.10267273.750108300.052602
hour_icu_intime2522.012.7613017.5307730.0000005.00000014.00000019.00000023.000000
icu_hour_flag2522.00.4052340.4910350.0000000.0000000.0000001.0000001.000000
icu_los_day2522.03.6557723.4033981.0005791.7084842.5245144.33710137.304780
hospital_los_day2522.08.4431357.8208240.0381943.8064246.44652810.523090123.687500
hosp_exp_flag2522.00.1312450.3377350.0000000.0000000.0000000.0000001.000000
icu_exp_flag2522.00.0908010.2873830.0000000.0000000.0000000.0000001.000000
mort_day910.0429.589134690.7778140.000694NaNNaNNaN3731.972222
day_28_flag2522.00.1558290.3627650.0000000.0000000.0000000.0000001.000000
mort_day_censored2522.0250.882677435.9939630.000694150.000000150.000000150.0000003731.972222
censor_flag2522.00.6391750.4803350.0000000.0000001.0000001.0000001.000000
aline_flag2522.00.5142740.4998950.0000000.0000001.0000001.0000001.000000
aline_time_day1297.00.2867100.7113490.000694NaNNaNNaN11.843750
weight_first2445.080.88748526.9263831.000000NaNNaNNaN710.000000
height_first896.0169.89368317.06940115.240000NaNNaNNaN444.500000
bmi896.00.0034340.0145130.000815NaNNaNNaN0.434431
sofa_first2522.04.3655831.9977110.0000003.0000004.0000005.00000015.000000
map_first2522.084.70393717.379634-6.00000073.00000084.00000095.000000259.000000
hr_first2522.087.08445719.4837480.00000074.00000085.00000099.000000174.000000
temp_first2513.036.8077910.91232732.222222NaNNaNNaN40.444446
spo2_first2519.098.6423183.02036647.000000NaNNaNNaN100.000000
bun_first2473.020.19005315.1389291.000000NaNNaNNaN139.000000
creatinine_first2473.01.1403561.1688190.100000NaNNaNNaN18.800000
chloride_first2480.0104.0754035.93622556.000000NaNNaNNaN129.000000
hgb_first2478.012.2261902.2363094.100000NaNNaNNaN19.800000
platelet_first2470.0240.640081101.2526156.000000NaNNaNNaN1313.000000
potassium_first2484.04.0654190.6810241.800000NaNNaNNaN9.200000
sodium_first2480.0139.5016134.87611774.000000NaNNaNNaN165.000000
tco2_first2496.025.1221965.2022713.000000NaNNaNNaN53.000000
wbc_first2469.012.2298506.1644980.200000NaNNaNNaN94.000000
chf_flag2522.00.0709750.2568350.0000000.0000000.0000000.0000001.000000
afib_flag2522.00.1383820.3453690.0000000.0000000.0000000.0000001.000000
renal_flag2522.00.0650280.2466240.0000000.0000000.0000000.0000001.000000
liver_flag2522.00.0578910.2335830.0000000.0000000.0000000.0000001.000000
copd_flag2522.00.0154640.1234130.0000000.0000000.0000000.0000001.000000
cad_flag2522.00.0935770.2912960.0000000.0000000.0000000.0000001.000000
stroke_flag2522.00.1586040.3653790.0000000.0000000.0000000.0000001.000000
malignancy_flag2522.00.1562250.3631410.0000000.0000000.0000000.0000001.000000
respfail_flag2522.00.3655830.4816890.0000000.0000000.0000001.0000001.000000
endocarditis_flag2522.00.0000000.0000000.0000000.0000000.0000000.0000000.000000
ards_flag2522.00.0170500.1294830.0000000.0000000.0000000.0000001.000000
pneumonia_flag2522.00.1780330.3826170.0000000.0000000.0000000.0000001.000000
sedative_flag2522.00.2113400.4083400.0000000.0000000.0000000.0000001.000000
midazolam_flag2522.00.0249800.1560960.0000000.0000000.0000000.0000001.000000
fentanyl_flag2522.00.0408410.1979600.0000000.0000000.0000000.0000001.000000
propofol_flag2522.00.1879460.3907470.0000000.0000000.0000000.0000001.000000
\n", + "
" + ], + "text/plain": [ + " count mean std min \\\n", + "subject_id 2522.0 41220.210547 29718.436646 22.000000 \n", + "hadm_id 2522.0 149910.327121 29250.749642 100016.000000 \n", + "icustay_id 2522.0 250851.056701 28929.896901 200019.000000 \n", + "age 2522.0 64.548664 50.095172 16.203184 \n", + "hour_icu_intime 2522.0 12.761301 7.530773 0.000000 \n", + "icu_hour_flag 2522.0 0.405234 0.491035 0.000000 \n", + "icu_los_day 2522.0 3.655772 3.403398 1.000579 \n", + "hospital_los_day 2522.0 8.443135 7.820824 0.038194 \n", + "hosp_exp_flag 2522.0 0.131245 0.337735 0.000000 \n", + "icu_exp_flag 2522.0 0.090801 0.287383 0.000000 \n", + "mort_day 910.0 429.589134 690.777814 0.000694 \n", + "day_28_flag 2522.0 0.155829 0.362765 0.000000 \n", + "mort_day_censored 2522.0 250.882677 435.993963 0.000694 \n", + "censor_flag 2522.0 0.639175 0.480335 0.000000 \n", + "aline_flag 2522.0 0.514274 0.499895 0.000000 \n", + "aline_time_day 1297.0 0.286710 0.711349 0.000694 \n", + "weight_first 2445.0 80.887485 26.926383 1.000000 \n", + "height_first 896.0 169.893683 17.069401 15.240000 \n", + "bmi 896.0 0.003434 0.014513 0.000815 \n", + "sofa_first 2522.0 4.365583 1.997711 0.000000 \n", + "map_first 2522.0 84.703937 17.379634 -6.000000 \n", + "hr_first 2522.0 87.084457 19.483748 0.000000 \n", + "temp_first 2513.0 36.807791 0.912327 32.222222 \n", + "spo2_first 2519.0 98.642318 3.020366 47.000000 \n", + "bun_first 2473.0 20.190053 15.138929 1.000000 \n", + "creatinine_first 2473.0 1.140356 1.168819 0.100000 \n", + "chloride_first 2480.0 104.075403 5.936225 56.000000 \n", + "hgb_first 2478.0 12.226190 2.236309 4.100000 \n", + "platelet_first 2470.0 240.640081 101.252615 6.000000 \n", + "potassium_first 2484.0 4.065419 0.681024 1.800000 \n", + "sodium_first 2480.0 139.501613 4.876117 74.000000 \n", + "tco2_first 2496.0 25.122196 5.202271 3.000000 \n", + "wbc_first 2469.0 12.229850 6.164498 0.200000 \n", + "chf_flag 2522.0 0.070975 0.256835 0.000000 \n", + "afib_flag 2522.0 0.138382 0.345369 0.000000 \n", + "renal_flag 2522.0 0.065028 0.246624 0.000000 \n", + "liver_flag 2522.0 0.057891 0.233583 0.000000 \n", + "copd_flag 2522.0 0.015464 0.123413 0.000000 \n", + "cad_flag 2522.0 0.093577 0.291296 0.000000 \n", + "stroke_flag 2522.0 0.158604 0.365379 0.000000 \n", + "malignancy_flag 2522.0 0.156225 0.363141 0.000000 \n", + "respfail_flag 2522.0 0.365583 0.481689 0.000000 \n", + "endocarditis_flag 2522.0 0.000000 0.000000 0.000000 \n", + "ards_flag 2522.0 0.017050 0.129483 0.000000 \n", + "pneumonia_flag 2522.0 0.178033 0.382617 0.000000 \n", + "sedative_flag 2522.0 0.211340 0.408340 0.000000 \n", + "midazolam_flag 2522.0 0.024980 0.156096 0.000000 \n", + "fentanyl_flag 2522.0 0.040841 0.197960 0.000000 \n", + "propofol_flag 2522.0 0.187946 0.390747 0.000000 \n", + "\n", + " 25% 50% 75% max \n", + "subject_id 15635.000000 31280.000000 66550.500000 99881.000000 \n", + "hadm_id 124288.500000 150295.000000 175301.000000 199962.000000 \n", + "icustay_id 226867.250000 251784.500000 275922.500000 299995.000000 \n", + "age 42.348493 57.102672 73.750108 300.052602 \n", + "hour_icu_intime 5.000000 14.000000 19.000000 23.000000 \n", + "icu_hour_flag 0.000000 0.000000 1.000000 1.000000 \n", + "icu_los_day 1.708484 2.524514 4.337101 37.304780 \n", + "hospital_los_day 3.806424 6.446528 10.523090 123.687500 \n", + "hosp_exp_flag 0.000000 0.000000 0.000000 1.000000 \n", + "icu_exp_flag 0.000000 0.000000 0.000000 1.000000 \n", + "mort_day NaN NaN NaN 3731.972222 \n", + "day_28_flag 0.000000 0.000000 0.000000 1.000000 \n", + "mort_day_censored 150.000000 150.000000 150.000000 3731.972222 \n", + "censor_flag 0.000000 1.000000 1.000000 1.000000 \n", + "aline_flag 0.000000 1.000000 1.000000 1.000000 \n", + "aline_time_day NaN NaN NaN 11.843750 \n", + "weight_first NaN NaN NaN 710.000000 \n", + "height_first NaN NaN NaN 444.500000 \n", + "bmi NaN NaN NaN 0.434431 \n", + "sofa_first 3.000000 4.000000 5.000000 15.000000 \n", + "map_first 73.000000 84.000000 95.000000 259.000000 \n", + "hr_first 74.000000 85.000000 99.000000 174.000000 \n", + "temp_first NaN NaN NaN 40.444446 \n", + "spo2_first NaN NaN NaN 100.000000 \n", + "bun_first NaN NaN NaN 139.000000 \n", + "creatinine_first NaN NaN NaN 18.800000 \n", + "chloride_first NaN NaN NaN 129.000000 \n", + "hgb_first NaN NaN NaN 19.800000 \n", + "platelet_first NaN NaN NaN 1313.000000 \n", + "potassium_first NaN NaN NaN 9.200000 \n", + "sodium_first NaN NaN NaN 165.000000 \n", + "tco2_first NaN NaN NaN 53.000000 \n", + "wbc_first NaN NaN NaN 94.000000 \n", + "chf_flag 0.000000 0.000000 0.000000 1.000000 \n", + "afib_flag 0.000000 0.000000 0.000000 1.000000 \n", + "renal_flag 0.000000 0.000000 0.000000 1.000000 \n", + "liver_flag 0.000000 0.000000 0.000000 1.000000 \n", + "copd_flag 0.000000 0.000000 0.000000 1.000000 \n", + "cad_flag 0.000000 0.000000 0.000000 1.000000 \n", + "stroke_flag 0.000000 0.000000 0.000000 1.000000 \n", + "malignancy_flag 0.000000 0.000000 0.000000 1.000000 \n", + "respfail_flag 0.000000 0.000000 1.000000 1.000000 \n", + "endocarditis_flag 0.000000 0.000000 0.000000 0.000000 \n", + "ards_flag 0.000000 0.000000 0.000000 1.000000 \n", + "pneumonia_flag 0.000000 0.000000 0.000000 1.000000 \n", + "sedative_flag 0.000000 0.000000 0.000000 1.000000 \n", + "midazolam_flag 0.000000 0.000000 0.000000 1.000000 \n", + "fentanyl_flag 0.000000 0.000000 0.000000 1.000000 \n", + "propofol_flag 0.000000 0.000000 0.000000 1.000000 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Load in the query from file\n", + "query = query_schema + \"\"\"\n", + "--FINAL QUERY\n", + "select\n", + " co.subject_id, co.hadm_id, co.icustay_id\n", + "\n", + " -- static variables from patient tracking tables\n", + " , co.age\n", + " , co.gender\n", + " -- , co.gender_num -- gender, 0=F, 1=M\n", + " , co.intime as icustay_intime\n", + " , co.day_icu_intime -- day of week, text\n", + " --, co.day_icu_intime_num -- day of week, numeric (0=Sun, 6=Sat)\n", + " , co.hour_icu_intime -- hour of ICU admission (24 hour clock)\n", + " , case \n", + " when co.hour_icu_intime >= 7\n", + " and co.hour_icu_intime < 19\n", + " then 1\n", + " else 0\n", + " end as icu_hour_flag\n", + " , co.outtime as icustay_outtime\n", + "\n", + " -- outcome variables\n", + " , co.icu_los_day\n", + " , co.hospital_los_day\n", + " , co.hosp_exp_flag -- 1/0 patient died within current hospital stay\n", + " , co.icu_exp_flag -- 1/0 patient died within current ICU stay\n", + " , co.mort_day -- days from ICU admission to mortality, if they died\n", + " , co.day_28_flag -- 1/0 whether the patient died 28 days after *ICU* admission\n", + " , co.mort_day_censored -- days until patient died *or* 150 days (150 days is our censor time)\n", + " , co.censor_flag -- 1/0 did this patient have 150 imputed in mort_day_censored\n", + "\n", + " -- aline flags\n", + " -- , co.initial_aline_flag -- always 0, we remove patients admitted w/ aline\n", + " , co.aline_flag -- 1/0 did the patient receive an aline\n", + " , co.aline_time_day -- if the patient received aline, fractional days until aline put in\n", + "\n", + " -- demographics extracted using regex + echos\n", + " , bmi.weight as weight_first\n", + " , bmi.height as height_first\n", + " , bmi.bmi\n", + "\n", + " -- service patient was admitted to the ICU under\n", + " , co.service_unit\n", + "\n", + " -- severity of illness just before ventilation\n", + " , so.sofa as sofa_first\n", + "\n", + " -- vital sign value just preceeding ventilation\n", + " , vi.map as map_first\n", + " , vi.heartrate as hr_first\n", + " , vi.temperature as temp_first\n", + " , vi.spo2 as spo2_first\n", + "\n", + " -- labs!\n", + " , labs.bun_first\n", + " , labs.creatinine_first\n", + " , labs.chloride_first\n", + " , labs.hgb_first\n", + " , labs.platelet_first\n", + " , labs.potassium_first\n", + " , labs.sodium_first\n", + " , labs.tco2_first\n", + " , labs.wbc_first\n", + "\n", + " -- comorbidities extracted using ICD-9 codes\n", + " , icd.chf as chf_flag\n", + " , icd.afib as afib_flag\n", + " , icd.renal as renal_flag\n", + " , icd.liver as liver_flag\n", + " , icd.copd as copd_flag\n", + " , icd.cad as cad_flag\n", + " , icd.stroke as stroke_flag\n", + " , icd.malignancy as malignancy_flag\n", + " , icd.respfail as respfail_flag\n", + " , icd.endocarditis as endocarditis_flag\n", + " , icd.ards as ards_flag\n", + " , icd.pneumonia as pneumonia_flag\n", + "\n", + " -- sedative use\n", + " , sed.sedative_flag\n", + " , sed.midazolam_flag\n", + " , sed.fentanyl_flag\n", + " , sed.propofol_flag\n", + " \n", + "from aline_cohort co\n", + "-- The following tables are generated by code within this repository\n", + "left join aline_sofa so\n", + "on co.icustay_id = so.icustay_id\n", + "left join aline_bmi bmi\n", + " on co.icustay_id = bmi.icustay_id\n", + "left join aline_icd icd\n", + " on co.hadm_id = icd.hadm_id\n", + "left join aline_vitals vi\n", + " on co.icustay_id = vi.icustay_id\n", + "left join aline_labs labs\n", + " on co.icustay_id = labs.icustay_id\n", + "left join aline_sedatives sed\n", + " on co.icustay_id = sed.icustay_id\n", + "order by co.icustay_id\n", + "\"\"\"\n", + "\n", + "# Load the result of the query into a dataframe\n", + "df = pd.read_sql_query(query, con)\n", + "df.describe().T" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we need to remove obvious outliers, including correcting ages > 200 to 91.4 (i.e. replace anonymized ages with 91.4, the median age of patients older than 89)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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MA5uuu7dtdQ1r6e5av9VB79ZJbr8P7b+wrELmNQ0t946trgM49BhNAgCAYRlN\nAgCAYQnDAAAMSxgGAGBYwjAAAMMShgEAGJYwDADAsP4/DDlpkjdptyYAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the rest of the distributions\n", + "for col in df.columns:\n", + " if df.dtypes[col] in ('int64','float64'):\n", + " plt.figure(figsize=[12,6])\n", + " plt.hist(df[col].dropna(), bins=50, normed=True)\n", + " plt.xlabel(col,fontsize=24)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# apply corrections\n", + "df.loc[df['age']>89, 'age'] = 91.4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3 - Write to file" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "df.to_csv('aline_data.csv',index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 4 - Create a propensity score using this data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will create the propensity score using R in the R markdown file `aline_propensity_score.Rmd`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5 - Close the connection to the database" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "con.close()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/aline/aline_bmi.sql b/notebooks/aline/aline_bmi.sql new file mode 100644 index 0000000..4fc3599 --- /dev/null +++ b/notebooks/aline/aline_bmi.sql @@ -0,0 +1,18 @@ + +DROP MATERIALIZED VIEW IF EXISTS ALINE_BMI CASCADE; +CREATE MATERIALIZED VIEW ALINE_BMI as + +select + co.icustay_id + , case + when hw.weight_first is not null and hw.height_first is not null + then (hw.weight_first / (hw.height_first*hw.height_first)) + else null + end as BMI + , hw.height_first as height + , hw.weight_first as weight + +from aline_cohort co +left join heightweight hw + on co.icustay_id = hw.icustay_id +order by co.icustay_id; diff --git a/notebooks/aline/aline_cohort.sql b/notebooks/aline/aline_cohort.sql new file mode 100644 index 0000000..9a68603 --- /dev/null +++ b/notebooks/aline/aline_cohort.sql @@ -0,0 +1,231 @@ +-- This query defines the cohort used for the ALINE study. + +-- Inclusion criteria: +-- adult patients +-- In ICU for at least 24 hours +-- First ICU admission +-- mechanical ventilation within the first 12 hours +-- medical or surgical ICU admission + +-- Exclusion criteria: +-- **Angus sepsis +-- **On vasopressors (?is this different than on dobutamine) +-- IAC placed before admission +-- CSRU patients + +-- **These exclusion criteria are applied in the data.sql file. + +-- This query also extracts demographics, and necessary preliminary flags needed +-- for data extraction. For example, since all data is extracted before +-- ventilation, we need to extract start times of ventilation + + +-- This query requires the following tables: +-- ventdurations - extracted by mimic-code/etc/ventilation-durations.sql + + +DROP MATERIALIZED VIEW IF EXISTS ALINE_COHORT_ALL CASCADE; +CREATE MATERIALIZED VIEW ALINE_COHORT_ALL as + +-- get start time of arterial line +-- Definition of arterial line insertion: +-- First measurement of invasive blood pressure +with a as +( + select icustay_id + , min(charttime) as starttime_aline + from chartevents + where icustay_id is not null + and valuenum is not null + and itemid in + ( + 51, -- Arterial BP [Systolic] + 6701, -- Arterial BP #2 [Systolic] + 220050, -- Arterial Blood Pressure systolic + + 8368, -- Arterial BP [Diastolic] + 8555, -- Arterial BP #2 [Diastolic] + 220051, -- Arterial Blood Pressure diastolic + + 52, --"Arterial BP Mean" + 6702, -- Arterial BP Mean #2 + 220052, --"Arterial Blood Pressure mean" + 225312 --"ART BP mean" + ) + group by icustay_id +) +-- get intime/outtime from vitals rather than administrative data +, co_intime as +( + select ie.icustay_id, min(charttime) as intime, max(charttime) as outtime + from icustays ie + left join chartevents ce + on ie.icustay_id = ce.icustay_id + and ce.charttime between ie.intime - interval '12' hour and ie.outtime + interval '12' hour + and ce.itemid in (211, 220045) + group by ie.icustay_id +) +-- first time ventilation was started +-- last time ventilation was stopped +, ve as +( + select icustay_id + , sum(extract(epoch from endtime-starttime))/24.0/60.0/60.0 as vent_day + , min(starttime) as starttime_first + , max(endtime) as endtime_last + from ventdurations vd + group by icustay_id +) +, serv as +( + select ie.icustay_id, se.curr_service + , ROW_NUMBER() over (partition by ie.icustay_id order by se.transfertime DESC) as rn + from icustays ie + inner join services se + on ie.hadm_id = se.hadm_id + and se.transfertime < ie.intime + interval '2' hour +) +-- cohort view - used to define other concepts +, co as +( + select + ie.subject_id, ie.hadm_id, ie.icustay_id + , co.intime + , to_char(co.intime, 'day') as day_icu_intime + , extract(dow from co.intime) as day_icu_intime_num + , extract(hour from co.intime) as hour_icu_intime + , co.outtime + + , ROW_NUMBER() over (partition by ie.subject_id order by adm.admittime, co.intime) as stay_num + , extract(epoch from (co.intime - pat.dob))/365.242/24.0/60.0/60.0 as age + , pat.gender + , case when pat.gender = 'M' then 1 else 0 end as gender_num + , vf.vaso_flag + , sep.angus + -- service + + -- collapse ethnicity into fixed categories + + -- time of a-line + , a.starttime_aline + , case when a.starttime_aline is not null then 1 else 0 end as aline_flag + , extract(epoch from (a.starttime_aline - co.intime))/24.0/60.0/60.0 as aline_time_day + , case + when a.starttime_aline is not null + and a.starttime_aline <= co.intime + then 1 + else 0 + end as initial_aline_flag + + -- ventilation + , case when ve.icustay_id is not null then 1 else 0 end as vent_flag + , case when ve.starttime_first < co.intime + interval '12' hour then 1 else 0 end as vent_1st_12hr + , case when ve.starttime_first < co.intime + interval '24' hour then 1 else 0 end as vent_1st_24hr + + -- binary flag: were they ventilated before a-line insertion? + , case + -- if they were never given an aline, this is a non-sensical question + when a.starttime_aline is null then null + -- aline given for sure after ventilation + when a.starttime_aline > co.intime + interval '1' hour and ve.starttime_first<=a.starttime_aline then 1 + -- aline given for sure after ventilation + when a.starttime_aline > co.intime + interval '1' hour and ve.starttime_first>a.starttime_aline then 0 + else NULL + end as vent_b4_aline + + -- number of days on a ventilator + , ve.vent_day + + -- number of days free of ventilator after *last* extubation + , extract(epoch from (ie.outtime - ve.endtime_last))/24.0/60.0/60.0 as vent_free_day + + -- number of days *not* on a ventilator + , extract(epoch from (ie.outtime - co.intime))/24.0/60.0/60.0 - vent_day as vent_off_day + + + , ve.starttime_first as vent_starttime + , ve.endtime_last as vent_endtime + + -- cohort flags // demographics + , extract(epoch from (ie.outtime - co.intime))/24.0/60.0/60.0 as icu_los_day + , extract(epoch from (adm.dischtime - adm.admittime))/24.0/60.0/60.0 as hospital_los_day + + -- will be used to exclude patients in CSRU + -- also only include those in CMED or SURG + , s.curr_service as service_unit + , case when s.curr_service like '%SURG' or s.curr_service like '%ORTHO%' then 1 + when s.curr_service = 'CMED' then 2 + when s.curr_service in ('CSURG','VSURG','TSURG') then 3 + else 0 + end + as service_num + + -- outcome + , case when adm.deathtime is not null then 1 else 0 end as hosp_exp_flag + , case when adm.deathtime <= ie.outtime then 1 else 0 end as icu_exp_flag + , case when pat.dod <= (co.intime + interval '28' day) then 1 else 0 end as day_28_flag + , extract(epoch from (pat.dod - adm.admittime))/24.0/60.0/60.0 as mort_day + + , case when pat.dod is null + then 150 -- patient deaths are censored 150 days after admission + else extract(epoch from (pat.dod - adm.admittime))/24.0/60.0/60.0 + end as mort_day_censored + , case when pat.dod is null then 1 else 0 end as censor_flag + + from co_intime co + inner join icustays ie + on co.icustay_id = ie.icustay_id + inner join admissions adm + on ie.hadm_id = adm.hadm_id + inner join patients pat + on ie.subject_id = pat.subject_id + left join a + on ie.icustay_id = a.icustay_id + left join ve + on ie.icustay_id = ve.icustay_id + left join serv s + on ie.icustay_id = s.icustay_id + and s.rn = 1 + left join aline_vaso_flag vf + on ie.icustay_id = vf.icustay_id + left join angus_sepsis sep + on ie.hadm_id = sep.hadm_id + where co.intime > (pat.dob + interval '16' year) -- only adults +) +select + co.* + , case when stay_num > 1 then 1 else 0 end as exclusion_readmission -- first ICU stay + , case when icu_los_day < 1 then 1 else 0 end exclusion_shortstay -- one day in the ICU + , case when vaso_flag = 1 then 1 else 0 end as exclusion_vasopressors + , case when angus = 1 then 1 else 0 end as exclusion_septic + , case when initial_aline_flag = 1 then 1 else 0 end exclusion_aline_before_admission -- aline must be placed later than admission + -- exclusion: IAC placement was performed prior to endotracheal intubation and initiation of mechanical ventilation + -- we do not apply this criteria since it's unclear if this was actually done in the original aline paper + -- , case when vent_b4_aline = 0 then 1 else 0 end as exclusion_aline_before_vent + , case when vent_starttime is null or vent_starttime > intime + interval '24' hour then 1 else 0 end exclusion_not_ventilated_first24hr -- were ventilated + -- above also requires ventilated within first 24 hours + , case when service_unit in + ( + -- we need to approximate CCU and CSRU using hospital service + -- paper only says CSRU but the code did both CCU/CSRU + -- this is the best guess + 'CMED','CSURG','VSURG','TSURG' -- cardiac/vascular/thoracic surgery + ) then 1 else 0 end as exclusion_service_surgical + -- "medical or surgical ICU admission" + +from co +order by icustay_id; + + +CREATE MATERIALIZED VIEW ALINE_COHORT AS +select + co.* +from ALINE_COHORT_ALL co +where exclusion_readmission = 0 -- first ICU stay +and exclusion_shortstay = 0 -- one day in the ICU +and exclusion_vasopressors = 0 +and exclusion_septic = 0 +and exclusion_aline_before_admission = 0 -- aline placed later than admission +-- and exclusion_aline_before_vent = 0 +and exclusion_not_ventilated_first24hr = 0 -- were ventilated within first 24 hours +and exclusion_service_surgical = 0; diff --git a/notebooks/aline/aline_icd.sql b/notebooks/aline/aline_icd.sql new file mode 100644 index 0000000..a14160b --- /dev/null +++ b/notebooks/aline/aline_icd.sql @@ -0,0 +1,67 @@ +-- Extract data which is based on ICD-9 codes +DROP MATERIALIZED VIEW IF EXISTS ALINE_ICD CASCADE; +CREATE MATERIALIZED VIEW ALINE_ICD AS +select + co.hadm_id + , max(case when icd9_code in + ( '03642','07422','09320','09321','09322','09323','09324','09884' + ,'11281','11504','11514','11594' + ,' 3911',' 4210',' 4211',' 4219' + ,'42490','42491','42499' + ) then 1 else 0 end) as endocarditis + + -- chf + , max(case when icd9_code in + ( '39891','40201','40291','40491','40413' + ,'40493','4280 ','4281 ','42820','42821' + ,'42822','42823','42830','42831','42832' + ,'42833','42840','42841','42842','42843' + ,'4289 ','428 ','4282 ','4283 ','4284 ' + ) then 1 else 0 end) as chf + + -- atrial fibrilliation or atrial flutter + , max(case when icd9_code like '4273%' then 1 else 0 end) as afib + + -- renal + , max(case when icd9_code like '585%' then 1 else 0 end) as renal + + -- liver + , max(case when icd9_code like '571%' then 1 else 0 end) as liver + + -- copd + , max(case when icd9_code in + ( '4660 ','490 ','4910 ','4911 ','49120' + ,'49121','4918 ','4919 ','4920 ','4928 ' + ,'494 ','4940 ','4941 ','496 ') then 1 else 0 end) as copd + + -- coronary artery disease + , max(case when icd9_code like '414%' then 1 else 0 end) as cad + + -- stroke + , max(case when icd9_code like '430%' + or icd9_code like '431%' + or icd9_code like '432%' + or icd9_code like '433%' + or icd9_code like '434%' + then 1 else 0 end) as stroke + + -- malignancy, includes remissions + , max(case when icd9_code between '140' and '239' then 1 else 0 end) as malignancy + + -- resp failure + , max(case when icd9_code like '518%' then 1 else 0 end) as respfail + + -- ARDS + , max(case when icd9_code = '51882' or icd9_code = '5185 ' then 1 else 0 end) as ards + + -- pneumonia + , max(case when icd9_code between '486' and '48881' + or icd9_code between '480' and '48099' + or icd9_code between '482' and '48299' + or icd9_code between '506' and '5078' + then 1 else 0 end) as pneumonia +from aline_cohort co +left join diagnoses_icd icd + on co.hadm_id = icd.hadm_id +group by co.hadm_id +order by co.hadm_id; diff --git a/notebooks/aline/aline_labs.sql b/notebooks/aline/aline_labs.sql new file mode 100644 index 0000000..f7efec0 --- /dev/null +++ b/notebooks/aline/aline_labs.sql @@ -0,0 +1,106 @@ + + +DROP MATERIALIZED VIEW IF EXISTS ALINE_LABS CASCADE; +CREATE MATERIALIZED VIEW ALINE_LABS as + +with labs_preceeding as +( + select co.icustay_id + , l.valuenum, l.charttime + , case + when itemid = 51006 then 'BUN' + when itemid = 50806 then 'CHLORIDE' + when itemid = 50902 then 'CHLORIDE' + when itemid = 50912 then 'CREATININE' + when itemid = 50811 then 'HEMOGLOBIN' + when itemid = 51222 then 'HEMOGLOBIN' + when itemid = 51265 then 'PLATELET' + when itemid = 50822 then 'POTASSIUM' + when itemid = 50971 then 'POTASSIUM' + when itemid = 50824 then 'SODIUM' + when itemid = 50983 then 'SODIUM' + when itemid = 50803 then 'TOTALCO2' -- actually is 'BICARBONATE' + when itemid = 50882 then 'TOTALCO2' -- actually is 'BICARBONATE' + when itemid = 50804 then 'TOTALCO2' + when itemid = 51300 then 'WBC' + when itemid = 51301 then 'WBC' + else null + end as label + , case when l.charttime > co.vent_starttime then 1 else 0 end as obs_after_vent + from ALINE_COHORT co + inner join labevents l + on l.subject_id = co.subject_id + and l.charttime <= co.vent_starttime + interval '4' hour + and l.charttime >= co.vent_starttime - interval '2' day + where l.itemid in + ( + 51300,51301 -- wbc + ,50811,51222 -- hgb + ,51265 -- platelet + ,50824, 50983 -- sodium + ,50822, 50971 -- potassium + ,50804 -- Total CO2 or ... + ,50803, 50882 -- bicarbonate + ,50806,50902 -- chloride + ,51006 -- bun + ,50912 -- creatinine + ) + and valuenum is not null +) +, labs_rn as +( + select + icustay_id, valuenum, label, obs_after_vent + , ROW_NUMBER() over (partition by icustay_id, label, obs_after_vent order by charttime DESC) as rn + from labs_preceeding +) +, labs_grp as +( + select + icustay_id + , coalesce(max(case when label = 'BUN' and obs_after_vent = 0 then valuenum else null end), + max(case when label = 'BUN' and obs_after_vent = 1 then valuenum else null end) + ) as BUN + , coalesce(max(case when label = 'CHLORIDE' and obs_after_vent = 0 then valuenum else null end), + max(case when label = 'CHLORIDE' and obs_after_vent = 1 then valuenum else null end) + ) as CHLORIDE + , coalesce(max(case when label = 'CREATININE' and obs_after_vent = 0 then valuenum else null end), + max(case when label = 'CREATININE' and obs_after_vent = 1 then valuenum else null end) + ) as CREATININE + , coalesce(max(case when label = 'HEMOGLOBIN' and obs_after_vent = 0 then valuenum else null end), + max(case when label = 'HEMOGLOBIN' and obs_after_vent = 1 then valuenum else null end) + ) as HEMOGLOBIN + , coalesce(max(case when label = 'PLATELET' and obs_after_vent = 0 then valuenum else null end), + max(case when label = 'PLATELET' and obs_after_vent = 1 then valuenum else null end) + ) as PLATELET + , coalesce(max(case when label = 'POTASSIUM' and obs_after_vent = 0 then valuenum else null end), + max(case when label = 'POTASSIUM' and obs_after_vent = 1 then valuenum else null end) + ) as POTASSIUM + , coalesce(max(case when label = 'SODIUM' and obs_after_vent = 0 then valuenum else null end), + max(case when label = 'SODIUM' and obs_after_vent = 1 then valuenum else null end) + ) as SODIUM + , coalesce(max(case when label = 'TOTALCO2' and obs_after_vent = 0 then valuenum else null end), + max(case when label = 'TOTALCO2' and obs_after_vent = 1 then valuenum else null end) + ) as TOTALCO2 + , coalesce(max(case when label = 'WBC' and obs_after_vent = 0 then valuenum else null end), + max(case when label = 'WBC' and obs_after_vent = 1 then valuenum else null end) + ) as WBC + + from labs_rn + where rn = 1 + group by icustay_id +) +select co.icustay_id + , lg.bun as bun_first + , lg.chloride as chloride_first + , lg.creatinine as creatinine_first + , lg.HEMOGLOBIN as hgb_first + , lg.platelet as platelet_first + , lg.potassium as potassium_first + , lg.sodium as sodium_first + , lg.TOTALCO2 as tco2_first + , lg.wbc as wbc_first + +from ALINE_COHORT co +left join labs_grp lg + on co.icustay_id = lg.icustay_id diff --git a/notebooks/aline/aline_propensity_score.Rmd b/notebooks/aline/aline_propensity_score.Rmd new file mode 100644 index 0000000..60f645c --- /dev/null +++ b/notebooks/aline/aline_propensity_score.Rmd @@ -0,0 +1,162 @@ +--- +title: "aline-propensity-score" +author: "Alistair Johnson, Jesse Raffa" +date: "May 15, 2017" +output: html_document +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) +``` + + +## Analysis of arterial line dataset + +This notebook creates a propensity score using a dataset of patients with indwelling arterial catheters (IACs). The propensity score is built using physiology and administrative data to predict the need for an IAC. Patients are then matched, and we statistically compare the mortality rate in the two matched groups. + +## Load data + +First, we load the data and convert some variables into factors. Note this code assumes that you have the csv file available in "~/git/mimic-code/notebooks/aline/". + +```{r load} +wdpath = paste(path.expand("~"),'git/mimic-code/notebooks/aline/',sep='/') +setwd(wdpath) +dataset = read.csv(file="aline_data.csv",head=TRUE,sep=",") +``` + + +```{r factorize, echo = FALSE} +dataset$icustay_id = factor(dataset$icustay_id) +dataset$day_28_flag = factor(dataset$day_28_flag, levels=c(0,1)) +dataset$gender = factor(dataset$gender, levels=c("F","M")) +dataset$day_icu_intime = factor(dataset$day_icu_intime) +dataset$hour_icu_intime = factor(dataset$hour_icu_intime) +dataset$icu_hour_flag = factor(dataset$icu_hour_flag, levels=c(0,1)) +#dataset$sepsis_flag = factor(dataset$sepsis_flag, levels=c(0,1)) +dataset$sedative_flag = factor(dataset$sedative_flag, levels=c(0,1)) +dataset$fentanyl_flag = factor(dataset$fentanyl_flag, levels=c(0,1)) +dataset$midazolam_flag = factor(dataset$midazolam_flag, levels=c(0,1)) +dataset$propofol_flag = factor(dataset$propofol_flag, levels=c(0,1)) +#dataset$dilaudid_flag = factor(dataset$dilaudid_flag, levels=c(0,1)) +dataset$chf_flag = factor(dataset$chf_flag, levels=c(0,1)) +dataset$afib_flag = factor(dataset$afib_flag, levels=c(0,1)) +dataset$renal_flag = factor(dataset$renal_flag, levels=c(0,1)) +dataset$liver_flag = factor(dataset$liver_flag, levels=c(0,1)) +dataset$copd_flag = factor(dataset$copd_flag, levels=c(0,1)) +dataset$cad_flag = factor(dataset$cad_flag, levels=c(0,1)) +dataset$stroke_flag = factor(dataset$stroke_flag, levels=c(0,1)) +dataset$malignancy_flag = factor(dataset$malignancy_flag, levels=c(0,1)) +dataset$respfail_flag = factor(dataset$respfail_flag, levels=c(0,1)) +dataset$ards_flag = factor(dataset$ards_flag, levels=c(0,1)) +dataset$pneumonia_flag = factor(dataset$pneumonia_flag, levels=c(0,1)) + +# custom factor +dataset$service_surg = factor( dataset$service_unit == 'SURG', levels=c(FALSE,TRUE)) +``` + +```{r impute, echo = FALSE} +# we could impute data if we like - e.g. the below imputes the mean +# we currently do complete case analysis however +imputeFlag = 0 +if (imputeFlag != 0){ + print("Imputing missing data for some features...") +for (col in c("weight_first","temp_first","spo2_first", + "bun_first","creatinine_first", "chloride_first", "hgb_first", + "platelet_first", "potassium_first", "sodium_first", "tco2_first", "wbc_first")) +{ + print(paste("Imputing data for: ", col)) + dataset[is.na(dataset[,col]),col] = mean(dataset[,col], na.rm=TRUE) +} +} +``` + +If we did not remove any missing data above, we need to subselect complete cases for analysis. + +```{r completecases, echo = FALSE} +# subselect the variables +dat = dataset[,c("aline_flag", + "age","gender","weight_first","sofa_first","service_surg", + "day_icu_intime","hour_icu_intime", + "chf_flag","afib_flag","renal_flag", + "liver_flag","copd_flag","cad_flag","stroke_flag", + "malignancy_flag","respfail_flag", + "map_first","hr_first","temp_first","spo2_first", + "bun_first","chloride_first","creatinine_first", + "hgb_first","platelet_first", + "potassium_first","sodium_first","tco2_first","wbc_first")] + +idxKeep = complete.cases(dat) +dat = dat[idxKeep,] +y <- dataset[idxKeep,"day_28_flag"] + +print(paste('Removed', sum(!idxKeep),'rows with missing data.')) +``` + +## Propensity score model + +Now, we build a logistic regression, using all the features, to predict the need for an arterial line catheter from physiology and administrative data. + +```{r glm} +# fit GLM +glm_fitted = glm(aline_flag ~ ., data=dat, family="binomial", na.action = na.exclude) +``` + +With our model fit, we now run step-wise AIC to remove features. We then plot the ROC curve, and calculate the area under the ROC curve. + +```{r stepwiseAIC} +# run step-wise AIC +library(MASS); +glm_fitted <- stepAIC(glm_fitted ) + +X <- fitted(glm_fitted, type="response") +Tr <- dat$aline_flag + +library("pROC") +roccurve <- roc(Tr ~ X) +plot(roccurve, col=rainbow(7), main="ROC curve", xlab="Specificity", ylab="Sensitivity") +auc(roccurve) +``` + +Our final model has a subset of features and OK AUROC. Let's plot the predictions it makes using a stacked bar chart. + +```{r stackedbar} +# plot stacked histogram of the predictions +xrange = seq(0,1,0.01) +# 3) subset your vectors to be inside xrange +g1 = subset(X,Tr==0) +g2 = subset(X,Tr==1) + +# 4) Now, use hist to compute the counts per interval +h1 = hist(g1,breaks=xrange,plot=F)$counts +h2 = hist(g2,breaks=xrange,plot=F)$counts + +barplot(rbind(h1,h2),col=3:2,names.arg=xrange[-1], + legend.text=c("No aline","Aline"),space=0,las=1,main="Stacked histogram of X") +``` + +We can see we have little support between 0-0.2, and above 0.9. We'll carry on with the knowledge that we'll have few pairs in these probability ranges. + +We have built the propensity score using logistic regression in the previous block. +We now use the `Matching` package to match patients with a caliper size of 0.1. +After matching, we'll apply McNemar's test for paired samples to determine if patients with and without an a-line have a difference in mortality. + +```{r ps} +library(Matching) + +set.seed(43770) + +ps <- Match(Y=NULL, Tr=Tr, X=X, M=1, estimand='ATT', caliper=0.1, exact=FALSE, replace=FALSE); + +# get pairs with treatment/outcome as cols +outcome <- data.frame(aline_pt=y[ps$index.treated], match_pt=y[ps$index.control]) +head(outcome) + +# mcnemar's test to see if iac related to mort (test should use matched pairs) +tab.match1 <- table(outcome$aline_pt,outcome$match_pt,dnn=c("Aline","Matched Control")) +tab.match1 +tab.match1[1,2]/tab.match1[2,1] +paste("95% Confint", round(exp(c(log(tab.match1[2,1]/tab.match1[1,2]) - qnorm(0.975)*sqrt(1/tab.match1[1,2] +1/tab.match1[2,1]),log(tab.match1[2,1]/tab.match1[1,2]) + qnorm(0.975)*sqrt(1/tab.match1[1,2] +1/tab.match1[2,1])) ),2)) +mcnemar.test(tab.match1) # for 1-1 pairs +``` + +The above p-value, which is > 0.05, tells us that we cannot reject the null hypothesis of the aline/non-aline groups having the same mortality rate. Assuming all assumptions of our modelling process are correct, we can infer from this that the use of an indwelling arterial catheter is not associated with a mortality benefit in these patients. diff --git a/notebooks/aline/aline_propensity_score.html b/notebooks/aline/aline_propensity_score.html new file mode 100644 index 0000000..b527994 --- /dev/null +++ b/notebooks/aline/aline_propensity_score.html @@ -0,0 +1,778 @@ + + + + + + + + + + + + + + + +aline-propensity-score + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + + + + + + + + + +
+

Analysis of arterial line dataset

+

This notebook creates a propensity score using a dataset of patients with indwelling arterial catheters (IACs). The propensity score is built using physiology and administrative data to predict the need for an IAC. Patients are then matched, and we statistically compare the mortality rate in the two matched groups.

+
+
+

Load data

+

First, we load the data and convert some variables into factors. Note this code assumes that you have the csv file available in “~/git/mimic-code/notebooks/aline/”.

+
wdpath = paste(path.expand("~"),'git/mimic-code/notebooks/aline/',sep='/')
+setwd(wdpath)
+dataset = read.csv(file="aline_data.csv",head=TRUE,sep=",")
+

If we did not remove any missing data above, we need to subselect complete cases for analysis.

+
## [1] "Removed 160 rows with missing data."
+
+
+

Propensity score model

+

Now, we build a logistic regression, using all the features, to predict the need for an arterial line catheter from physiology and administrative data.

+
# fit GLM
+glm_fitted = glm(aline_flag ~ ., data=dat, family="binomial", na.action = na.exclude)
+

With our model fit, we now run step-wise AIC to remove features. We then plot the ROC curve, and calculate the area under the ROC curve.

+
# run step-wise AIC
+library(MASS);  
+glm_fitted  <- stepAIC(glm_fitted )
+
## Start:  AIC=3054.63
+## aline_flag ~ age + gender + weight_first + sofa_first + service_surg + 
+##     day_icu_intime + hour_icu_intime + chf_flag + afib_flag + 
+##     renal_flag + liver_flag + copd_flag + cad_flag + stroke_flag + 
+##     malignancy_flag + respfail_flag + map_first + hr_first + 
+##     temp_first + spo2_first + bun_first + chloride_first + creatinine_first + 
+##     hgb_first + platelet_first + potassium_first + sodium_first + 
+##     tco2_first + wbc_first
+## 
+##                    Df Deviance    AIC
+## - hour_icu_intime  23   2977.9 3045.9
+## - day_icu_intime    6   2949.4 3051.4
+## - creatinine_first  1   2940.7 3052.7
+## - hr_first          1   2940.8 3052.8
+## - afib_flag         1   2940.8 3052.8
+## - temp_first        1   2940.8 3052.8
+## - cad_flag          1   2940.9 3052.9
+## - age               1   2941.2 3053.2
+## - chf_flag          1   2941.2 3053.2
+## - malignancy_flag   1   2941.2 3053.2
+## - hgb_first         1   2941.2 3053.2
+## - spo2_first        1   2941.2 3053.2
+## - gender            1   2941.3 3053.3
+## - respfail_flag     1   2941.6 3053.6
+## - bun_first         1   2941.8 3053.8
+## - potassium_first   1   2942.6 3054.6
+## <none>                  2940.6 3054.6
+## - platelet_first    1   2943.1 3055.1
+## - weight_first      1   2943.9 3055.9
+## - renal_flag        1   2946.0 3058.0
+## - liver_flag        1   2946.1 3058.1
+## - tco2_first        1   2946.5 3058.5
+## - copd_flag         1   2948.3 3060.3
+## - map_first         1   2950.6 3062.6
+## - sodium_first      1   2962.5 3074.5
+## - sofa_first        1   2966.3 3078.3
+## - wbc_first         1   2969.6 3081.6
+## - stroke_flag       1   2979.0 3091.0
+## - chloride_first    1   2980.9 3092.9
+## - service_surg      1   2993.1 3105.1
+## 
+## Step:  AIC=3045.91
+## aline_flag ~ age + gender + weight_first + sofa_first + service_surg + 
+##     day_icu_intime + chf_flag + afib_flag + renal_flag + liver_flag + 
+##     copd_flag + cad_flag + stroke_flag + malignancy_flag + respfail_flag + 
+##     map_first + hr_first + temp_first + spo2_first + bun_first + 
+##     chloride_first + creatinine_first + hgb_first + platelet_first + 
+##     potassium_first + sodium_first + tco2_first + wbc_first
+## 
+##                    Df Deviance    AIC
+## - day_icu_intime    6   2984.5 3040.5
+## - creatinine_first  1   2977.9 3043.9
+## - hr_first          1   2978.0 3044.0
+## - afib_flag         1   2978.1 3044.1
+## - age               1   2978.2 3044.2
+## - spo2_first        1   2978.2 3044.2
+## - gender            1   2978.3 3044.3
+## - temp_first        1   2978.4 3044.4
+## - cad_flag          1   2978.4 3044.4
+## - malignancy_flag   1   2978.4 3044.4
+## - chf_flag          1   2978.6 3044.6
+## - hgb_first         1   2978.6 3044.6
+## - respfail_flag     1   2978.7 3044.7
+## - bun_first         1   2979.1 3045.1
+## - potassium_first   1   2979.1 3045.1
+## <none>                  2977.9 3045.9
+## - platelet_first    1   2980.8 3046.8
+## - weight_first      1   2980.9 3046.9
+## - renal_flag        1   2983.8 3049.8
+## - liver_flag        1   2984.0 3050.0
+## - tco2_first        1   2984.6 3050.6
+## - copd_flag         1   2987.2 3053.2
+## - map_first         1   2988.2 3054.2
+## - sodium_first      1   3000.7 3066.7
+## - sofa_first        1   3004.0 3070.0
+## - wbc_first         1   3006.3 3072.3
+## - stroke_flag       1   3016.5 3082.5
+## - chloride_first    1   3020.5 3086.5
+## - service_surg      1   3034.2 3100.2
+## 
+## Step:  AIC=3040.52
+## aline_flag ~ age + gender + weight_first + sofa_first + service_surg + 
+##     chf_flag + afib_flag + renal_flag + liver_flag + copd_flag + 
+##     cad_flag + stroke_flag + malignancy_flag + respfail_flag + 
+##     map_first + hr_first + temp_first + spo2_first + bun_first + 
+##     chloride_first + creatinine_first + hgb_first + platelet_first + 
+##     potassium_first + sodium_first + tco2_first + wbc_first
+## 
+##                    Df Deviance    AIC
+## - creatinine_first  1   2984.5 3038.5
+## - hr_first          1   2984.6 3038.6
+## - afib_flag         1   2984.7 3038.7
+## - age               1   2984.8 3038.8
+## - spo2_first        1   2984.8 3038.8
+## - temp_first        1   2984.9 3038.9
+## - cad_flag          1   2985.0 3039.0
+## - gender            1   2985.1 3039.1
+## - malignancy_flag   1   2985.1 3039.1
+## - hgb_first         1   2985.1 3039.1
+## - chf_flag          1   2985.2 3039.2
+## - respfail_flag     1   2985.3 3039.3
+## - bun_first         1   2985.8 3039.8
+## - potassium_first   1   2985.9 3039.9
+## <none>                  2984.5 3040.5
+## - platelet_first    1   2987.2 3041.2
+## - weight_first      1   2987.3 3041.3
+## - renal_flag        1   2990.4 3044.4
+## - liver_flag        1   2990.6 3044.6
+## - tco2_first        1   2991.0 3045.0
+## - copd_flag         1   2993.8 3047.8
+## - map_first         1   2994.6 3048.6
+## - sodium_first      1   3007.9 3061.9
+## - sofa_first        1   3011.4 3065.4
+## - wbc_first         1   3013.2 3067.2
+## - stroke_flag       1   3023.1 3077.1
+## - chloride_first    1   3027.6 3081.6
+## - service_surg      1   3041.1 3095.1
+## 
+## Step:  AIC=3038.52
+## aline_flag ~ age + gender + weight_first + sofa_first + service_surg + 
+##     chf_flag + afib_flag + renal_flag + liver_flag + copd_flag + 
+##     cad_flag + stroke_flag + malignancy_flag + respfail_flag + 
+##     map_first + hr_first + temp_first + spo2_first + bun_first + 
+##     chloride_first + hgb_first + platelet_first + potassium_first + 
+##     sodium_first + tco2_first + wbc_first
+## 
+##                   Df Deviance    AIC
+## - hr_first         1   2984.6 3036.6
+## - afib_flag        1   2984.7 3036.7
+## - age              1   2984.8 3036.8
+## - spo2_first       1   2984.8 3036.8
+## - temp_first       1   2984.9 3036.9
+## - cad_flag         1   2985.1 3037.1
+## - gender           1   2985.1 3037.1
+## - malignancy_flag  1   2985.1 3037.1
+## - hgb_first        1   2985.1 3037.1
+## - chf_flag         1   2985.2 3037.2
+## - respfail_flag    1   2985.3 3037.3
+## - potassium_first  1   2985.9 3037.9
+## - bun_first        1   2986.4 3038.4
+## <none>                 2984.5 3038.5
+## - platelet_first   1   2987.2 3039.2
+## - weight_first     1   2987.3 3039.3
+## - liver_flag       1   2990.6 3042.6
+## - renal_flag       1   2990.9 3042.9
+## - tco2_first       1   2991.1 3043.1
+## - copd_flag        1   2993.8 3045.8
+## - map_first        1   2994.6 3046.6
+## - sodium_first     1   3008.1 3060.1
+## - sofa_first       1   3012.7 3064.7
+## - wbc_first        1   3013.2 3065.2
+## - stroke_flag      1   3023.1 3075.1
+## - chloride_first   1   3028.7 3080.7
+## - service_surg     1   3041.1 3093.1
+## 
+## Step:  AIC=3036.62
+## aline_flag ~ age + gender + weight_first + sofa_first + service_surg + 
+##     chf_flag + afib_flag + renal_flag + liver_flag + copd_flag + 
+##     cad_flag + stroke_flag + malignancy_flag + respfail_flag + 
+##     map_first + temp_first + spo2_first + bun_first + chloride_first + 
+##     hgb_first + platelet_first + potassium_first + sodium_first + 
+##     tco2_first + wbc_first
+## 
+##                   Df Deviance    AIC
+## - afib_flag        1   2984.8 3034.8
+## - age              1   2984.8 3034.8
+## - temp_first       1   2985.0 3035.0
+## - spo2_first       1   2985.0 3035.0
+## - cad_flag         1   2985.2 3035.2
+## - gender           1   2985.2 3035.2
+## - malignancy_flag  1   2985.2 3035.2
+## - hgb_first        1   2985.2 3035.2
+## - chf_flag         1   2985.3 3035.3
+## - respfail_flag    1   2985.5 3035.5
+## - potassium_first  1   2986.0 3036.0
+## - bun_first        1   2986.5 3036.5
+## <none>                 2984.6 3036.6
+## - platelet_first   1   2987.3 3037.3
+## - weight_first     1   2987.4 3037.4
+## - liver_flag       1   2990.9 3040.9
+## - renal_flag       1   2990.9 3040.9
+## - tco2_first       1   2991.3 3041.3
+## - copd_flag        1   2994.1 3044.1
+## - map_first        1   2994.7 3044.7
+## - sodium_first     1   3008.5 3058.5
+## - sofa_first       1   3012.8 3062.8
+## - wbc_first        1   3013.3 3063.3
+## - stroke_flag      1   3023.8 3073.8
+## - chloride_first   1   3029.1 3079.1
+## - service_surg     1   3041.1 3091.1
+## 
+## Step:  AIC=3034.76
+## aline_flag ~ age + gender + weight_first + sofa_first + service_surg + 
+##     chf_flag + renal_flag + liver_flag + copd_flag + cad_flag + 
+##     stroke_flag + malignancy_flag + respfail_flag + map_first + 
+##     temp_first + spo2_first + bun_first + chloride_first + hgb_first + 
+##     platelet_first + potassium_first + sodium_first + tco2_first + 
+##     wbc_first
+## 
+##                   Df Deviance    AIC
+## - age              1   2984.9 3032.9
+## - temp_first       1   2985.1 3033.1
+## - spo2_first       1   2985.1 3033.1
+## - cad_flag         1   2985.3 3033.3
+## - gender           1   2985.3 3033.3
+## - malignancy_flag  1   2985.4 3033.4
+## - hgb_first        1   2985.4 3033.4
+## - chf_flag         1   2985.5 3033.5
+## - respfail_flag    1   2985.6 3033.6
+## - potassium_first  1   2986.2 3034.2
+## - bun_first        1   2986.7 3034.7
+## <none>                 2984.8 3034.8
+## - platelet_first   1   2987.5 3035.5
+## - weight_first     1   2987.6 3035.6
+## - renal_flag       1   2991.0 3039.0
+## - liver_flag       1   2991.2 3039.2
+## - tco2_first       1   2991.6 3039.6
+## - copd_flag        1   2994.2 3042.2
+## - map_first        1   2994.8 3042.8
+## - sodium_first     1   3008.6 3056.6
+## - sofa_first       1   3012.8 3060.8
+## - wbc_first        1   3013.5 3061.5
+## - stroke_flag      1   3024.6 3072.6
+## - chloride_first   1   3029.2 3077.2
+## - service_surg     1   3041.1 3089.1
+## 
+## Step:  AIC=3032.89
+## aline_flag ~ gender + weight_first + sofa_first + service_surg + 
+##     chf_flag + renal_flag + liver_flag + copd_flag + cad_flag + 
+##     stroke_flag + malignancy_flag + respfail_flag + map_first + 
+##     temp_first + spo2_first + bun_first + chloride_first + hgb_first + 
+##     platelet_first + potassium_first + sodium_first + tco2_first + 
+##     wbc_first
+## 
+##                   Df Deviance    AIC
+## - temp_first       1   2985.2 3031.2
+## - spo2_first       1   2985.3 3031.3
+## - hgb_first        1   2985.4 3031.4
+## - gender           1   2985.5 3031.5
+## - cad_flag         1   2985.5 3031.5
+## - malignancy_flag  1   2985.6 3031.6
+## - chf_flag         1   2985.6 3031.6
+## - respfail_flag    1   2985.8 3031.8
+## - potassium_first  1   2986.4 3032.4
+## - bun_first        1   2986.7 3032.7
+## <none>                 2984.9 3032.9
+## - platelet_first   1   2987.6 3033.6
+## - weight_first     1   2987.9 3033.9
+## - renal_flag       1   2991.1 3037.1
+## - liver_flag       1   2991.2 3037.2
+## - tco2_first       1   2991.6 3037.6
+## - copd_flag        1   2994.5 3040.5
+## - map_first        1   2995.0 3041.0
+## - sodium_first     1   3008.8 3054.8
+## - sofa_first       1   3012.9 3058.9
+## - wbc_first        1   3014.5 3060.5
+## - stroke_flag      1   3027.3 3073.3
+## - chloride_first   1   3030.0 3076.0
+## - service_surg     1   3041.1 3087.1
+## 
+## Step:  AIC=3031.24
+## aline_flag ~ gender + weight_first + sofa_first + service_surg + 
+##     chf_flag + renal_flag + liver_flag + copd_flag + cad_flag + 
+##     stroke_flag + malignancy_flag + respfail_flag + map_first + 
+##     spo2_first + bun_first + chloride_first + hgb_first + platelet_first + 
+##     potassium_first + sodium_first + tco2_first + wbc_first
+## 
+##                   Df Deviance    AIC
+## - spo2_first       1   2985.6 3029.6
+## - hgb_first        1   2985.8 3029.8
+## - malignancy_flag  1   2985.9 3029.9
+## - gender           1   2985.9 3029.9
+## - chf_flag         1   2985.9 3029.9
+## - cad_flag         1   2986.0 3030.0
+## - respfail_flag    1   2986.1 3030.1
+## - potassium_first  1   2986.8 3030.8
+## - bun_first        1   2987.0 3031.0
+## <none>                 2985.2 3031.2
+## - platelet_first   1   2987.9 3031.9
+## - weight_first     1   2988.3 3032.3
+## - renal_flag       1   2991.4 3035.4
+## - liver_flag       1   2991.5 3035.5
+## - tco2_first       1   2991.9 3035.9
+## - copd_flag        1   2995.0 3039.0
+## - map_first        1   2995.2 3039.2
+## - sodium_first     1   3009.0 3053.0
+## - sofa_first       1   3013.2 3057.2
+## - wbc_first        1   3015.5 3059.5
+## - stroke_flag      1   3027.9 3071.9
+## - chloride_first   1   3030.1 3074.1
+## - service_surg     1   3041.9 3085.9
+## 
+## Step:  AIC=3029.62
+## aline_flag ~ gender + weight_first + sofa_first + service_surg + 
+##     chf_flag + renal_flag + liver_flag + copd_flag + cad_flag + 
+##     stroke_flag + malignancy_flag + respfail_flag + map_first + 
+##     bun_first + chloride_first + hgb_first + platelet_first + 
+##     potassium_first + sodium_first + tco2_first + wbc_first
+## 
+##                   Df Deviance    AIC
+## - hgb_first        1   2986.2 3028.2
+## - chf_flag         1   2986.3 3028.3
+## - gender           1   2986.3 3028.3
+## - malignancy_flag  1   2986.3 3028.3
+## - cad_flag         1   2986.4 3028.4
+## - respfail_flag    1   2986.6 3028.6
+## - potassium_first  1   2987.2 3029.2
+## - bun_first        1   2987.3 3029.3
+## <none>                 2985.6 3029.6
+## - platelet_first   1   2988.3 3030.3
+## - weight_first     1   2988.6 3030.6
+## - renal_flag       1   2991.8 3033.8
+## - liver_flag       1   2992.0 3034.0
+## - tco2_first       1   2992.1 3034.1
+## - map_first        1   2995.5 3037.5
+## - copd_flag        1   2995.8 3037.8
+## - sodium_first     1   3009.2 3051.2
+## - sofa_first       1   3013.9 3055.9
+## - wbc_first        1   3015.5 3057.5
+## - stroke_flag      1   3028.4 3070.4
+## - chloride_first   1   3030.3 3072.3
+## - service_surg     1   3042.0 3084.0
+## 
+## Step:  AIC=3028.18
+## aline_flag ~ gender + weight_first + sofa_first + service_surg + 
+##     chf_flag + renal_flag + liver_flag + copd_flag + cad_flag + 
+##     stroke_flag + malignancy_flag + respfail_flag + map_first + 
+##     bun_first + chloride_first + platelet_first + potassium_first + 
+##     sodium_first + tco2_first + wbc_first
+## 
+##                   Df Deviance    AIC
+## - gender           1   2986.6 3026.6
+## - malignancy_flag  1   2986.7 3026.7
+## - chf_flag         1   2986.9 3026.9
+## - cad_flag         1   2986.9 3026.9
+## - respfail_flag    1   2987.1 3027.1
+## - potassium_first  1   2987.9 3027.9
+## <none>                 2986.2 3028.2
+## - bun_first        1   2988.4 3028.4
+## - platelet_first   1   2988.8 3028.8
+## - weight_first     1   2989.0 3029.0
+## - renal_flag       1   2992.2 3032.2
+## - liver_flag       1   2992.2 3032.2
+## - tco2_first       1   2994.0 3034.0
+## - map_first        1   2995.9 3035.9
+## - copd_flag        1   2996.2 3036.2
+## - sofa_first       1   3015.0 3055.0
+## - wbc_first        1   3015.6 3055.6
+## - sodium_first     1   3016.9 3056.9
+## - stroke_flag      1   3028.4 3068.4
+## - service_surg     1   3043.1 3083.1
+## - chloride_first   1   3044.2 3084.2
+## 
+## Step:  AIC=3026.62
+## aline_flag ~ weight_first + sofa_first + service_surg + chf_flag + 
+##     renal_flag + liver_flag + copd_flag + cad_flag + stroke_flag + 
+##     malignancy_flag + respfail_flag + map_first + bun_first + 
+##     chloride_first + platelet_first + potassium_first + sodium_first + 
+##     tco2_first + wbc_first
+## 
+##                   Df Deviance    AIC
+## - malignancy_flag  1   2987.2 3025.2
+## - chf_flag         1   2987.2 3025.2
+## - cad_flag         1   2987.3 3025.3
+## - respfail_flag    1   2987.6 3025.6
+## - potassium_first  1   2988.2 3026.2
+## <none>                 2986.6 3026.6
+## - bun_first        1   2988.8 3026.8
+## - platelet_first   1   2989.5 3027.5
+## - weight_first     1   2990.3 3028.3
+## - renal_flag       1   2992.7 3030.7
+## - liver_flag       1   2992.7 3030.7
+## - tco2_first       1   2994.3 3032.3
+## - map_first        1   2996.2 3034.2
+## - copd_flag        1   2996.7 3034.7
+## - wbc_first        1   3016.5 3054.5
+## - sofa_first       1   3016.6 3054.6
+## - sodium_first     1   3016.9 3054.9
+## - stroke_flag      1   3028.4 3066.4
+## - service_surg     1   3043.2 3081.2
+## - chloride_first   1   3044.3 3082.3
+## 
+## Step:  AIC=3025.15
+## aline_flag ~ weight_first + sofa_first + service_surg + chf_flag + 
+##     renal_flag + liver_flag + copd_flag + cad_flag + stroke_flag + 
+##     respfail_flag + map_first + bun_first + chloride_first + 
+##     platelet_first + potassium_first + sodium_first + tco2_first + 
+##     wbc_first
+## 
+##                   Df Deviance    AIC
+## - chf_flag         1   2987.8 3023.8
+## - cad_flag         1   2987.8 3023.8
+## - respfail_flag    1   2988.2 3024.2
+## - potassium_first  1   2988.8 3024.8
+## <none>                 2987.2 3025.2
+## - bun_first        1   2989.2 3025.2
+## - platelet_first   1   2990.2 3026.2
+## - weight_first     1   2991.0 3027.0
+## - renal_flag       1   2993.1 3029.1
+## - liver_flag       1   2993.3 3029.3
+## - tco2_first       1   2994.6 3030.6
+## - map_first        1   2996.9 3032.9
+## - copd_flag        1   2997.3 3033.3
+## - sofa_first       1   3016.8 3052.8
+## - sodium_first     1   3016.9 3052.9
+## - wbc_first        1   3017.7 3053.7
+## - stroke_flag      1   3029.6 3065.6
+## - service_surg     1   3043.3 3079.3
+## - chloride_first   1   3044.4 3080.4
+## 
+## Step:  AIC=3023.75
+## aline_flag ~ weight_first + sofa_first + service_surg + renal_flag + 
+##     liver_flag + copd_flag + cad_flag + stroke_flag + respfail_flag + 
+##     map_first + bun_first + chloride_first + platelet_first + 
+##     potassium_first + sodium_first + tco2_first + wbc_first
+## 
+##                   Df Deviance    AIC
+## - cad_flag         1   2988.2 3022.2
+## - respfail_flag    1   2988.7 3022.7
+## - potassium_first  1   2989.3 3023.3
+## <none>                 2987.8 3023.8
+## - bun_first        1   2990.2 3024.2
+## - platelet_first   1   2990.8 3024.8
+## - weight_first     1   2991.7 3025.7
+## - renal_flag       1   2993.4 3027.4
+## - liver_flag       1   2993.9 3027.9
+## - tco2_first       1   2995.9 3029.9
+## - map_first        1   2997.4 3031.4
+## - copd_flag        1   2997.4 3031.4
+## - sofa_first       1   3017.1 3051.1
+## - sodium_first     1   3017.2 3051.2
+## - wbc_first        1   3018.2 3052.2
+## - stroke_flag      1   3029.7 3063.7
+## - service_surg     1   3043.7 3077.7
+## - chloride_first   1   3044.5 3078.5
+## 
+## Step:  AIC=3022.25
+## aline_flag ~ weight_first + sofa_first + service_surg + renal_flag + 
+##     liver_flag + copd_flag + stroke_flag + respfail_flag + map_first + 
+##     bun_first + chloride_first + platelet_first + potassium_first + 
+##     sodium_first + tco2_first + wbc_first
+## 
+##                   Df Deviance    AIC
+## - respfail_flag    1   2989.1 3021.1
+## - potassium_first  1   2989.8 3021.8
+## <none>                 2988.2 3022.2
+## - bun_first        1   2990.5 3022.5
+## - platelet_first   1   2991.3 3023.3
+## - weight_first     1   2992.1 3024.1
+## - liver_flag       1   2994.3 3026.3
+## - renal_flag       1   2994.7 3026.7
+## - tco2_first       1   2996.4 3028.4
+## - copd_flag        1   2998.0 3030.0
+## - map_first        1   2998.0 3030.0
+## - sodium_first     1   3017.5 3049.5
+## - sofa_first       1   3018.1 3050.1
+## - wbc_first        1   3018.9 3050.9
+## - stroke_flag      1   3029.7 3061.7
+## - service_surg     1   3044.0 3076.0
+## - chloride_first   1   3044.9 3076.9
+## 
+## Step:  AIC=3021.13
+## aline_flag ~ weight_first + sofa_first + service_surg + renal_flag + 
+##     liver_flag + copd_flag + stroke_flag + map_first + bun_first + 
+##     chloride_first + platelet_first + potassium_first + sodium_first + 
+##     tco2_first + wbc_first
+## 
+##                   Df Deviance    AIC
+## - potassium_first  1   2990.7 3020.7
+## - bun_first        1   2991.1 3021.1
+## <none>                 2989.1 3021.1
+## - platelet_first   1   2992.2 3022.2
+## - weight_first     1   2992.9 3022.9
+## - liver_flag       1   2995.2 3025.2
+## - renal_flag       1   2995.6 3025.6
+## - tco2_first       1   2996.7 3026.7
+## - map_first        1   2998.8 3028.8
+## - copd_flag        1   2999.1 3029.1
+## - sofa_first       1   3018.3 3048.3
+## - sodium_first     1   3018.4 3048.4
+## - wbc_first        1   3019.5 3049.5
+## - stroke_flag      1   3032.1 3062.1
+## - chloride_first   1   3046.0 3076.0
+## - service_surg     1   3046.2 3076.2
+## 
+## Step:  AIC=3020.68
+## aline_flag ~ weight_first + sofa_first + service_surg + renal_flag + 
+##     liver_flag + copd_flag + stroke_flag + map_first + bun_first + 
+##     chloride_first + platelet_first + sodium_first + tco2_first + 
+##     wbc_first
+## 
+##                  Df Deviance    AIC
+## - bun_first       1   2992.0 3020.0
+## <none>                2990.7 3020.7
+## - weight_first    1   2994.1 3022.1
+## - platelet_first  1   2994.2 3022.2
+## - liver_flag      1   2996.9 3024.9
+## - renal_flag      1   2997.9 3025.9
+## - tco2_first      1   2998.0 3026.0
+## - map_first       1   3000.2 3028.2
+## - copd_flag       1   3001.0 3029.0
+## - sodium_first    1   3018.7 3046.7
+## - sofa_first      1   3019.3 3047.3
+## - wbc_first       1   3020.6 3048.6
+## - stroke_flag     1   3034.3 3062.3
+## - chloride_first  1   3046.4 3074.4
+## - service_surg    1   3049.0 3077.0
+## 
+## Step:  AIC=3020
+## aline_flag ~ weight_first + sofa_first + service_surg + renal_flag + 
+##     liver_flag + copd_flag + stroke_flag + map_first + chloride_first + 
+##     platelet_first + sodium_first + tco2_first + wbc_first
+## 
+##                  Df Deviance    AIC
+## <none>                2992.0 3020.0
+## - weight_first    1   2995.5 3021.5
+## - platelet_first  1   2995.7 3021.7
+## - renal_flag      1   2998.0 3024.0
+## - liver_flag      1   2998.3 3024.3
+## - tco2_first      1   2999.3 3025.3
+## - map_first       1   3001.6 3027.6
+## - copd_flag       1   3002.1 3028.1
+## - sodium_first    1   3019.8 3045.8
+## - wbc_first       1   3022.8 3048.8
+## - sofa_first      1   3026.8 3052.8
+## - stroke_flag     1   3035.9 3061.9
+## - chloride_first  1   3047.2 3073.2
+## - service_surg    1   3049.9 3075.9
+
X <- fitted(glm_fitted, type="response")
+Tr <- dat$aline_flag
+
+library("pROC")
+
## Type 'citation("pROC")' for a citation.
+
## 
+## Attaching package: 'pROC'
+
## The following objects are masked from 'package:stats':
+## 
+##     cov, smooth, var
+
roccurve <- roc(Tr ~ X)
+plot(roccurve, col=rainbow(7), main="ROC curve", xlab="Specificity", ylab="Sensitivity")
+

+
auc(roccurve)
+
## Area under the curve: 0.6945
+

Our final model has a subset of features and OK AUROC. Let’s plot the predictions it makes using a stacked bar chart.

+
# plot stacked histogram of the predictions
+xrange = seq(0,1,0.01)
+# 3) subset your vectors to be inside xrange
+g1 = subset(X,Tr==0)
+g2 = subset(X,Tr==1)
+
+# 4) Now, use hist to compute the counts per interval
+h1 = hist(g1,breaks=xrange,plot=F)$counts
+h2 = hist(g2,breaks=xrange,plot=F)$counts
+
+barplot(rbind(h1,h2),col=3:2,names.arg=xrange[-1],
+        legend.text=c("No aline","Aline"),space=0,las=1,main="Stacked histogram of X")
+

+

We can see we have little support between 0-0.2, and above 0.9. We’ll carry on with the knowledge that we’ll have few pairs in these probability ranges.

+

We have built the propensity score using logistic regression in the previous block. We now use the Matching package to match patients with a caliper size of 0.1. After matching, we’ll apply McNemar’s test for paired samples to determine if patients with and without an a-line have a difference in mortality.

+
library(Matching)
+
## ## 
+## ##  Matching (Version 4.9-2, Build Date: 2015-12-25)
+## ##  See http://sekhon.berkeley.edu/matching for additional documentation.
+## ##  Please cite software as:
+## ##   Jasjeet S. Sekhon. 2011. ``Multivariate and Propensity Score Matching
+## ##   Software with Automated Balance Optimization: The Matching package for R.''
+## ##   Journal of Statistical Software, 42(7): 1-52. 
+## ##
+
set.seed(43770)
+
+ps <- Match(Y=NULL, Tr=Tr, X=X, M=1, estimand='ATT', caliper=0.1, exact=FALSE, replace=FALSE);
+
## Warning in Match(Y = NULL, Tr = Tr, X = X, M = 1, estimand = "ATT", caliper
+## = 0.1, : replace==FALSE, but there are more (weighted) treated obs than
+## control obs. Some treated obs will not be matched. You may want to estimate
+## ATC instead.
+
# get pairs with treatment/outcome as cols
+outcome <- data.frame(aline_pt=y[ps$index.treated], match_pt=y[ps$index.control])
+head(outcome)
+
##   aline_pt match_pt
+## 1        0        0
+## 2        0        1
+## 3        0        0
+## 4        0        1
+## 5        1        0
+## 6        0        0
+
# mcnemar's test to see if iac related to mort (test should use matched pairs)
+tab.match1 <- table(outcome$aline_pt,outcome$match_pt,dnn=c("Aline","Matched Control"))
+tab.match1
+
##      Matched Control
+## Aline   0   1
+##     0 582 118
+##     1 104  19
+
tab.match1[1,2]/tab.match1[2,1]
+
## [1] 1.134615
+
paste("95% Confint", round(exp(c(log(tab.match1[2,1]/tab.match1[1,2]) - qnorm(0.975)*sqrt(1/tab.match1[1,2] +1/tab.match1[2,1]),log(tab.match1[2,1]/tab.match1[1,2]) + qnorm(0.975)*sqrt(1/tab.match1[1,2] +1/tab.match1[2,1])) ),2))
+
## [1] "95% Confint 0.68" "95% Confint 1.15"
+
mcnemar.test(tab.match1) # for 1-1 pairs
+
## 
+##  McNemar's Chi-squared test with continuity correction
+## 
+## data:  tab.match1
+## McNemar's chi-squared = 0.76126, df = 1, p-value = 0.3829
+

The above p-value, which is > 0.05, tells us that we cannot reject the null hypothesis of the aline/non-aline groups having the same mortality rate. Assuming all assumptions of our modelling process are correct, we can infer from this that the use of an indwelling arterial catheter is not associated with a mortality benefit in these patients.

+
+ + + + +
+ + + + + + + + diff --git a/notebooks/aline/aline_sedatives.sql b/notebooks/aline/aline_sedatives.sql new file mode 100644 index 0000000..5be8296 --- /dev/null +++ b/notebooks/aline/aline_sedatives.sql @@ -0,0 +1,61 @@ +-- Create a table which indicates if a patient was ever on a sedative before IAC + +-- List of sedatives used (CareVue): +-- midazolam - 30124 +-- fentanyl - 30150, 30308, 30118, 30149 +-- propofol - 30131 + +-- List of sedatives used (MetaVision): +-- midazolam - 221668 +-- fentanyl - 221744, 225972, 225942 +-- propofol - 222168 + +DROP MATERIALIZED VIEW IF EXISTS ALINE_SEDATIVES CASCADE; +CREATE MATERIALIZED VIEW ALINE_SEDATIVES as +with io_cv as +( + select + icustay_id, charttime, itemid, stopped, rate, amount + from mimiciii.inputevents_cv + where itemid in + ( + 30124 -- midazolam + , 30150, 30308, 30118, 30149 -- fentanyl + , 30131 -- propofol + ) + and coalesce(rate,amount) is not null + and (rate > 0 OR amount > 0) +) +-- select only the ITEMIDs from the inputevents_mv table related to vasopressors +, io_mv as +( + select + icustay_id, linkorderid, itemid, starttime, endtime, rate, amount + from mimiciii.inputevents_mv io + -- Subselect the vasopressor ITEMIDs + where itemid in + ( + 221668 -- midazolam + , 221744, 225972, 225942 -- fentanyl + , 222168 -- propofol + ) + and coalesce(rate,amount) is not null + and (rate > 0 OR amount > 0) + and statusdescription != 'Rewritten' -- only valid orders +) +select + co.subject_id, co.hadm_id, co.icustay_id + , max(case when coalesce(io_mv.icustay_id, io_cv.icustay_id) is not null then 1 else 0 end) as sedative_flag + , max(case when coalesce(io_mv.itemid, io_cv.itemid) in (30124, 221668) then 1 else 0 end) as midazolam_flag + , max(case when coalesce(io_mv.itemid, io_cv.itemid) in (30150, 30308, 30118, 30149, 221744, 225972, 225942) then 1 else 0 end) as fentanyl_flag + , max(case when coalesce(io_mv.itemid, io_cv.itemid) in (30131, 222168) then 1 else 0 end) as propofol_flag +from aline_cohort co +left join io_mv + on co.icustay_id = io_mv.icustay_id + and co.starttime_aline > io_mv.starttime + and co.starttime_aline <= io_mv.endtime +left join io_cv + on co.icustay_id = io_cv.icustay_id + and co.starttime_aline > io_cv.charttime +group by co.subject_id, co.hadm_id, co.icustay_id +order by icustay_id; diff --git a/notebooks/aline/aline_sofa.sql b/notebooks/aline/aline_sofa.sql new file mode 100644 index 0000000..59b7ca3 --- /dev/null +++ b/notebooks/aline/aline_sofa.sql @@ -0,0 +1,424 @@ +-- This query extracts the sequential organ failure assessment (formally: sepsis-related organ failure assessment). +-- This query is *specifically designed for the arterial line study*. +-- It makes many assumptions which are only valid in that cohort: no patients are on vasopressors, no patients are ventilated during data extraction. + +-- Reference for SOFA: +-- Jean-Louis Vincent, Rui Moreno, Jukka Takala, Sheila Willatts, Arnaldo De Mendonça, +-- Hajo Bruining, C. K. Reinhart, Peter M Suter, and L. G. Thijs. +-- "The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure." +-- Intensive care medicine 22, no. 7 (1996): 707-710. + +-- Variables used in SOFA: +-- GCS, MAP, FiO2, Ventilation status (sourced from CHARTEVENTS) +-- Creatinine, Bilirubin, FiO2, PaO2, Platelets (sourced from LABEVENTS) +-- Dobutamine, Epinephrine, Norepinephrine (sourced from INPUTEVENTS_MV and INPUTEVENTS_CV) +-- Urine output (sourced from OUTPUTEVENTS) + +DROP MATERIALIZED VIEW IF EXISTS ALINE_SOFA CASCADE; +CREATE MATERIALIZED VIEW ALINE_SOFA AS +-- extract PaO2/FiO2 +-- do not need to worry about patient ventilation +with co as +( + select + co.subject_id, co.hadm_id, co.icustay_id, co.intime, co.outtime + , co.vent_starttime - interval '1' day as starttime + , co.vent_starttime + interval '2' hour as endtime + from aline_cohort co +) +, stg_fio2 as +( + select co.ICUSTAY_ID, ce.CHARTTIME + -- pre-process the FiO2s to ensure they are between 21-100% + , max( + case + when itemid = 223835 + then case + when valuenum > 0 and valuenum <= 1 + then valuenum * 100 + -- improperly input data - looks like O2 flow in litres + when valuenum > 1 and valuenum < 21 + then null + when valuenum >= 21 and valuenum <= 100 + then valuenum + else null end -- unphysiological + when itemid in (3420, 3422) + -- all these values are well formatted + then valuenum + when itemid = 190 and valuenum > 0.20 and valuenum < 1 + -- well formatted but not in % + then valuenum * 100 + else null end + ) as fio2_chartevents + from co + left join CHARTEVENTS ce + on co.icustay_id = ce.icustay_id + and ce.ITEMID in + ( + 3420 -- FiO2 + , 190 -- FiO2 set + , 223835 -- Inspired O2 Fraction (FiO2) + , 3422 -- FiO2 [measured] + ) + group by co.ICUSTAY_ID, ce.CHARTTIME +) +, bg as +( + select pvt.ICUSTAY_ID, pvt.CHARTTIME + , max(case when label = 'SPECIMEN' then value else null end) as SPECIMEN + , max(case when label = 'FIO2' then valuenum else null end) as FIO2 + , max(case when label = 'PO2' then valuenum else null end) as PO2 + from + ( -- begin query that extracts the data + select co.icustay_id, charttime + -- here we assign labels to ITEMIDs + -- this also fuses together multiple ITEMIDs containing the same data + , case + when itemid = 50800 then 'SPECIMEN' + when itemid = 50816 then 'FIO2' + when itemid = 50821 then 'PO2' + else null + end as label + , value + -- add in some sanity checks on the values + , case + when valuenum <= 0 then null + when itemid = 50816 and valuenum > 100 then null -- FiO2 + -- conservative upper limit + when itemid = 50821 and valuenum > 800 then null -- PO2 + else valuenum + end as valuenum + + from co + left join labevents le + on co.subject_id = le.subject_id + and le.charttime between co.starttime and co.endtime + and le.ITEMID in (50800, 50816, 50821) + ) pvt + group by pvt.icustay_id, pvt.CHARTTIME + -- we only want rows with a PO2 measurement + having max(case when label = 'PO2' then valuenum else null end) is not null +) +, stg_pafi as +( +select + bg.icustay_id, bg.charttime + , bg.PO2, bg.FIO2, s2.fio2_chartevents + , case when coalesce(bg.FIO2, s2.fio2_chartevents, 0) > 0 and coalesce(bg.FIO2, s2.fio2_chartevents, 100) < 100 + then 100*bg.PO2/(coalesce(bg.FIO2, s2.fio2_chartevents)) + else null end as pao2fio2 + , ROW_NUMBER() over (partition by bg.icustay_id order by bg.charttime DESC, s2.charttime DESC) as rn +from bg +left join stg_fio2 s2 + -- same patient + on bg.icustay_id = s2.icustay_id + -- fio2 occurred at most 4 hours before this blood gas + and s2.charttime between bg.charttime - interval '4' hour and bg.charttime +where coalesce(bg.SPECIMEN,'ART') = 'ART' +) + +------------------------------------------- +-- LABS -- +------------------------------------------- +, labs as ( +select + pvt.icustay_id + , max(case when label = 'BILIRUBIN' then valuenum else null end) as BILIRUBIN_max + , max(case when label = 'CREATININE' then valuenum else null end) as CREATININE_max + , min(case when label = 'PLATELET' then valuenum else null end) as PLATELET_min +from +( -- begin query that extracts the data + select co.icustay_id + -- here we assign labels to ITEMIDs + -- this also fuses together multiple ITEMIDs containing the same data + , case + when itemid = 50885 then 'BILIRUBIN' + when itemid = 50912 then 'CREATININE' + when itemid = 51265 then 'PLATELET' + else null + end as label + , -- add in some sanity checks on the values + -- the where clause below requires all valuenum to be > 0, so these are only upper limit checks + case + when itemid = 50885 and valuenum > 150 then null -- mg/dL 'BILIRUBIN' + when itemid = 50912 and valuenum > 150 then null -- mg/dL 'CREATININE' + when itemid = 51265 and valuenum > 10000 then null -- K/uL 'PLATELET' + else le.valuenum + end as valuenum + + from co + + left join labevents le + on co.subject_id = le.subject_id + and le.charttime between co.starttime and co.endtime + and le.ITEMID in + ( + -- comment is: LABEL | CATEGORY | FLUID | NUMBER OF ROWS IN LABEVENTS + 50885, -- BILIRUBIN, TOTAL | CHEMISTRY | BLOOD | 238277 + 50912, -- CREATININE | CHEMISTRY | BLOOD | 797476 + 51265 -- PLATELET COUNT | HEMATOLOGY | BLOOD | 778444 + ) + and valuenum is not null and valuenum > 0 -- lab values cannot be 0 and cannot be negative +) pvt +group by pvt.icustay_id +) +-- VITALS -- +, vitals as +( + select + co.icustay_id, min(valuenum) as MeanBP_min + from co + inner join chartevents ce + on ce.subject_id = co.subject_id + and ce.charttime between co.starttime and co.endtime + and ce.itemid in (456,52,6702,443,220052,220181,225312) + group by co.icustay_id +) + +, uo as +( + select co.icustay_id + -- volumes associated with urine output ITEMIDs + , sum(case when itemid = 227488 then -1.0*VALUE else VALUE end)/ + ( + case when max(oe.charttime) < min(co.intime) and max(oe.charttime) <= min(oe.charttime) then 1 + else + (extract(epoch from (max(oe.charttime)-coalesce(min(oe.charttime),min(co.intime))))/60.0/60.0) + 1 + end + )*24.0 as UrineOutput + + from co + -- Join to the outputevents table to get urine output + left join outputevents oe + on co.subject_id = oe.subject_id + -- ensure the data occurs during the first day + and oe.charttime between co.starttime and co.endtime + and itemid in + ( + -- these are the most frequently occurring urine output observations in CareVue + 40055, -- "Urine Out Foley" + 43175, -- "Urine ." + 40069, -- "Urine Out Void" + 40094, -- "Urine Out Condom Cath" + 40715, -- "Urine Out Suprapubic" + 40473, -- "Urine Out IleoConduit" + 40085, -- "Urine Out Incontinent" + 40057, -- "Urine Out Rt Nephrostomy" + 40056, -- "Urine Out Lt Nephrostomy" + 40405, -- "Urine Out Other" + 40428, -- "Urine Out Straight Cath" + 40086,-- Urine Out Incontinent + 40096, -- "Urine Out Ureteral Stent #1" + 40651, -- "Urine Out Ureteral Stent #2" + + -- these are the most frequently occurring urine output observations in MetaVision + 226559, -- "Foley" + 226560, -- "Void" + 226561, -- "Condom Cath" + 226584, -- "Ileoconduit" + 226563, -- "Suprapubic" + 226564, -- "R Nephrostomy" + 226565, -- "L Nephrostomy" + 226567, -- Straight Cath + 226557, -- R Ureteral Stent + 226558, -- L Ureteral Stent + 227488, -- GU Irrigant Volume In + 227489 -- GU Irrigant/Urine Volume Out + ) + group by co.icustay_id +) + +--------- +-- GCS -- +--------- + +, gcs_base as +( + select co.ICUSTAY_ID, l.CHARTTIME + , ROW_NUMBER () + OVER (PARTITION BY co.ICUSTAY_ID ORDER BY l.charttime ASC) as rn + + -- merge the ITEMIDs so that the pivot applies to both metavision/carevue data + , max(case when l.ITEMID in (454,223901) then l.valuenum else null end) as GCSMotor + , max(case when l.ITEMID in (723,223900) then l.valuenum else null end) as GCSVerbal + , max(case when l.ITEMID in (184,220739) then l.valuenum else null end) as GCSEyes + + -- flag indicating gcs verbal set to 0 due to mechanical ventilation + , max(case + -- endotrach/vent is assigned a value of 0, later parsed specially + when l.ITEMID = 723 and l.VALUE = '1.0 ET/Trach' then 1 -- carevue + when l.ITEMID = 223900 and l.VALUE = 'No Response-ETT' then 1 -- metavision + else 0 end) as EndoTrachFlag + + from co + inner join chartevents l + on co.subject_id = l.subject_id + and l.charttime between co.starttime and co.endtime + and l.ITEMID in -- Isolate the desired GCS variables + ( + -- 198 -- GCS + -- GCS components, CareVue + 184, 454, 723 + -- GCS components, Metavision + , 223900, 223901, 220739 + ) + group by co.ICUSTAY_ID, l.charttime +) +, gcs as +( + select b.icustay_id + -- Calculate GCS, factoring in special case when they are intubated and prev vals + -- note that the coalesce are used to implement the following if: + -- if current value exists, use it + -- if previous value exists, use it + -- otherwise, default to normal + , min(case + -- replace GCS during sedation with 15 + when b.GCSVerbal = 0 + then 15 + when b.GCSVerbal is null and b2.GCSVerbal = 0 + then 15 + -- if previously they were intub, but they aren't now, do not use previous GCS values + when b2.GCSVerbal = 0 + then + coalesce(b.GCSMotor,6) + + coalesce(b.GCSVerbal,5) + + coalesce(b.GCSEyes,4) + -- otherwise, add up score normally, imputing previous value if none available at current time + else + coalesce(b.GCSMotor,coalesce(b2.GCSMotor,6)) + + coalesce(b.GCSVerbal,coalesce(b2.GCSVerbal,5)) + + coalesce(b.GCSEyes,coalesce(b2.GCSEyes,4)) + end) as MinGCS + + from gcs_base b + -- join to itself within 6 hours to get previous value + left join gcs_base b2 + on b.ICUSTAY_ID = b2.ICUSTAY_ID and b.rn = b2.rn+1 and b2.charttime > b.charttime - interval '6' hour + group by b.icustay_id +) + +-- Aggregate the components for the score +, scorecomp as +( +select co.icustay_id + , v.MeanBP_Min + + -- by the cohort definition, patients are never on vasopressors + , 0 as rate_norepinephrine + , 0 as rate_epinephrine + , 0 as rate_dopamine + , 0 as rate_dobutamine + + , l.Creatinine_Max + , l.Bilirubin_Max + , l.Platelet_Min + + , pf.PaO2FiO2 + , uo.UrineOutput + , gcs.MinGCS + +from co +left join stg_pafi pf + on co.icustay_id = pf.icustay_id + and pf.rn = 1 +left join vitals v + on co.icustay_id = v.icustay_id +left join labs l + on co.icustay_id = l.icustay_id +left join uo + on co.icustay_id = uo.icustay_id +left join gcs gcs + on co.icustay_id = gcs.icustay_id +) +, scorecalc as +( + -- Calculate the final score + -- note that if the underlying data is missing, the component is null + -- eventually these are treated as 0 (normal), but knowing when data is missing is useful for debugging + select icustay_id + -- Respiration + , case + -- patient is never ventilated + -- when PaO2FiO2_vent_min < 100 then 4 + -- when PaO2FiO2_vent_min < 200 then 3 + when PaO2FiO2 < 300 then 2 + when PaO2FiO2 < 400 then 1 + when PaO2FiO2 is null then null + else 0 + end as respiration + + -- Coagulation + , case + when platelet_min < 20 then 4 + when platelet_min < 50 then 3 + when platelet_min < 100 then 2 + when platelet_min < 150 then 1 + when platelet_min is null then null + else 0 + end as coagulation + + -- Liver + , case + -- Bilirubin checks in mg/dL + when Bilirubin_Max >= 12.0 then 4 + when Bilirubin_Max >= 6.0 then 3 + when Bilirubin_Max >= 2.0 then 2 + when Bilirubin_Max >= 1.2 then 1 + when Bilirubin_Max is null then null + else 0 + end as liver + + -- Cardiovascular + , case + -- when rate_dopamine > 15 or rate_epinephrine > 0.1 or rate_norepinephrine > 0.1 then 4 + -- when rate_dopamine > 5 or rate_epinephrine <= 0.1 or rate_norepinephrine <= 0.1 then 3 + -- when rate_dopamine > 0 or rate_dobutamine > 0 then 2 + when MeanBP_Min < 70 then 1 + when MeanBP_Min is null then null + else 0 + end as cardiovascular + + -- Neurological failure (GCS) + , case + when (MinGCS >= 13 and MinGCS <= 14) then 1 + when (MinGCS >= 10 and MinGCS <= 12) then 2 + when (MinGCS >= 6 and MinGCS <= 9) then 3 + when MinGCS < 6 then 4 + when MinGCS is null then null + else 0 end + as cns + + -- Renal failure - high creatinine or low urine output + , case + when (Creatinine_Max >= 5.0) then 4 + when UrineOutput < 200 then 4 + when (Creatinine_Max >= 3.5 and Creatinine_Max < 5.0) then 3 + when UrineOutput < 500 then 3 + when (Creatinine_Max >= 2.0 and Creatinine_Max < 3.5) then 2 + when (Creatinine_Max >= 1.2 and Creatinine_Max < 2.0) then 1 + when coalesce(UrineOutput, Creatinine_Max) is null then null + else 0 end + as renal + from scorecomp +) +select co.icustay_id + -- Combine all the scores to get SOFA + -- Impute 0 if the score is missing + , coalesce(respiration,0) + + coalesce(coagulation,0) + + coalesce(liver,0) + + coalesce(cardiovascular,0) + + coalesce(cns,0) + + coalesce(renal,0) + as SOFA +, respiration +, coagulation +, liver +, cardiovascular +, cns +, renal +from co +left join scorecalc s + on co.icustay_id = s.icustay_id +order by co.icustay_id; diff --git a/notebooks/aline/aline_vaso_flag.sql b/notebooks/aline/aline_vaso_flag.sql new file mode 100644 index 0000000..4abc9c4 --- /dev/null +++ b/notebooks/aline/aline_vaso_flag.sql @@ -0,0 +1,59 @@ +-- Create a table which indicates if a patient was ever on a vasopressor during their ICU stay + +-- List of vasopressors used: +-- norepinephrine - 30047,30120,221906 +-- epinephrine - 30044,30119,30309,221289 +-- phenylephrine - 30127,30128,221749 +-- vasopressin - 30051,222315 +-- dopamine - 30043,30307,221662 +-- Isuprel - 30046,227692 + +DROP MATERIALIZED VIEW IF EXISTS ALINE_VASO_FLAG CASCADE; +CREATE MATERIALIZED VIEW ALINE_VASO_FLAG as +with io_cv as +( + select + icustay_id, charttime, itemid, stopped, rate, amount + from mimiciii.inputevents_cv + where itemid in + ( + 30047,30120 -- norepinephrine + ,30044,30119,30309 -- epinephrine + ,30127,30128 -- phenylephrine + ,30051 -- vasopressin + ,30043,30307,30125 -- dopamine + ,30046 -- isuprel + ) + and rate is not null + and rate > 0 +) +-- select only the ITEMIDs from the inputevents_mv table related to vasopressors +, io_mv as +( + select + icustay_id, linkorderid, starttime, endtime + from mimiciii.inputevents_mv io + -- Subselect the vasopressor ITEMIDs + where itemid in + ( + 221906 -- norepinephrine + ,221289 -- epinephrine + ,221749 -- phenylephrine + ,222315 -- vasopressin + ,221662 -- dopamine + ,227692 -- isuprel + ) + and rate is not null + and rate > 0 + and statusdescription != 'Rewritten' -- only valid orders +) +select + co.subject_id, co.hadm_id, co.icustay_id + , max(case when coalesce(io_mv.icustay_id, io_cv.icustay_id) is not null then 1 else 0 end) as vaso_flag +from icustays co +left join io_mv + on co.icustay_id = io_mv.icustay_id +left join io_cv + on co.icustay_id = io_cv.icustay_id +group by co.subject_id, co.hadm_id, co.icustay_id +order by icustay_id; diff --git a/notebooks/aline/aline_vitals.sql b/notebooks/aline/aline_vitals.sql new file mode 100644 index 0000000..9ee60b7 --- /dev/null +++ b/notebooks/aline/aline_vitals.sql @@ -0,0 +1,71 @@ + +DROP MATERIALIZED VIEW IF EXISTS ALINE_VITALS CASCADE; +CREATE MATERIALIZED VIEW ALINE_VITALS as + +-- first, group together ITEMIDs for the same vital sign +with vitals_stg0 as +( + select + co.icustay_id, charttime + , case + -- MAP, Temperature, HR, CVP, SpO2, + when itemid in (456,52,6702,443,220052,220181,225312) then 'MAP' + when itemid in (223762,676,223761,678) then 'Temperature' + when itemid in (211,220045) then 'HeartRate' + when itemid in (646,220277) then 'SpO2' + else null end as label + + , case when itemid in (223761,678) and ((valuenum-32)/1.8)<10 then null + when itemid in (223762,676) and valuenum < 10 then null + -- convert F to C + when itemid in (223761,678) then (valuenum-32)/1.8 + -- sanity checks on data - one outliter with spo2 < 25 + when itemid in (646,220277) and valuenum <= 25 then null + else valuenum end as valuenum + , case when ce.charttime > co.vent_starttime then 1 else 0 end as obs_after_vent + from ALINE_COHORT co + inner join chartevents ce + on ce.icustay_id = co.icustay_id + and ce.charttime <= co.vent_starttime + interval '4' hour + and ce.charttime >= co.vent_starttime - interval '1' day + and itemid in + ( + 456,52,6702,443,220052,220181,225312 -- map + , 223762,676,223761,678 -- temp + , 211,220045 -- hr + , 646,220277 -- spo2 + ) + and valuenum is not null + and coalesce(error,0) != 1 +) +-- next, assign an integer where rn=1 is the vital sign just preceeding vent +, vitals_stg1 as +( + select + icustay_id, label, valuenum, obs_after_vent + , ROW_NUMBER() over (partition by icustay_id, label, obs_after_vent order by charttime DESC) as rn + from vitals_stg0 +) +-- now aggregate where rn=1 to give the vital sign just before the vent starttime +, vitals as +( + select + icustay_id + -- this code prioritizes observations made before ventilation + -- but if they are admitted ventilated then we allow some fuzziness + , coalesce(min(case when rn = 1 and obs_after_vent = 0 and label = 'MAP' then valuenum else null end), + min(case when rn = 1 and obs_after_vent = 1 and label = 'MAP' then valuenum else null end)) as MAP + , coalesce(min(case when rn = 1 and obs_after_vent = 0 and label = 'Temperature' then valuenum else null end), + min(case when rn = 1 and obs_after_vent = 1 and label = 'Temperature' then valuenum else null end)) as Temperature + , coalesce(min(case when rn = 1 and obs_after_vent = 0 and label = 'HeartRate' then valuenum else null end), + min(case when rn = 1 and obs_after_vent = 1 and label = 'HeartRate' then valuenum else null end)) as HeartRate + , coalesce(min(case when rn = 1 and obs_after_vent = 0 and label = 'SpO2' then valuenum else null end), + min(case when rn = 1 and obs_after_vent = 1 and label = 'SpO2' then valuenum else null end)) as SpO2 + from vitals_stg1 + group by icustay_id +) +select + co.icustay_id, v.MAP, v.Temperature, v.HeartRate, v.SpO2 +from aline_cohort co +left join vitals v + on co.icustay_id = v.icustay_id; diff --git a/notebooks/consort_diagram/plot_consort_diagram.Rmd b/notebooks/consort_diagram/plot_consort_diagram.Rmd new file mode 100644 index 0000000..cdb014b --- /dev/null +++ b/notebooks/consort_diagram/plot_consort_diagram.Rmd @@ -0,0 +1,75 @@ +--- +title: "Plotting a CONSORT Flow Diagram" +output: + html_document: default + pdf_document: default +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) +``` + +CONSORT Flow Diagrams can be used to plot the flow of data selection of a patient cohort. +For more details, see: +[http://www.consort-statement.org/consort-statement/flow-diagram](The CONSORT Flow Diagram) + +```{r loadpackages, include=FALSE} +.packages <- c("diagram") +.inst <- .packages %in% installed.packages() +if(length(.packages[!.inst]) > 0) { + install.packages(.packages[!.inst], repos = "http://cran.rstudio.com") +} +lapply(.packages, library, character.only=TRUE) +``` + +```{r plot, echo=FALSE} +# set margins and multiplot +par(mfrow = c(1, 1)) +par(mar = c(0, 0, 0, 0)) + +# initialise a plot device +openplotmat() + +# position of boxes +# 1st column indicates x axis position between 0 and 1 +# 2nd column indicates y axis position between 0 and 1 +# automatically assigns vertical position +num_of_boxes <- 6 +auto_coords = coordinates(num_of_boxes) +vert_pos = rev(auto_coords[,1]) +box_pos <- matrix(nrow = num_of_boxes, ncol = 2, data = 0) +box_pos[1,] = c(0.20, vert_pos[1]) # 1st box +box_pos[2,] = c(0.70, vert_pos[2]) # 2nd box +box_pos[3,] = c(0.70, vert_pos[3]) # 3rd box +box_pos[4,] = c(0.70, vert_pos[4]) # etc... +box_pos[5,] = c(0.70, vert_pos[5]) +box_pos[6,] = c(0.20, vert_pos[6]) + +# content of boxes +box_content <- matrix(nrow = num_of_boxes, ncol = 1, data = 0) +box_content[1] = "All patients in MIMIC-III \n n = 58,976" # 1st box +box_content[2] = "Exclude patients of age < 18 \n n = 8,180" # 2nd box +box_content[3] = "Exclude patients with no ICU admissions \n n = 1,071" # 3rd box +box_content[4] = "Exclude patients with diabetes \n n = 1,324" # etc... +box_content[5] = "Exclude patients with sepsis \n n = 4,804" +box_content[6] = "Study cohort \n n = 43,597" + +# adjust the size of boxes to fit content +box_x <- c(0.20, 0.25, 0.25, 0.25, 0.25, 0.20) +box_y <- c(0.07, 0.07, 0.07, 0.07, 0.07, 0.07) + +# Draw the arrows +straightarrow(from = c(box_pos[1,1],box_pos[2,2]), to = box_pos[2,], lwd = 1) +straightarrow(from = c(box_pos[1,1],box_pos[3,2]), to = box_pos[3,], lwd = 1) +straightarrow(from = c(box_pos[1,1],box_pos[4,2]), to = box_pos[4,], lwd = 1) +straightarrow(from = c(box_pos[1,1],box_pos[5,2]), to = box_pos[5,], lwd = 1) +straightarrow(from = box_pos[1,], to = box_pos[6,], lwd = 1) + +# Draw the boxes +for (i in 1:num_of_boxes) { + textrect(mid = box_pos[i,], radx = box_x[i], rady = box_y[i], + lab = box_content[i], + shadow.col = "grey") + } + +``` diff --git a/notebooks/consort_diagram/plot_consort_diagram.html b/notebooks/consort_diagram/plot_consort_diagram.html new file mode 100644 index 0000000..bd43439 --- /dev/null +++ b/notebooks/consort_diagram/plot_consort_diagram.html @@ -0,0 +1,156 @@ + + + + + + + + + + + + + +Plotting a CONSORT Flow Diagram + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + + + + + + + + +

CONSORT Flow Diagrams can be used to plot the flow of data selection of a patient cohort.
+For more details, see: http://www.consort-statement.org/consort-statement/flow-diagram

+

+ + + + +
+ + + + + + + + diff --git a/notebooks/consort_diagram/plot_consort_diagram.pdf b/notebooks/consort_diagram/plot_consort_diagram.pdf new file mode 100644 index 0000000..aeca82c Binary files /dev/null and b/notebooks/consort_diagram/plot_consort_diagram.pdf differ diff --git a/notebooks/crrt-notebook.ipynb b/notebooks/crrt-notebook.ipynb new file mode 100644 index 0000000..636307b --- /dev/null +++ b/notebooks/crrt-notebook.ipynb @@ -0,0 +1,54615 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Continuous renal replacement therapy (CRRT)\n", + "\n", + "This notebook overviews the process of defining CRRT: a treatment used to dialyse or filter a patient's blood continuously. Key to CRRT is its lower speed compared to conventional dialysis: avoidance of rapid solute/fluid loss is suspected to be the main reason why CRRT tends to be tolerated better than intermittent hemodialysis.\n", + "\n", + "The primary aim of this notebook is to define the start and end times of CRRT for patients in the MIMIC-III database v1.4.\n", + "\n", + "A secondary aim of this notebook is to provide insight into how to extract clinical concepts from the MIMIC-III database.\n", + "\n", + "Many thanks to Sharon O'Donoghue for her invaluable advice in the creation of this notebook." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Outline\n", + "\n", + "The main steps in defining a clinical concept in MIMIC-III are as follows:\n", + "\n", + "1. Identification of key terms and phrases which describe the concept\n", + "2. Search for these terms in D_ITEMS (or D_LABITEMS if searching for a laboratory measurement)\n", + "3. Extraction of the data from tables specified in the `LINKSTO` column of D_ITEMS\n", + "4. Definition of the concept using rules applied to the data extracted\n", + "5. Validation of the concepts by individual inspection and aggregate statistics\n", + "\n", + "This process is iterative and not as clear cut as the above - validation may lead you to redefine data extraction, and so on. Furthermore, in the case of MIMIC-III v1.4, this process must be repeated twice: once for Metavision, once for CareVue.\n", + "\n", + "## MetaVision vs. CareVue\n", + "\n", + "One issue in MIMIC-III is that it is a combination of two ICU database systems. As a result, concepts are split among different `ITEMID` values. For example, a patient's heart rate is a relatively simple concept to extract, however, if we look in the D_ITEMS table for labels matching 'heart rate', we find at least two `ITEMID`:\n", + "\n", + "itemid | label | abbreviation | dbsource | linksto\n", + "--------|-------------------------|-----------------|------------|-------------\n", + "211 | Heart Rate | | carevue | chartevents\n", + "220045 | Heart Rate | HR | metavision | chartevents\n", + "\n", + "Both these `ITEMID` values capture heart rate - but one is used for the CareVue database system (`dbsource = 'carevue'`) and one is used for the MetaVision database system (`dbsource = 'metavision'`). The data extraction step must be repeated twice: once for `dbsource = 'carevue'` and once for `dbsource = 'metavision'`. In general, it is recommended to extract data from MetaVision first, as the data is better structured and provides useful information for what data elements to include. For example, `ITEMID` values in MetaVision have abbrevations with each label - these abbreviations can then be used to search for data elements in CareVue." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 0: import libraries, connect to the database" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Import libraries\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import psycopg2\n", + "from IPython.display import display, HTML # used to print out pretty pandas dataframes\n", + "import matplotlib.dates as dates\n", + "import matplotlib.lines as mlines\n", + "\n", + "%matplotlib inline\n", + "plt.style.use('ggplot') \n", + "\n", + "# specify user/password/where the database is\n", + "sqluser = 'postgres'\n", + "sqlpass = 'postgres'\n", + "dbname = 'mimic'\n", + "schema_name = 'mimiciii'\n", + "host = 'localhost'\n", + "\n", + "query_schema = 'SET search_path to ' + schema_name + ';'\n", + "\n", + "# connect to the database\n", + "con = psycopg2.connect(dbname=dbname, user=sqluser, password=sqlpass, host=host)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 1: Identification of key terms\n", + "\n", + "We are interested in continuous renal replacement therapy (CRRT). First, we look for 'CRRT' in the database, isolating ourselves to metavision data:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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0227290CRRT modeDialysischartevents
1225436CRRT Filter ChangeDialysisprocedureevents_mv
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5225956Reason for CRRT Filter ChangeDialysischartevents
\n", + "
" + ], + "text/plain": [ + " itemid label category linksto\n", + "0 227290 CRRT mode Dialysis chartevents\n", + "1 225436 CRRT Filter Change Dialysis procedureevents_mv\n", + "2 227525 Calcium Gluconate (CRRT) Medications inputevents_mv\n", + "3 225802 Dialysis - CRRT Dialysis procedureevents_mv\n", + "4 227536 KCl (CRRT) Medications inputevents_mv\n", + "5 225956 Reason for CRRT Filter Change Dialysis chartevents" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "\n", + "query = query_schema + \"\"\"\n", + "select itemid, label, category, linksto\n", + "from d_items\n", + "where dbsource = 'metavision'\n", + "and lower(label) like '%crrt%'\n", + "\"\"\"\n", + "df = pd.read_sql_query(query,con)\n", + "\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above gives us some hints to expand our initial search:\n", + "\n", + "* `category = 'Dialysis'`\n", + "* `lower(label) like '%dialysis%'`\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 2: Extraction of `ITEMID`s from tables\n", + "\n", + "## Get list of `itemid` related to CRRT" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
itemidlabelcategorylinksto
0225740Dialysis Catheter DiscontinuedAccess Lines - Invasivechartevents
1227357Dialysis Catheter Dressing OcclusiveAccess Lines - Invasivechartevents
2225776Dialysis Catheter Dressing TypeAccess Lines - Invasivechartevents
3226118Dialysis Catheter placed in outside facilityAccess Lines - Invasivechartevents
4227753Dialysis Catheter Placement Confirmed by X-rayAccess Lines - Invasivechartevents
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "query = query_schema + \"\"\"\n", + "select itemid, label, category, linksto\n", + "from d_items di\n", + "where dbsource = 'metavision'\n", + "and (lower(label) like '%dialy%'\n", + "or category = 'Dialysis'\n", + "or lower(label) like '%crrt%'\n", + ")\n", + "order by linksto, category, label\n", + "\"\"\"\n", + "df = pd.read_sql_query(query,con)\n", + "\n", + "HTML(df.head().to_html().replace('NaN', ''))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Manually label above `itemid`\n", + "\n", + "The above is a list of all the potential data elements which could be used to define CRRT. The next step is to identify the specific elements which can be used to define start/stop time. This process requires clinical expertise in the area.\n", + "\n", + "The following tables are a result of reviewing all `ITEMID` labels and flagging them as \"consider for further review\" or \"not relevant\".\n", + "\n", + "\n", + "### Links to CHARTEVENTS\n", + "\n", + "itemid | label | category | linksto | Included/comment\n", + "--- | --- | --- | --- | ---\n", + "225740 | Dialysis Catheter Discontinued | Access Lines - Invasive | chartevents | No - access line\n", + "227357 | Dialysis Catheter Dressing Occlusive | Access Lines - Invasive | chartevents | No - access line\n", + "225776 | Dialysis Catheter Dressing Type | Access Lines - Invasive | chartevents | No - access line\n", + "226118 | Dialysis Catheter placed in outside facility | Access Lines - Invasive | chartevents | No - access line\n", + "227753 | Dialysis Catheter Placement Confirmed by X-ray | Access Lines - Invasive | chartevents | No - access line\n", + "225323 | Dialysis Catheter Site Appear | Access Lines - Invasive | chartevents | No - access line\n", + "225725 | Dialysis Catheter Tip Cultured | Access Lines - Invasive | chartevents | No - access line\n", + "227124 | Dialysis Catheter Type | Access Lines - Invasive | chartevents | No - access line\n", + "225126 | Dialysis patient | Adm History/FHPA | chartevents | No - admission information\n", + "224149 | Access Pressure | Dialysis | chartevents | Yes - CRRT setting\n", + "224404 | ART Lumen Volume | Dialysis | chartevents | Yes - CRRT setting\n", + "224144 | Blood Flow (ml/min) | Dialysis | chartevents | Yes - CRRT setting\n", + "228004 | Citrate (ACD-A) | Dialysis | chartevents | Yes - CRRT setting\n", + "227290 | CRRT mode | Dialysis | chartevents | Yes - CRRT setting\n", + "225183 | Current Goal | Dialysis | chartevents | Yes - CRRT setting\n", + "225977 | Dialysate Fluid | Dialysis | chartevents | Yes - CRRT setting\n", + "224154 | Dialysate Rate | Dialysis | chartevents | Yes - CRRT setting\n", + "224135 | Dialysis Access Site | Dialysis | chartevents | No - access line\n", + "225954 | Dialysis Access Type | Dialysis | chartevents | No - access line\n", + "224139 | Dialysis Site Appearance | Dialysis | chartevents | No - access line\n", + "225810 | Dwell Time (Peritoneal Dialysis) | Dialysis | chartevents | No - peritoneal dialysis\n", + "224151 | Effluent Pressure | Dialysis | chartevents | Yes - CRRT setting\n", + "224150 | Filter Pressure | Dialysis | chartevents | Yes - CRRT setting\n", + "226499 | Hemodialysis Output | Dialysis | chartevents | No - hemodialysis\n", + "225958 | Heparin Concentration (units/mL) | Dialysis | chartevents | Yes - CRRT setting\n", + "224145 | Heparin Dose (per hour) | Dialysis | chartevents | Yes - CRRT setting\n", + "224191 | Hourly Patient Fluid Removal | Dialysis | chartevents | Yes - CRRT setting\n", + "225952 | Medication Added #1 (Peritoneal Dialysis) | Dialysis | chartevents | No - peritoneal dialysis\n", + "227638 | Medication Added #2 (Peritoneal Dialysis) | Dialysis | chartevents | No - peritoneal dialysis\n", + "225959 | Medication Added Amount #1 (Peritoneal Dialysis) | Dialysis | chartevents | No - peritoneal dialysis\n", + "227639 | Medication Added Amount #2 (Peritoneal Dialysis) | Dialysis | chartevents | No - peritoneal dialysis\n", + "225961 | Medication Added Units #1 (Peritoneal Dialysis) | Dialysis | chartevents | No - peritoneal dialysis\n", + "227640 | Medication Added Units #2 (Peritoneal Dialysis) | Dialysis | chartevents | No - peritoneal dialysis\n", + "228005 | PBP (Prefilter) Replacement Rate | Dialysis | chartevents | Yes - CRRT setting\n", + "225965 | Peritoneal Dialysis Catheter Status | Dialysis | chartevents | No - peritoneal dialysis\n", + "225963 | Peritoneal Dialysis Catheter Type | Dialysis | chartevents | No - peritoneal dialysis\n", + "225951 | Peritoneal Dialysis Fluid Appearance | Dialysis | chartevents | No - peritoneal dialysis\n", + "228006 | Post Filter Replacement Rate | Dialysis | chartevents | Yes - CRRT setting\n", + "225956 | Reason for CRRT Filter Change | Dialysis | chartevents | Yes - CRRT setting\n", + "225976 | Replacement Fluid | Dialysis | chartevents | Yes - CRRT setting\n", + "224153 | Replacement Rate | Dialysis | chartevents | Yes - CRRT setting\n", + "224152 | Return Pressure | Dialysis | chartevents | Yes - CRRT setting\n", + "225953 | Solution (Peritoneal Dialysis) | Dialysis | chartevents | No - peritoneal dialysis\n", + "224146 | System Integrity | Dialysis | chartevents | Yes - CRRT setting\n", + "226457 | Ultrafiltrate Output | Dialysis | chartevents | Yes - CRRT setting\n", + "224406 | VEN Lumen Volume | Dialysis | chartevents | Yes - CRRT setting\n", + "225806 | Volume In (PD) | Dialysis | chartevents | No - peritoneal dialysis\n", + "227438 | Volume not removed | Dialysis | chartevents | No - peritoneal dialysis\n", + "225807 | Volume Out (PD) | Dialysis | chartevents | No - peritoneal dialysis\n", + "\n", + "### Links to DATETIMEEVENTS\n", + "\n", + "itemid | label | category | linksto | Included/comment\n", + "--- | --- | --- | --- | ---\n", + "225318 | Dialysis Catheter Cap Change | Access Lines - Invasive | datetimeevents | No - access lines\n", + "225319 | Dialysis Catheter Change over Wire Date | Access Lines - Invasive | datetimeevents | No - access lines\n", + "225321 | Dialysis Catheter Dressing Change | Access Lines - Invasive | datetimeevents | No - access lines\n", + "225322 | Dialysis Catheter Insertion Date | Access Lines - Invasive | datetimeevents | No - access lines\n", + "225324 | Dialysis CatheterTubing Change | Access Lines - Invasive | datetimeevents | No - access lines\n", + "225128 | Last dialysis | Adm History/FHPA | datetimeevents | No - admission information\n", + "\n", + "### Links to INPUTEVENTS_MV\n", + "\n", + "itemid | label | category | linksto | Included/comment\n", + "--- | --- | --- | --- | ---\n", + "227525 | Calcium Gluconate (CRRT) | Medications | inputevents_mv | Yes - CRRT setting\n", + "227536 | KCl (CRRT) | Medications | inputevents_mv | Yes - CRRT setting\n", + "\n", + "### Links to PROCEDUREEVENTS_MV\n", + "\n", + "itemid | label | category | linksto | Included/comment\n", + "--- | --- | --- | --- | ---\n", + "225441 | Hemodialysis | 4-Procedures | procedureevents_mv | No - hemodialysis\n", + "224270 | Dialysis Catheter | Access Lines - Invasive | procedureevents_mv | No - access lines\n", + "225436 | CRRT Filter Change | Dialysis | procedureevents_mv | Yes - CRRT setting\n", + "225802 | Dialysis - CRRT | Dialysis | procedureevents_mv | Yes - CRRT setting\n", + "225803 | Dialysis - CVVHD | Dialysis | procedureevents_mv | Yes - CRRT setting\n", + "225809 | Dialysis - CVVHDF | Dialysis | procedureevents_mv | Yes - CRRT setting\n", + "225955 | Dialysis - SCUF | Dialysis | procedureevents_mv | Yes - CRRT setting\n", + "225805 | Peritoneal Dialysis | Dialysis | procedureevents_mv | No - peritoneal dialysis\n", + "\n", + "## Reasons for inclusion/exclusion\n", + "\n", + "* CRRT Setting - yes (included) - these settings are only documented when a patient is receiving CRRT.\n", + "* Access lines- no (excluded) - these ITEMIDs are not included as the presence of an access line does *not* guarantee that CRRT is being delivered. While having an access line is a requirement of performing CRRT, these lines are present even when a patient is not actively being hemodialysed.\n", + "* Peritoneal dialysis - no (excluded) - Peritoneal dialysis is a different form of dialysis, and is not CRRT\n", + "* Hemodialysis - no (excluded) - Similar as above, hemodialysis is a different form of dialysis and is not CRRT" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Define rules based upon ITEMIDs\n", + "\n", + "Above, we acquired a list of `itemid` which we determined to be related to administration of CRRT. The next step is to determine *how* these `itemid` relate to CRRT: do they indicate it is started, stopped, continuing, or something else.\n", + "\n", + "We will evaluate `itemid` from three tables, in turn: CHARTEVENTS, INPUTEVENTS_MV, and PROCEDUREEVENTS_MV. Note that the \\_MV subscript indicates that the table only has data from MetaVision (half the patients), while \\_CV indicates the table only has data from CareVue (the other half of patients). Note that after we extract data from MetaVision patients, we will repeat this exercise for CareVue patients.\n", + "\n", + "### table 1 of 3: `itemid` from CHARTEVENTS\n", + "\n", + "These are the included CRRT settings in CHARTEVENTS:\n", + "\n", + "itemid | label | param_type \n", + "--------|----------------------------------|------------\n", + "224144 | Blood Flow (ml/min) | Numeric\n", + "224145 | Heparin Dose (per hour) | Numeric\n", + "224146 | System Integrity | Text\n", + "224149 | Access Pressure | Numeric\n", + "224150 | Filter Pressure | Numeric\n", + "224151 | Effluent Pressure | Numeric\n", + "224152 | Return Pressure | Numeric\n", + "224153 | Replacement Rate | Numeric\n", + "224154 | Dialysate Rate | Numeric\n", + "224191 | Hourly Patient Fluid Removal | Numeric\n", + "224404 | ART Lumen Volume | Numeric\n", + "224406 | VEN Lumen Volume | Numeric\n", + "225183 | Current Goal | Numeric\n", + "225956 | Reason for CRRT Filter Change | Text\n", + "225958 | Heparin Concentration (units/mL) | Text\n", + "225976 | Replacement Fluid | Text\n", + "225977 | Dialysate Fluid | Text\n", + "226457 | Ultrafiltrate Output | Numeric\n", + "227290 | CRRT mode | Text\n", + "228004 | Citrate (ACD-A) | Numeric\n", + "228005 | PBP (Prefilter) Replacement Rate | Numeric\n", + "228006 | Post Filter Replacement Rate | Numeric\n", + "\n", + "First, we examine the numeric fields. These fields are the core CRRT settings which, according to clinical advice, should be documented hourly for patients actively on CRRT:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
icustay_idlabelcharttimevaluevalueuom
0246866ART Lumen Volume2161-12-11 20:00:001.3mL
1246866VEN Lumen Volume2161-12-11 20:00:001.2mL
2246866Access Pressure2161-12-11 23:43:00-87mmHg
3246866Blood Flow (ml/min)2161-12-11 23:43:00200ml/min
4246866Citrate (ACD-A)2161-12-11 23:43:000ml/hr
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "query = query_schema + \"\"\"\n", + "select\n", + " ce.icustay_id, di.label, ce.charttime\n", + " , ce.value\n", + " , ce.valueuom\n", + "from chartevents ce\n", + "inner join d_items di\n", + "on ce.itemid = di.itemid\n", + "where ce.icustay_id = 246866\n", + "and ce.itemid in\n", + "(\n", + " 224404, -- | ART Lumen Volume\n", + " 224406, -- | VEN Lumen Volume\n", + " 228004, -- | Citrate (ACD-A)\n", + " 224145, -- | Heparin Dose (per hour)\n", + " 225183, -- | Current Goal\n", + " 224149, -- | Access Pressure\n", + " 224144, -- | Blood Flow (ml/min)\n", + " 224154, -- | Dialysate Rate\n", + " 224151, -- | Effluent Pressure\n", + " 224150, -- | Filter Pressure\n", + " 224191, -- | Hourly Patient Fluid Removal\n", + " 228005, -- | PBP (Prefilter) Replacement Rate\n", + " 228006, -- | Post Filter Replacement Rate\n", + " 224153, -- | Replacement Rate\n", + " 224152, -- | Return Pressure\n", + " 226457 -- | Ultrafiltrate Output\n", + ")\n", + "order by ce.icustay_id, ce.charttime, di.label;\n", + "\"\"\"\n", + "df = pd.read_sql_query(query,con)\n", + "\n", + "HTML(df.head().to_html().replace('NaN', ''))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Above we can see that `ART Lumen Volume and VEN Lumen Volume` are documented at a drastically different time than the other settings. Upon discussion with a clinical expert, they confirmed that this is expected, as these volumes indicate settings to keep open the line and are not directly relevant to the administration of CRRT - at best they are superfluous and at worst they can mislead the start/stop times. As a result `ART Lumen Volume` and `VEN Lumen Volume` are excluded. This leaves us with the final set of `ITEMID`s:\n", + "\n", + "```sql\n", + "224149, -- Access Pressure\n", + "224144, -- Blood Flow (ml/min)\n", + "228004, -- Citrate (ACD-A)\n", + "225183, -- Current Goal\n", + "224154, -- Dialysate Rate\n", + "224151, -- Effluent Pressure\n", + "224150, -- Filter Pressure\n", + "224145, -- Heparin Dose (per hour)\n", + "224191, -- Hourly Patient Fluid Removal\n", + "228005, -- PBP (Prefilter) Replacement Rate\n", + "228006, -- Post Filter Replacement Rate\n", + "224153, -- Replacement Rate\n", + "224152, -- Return Pressure\n", + "226457 -- Ultrafiltrate Output\n", + "```\n", + "\n", + "The next step is to examine the remaining text based `ITEMID`:\n", + "\n", + "\n", + "\n", + "itemid | label | param_type \n", + "--------|----------------------------------|------------\n", + "224146 | System Integrity | Text\n", + "225956 | Reason for CRRT Filter Change | Text\n", + "225958 | Heparin Concentration (units/mL) | Text\n", + "225976 | Replacement Fluid | Text\n", + "225977 | Dialysate Fluid | Text\n", + "227290 | CRRT mode | Text\n", + "\n", + "We define a helper function which prints out the number of observations for a given `itemid`:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def print_itemid_info(con, itemid):\n", + " # get name of itemid\n", + " query = query_schema + \"\"\"\n", + " select label\n", + " from d_items\n", + " where itemid = \"\"\" + str(itemid)\n", + " \n", + " df = pd.read_sql_query(query,con)\n", + " print('Values for {} - {}...'.format(itemid, df['label'][0]))\n", + " \n", + " \n", + " query = query_schema + \"\"\"\n", + " select value\n", + " , count(distinct icustay_id) as number_of_patients\n", + " , count(icustay_id) as number_of_observations\n", + " from chartevents\n", + " where itemid = \"\"\" + str(itemid) + \"\"\" \n", + " group by value\n", + " order by value\n", + " \"\"\"\n", + " df = pd.read_sql_query(query,con)\n", + " display(HTML(df.to_html().replace('NaN', '')))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 224146 - System Integrity" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Values for 224146 - System Integrity...\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
valuenumber_of_patientsnumber_of_observations
0Active53948072
1Clots Increasing2451419
2Clots Present42716836
3Clotted233441
4Discontinued339771
5Line pressure inconsistent127431
6New Filter3571040
7No Clot Present2752615
8Recirculating172466
9Reinitiated3361207
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print_itemid_info(con, 224146)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In discussion with a clinical expert, each of these settings indicate different stages of the CRRT treatment. We can simplify them into three modes: started, stopped, or active. Since active implies that the CRRT is running, the first active event could also be a start time, therefore we call it \"active/started\". Here we list the manually curated mapping:\n", + "\n", + "value | count | interpretation\n", + "--- | --- | ---\n", + "Active | 539 | CRRT active/started\n", + "Clots Increasing | 245 | CRRT active/started\n", + "Clots Present | 427 | CRRT active/started\n", + "Clotted | 233 | CRRT **stopped**\n", + "Discontinued | 339 | CRRT **stopped**\n", + "Line pressure inconsistent | 127 | CRRT active/started\n", + "New Filter | 357 | CRRT **started**\n", + "No Clot Present | 275 | CRRT active/started\n", + "Recirculating | 172 | CRRT **stopped**\n", + "Reinitiated | 336 | CRRT **started**\n", + "\n", + "Later on we will code special rules to incorporate this `itemid`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 225956 - Reason for CRRT Filter Change" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Values for 225956 - Reason for CRRT Filter Change...\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
valuenumber_of_patientsnumber_of_observations
0Clotted5069
1Line changed911
2Procedure2031
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print_itemid_info(con, 225956)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above is a **stop** time as the filter needed to be changed at this time. Any subsequent CRRT would be a restart of CRRT - and not a continuation of an ongoing CRRT session." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 225958 - Heparin Concentration (units/mL)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Values for 225958 - Heparin Concentration (units/mL)...\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
valuenumber_of_patientsnumber_of_observations
010016995
110004194
2Not applicable1208796
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print_itemid_info(con, 225958)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above is a normal setting and can be combined with the numeric fields." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 225976 - Replacement Fluid" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Values for 225976 - Replacement Fluid...\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
valuenumber_of_patientsnumber_of_observations
0None1419
1Normal Saline 0.9%112
2Prismasate K078201
3Prismasate K245927603
4Prismasate K438730872
5Sodium Bicarb 150/D5W28
6Sodium Bicarb 75/0.45NS648
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print_itemid_info(con, 225976)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above is a normal setting and can be combined with the numeric fields." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 225977 - Dialysate Fluid" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Values for 225977 - Dialysate Fluid...\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
valuenumber_of_patientsnumber_of_observations
0None976025
1Normal Saline32695
2Prismasate K089231
3Prismasate K243824271
4Prismasate K435727320
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print_itemid_info(con, 225977)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above is a normal setting and can be combined with the numeric fields." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 227290 - CRRT mode" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Values for 227290 - CRRT mode...\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
valuenumber_of_patientsnumber_of_observations
0CVVH401280
1CVVHD24583
2CVVHDF49825533
3SCUF17
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print_itemid_info(con, 227290)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "While all of this looks good, it's feasible that the documentation of the CRRT mode is not done directly concurrent to the actual administration of CRRT. We thus investigate whether CRRT mode is available for all patients with a CRRT setting." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
num_icustay_idnum_with_mode
0784533
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Examining CRRT mode\n", + "query = query_schema + \"\"\"\n", + "with t1 as \n", + "(\n", + "select icustay_id,\n", + " max(case when itemid = 227290 then 1 else 0 end) as HasMode\n", + "from chartevents ce\n", + "where itemid in\n", + "(\n", + " 227290, -- CRRT mode\n", + " 228004, -- Citrate (ACD-A)\n", + " 225958, -- Heparin Concentration (units/mL)\n", + " 224145, -- Heparin Dose (per hour)\n", + " 225183, -- Current Goal -- always there\n", + " 224149, -- Access Pressure\n", + " 224144, -- Blood Flow (ml/min)\n", + " 225977, -- Dialysate Fluid\n", + " 224154, -- Dialysate Rate\n", + " 224151, -- Effluent Pressure\n", + " 224150, -- Filter Pressure\n", + " 224191, -- Hourly Patient Fluid Removal\n", + " 228005, -- PBP (Prefilter) Replacement Rate\n", + " 228006, -- Post Filter Replacement Rate\n", + " 225976, -- Replacement Fluid\n", + " 224153, -- Replacement Rate\n", + " 224152, -- Return Pressure\n", + " 226457 -- Ultrafiltrate Output\n", + ")\n", + "group by icustay_id\n", + ")\n", + "select count(icustay_id) as Num_ICUSTAY_ID\n", + ", sum(hasmode) as Num_With_Mode\n", + "from t1\n", + "\"\"\"\n", + "df = pd.read_sql_query(query,con)\n", + "\n", + "HTML(df.to_html().replace('NaN', ''))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can take this analysis a bit further and ask: is CRRT mode is present when *none* of the other settings are present?" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
numobsbothonlycrrtmodenocrrtmode
08116227446153778
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "query = query_schema + \"\"\"\n", + "with t1 as \n", + "(\n", + "select icustay_id, charttime\n", + " , max(case when itemid = 227290 then 1 else 0 end) as HasCRRTMode\n", + " , max(case when itemid != 227290 then 1 else 0 end) as OtherITEMID\n", + "from chartevents ce\n", + "where itemid in\n", + "(\n", + " 227290, -- CRRT mode\n", + " 228004, -- Citrate (ACD-A)\n", + " 225958, -- Heparin Concentration (units/mL)\n", + " 224145, -- Heparin Dose (per hour)\n", + " 225183, -- Current Goal -- always there\n", + " 224149, -- Access Pressure\n", + " 224144, -- Blood Flow (ml/min)\n", + " 225977, -- Dialysate Fluid\n", + " 224154, -- Dialysate Rate\n", + " 224151, -- Effluent Pressure\n", + " 224150, -- Filter Pressure\n", + " 224191, -- Hourly Patient Fluid Removal\n", + " 228005, -- PBP (Prefilter) Replacement Rate\n", + " 228006, -- Post Filter Replacement Rate\n", + " 225976, -- Replacement Fluid\n", + " 224153, -- Replacement Rate\n", + " 224152, -- Return Pressure\n", + " 226457 -- Ultrafiltrate Output\n", + ")\n", + "group by icustay_id, charttime\n", + ")\n", + "select count(icustay_id) as NumObs\n", + ", sum(case when HasCRRTMode = 1 and OtherITEMID = 1 then 1 else 0 end) as Both\n", + ", sum(case when HasCRRTMode = 1 and OtherITEMID = 0 then 1 else 0 end) as OnlyCRRTMode\n", + ", sum(case when HasCRRTMode = 0 and OtherITEMID = 1 then 1 else 0 end) as NoCRRTMode\n", + "from t1\n", + "\"\"\"\n", + "df = pd.read_sql_query(query,con)\n", + "\n", + "HTML(df.to_html().replace('NaN', ''))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As CRRT mode is relatively redundant, doesn't necessarily indicate CRRT is being actively performed, and documentation for it is not 100% compliant, we exclude it from the list of `ITEMID`.\n", + "\n", + "### CHARTEVENTS wrap up\n", + "\n", + "The following is the final set of `ITEMID` from CHARTEVENTS which indicate CRRT is started/ongoing:\n", + "\n", + "```sql\n", + "224149, -- Access Pressure\n", + "224144, -- Blood Flow (ml/min)\n", + "228004, -- Citrate (ACD-A)\n", + "225183, -- Current Goal\n", + "225977, -- Dialysate Fluid\n", + "224154, -- Dialysate Rate\n", + "224151, -- Effluent Pressure\n", + "224150, -- Filter Pressure\n", + "225958, -- Heparin Concentration (units/mL)\n", + "224145, -- Heparin Dose (per hour)\n", + "224191, -- Hourly Patient Fluid Removal\n", + "228005, -- PBP (Prefilter) Replacement Rate\n", + "228006, -- Post Filter Replacement Rate\n", + "225976, -- Replacement Fluid\n", + "224153, -- Replacement Rate\n", + "224152, -- Return Pressure\n", + "226457 -- Ultrafiltrate Output\n", + "```\n", + "\n", + "The following `ITEMID` are the final set which indicate CRRT is started/stopped/ongoing (i.e. require special rules):\n", + "\n", + "```sql\n", + "224146, -- System Integrity\n", + "225956 -- Reason for CRRT Filter Change\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### table 2 of 3: INPUTEVENTS_MV\n", + "\n", + "The following is the final set of ITEMID from INPUTEVENTS_MV:\n", + "\n", + "```sql\n", + "227525,-- Calcium Gluconate (CRRT)\n", + "227536 -- KCl (CRRT)\n", + "```\n", + "\n", + "No special examination is required for these fields - they are guaranteed to be CRRT (as verified by a clinician) - we can use these to indicate that CRRT is active/started." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### table 3 of 3: PROCEDUREEVENTS_MV" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following are the set of `itemid` from above related to PROCEDUREEVENTS_MV:\n", + "\n", + "itemid | label\n", + "--- | ---\n", + "225436 | CRRT Filter Change\n", + "225802 | Dialysis - CRRT\n", + "225803 | Dialysis - CVVHD\n", + "225809 | Dialysis - CVVHDF\n", + "225955 | Dialysis - SCUF\n", + "\n", + "The only contentious `ITEMID` is 225436 (CRRT Filter Change). This `ITEMID` indicates a break from CRRT, and it reinitiates at the end of this change. While in principle this could be used as an end time, documentation on it is not 100%, and as recommended by staff it's easier to ignore this and use the filter change field from CHARTEVENTS to define the end of CRRT events.\n", + "\n", + "The final set of `ITEMID` used for CRRT are:\n", + "\n", + "```sql\n", + "225802, -- Dialysis - CRRT\n", + "225803, -- Dialysis - CVVHD\n", + "225809, -- Dialysis - CVVHDF\n", + "225955 -- Dialysis - SCUF\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Step 4: definition of concept using rules\n", + "\n", + "Let's review the goal of this notebook. We would like to define the duration of CRRT for each patient. Concretely, this means we must define, for each `ICUSTAY_ID`:\n", + "\n", + "* a `STARTTIME`\n", + "* an `ENDTIME`\n", + "\n", + "As CRRT can be started/stopped throughout a patient's stay, there may be multiple `STARTTIME` and `ENDTIME` for a single `ICUSTAY_ID` - but they should not overlap.\n", + "\n", + "Recall that CHARTEVENTS stores data at charted times (`CHARTTIME`), and as a result the settings are stored at a single point in time. For CHARTEVENTS, the main task thus becomes converting a series of `CHARTTIME` into pairs of `STARTTIME` and `ENDTIME`. Intuitively this can be done by looking for consecutive settings each hour, and combining these into a single CRRT event. The first observed `CHARTTIME` becomes the `STARTTIME`, and the last observed `CHARTTIME` becomes the `ENDTIME`. However, CHARTEVENTS is not the only source of data. To improve the accuracy of our calculation, we also include data from INPUTEVENTS_MV and PROCEDUREEVENTS_MV. For INPUTEVENTS_MV, this does not complicate things too much. Each observation in INPUTEVENTS_MV is also stored at a single `CHARTTIME`, and so we simply need to combine this table with CHARTEVENTS before proceeding (likely by using the SQL `UNION` command).\n", + "\n", + "PROCEDUREEVENTS_MV is more complicated as it actually stores data with a `STARTTIME` and an `ENDTIME` column already. We need to merge the extracted data from CHARTEVENTS/INPUTEVENTS_MV with this already nicely formatted data from PROCEDUREEVENTS_MV.\n", + "\n", + "With the task laid out, let's get started. We will:\n", + "\n", + "1. Aggregate INPUTEVENTS_MV into durations\n", + "2. Convert CHARTEVENTS into durations\n", + "2. Compare these durations with PROCEDUREVENTS_MV and decide on a rule for merging the two\n", + "3. Merge PROCEDUREEVENTS_MV with INPUTEVENTS_MV/CHARTEVENTS for a final durations table for Metavision" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# define the example ICUSTAY_ID for the below code\n", + "# originally, this was 246866 - if changed, the interpretation provided will no longer make sense\n", + "query_where_clause = \"and icustay_id = 246866\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To make sure we don't display data we don't have to, we define a function which: (i) doesn't display icustay_id, and (ii) simplifies the date by removing the month/year." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def display_df(df):\n", + " col = [x for x in df.columns if x != 'icustay_id']\n", + " df_tmp = df[col].copy()\n", + " for c in df_tmp.columns:\n", + " if '[ns]' in str(df_tmp[c].dtype):\n", + " df_tmp[c] = df_tmp[c].dt.strftime('Day %d, %H:%M')\n", + " \n", + " display(HTML(df_tmp.to_html().replace('NaN', '')))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Aggregating INPUTEVENTS_MV\n", + "\n", + "First, let's look at INPUTEVENTS_MV. Each entry is stored with a `starttime` and an `endtime`. Note we have to exclude `statusdescription = 'Rewritten'` as these are undelivered medications which have been rewritten (useful for auditing purposes but does not give you information about drugs delivered to the patient)." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Durations from INPUTEVENTS for one patient with KCl...\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
linkorderidorderidlabelstarttimeendtimeraterateuomstatusdescription
085222578522257KClDay 11, 21:30Day 12, 02:304.000000mEq./hourFinishedRunning
193704849370484KClDay 11, 23:45Day 12, 02:4110.002273mEq./hourFinishedRunning
235072523507252KClDay 12, 02:41Day 12, 05:369.997713mEq./hourFinishedRunning
395259619525961KClDay 12, 05:36Day 12, 08:3110.285715mEq./hourFinishedRunning
471189857118985KClDay 12, 08:31Day 12, 11:2910.112360mEq./hourFinishedRunning
553950955395095KClDay 12, 11:29Day 12, 14:2810.055866mEq./hourFinishedRunning
680655418065541KClDay 12, 14:28Day 12, 17:2510.169492mEq./hourFinishedRunning
757588995758899KClDay 12, 17:25Day 12, 20:2410.055866mEq./hourFinishedRunning
871571267157126KClDay 12, 20:24Day 12, 20:3010.000000mEq./hourPaused
971571263853798KClDay 12, 21:30Day 12, 21:359.997634mEq./hourChanged
1071571266292957KClDay 12, 21:35Day 13, 02:086.190549mEq./hourFinishedRunning
11271343271343KClDay 13, 02:08Day 13, 07:066.040268mEq./hourFinishedRunning
1294079429407942KClDay 13, 07:06Day 13, 12:036.006060mEq./hourFinishedRunning
1391821199182119KClDay 13, 12:03Day 13, 16:296.005904mEq./hourStopped
1497206239720623KClDay 13, 18:15Day 13, 23:156.000000mEq./hourFinishedRunning
1521945782194578KClDay 14, 15:28Day 14, 18:476.000000mEq./hourFinishedRunning
1675675257567525KClDay 14, 18:47Day 14, 19:016.000000mEq./hourChanged
1775675254605649KClDay 14, 19:01Day 14, 23:044.007380mEq./hourChanged
1875675255699592KClDay 14, 23:04Day 14, 23:185.871428mEq./hourFinishedRunning
1987437158743715KClDay 14, 23:18Day 15, 02:285.905264mEq./hourFinishedRunning
2040807094080709KClDay 15, 02:28Day 15, 05:445.969388mEq./hourFinishedRunning
2146447824644782KClDay 15, 05:44Day 15, 08:575.906736mEq./hourFinishedRunning
22741589741589KClDay 15, 08:57Day 15, 12:085.905759mEq./hourFinishedRunning
2361902206190220KClDay 15, 12:08Day 15, 15:175.904762mEq./hourFinishedRunning
2419210101921010KClDay 15, 15:17Day 15, 18:345.908629mEq./hourFinishedRunning
2530119123011912KClDay 15, 18:34Day 15, 21:465.906250mEq./hourFinishedRunning
2611073181107318KClDay 15, 21:46Day 16, 01:015.907693mEq./hourFinishedRunning
27609665609665KClDay 16, 01:01Day 16, 04:185.908629mEq./hourFinishedRunning
2849951984995198KClDay 16, 04:18Day 16, 07:365.903030mEq./hourFinishedRunning
2946674234667423KClDay 16, 07:36Day 16, 10:545.903030mEq./hourFinishedRunning
3048020774802077KClDay 16, 10:54Day 16, 14:125.903030mEq./hourFinishedRunning
3184270078427007KClDay 16, 14:12Day 16, 16:045.905613mEq./hourStopped
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Durations from INPUTEVENTS for one patient with KCl...\")\n", + "query = query_schema + \"\"\"\n", + "select \n", + " linkorderid\n", + " , orderid\n", + " , case when itemid = 227525 then 'Calcium' else 'KCl' end as label\n", + " , starttime, endtime\n", + " , rate, rateuom\n", + " , statusdescription\n", + "from inputevents_mv\n", + "where itemid in\n", + "(\n", + " --227525,-- Calcium Gluconate (CRRT)\n", + " 227536 -- KCl (CRRT)\n", + ")\n", + "and statusdescription != 'Rewritten'\n", + "\"\"\" + query_where_clause + \"\"\"\n", + "order by starttime, endtime\n", + "\"\"\"\n", + "ie = pd.read_sql_query(query,con)\n", + "\n", + "display_df(ie)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Normally `linkorderid` links together administrations which are consecutive but may have changes in rate, but from the above we can note that `linkorderid` seems to rarely group entries. Rows 8-10 and 16-18 are grouped (i.e. they are sequential administrations where the rate may or may not have changed), but many aren't *even though* they occur sequentially. We'd like to merge together sequential events to simplify the durations - and it appears we can greatly simplify this data by merging two rows if `endtime(row-1) == starttime(row)`.\n", + "\n", + "We can do this in three steps:\n", + "\n", + "1. Create a binary flag that indicates when new \"events\" occur, where an \"event\" is defined as a continuous segment of administration, i.e. the binary flag is 1 if the row does not immediately follow the previous row, and 0 if the row does immediately follow the previous row\n", + "2. Aggregate this binary flag so each individual event is assigned a unique integer (i.e. create a partition over these events)\n", + "3. Create an integer to identify the last row in the event (so we can get useful information from this row)\n", + "4. Group the data based off the partition to result in a single `starttime` and `endtime` for each continguous medication administration\n", + "\n", + "Now we'll go through the code for doing this step by step." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 1: create a binary flag for new events\n", + "\n", + "```sql\n", + "with t1 as\n", + "(\n", + "select icustay_id\n", + " , case when itemid = 227525 then 'Calcium' else 'KCl' end as label\n", + " , starttime\n", + " , endtime\n", + " , case\n", + " when lag(endtime) over (partition by icustay_id, itemid order by starttime, endtime) = starttime\n", + " then 0\n", + " else 1 end\n", + " as new_event_flag\n", + " , rate, rateuom\n", + " , statusdescription\n", + "from inputevents_mv\n", + "where itemid in\n", + "(\n", + " --227525,-- Calcium Gluconate (CRRT)\n", + " 227536 -- KCl (CRRT)\n", + ")\n", + "and statusdescription != 'Rewritten'\n", + "\"\"\" + query_where_clause + \"\"\"\n", + "```\n", + "\n", + "This selects data from INPUTEVENTS_MV for just KCl using a single patient specified by the `query_where_clause` (this is so it can act as an example - you can omit the single patient and it will work on all the data).\n", + "\n", + "The key code block is here:\n", + "\n", + "```sql\n", + " , case\n", + " when lag(endtime) over (partition by icustay_id, itemid order by starttime, endtime) = starttime\n", + " then 0\n", + " else 1 end\n", + " as new_event_flag\n", + "```\n", + "\n", + "This creates a boolean flag which is 1 every time the current `starttime` is not equal to the previous `endtime`, i.e. it marks new \"events\". We can see it in action here:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Durations from INPUTEVENTS_MV, new events noted with time_partition...\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
labelstarttimeendtimeendtime_lagnew_event_flagraterateuomstatusdescription
0KClDay 11, 21:30Day 12, 02:30NaT14.000000mEq./hourFinishedRunning
1KClDay 11, 23:45Day 12, 02:41Day 12, 02:30110.002273mEq./hourFinishedRunning
2KClDay 12, 02:41Day 12, 05:36Day 12, 02:4109.997713mEq./hourFinishedRunning
3KClDay 12, 05:36Day 12, 08:31Day 12, 05:36010.285715mEq./hourFinishedRunning
4KClDay 12, 08:31Day 12, 11:29Day 12, 08:31010.112360mEq./hourFinishedRunning
5KClDay 12, 11:29Day 12, 14:28Day 12, 11:29010.055866mEq./hourFinishedRunning
6KClDay 12, 14:28Day 12, 17:25Day 12, 14:28010.169492mEq./hourFinishedRunning
7KClDay 12, 17:25Day 12, 20:24Day 12, 17:25010.055866mEq./hourFinishedRunning
8KClDay 12, 20:24Day 12, 20:30Day 12, 20:24010.000000mEq./hourPaused
9KClDay 12, 21:30Day 12, 21:35Day 12, 20:3019.997634mEq./hourChanged
10KClDay 12, 21:35Day 13, 02:08Day 12, 21:3506.190549mEq./hourFinishedRunning
11KClDay 13, 02:08Day 13, 07:06Day 13, 02:0806.040268mEq./hourFinishedRunning
12KClDay 13, 07:06Day 13, 12:03Day 13, 07:0606.006060mEq./hourFinishedRunning
13KClDay 13, 12:03Day 13, 16:29Day 13, 12:0306.005904mEq./hourStopped
14KClDay 13, 18:15Day 13, 23:15Day 13, 16:2916.000000mEq./hourFinishedRunning
15KClDay 14, 15:28Day 14, 18:47Day 13, 23:1516.000000mEq./hourFinishedRunning
16KClDay 14, 18:47Day 14, 19:01Day 14, 18:4706.000000mEq./hourChanged
17KClDay 14, 19:01Day 14, 23:04Day 14, 19:0104.007380mEq./hourChanged
18KClDay 14, 23:04Day 14, 23:18Day 14, 23:0405.871428mEq./hourFinishedRunning
19KClDay 14, 23:18Day 15, 02:28Day 14, 23:1805.905264mEq./hourFinishedRunning
20KClDay 15, 02:28Day 15, 05:44Day 15, 02:2805.969388mEq./hourFinishedRunning
21KClDay 15, 05:44Day 15, 08:57Day 15, 05:4405.906736mEq./hourFinishedRunning
22KClDay 15, 08:57Day 15, 12:08Day 15, 08:5705.905759mEq./hourFinishedRunning
23KClDay 15, 12:08Day 15, 15:17Day 15, 12:0805.904762mEq./hourFinishedRunning
24KClDay 15, 15:17Day 15, 18:34Day 15, 15:1705.908629mEq./hourFinishedRunning
25KClDay 15, 18:34Day 15, 21:46Day 15, 18:3405.906250mEq./hourFinishedRunning
26KClDay 15, 21:46Day 16, 01:01Day 15, 21:4605.907693mEq./hourFinishedRunning
27KClDay 16, 01:01Day 16, 04:18Day 16, 01:0105.908629mEq./hourFinishedRunning
28KClDay 16, 04:18Day 16, 07:36Day 16, 04:1805.903030mEq./hourFinishedRunning
29KClDay 16, 07:36Day 16, 10:54Day 16, 07:3605.903030mEq./hourFinishedRunning
30KClDay 16, 10:54Day 16, 14:12Day 16, 10:5405.903030mEq./hourFinishedRunning
31KClDay 16, 14:12Day 16, 16:04Day 16, 14:1205.905613mEq./hourStopped
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Durations from INPUTEVENTS_MV, new events noted with time_partition...\")\n", + "query = query_schema + \"\"\"\n", + "with t1 as\n", + "(\n", + "select \n", + " icustay_id\n", + " , case when itemid = 227525 then 'Calcium' else 'KCl' end as label\n", + " , starttime, endtime\n", + " , lag(endtime) over (partition by icustay_id, itemid order by starttime, endtime) as endtime_lag\n", + " , case\n", + " when lag(endtime) over (partition by icustay_id, itemid order by starttime, endtime) = starttime\n", + " then 0\n", + " else 1 end\n", + " as new_event_flag\n", + " , rate, rateuom\n", + " , statusdescription\n", + "from inputevents_mv\n", + "where itemid in\n", + "(\n", + " --227525,-- Calcium Gluconate (CRRT)\n", + " 227536 -- KCl (CRRT)\n", + ")\n", + "and statusdescription != 'Rewritten'\n", + "\"\"\" + query_where_clause + \"\"\"\n", + ")\n", + "select \n", + " label\n", + ", starttime\n", + ", endtime\n", + ", endtime_lag\n", + ", new_event_flag\n", + ", rate, rateuom\n", + ", statusdescription\n", + "from t1\n", + "\"\"\"\n", + "ie = pd.read_sql_query(query,con)\n", + "\n", + "display_df(ie)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note we have added the `endtime_lag` column to give a clearer idea of how the query is working. We can see the first row starts with `new_event_flag = 1` since `endtime_lag` is null. Next, the `endtime_lag != starttime`, so `new_event_flag` is again `= 1`.\n", + "\n", + "Finally, for row 2 (marked by **2** on the far left), the `endtime_lag == starttime` - and so `new_event_flag` is 0. This continues all the way until row **9**, where we can again see `endtime_lag != starttime`. Note that the `statusdescription` on row **8** even informs us why: it states that the administration has been \"Paused\". This is why we mentioned earlier that we were interested in the last row from an event.\n", + "\n", + "### Step 2: create a binary flag for new events\n", + "\n", + "With SQL, in order to aggregate groups of rows, we need a *partition*. That is, we need some key (usually an integer) which is unique for that set of rows. Once we have this unique key, we can do all the standard SQL aggregations like `max()`, `min()`, and so on (note: SQL \"window\" functions operate on the same principle, except you define the partition explicitly from a combination of columns).\n", + "\n", + "With this in mind, our next step is to use this flag to create a unique integer for each set of rows we'd like grouped. Since we'd like to group new events together, we can run a cumulative sum along `new_event_flag`: every time a new event occurs, the integer will increase and consequently that event will all have the same unique key. The code to do this is:\n", + "\n", + "```sql\n", + "SUM(new_event_flag) OVER (partition by icustay_id, label order by starttime, endtime) as time_partition \n", + "```\n", + "\n", + "Let's see this in action:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Durations from INPUTEVENTS for one patient with KCl...\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
labelstarttimeendtimenew_event_flagtime_partitionraterateuomstatusdescription
0KClDay 11, 21:30Day 12, 02:30114.000000mEq./hourFinishedRunning
1KClDay 11, 23:45Day 12, 02:411210.002273mEq./hourFinishedRunning
2KClDay 12, 02:41Day 12, 05:36029.997713mEq./hourFinishedRunning
3KClDay 12, 05:36Day 12, 08:310210.285715mEq./hourFinishedRunning
4KClDay 12, 08:31Day 12, 11:290210.112360mEq./hourFinishedRunning
5KClDay 12, 11:29Day 12, 14:280210.055866mEq./hourFinishedRunning
6KClDay 12, 14:28Day 12, 17:250210.169492mEq./hourFinishedRunning
7KClDay 12, 17:25Day 12, 20:240210.055866mEq./hourFinishedRunning
8KClDay 12, 20:24Day 12, 20:300210.000000mEq./hourPaused
9KClDay 12, 21:30Day 12, 21:35139.997634mEq./hourChanged
10KClDay 12, 21:35Day 13, 02:08036.190549mEq./hourFinishedRunning
11KClDay 13, 02:08Day 13, 07:06036.040268mEq./hourFinishedRunning
12KClDay 13, 07:06Day 13, 12:03036.006060mEq./hourFinishedRunning
13KClDay 13, 12:03Day 13, 16:29036.005904mEq./hourStopped
14KClDay 13, 18:15Day 13, 23:15146.000000mEq./hourFinishedRunning
15KClDay 14, 15:28Day 14, 18:47156.000000mEq./hourFinishedRunning
16KClDay 14, 18:47Day 14, 19:01056.000000mEq./hourChanged
17KClDay 14, 19:01Day 14, 23:04054.007380mEq./hourChanged
18KClDay 14, 23:04Day 14, 23:18055.871428mEq./hourFinishedRunning
19KClDay 14, 23:18Day 15, 02:28055.905264mEq./hourFinishedRunning
20KClDay 15, 02:28Day 15, 05:44055.969388mEq./hourFinishedRunning
21KClDay 15, 05:44Day 15, 08:57055.906736mEq./hourFinishedRunning
22KClDay 15, 08:57Day 15, 12:08055.905759mEq./hourFinishedRunning
23KClDay 15, 12:08Day 15, 15:17055.904762mEq./hourFinishedRunning
24KClDay 15, 15:17Day 15, 18:34055.908629mEq./hourFinishedRunning
25KClDay 15, 18:34Day 15, 21:46055.906250mEq./hourFinishedRunning
26KClDay 15, 21:46Day 16, 01:01055.907693mEq./hourFinishedRunning
27KClDay 16, 01:01Day 16, 04:18055.908629mEq./hourFinishedRunning
28KClDay 16, 04:18Day 16, 07:36055.903030mEq./hourFinishedRunning
29KClDay 16, 07:36Day 16, 10:54055.903030mEq./hourFinishedRunning
30KClDay 16, 10:54Day 16, 14:12055.903030mEq./hourFinishedRunning
31KClDay 16, 14:12Day 16, 16:04055.905613mEq./hourStopped
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Durations from INPUTEVENTS for one patient with KCl...\")\n", + "query = query_schema + \"\"\"\n", + "with t1 as\n", + "(\n", + "select icustay_id\n", + " , case when itemid = 227525 then 'Calcium' else 'KCl' end as label\n", + " , starttime\n", + " , endtime\n", + " , case\n", + " when lag(endtime) over (partition by icustay_id, itemid order by starttime, endtime) = starttime\n", + " then 0\n", + " else 1 end\n", + " as new_event_flag\n", + " , rate, rateuom\n", + " , statusdescription\n", + "from inputevents_mv\n", + "where itemid in\n", + "(\n", + " --227525,-- Calcium Gluconate (CRRT)\n", + " 227536 -- KCl (CRRT)\n", + ")\n", + "and statusdescription != 'Rewritten'\n", + "\"\"\" + query_where_clause + \"\"\"\n", + ")\n", + ", t2 as\n", + "(\n", + " select \n", + " icustay_id\n", + " , label\n", + " , starttime, endtime\n", + " , new_event_flag\n", + " , SUM(new_event_flag) OVER (partition by icustay_id, label order by starttime, endtime) as time_partition \n", + " , rate, rateuom, statusdescription\n", + " from t1\n", + ")\n", + "select \n", + " label\n", + " , starttime\n", + " , endtime\n", + " , new_event_flag\n", + " , time_partition \n", + " , rate, rateuom, statusdescription\n", + "\n", + "from t2\n", + "order by starttime, endtime\n", + "\"\"\"\n", + "ie = pd.read_sql_query(query,con)\n", + "\n", + "display_df(ie)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above (hopefully) makes it clear how a unique partition for each continuous segment of KCl administration can be delineated by cumulatively summing `new_event_flag` to create `time_partition`.\n", + "\n", + "### Step 3: create an integer to mark the last row of an event\n", + "\n", + "From above, it appears as though the *last* `statusdescription` would provide us useful debugging information as to why the administration event stopped - so we have another inline view where we create an integer which is 1 for the last `statusdescription`." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Durations from INPUTEVENTS for one patient with KCl...\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
labelstarttimeendtimetime_partitionraterateuomstatusdescriptionlastrow
0KClDay 11, 21:30Day 12, 02:3014.000000mEq./hourFinishedRunning1
1KClDay 11, 23:45Day 12, 02:41210.002273mEq./hourFinishedRunning8
2KClDay 12, 02:41Day 12, 05:3629.997713mEq./hourFinishedRunning7
3KClDay 12, 05:36Day 12, 08:31210.285715mEq./hourFinishedRunning6
4KClDay 12, 08:31Day 12, 11:29210.112360mEq./hourFinishedRunning5
5KClDay 12, 11:29Day 12, 14:28210.055866mEq./hourFinishedRunning4
6KClDay 12, 14:28Day 12, 17:25210.169492mEq./hourFinishedRunning3
7KClDay 12, 17:25Day 12, 20:24210.055866mEq./hourFinishedRunning2
8KClDay 12, 20:24Day 12, 20:30210.000000mEq./hourPaused1
9KClDay 12, 21:30Day 12, 21:3539.997634mEq./hourChanged5
10KClDay 12, 21:35Day 13, 02:0836.190549mEq./hourFinishedRunning4
11KClDay 13, 02:08Day 13, 07:0636.040268mEq./hourFinishedRunning3
12KClDay 13, 07:06Day 13, 12:0336.006060mEq./hourFinishedRunning2
13KClDay 13, 12:03Day 13, 16:2936.005904mEq./hourStopped1
14KClDay 13, 18:15Day 13, 23:1546.000000mEq./hourFinishedRunning1
15KClDay 14, 15:28Day 14, 18:4756.000000mEq./hourFinishedRunning17
16KClDay 14, 18:47Day 14, 19:0156.000000mEq./hourChanged16
17KClDay 14, 19:01Day 14, 23:0454.007380mEq./hourChanged15
18KClDay 14, 23:04Day 14, 23:1855.871428mEq./hourFinishedRunning14
19KClDay 14, 23:18Day 15, 02:2855.905264mEq./hourFinishedRunning13
20KClDay 15, 02:28Day 15, 05:4455.969388mEq./hourFinishedRunning12
21KClDay 15, 05:44Day 15, 08:5755.906736mEq./hourFinishedRunning11
22KClDay 15, 08:57Day 15, 12:0855.905759mEq./hourFinishedRunning10
23KClDay 15, 12:08Day 15, 15:1755.904762mEq./hourFinishedRunning9
24KClDay 15, 15:17Day 15, 18:3455.908629mEq./hourFinishedRunning8
25KClDay 15, 18:34Day 15, 21:4655.906250mEq./hourFinishedRunning7
26KClDay 15, 21:46Day 16, 01:0155.907693mEq./hourFinishedRunning6
27KClDay 16, 01:01Day 16, 04:1855.908629mEq./hourFinishedRunning5
28KClDay 16, 04:18Day 16, 07:3655.903030mEq./hourFinishedRunning4
29KClDay 16, 07:36Day 16, 10:5455.903030mEq./hourFinishedRunning3
30KClDay 16, 10:54Day 16, 14:1255.903030mEq./hourFinishedRunning2
31KClDay 16, 14:12Day 16, 16:0455.905613mEq./hourStopped1
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Durations from INPUTEVENTS for one patient with KCl...\")\n", + "query = query_schema + \"\"\"\n", + "with t1 as\n", + "(\n", + "select icustay_id\n", + " , case when itemid = 227525 then 'Calcium' else 'KCl' end as label\n", + " , starttime\n", + " , endtime\n", + " , case\n", + " when lag(endtime) over (partition by icustay_id, itemid order by starttime, endtime) = starttime\n", + " then 0\n", + " else 1 end\n", + " as new_event_flag\n", + " , rate, rateuom\n", + " , statusdescription\n", + "from inputevents_mv\n", + "where itemid in\n", + "(\n", + " --227525,-- Calcium Gluconate (CRRT)\n", + " 227536 -- KCl (CRRT)\n", + ")\n", + "and statusdescription != 'Rewritten'\n", + "\"\"\" + query_where_clause + \"\"\"\n", + ")\n", + ", t2 as\n", + "(\n", + " select \n", + " icustay_id\n", + " , label\n", + " , starttime, endtime\n", + " , SUM(new_event_flag) OVER (partition by icustay_id, label order by starttime, endtime) as time_partition \n", + " , rate, rateuom, statusdescription\n", + " from t1\n", + ")\n", + ", t3 as\n", + "(\n", + "select\n", + " icustay_id\n", + " , label\n", + " , starttime, endtime\n", + " , time_partition \n", + " , rate, rateuom, statusdescription\n", + " , ROW_NUMBER() over (PARTITION BY icustay_id, label, time_partition order by starttime desc, endtime desc) as lastrow\n", + "from t2\n", + ")\n", + "select \n", + "label\n", + ", starttime\n", + ", endtime\n", + ", time_partition\n", + ", rate, rateuom\n", + ", statusdescription\n", + ", lastrow\n", + " from t3\n", + "order by starttime, endtime\n", + "\"\"\"\n", + "ie = pd.read_sql_query(query,con)\n", + "\n", + "display_df(ie)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step 4: aggregate to merge together contiguous start/end times\n", + "\n", + "Now we aggregate the `starttime` and `endtime` together by grouping by `time_partition`, as follows:\n", + "\n", + "* we want the first `starttime`, so we use `min(starttime)`\n", + "* we want the last `endtime`, so we use `max(endtime)`\n", + "* we want the `statusdescription` at the last row, so we aggregate a column where all rows except the last are null\n", + "\n", + "To give more detail on the last step, let's look at the SQL code:\n", + "\n", + "```sql\n", + ", min(case when lastrow = 1 then statusdescription else null end) as statusdescription\n", + "```\n", + "\n", + "Aggregate functions ignore null values, so if we set the column to null for all but `lastrow = 1`, then the aggregate function is guaranteed to only return the value at `lastrow = 1`. The use of aggregate function could be either `min()` or `max()` - since it only effectively operates on a single value.\n", + "\n", + "Tying it all together, we have the final query:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Durations from INPUTEVENTS for one patient with KCl...\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
labelstarttimeendtimerate_minrate_maxrateuomstatusdescription
0KClDay 11, 21:30Day 12, 02:304.0000004.000000mEq./hourFinishedRunning
1KClDay 11, 23:45Day 12, 20:309.99771310.285715mEq./hourPaused
2CalciumDay 11, 23:45Day 12, 20:301.2016252.002708grams/hourPaused
3CalciumDay 12, 21:30Day 13, 15:541.2066901.805171grams/hourFinishedRunning
4KClDay 12, 21:30Day 13, 16:296.0059049.997634mEq./hourStopped
5KClDay 13, 18:15Day 13, 23:156.0000006.000000mEq./hourFinishedRunning
6CalciumDay 13, 18:15Day 13, 23:151.6021361.602136grams/hourPaused
7KClDay 14, 15:28Day 16, 16:044.0073806.000000mEq./hourStopped
8CalciumDay 14, 15:28Day 16, 16:051.1960131.990426grams/hourStopped
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Durations from INPUTEVENTS for one patient with KCl...\")\n", + "query = query_schema + \"\"\"\n", + "with t1 as\n", + "(\n", + "select icustay_id\n", + " , case when itemid = 227525 then 'Calcium' else 'KCl' end as label\n", + " , starttime\n", + " , endtime\n", + " , case\n", + " when lag(endtime) over (partition by icustay_id, itemid order by starttime, endtime) = starttime\n", + " then 0\n", + " else 1 end\n", + " as new_event_flag\n", + " , rate, rateuom\n", + " , statusdescription\n", + "from inputevents_mv\n", + "where itemid in\n", + "(\n", + " 227525,-- Calcium Gluconate (CRRT)\n", + " 227536 -- KCl (CRRT)\n", + ")\n", + "and statusdescription != 'Rewritten'\n", + "\"\"\" + query_where_clause + \"\"\"\n", + ")\n", + ", t2 as\n", + "(\n", + " select \n", + " icustay_id\n", + " , label\n", + " , starttime, endtime\n", + " , SUM(new_event_flag) OVER (partition by icustay_id, label order by starttime, endtime) as time_partition \n", + " , rate, rateuom, statusdescription\n", + " from t1\n", + ")\n", + ", t3 as\n", + "(\n", + "select\n", + " icustay_id\n", + " , label\n", + " , starttime, endtime\n", + " , time_partition \n", + " , rate, rateuom, statusdescription\n", + " , ROW_NUMBER() over (PARTITION BY icustay_id, label, time_partition order by starttime desc, endtime desc) as lastrow\n", + "from t2\n", + ")\n", + "select\n", + " label\n", + " --, time_partition\n", + " , min(starttime) AS starttime\n", + " , max(endtime) AS endtime\n", + " , min(rate) AS rate_min\n", + " , max(rate) AS rate_max\n", + " , min(rateuom) AS rateuom\n", + " , min(case when lastrow = 1 then statusdescription else null end) as statusdescription\n", + "from t3\n", + "group by icustay_id, label, time_partition\n", + "order by starttime, endtime\n", + "\"\"\"\n", + "ie = pd.read_sql_query(query,con)\n", + "\n", + "display_df(ie)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above looks good - so we save the query to `query_inputevents` without the clause that isolates the data to one patient." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "query_inputevents = query_schema + \"\"\"\n", + "with t1 as\n", + "(\n", + "select icustay_id\n", + " , case when itemid = 227525 then 'Calcium' else 'KCl' end as label\n", + " , starttime\n", + " , endtime\n", + " , case\n", + " when lag(endtime) over (partition by icustay_id, itemid order by starttime, endtime) = starttime\n", + " then 0\n", + " else 1 end\n", + " as new_event_flag\n", + " , rate, rateuom\n", + " , statusdescription\n", + "from inputevents_mv\n", + "where itemid in\n", + "(\n", + " 227525,-- Calcium Gluconate (CRRT)\n", + " 227536 -- KCl (CRRT)\n", + ")\n", + "and statusdescription != 'Rewritten'\n", + ")\n", + ", t2 as\n", + "(\n", + " select \n", + " icustay_id\n", + " , label\n", + " , starttime, endtime\n", + " , SUM(new_event_flag) OVER (partition by icustay_id, label order by starttime, endtime) as time_partition \n", + " , rate, rateuom, statusdescription\n", + " from t1\n", + ")\n", + ", t3 as\n", + "(\n", + "select\n", + " icustay_id\n", + " , label\n", + " , starttime, endtime\n", + " , time_partition \n", + " , rate, rateuom, statusdescription\n", + " , ROW_NUMBER() over (PARTITION BY icustay_id, label, time_partition order by starttime desc, endtime desc) as lastrow\n", + "from t2\n", + ")\n", + "select\n", + " icustay_id\n", + " , time_partition as num\n", + " , min(starttime) AS starttime\n", + " , max(endtime) AS endtime\n", + " , label\n", + " --, min(rate) AS rate_min\n", + " --, max(rate) AS rate_max\n", + " --, min(rateuom) AS rateuom\n", + " --, min(case when lastrow = 1 then statusdescription else null end) as statusdescription\n", + "from t3\n", + "group by icustay_id, label, time_partition\n", + "order by starttime, endtime\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Conclusion\n", + "\n", + "We now have a good method of combining contiguous events from `INPUTEVENTS_MV`. Note that this is *usually* not required, as the `linkorderid` is meant to partition these events for us. For example, lets look at a very common sedative agent used in the ICU, propofol:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Durations from INPUTEVENTS for one patient given propofol...\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
labellinkorderidorderidstarttimeendtimeraterateuomamountamountuom
0Propofol14058161405816Day 09, 18:29Day 10, 00:1450.002502mcg/kg/min17.250863mg
1Propofol14058162101314Day 10, 01:01Day 10, 01:0550.002502mcg/kg/min0.200010mg
2Propofol14058167312240Day 10, 01:05Day 10, 08:0540.001221mcg/kg/min16.800513mg
3Propofol14058167169415Day 10, 08:15Day 10, 12:0040.001221mcg/kg/min9.000275mg
4Propofol14058165852722Day 10, 12:05Day 10, 12:4040.001221mcg/kg/min1.400043mg
5Propofol14058163365285Day 10, 12:40Day 10, 14:0020.000627mcg/kg/min1.600050mg
6Propofol522225522225Day 10, 14:00Day 10, 14:01None10.000001mg
7Propofol14058165245063Day 10, 14:00Day 10, 14:0740.001254mcg/kg/min0.280009mg
8Propofol27035532703553Day 10, 14:05Day 10, 14:06None10.000001mg
9Propofol14058166687581Day 10, 14:07Day 11, 08:4530.001253mcg/kg/min33.541401mg
10Propofol49126964912696Day 10, 16:10Day 10, 16:11None10.000001mg
11Propofol38380863838086Day 10, 16:55Day 10, 16:56None10.000001mg
12Propofol56658085665808Day 11, 01:51Day 11, 01:52None10.000001mg
13Propofol14058163755617Day 11, 09:10Day 11, 13:3630.001253mcg/kg/min7.980333mg
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Durations from INPUTEVENTS for one patient given propofol...\")\n", + "query = query_schema + \"\"\"\n", + "with t1 as\n", + "(\n", + "select icustay_id\n", + " , di.label\n", + " , mv.linkorderid, mv.orderid\n", + " , starttime\n", + " , endtime\n", + " , rate, rateuom\n", + " , amount, amountuom\n", + "from inputevents_mv mv\n", + "inner join d_items di\n", + "on mv.itemid = di.itemid\n", + "and statusdescription != 'Rewritten'\n", + "\"\"\" + query_where_clause + \"\"\"\n", + "and mv.itemid = 222168\n", + ")\n", + "select \n", + " label\n", + " , linkorderid, orderid\n", + " , starttime\n", + " , endtime\n", + " , rate, rateuom\n", + " , amount, amountuom\n", + "from t1\n", + "order by starttime, endtime\n", + "\"\"\"\n", + "ie = pd.read_sql_query(query,con)\n", + "\n", + "display_df(ie)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we see that `linkorderid` nicely delineates contiguous events without us having to put in the effort of above. It also separates *distinct* administrations. Above, at row **6**, we can see a \"1 minute\" delivery of propofol. This is how MetaVision tables (those which end in `_mv`) mark \"instant\" events - in the case of drug delivery, these are boluses of drugs administered to the patient. \n", + "\n", + "When using this data, we can group like events on a partition (as we did above), but we don't have to create the partition: it already exists with `linkorderid`. " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Grouped durations from INPUTEVENTS for one patient given propofol...\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
labellinkorderidstarttimeendtimerate_minrate_maxrateuomamount_minamount_maxamountuom
0Propofol1405816Day 09, 18:29Day 11, 13:3620.00062750.002502mcg/kg/min0.20001033.541401mg
1Propofol522225Day 10, 14:00Day 10, 14:01None10.00000110.000001mg
2Propofol2703553Day 10, 14:05Day 10, 14:06None10.00000110.000001mg
3Propofol4912696Day 10, 16:10Day 10, 16:11None10.00000110.000001mg
4Propofol3838086Day 10, 16:55Day 10, 16:56None10.00000110.000001mg
5Propofol5665808Day 11, 01:51Day 11, 01:52None10.00000110.000001mg
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Grouped durations from INPUTEVENTS for one patient given propofol...\")\n", + "query = query_schema + \"\"\"\n", + "with t1 as\n", + "(\n", + "select icustay_id\n", + " , di.itemid, di.label\n", + " , mv.linkorderid, mv.orderid\n", + " , starttime\n", + " , endtime\n", + " , amount, amountuom\n", + " , rate, rateuom\n", + "from inputevents_mv mv\n", + "inner join d_items di\n", + "on mv.itemid = di.itemid\n", + "and statusdescription != 'Rewritten'\n", + "\"\"\" + query_where_clause + \"\"\"\n", + "and mv.itemid = 222168\n", + ")\n", + "select icustay_id\n", + " , label\n", + " , linkorderid\n", + " , min(starttime) as starttime\n", + " , max(endtime) as endtime\n", + " , min(rate) as rate_min\n", + " , max(rate) as rate_max\n", + " , max(rateuom) as rateuom\n", + " , min(amount) as amount_min\n", + " , max(amount) as amount_max\n", + " , max(amountuom) as amountuom\n", + "from t1\n", + "group by icustay_id, itemid, label, linkorderid\n", + "order by starttime, endtime\n", + "\"\"\"\n", + "ie = pd.read_sql_query(query,con)\n", + "\n", + "display_df(ie)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's also worth noting that bolus administrations *do not have a `rate`*. They only have an `amount`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Convert CHARTEVENTS into durations" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Durations from CHARTEVENTS...\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
numstarttimeendtime
01Day 11, 23:43Day 12, 20:00
12Day 12, 22:00Day 13, 16:30
23Day 13, 18:15Day 13, 23:00
34Day 14, 15:27Day 16, 16:00
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# convert CHARTEVENTS into durations\n", + "# NOTE: we only look at a single patient as an exemplar\n", + "print(\"Durations from CHARTEVENTS...\")\n", + "query = query_schema + \"\"\"\n", + "with crrt_settings as\n", + "(\n", + "select ce.icustay_id, ce.charttime\n", + ", max(\n", + " case\n", + " when ce.itemid in\n", + " (\n", + " 224149, -- Access Pressure\n", + " 224144, -- Blood Flow (ml/min)\n", + " 228004, -- Citrate (ACD-A)\n", + " 225183, -- Current Goal\n", + " 225977, -- Dialysate Fluid\n", + " 224154, -- Dialysate Rate\n", + " 224151, -- Effluent Pressure\n", + " 224150, -- Filter Pressure\n", + " 225958, -- Heparin Concentration (units/mL)\n", + " 224145, -- Heparin Dose (per hour)\n", + " 224191, -- Hourly Patient Fluid Removal\n", + " 228005, -- PBP (Prefilter) Replacement Rate\n", + " 228006, -- Post Filter Replacement Rate\n", + " 225976, -- Replacement Fluid\n", + " 224153, -- Replacement Rate\n", + " 224152, -- Return Pressure\n", + " 226457 -- Ultrafiltrate Output\n", + " ) then 1\n", + " else 0 end)\n", + " as RRT\n", + "-- Below indicates that a new instance of CRRT has started\n", + ", max(\n", + " case\n", + " -- System Integrity\n", + " when ce.itemid = 224146 and value in ('New Filter','Reinitiated')\n", + " then 1\n", + " else 0\n", + " end ) as RRT_start\n", + "-- Below indicates that the current instance of CRRT has ended\n", + ", max(\n", + " case\n", + " -- System Integrity\n", + " when ce.itemid = 224146 and value in ('Discontinued','Recirculating')\n", + " then 1\n", + " when ce.itemid = 225956\n", + " then 1\n", + " else 0\n", + " end ) as RRT_end\n", + "from chartevents ce\n", + "where ce.itemid in\n", + "(\n", + " -- MetaVision ITEMIDs\n", + " -- Below require special handling\n", + " 224146, -- System Integrity\n", + " 225956, -- Reason for CRRT Filter Change\n", + "\n", + " -- Below are settings which indicate CRRT is started/continuing\n", + " 224149, -- Access Pressure\n", + " 224144, -- Blood Flow (ml/min)\n", + " 228004, -- Citrate (ACD-A)\n", + " 225183, -- Current Goal\n", + " 225977, -- Dialysate Fluid\n", + " 224154, -- Dialysate Rate\n", + " 224151, -- Effluent Pressure\n", + " 224150, -- Filter Pressure\n", + " 225958, -- Heparin Concentration (units/mL)\n", + " 224145, -- Heparin Dose (per hour)\n", + " 224191, -- Hourly Patient Fluid Removal\n", + " 228005, -- PBP (Prefilter) Replacement Rate\n", + " 228006, -- Post Filter Replacement Rate\n", + " 225976, -- Replacement Fluid\n", + " 224153, -- Replacement Rate\n", + " 224152, -- Return Pressure\n", + " 226457 -- Ultrafiltrate Output\n", + ")\n", + "and ce.value is not null\n", + "\"\"\" + query_where_clause + \"\"\"\n", + "group by icustay_id, charttime\n", + ")\n", + "\n", + "-- create the durations for each CRRT instance\n", + "select icustay_id\n", + " , ROW_NUMBER() over (partition by icustay_id order by num) as num\n", + " , min(charttime) as starttime\n", + " , max(charttime) as endtime\n", + "from\n", + "(\n", + " select vd1.*\n", + " -- create a cumulative sum of the instances of new CRRT\n", + " -- this results in a monotonically increasing integer assigned to each CRRT\n", + " , case when RRT_start = 1 or RRT=1 or RRT_end = 1 then\n", + " SUM( NewCRRT )\n", + " OVER ( partition by icustay_id order by charttime )\n", + " else null end\n", + " as num\n", + " --- now we convert CHARTTIME of CRRT settings into durations\n", + " from ( -- vd1\n", + " select\n", + " icustay_id\n", + " -- this carries over the previous charttime\n", + " , case\n", + " when RRT=1 then\n", + " LAG(CHARTTIME, 1) OVER (partition by icustay_id, RRT order by charttime)\n", + " else null\n", + " end as charttime_lag\n", + " , charttime\n", + " , RRT\n", + " , RRT_start\n", + " , RRT_end\n", + " -- calculate the time since the last event\n", + " , case\n", + " -- non-null iff the current observation indicates settings are present\n", + " when RRT=1 then\n", + " CHARTTIME -\n", + " (\n", + " LAG(CHARTTIME, 1) OVER\n", + " (\n", + " partition by icustay_id, RRT\n", + " order by charttime\n", + " )\n", + " )\n", + " else null\n", + " end as CRRT_duration\n", + "\n", + " -- now we determine if the current event is a new instantiation\n", + " , case\n", + " when RRT_start = 1\n", + " then 1\n", + " -- if there is an end flag, we mark any subsequent event as new\n", + " when RRT_end = 1\n", + " -- note the end is *not* a new event, the *subsequent* row is\n", + " -- so here we output 0\n", + " then 0\n", + " when\n", + " LAG(RRT_end,1)\n", + " OVER\n", + " (\n", + " partition by icustay_id, case when RRT=1 or RRT_end=1 then 1 else 0 end\n", + " order by charttime\n", + " ) = 1\n", + " then 1\n", + " -- if there is less than 2 hours between CRRT settings, we do not treat this as a new CRRT event\n", + " when (CHARTTIME - (LAG(CHARTTIME, 1)\n", + " OVER\n", + " (\n", + " partition by icustay_id, case when RRT=1 or RRT_end=1 then 1 else 0 end\n", + " order by charttime\n", + " ))) <= interval '2' hour\n", + " then 0\n", + " else 1\n", + " end as NewCRRT\n", + " -- use the temp table with only settings from chartevents\n", + " FROM crrt_settings\n", + " ) AS vd1\n", + " -- now we can isolate to just rows with settings\n", + " -- (before we had rows with start/end flags)\n", + " -- this removes any null values for NewCRRT\n", + " where\n", + " RRT_start = 1 or RRT = 1 or RRT_end = 1\n", + ") AS vd2\n", + "group by icustay_id, num\n", + "having min(charttime) != max(charttime)\n", + "order by icustay_id, num\n", + "\"\"\"\n", + "ce = pd.read_sql_query(query,con)\n", + "\n", + "display_df(ce)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# happy with the above query - repeat it without the isolation to a single ICUSTAY_ID\n", + "query_chartevents = query_schema + \"\"\"\n", + "with crrt_settings as\n", + "(\n", + "select ce.icustay_id, ce.charttime\n", + ", max(\n", + " case\n", + " when ce.itemid in\n", + " (\n", + " 224149, -- Access Pressure\n", + " 224144, -- Blood Flow (ml/min)\n", + " 228004, -- Citrate (ACD-A)\n", + " 225183, -- Current Goal\n", + " 225977, -- Dialysate Fluid\n", + " 224154, -- Dialysate Rate\n", + " 224151, -- Effluent Pressure\n", + " 224150, -- Filter Pressure\n", + " 225958, -- Heparin Concentration (units/mL)\n", + " 224145, -- Heparin Dose (per hour)\n", + " 224191, -- Hourly Patient Fluid Removal\n", + " 228005, -- PBP (Prefilter) Replacement Rate\n", + " 228006, -- Post Filter Replacement Rate\n", + " 225976, -- Replacement Fluid\n", + " 224153, -- Replacement Rate\n", + " 224152, -- Return Pressure\n", + " 226457 -- Ultrafiltrate Output\n", + " ) then 1\n", + " else 0 end)\n", + " as RRT\n", + "-- Below indicates that a new instance of CRRT has started\n", + ", max(\n", + " case\n", + " -- System Integrity\n", + " when ce.itemid = 224146 and value in ('New Filter','Reinitiated')\n", + " then 1\n", + " else 0\n", + " end ) as RRT_start\n", + "-- Below indicates that the current instance of CRRT has ended\n", + ", max(\n", + " case\n", + " -- System Integrity\n", + " when ce.itemid = 224146 and value in ('Discontinued','Recirculating')\n", + " then 1\n", + " when ce.itemid = 225956\n", + " then 1\n", + " else 0\n", + " end ) as RRT_end\n", + "from chartevents ce\n", + "where ce.itemid in\n", + "(\n", + " -- MetaVision ITEMIDs\n", + " -- Below require special handling\n", + " 224146, -- System Integrity\n", + " 225956, -- Reason for CRRT Filter Change\n", + "\n", + " -- Below are settings which indicate CRRT is started/continuing\n", + " 224149, -- Access Pressure\n", + " 224144, -- Blood Flow (ml/min)\n", + " 228004, -- Citrate (ACD-A)\n", + " 225183, -- Current Goal\n", + " 225977, -- Dialysate Fluid\n", + " 224154, -- Dialysate Rate\n", + " 224151, -- Effluent Pressure\n", + " 224150, -- Filter Pressure\n", + " 225958, -- Heparin Concentration (units/mL)\n", + " 224145, -- Heparin Dose (per hour)\n", + " 224191, -- Hourly Patient Fluid Removal\n", + " 228005, -- PBP (Prefilter) Replacement Rate\n", + " 228006, -- Post Filter Replacement Rate\n", + " 225976, -- Replacement Fluid\n", + " 224153, -- Replacement Rate\n", + " 224152, -- Return Pressure\n", + " 226457 -- Ultrafiltrate Output\n", + ")\n", + "and ce.value is not null\n", + "group by icustay_id, charttime\n", + ")\n", + "\n", + "-- create the durations for each CRRT instance\n", + "select icustay_id\n", + " , ROW_NUMBER() over (partition by icustay_id order by num) as num\n", + " , min(charttime) as starttime\n", + " , max(charttime) as endtime\n", + "from\n", + "(\n", + " select vd1.*\n", + " -- create a cumulative sum of the instances of new CRRT\n", + " -- this results in a monotonically increasing integer assigned to each CRRT\n", + " , case when RRT_start = 1 or RRT=1 or RRT_end = 1 then\n", + " SUM( NewCRRT )\n", + " OVER ( partition by icustay_id order by charttime )\n", + " else null end\n", + " as num\n", + " --- now we convert CHARTTIME of CRRT settings into durations\n", + " from ( -- vd1\n", + " select\n", + " icustay_id\n", + " -- this carries over the previous charttime\n", + " , case\n", + " when RRT=1 then\n", + " LAG(CHARTTIME, 1) OVER (partition by icustay_id, RRT order by charttime)\n", + " else null\n", + " end as charttime_lag\n", + " , charttime\n", + " , RRT\n", + " , RRT_start\n", + " , RRT_end\n", + " -- calculate the time since the last event\n", + " , case\n", + " -- non-null iff the current observation indicates settings are present\n", + " when RRT=1 then\n", + " CHARTTIME -\n", + " (\n", + " LAG(CHARTTIME, 1) OVER\n", + " (\n", + " partition by icustay_id, RRT\n", + " order by charttime\n", + " )\n", + " )\n", + " else null\n", + " end as CRRT_duration\n", + "\n", + " -- now we determine if the current event is a new instantiation\n", + " , case\n", + " when RRT_start = 1\n", + " then 1\n", + " -- if there is an end flag, we mark any subsequent event as new\n", + " when RRT_end = 1\n", + " -- note the end is *not* a new event, the *subsequent* row is\n", + " -- so here we output 0\n", + " then 0\n", + " when\n", + " LAG(RRT_end,1)\n", + " OVER\n", + " (\n", + " partition by icustay_id, case when RRT=1 or RRT_end=1 then 1 else 0 end\n", + " order by charttime\n", + " ) = 1\n", + " then 1\n", + " -- if there is less than 2 hours between CRRT settings, we do not treat this as a new CRRT event\n", + " when (CHARTTIME - (LAG(CHARTTIME, 1)\n", + " OVER\n", + " (\n", + " partition by icustay_id, case when RRT=1 or RRT_end=1 then 1 else 0 end\n", + " order by charttime\n", + " ))) <= interval '2' hour\n", + " then 0\n", + " else 1\n", + " end as NewCRRT\n", + " -- use the temp table with only settings from chartevents\n", + " FROM crrt_settings\n", + " ) AS vd1\n", + " -- now we can isolate to just rows with settings\n", + " -- (before we had rows with start/end flags)\n", + " -- this removes any null values for NewCRRT\n", + " where\n", + " RRT_start = 1 or RRT = 1 or RRT_end = 1\n", + ") AS vd2\n", + "group by icustay_id, num\n", + "having min(charttime) != max(charttime)\n", + "order by icustay_id, num\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Extract durations from PROCEDUREEVENTS_MV\n", + "\n", + "PROCEDUREEVENTS_MV contains entries for dialysis. As a reminder from the above, we picked the following `itemid`:\n", + "\n", + "* 225802 -- Dialysis - CRRT\n", + "* 225803 -- Dialysis - CVVHD\n", + "* 225809 -- Dialysis - CVVHDF\n", + "* 225955 -- Dialysis - SCUF\n", + "\n", + "Extracting data for these entries is straightforward. Each instance of CRRT is documented with a single `starttime` and a single `stoptime`, with no need to merge together different rows." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Durations from PROCEDUREEVENTS_MV...\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
numstarttimeendtime
01Day 11, 23:45Day 12, 20:30
12Day 12, 21:30Day 13, 23:15
23Day 14, 15:27Day 16, 16:02
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# extract the durations from PROCEDUREEVENTS_MV\n", + "# NOTE: we only look at a single patient as an exemplar\n", + "print(\"Durations from PROCEDUREEVENTS_MV...\")\n", + "query = query_schema + \"\"\"\n", + "select icustay_id\n", + " , ROW_NUMBER() over (partition by icustay_id order by starttime, endtime) as num\n", + " , starttime, endtime\n", + "from procedureevents_mv\n", + "where itemid in\n", + "(\n", + " 225802 -- Dialysis - CRRT\n", + " , 225803 -- Dialysis - CVVHD\n", + " , 225809 -- Dialysis - CVVHDF\n", + " , 225955 -- Dialysis - SCUF\n", + ")\n", + "\"\"\" + query_where_clause + \"\"\"\n", + "order by icustay_id, num\n", + "\"\"\"\n", + "pe = pd.read_sql_query(query,con)\n", + "\n", + "display_df(pe)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that the above documentation is quite dilligent: the entry pauses between the first and second row for 1 hour representing an actual pause in the administration of CRRT." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# happy with above query\n", + "query_procedureevents = query_schema + \"\"\"\n", + "select icustay_id\n", + " , ROW_NUMBER() over (partition by icustay_id order by starttime, endtime) as num\n", + " , starttime, endtime\n", + "from procedureevents_mv\n", + "where itemid in\n", + "(\n", + " 225802 -- Dialysis - CRRT\n", + " , 225803 -- Dialysis - CVVHD\n", + " , 225809 -- Dialysis - CVVHDF\n", + " , 225955 -- Dialysis - SCUF\n", + ")\n", + "order by icustay_id, num\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Roundup: data from INPUTEVENTS_MV, CHARTEVENTS, and PROCEDUREEVENTS_MV" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": 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numstarttimeendtimelabel
01Day 09, 18:10Day 13, 15:44Calcium
11Day 02, 21:20Day 04, 16:05Calcium
21Day 03, 08:56Day 04, 16:05KCl
31Day 21, 01:00Day 21, 09:00Calcium
42Day 21, 11:00Day 27, 11:00Calcium
51Day 25, 20:00Day 26, 02:33KCl
62Day 26, 02:36Day 27, 11:00KCl
71Day 28, 06:30Day 30, 05:04KCl
81Day 28, 06:30Day 30, 05:06Calcium
91Day 28, 23:15Day 29, 04:30Calcium
101Day 28, 23:15Day 29, 04:30KCl
112Day 29, 12:15Day 01, 17:19Calcium
122Day 29, 12:15Day 01, 20:01KCl
132Day 30, 07:01Day 30, 09:09KCl
142Day 30, 07:01Day 30, 09:43Calcium
153Day 30, 16:58Day 01, 14:24Calcium
163Day 30, 16:59Day 31, 16:06KCl
174Day 31, 18:45Day 01, 14:26KCl
185Day 01, 17:15Day 01, 18:21KCl
194Day 01, 17:15Day 03, 09:01Calcium
206Day 01, 19:16Day 03, 09:01KCl
213Day 02, 01:30Day 02, 11:30KCl
223Day 02, 01:30Day 02, 11:30Calcium
234Day 02, 12:45Day 03, 10:02KCl
244Day 02, 12:45Day 03, 10:03Calcium
251Day 20, 22:45Day 23, 20:15Calcium
261Day 20, 22:45Day 23, 20:15KCl
272Day 23, 22:44Day 25, 22:59Calcium
282Day 23, 22:44Day 25, 23:00KCl
291Day 13, 20:50Day 16, 07:00Calcium
301Day 13, 20:51Day 16, 07:00KCl
311Day 26, 20:30Day 27, 01:00Calcium
321Day 27, 00:00Day 27, 01:00KCl
332Day 27, 03:30Day 28, 03:06KCl
342Day 27, 03:30Day 28, 03:06Calcium
353Day 28, 04:51Day 28, 06:58KCl
363Day 28, 04:51Day 28, 07:30Calcium
374Day 28, 13:12Day 29, 13:15Calcium
384Day 28, 13:13Day 29, 13:15KCl
391Day 03, 21:00Day 06, 20:02Calcium
401Day 03, 21:00Day 06, 20:02KCl
412Day 06, 21:52Day 07, 11:37Calcium
422Day 06, 21:53Day 07, 11:37KCl
431Day 30, 18:40Day 01, 15:51KCl
441Day 30, 18:42Day 01, 15:05Calcium
452Day 01, 16:45Day 08, 15:00Calcium
462Day 01, 16:45Day 08, 15:00KCl
473Day 11, 16:06Day 12, 10:02Calcium
483Day 12, 01:30Day 12, 10:01KCl
494Day 12, 12:00Day 12, 17:59KCl
504Day 12, 12:00Day 12, 18:01Calcium
515Day 13, 17:15Day 15, 03:00Calcium
525Day 13, 17:15Day 15, 03:02KCl
536Day 15, 05:00Day 16, 07:42KCl
546Day 15, 05:00Day 16, 08:30Calcium
557Day 16, 10:59Day 17, 12:06KCl
567Day 16, 10:59Day 17, 12:09Calcium
571Day 12, 02:00Day 13, 07:00Calcium
581Day 03, 23:00Day 04, 12:47Calcium
591Day 03, 23:00Day 04, 12:52KCl
601Day 02, 20:00Day 02, 20:40Calcium
611Day 02, 20:00Day 02, 21:00KCl
622Day 02, 20:00Day 02, 21:00Calcium
632Day 02, 20:00Day 05, 08:46KCl
643Day 02, 20:00Day 05, 08:46Calcium
651Day 07, 18:00Day 08, 01:31KCl
661Day 07, 18:00Day 08, 01:33Calcium
672Day 08, 04:30Day 08, 10:39KCl
682Day 08, 04:30Day 08, 13:17Calcium
691Day 13, 17:15Day 16, 15:33Calcium
701Day 13, 22:45Day 16, 15:41KCl
712Day 16, 18:17Day 19, 08:30Calcium
722Day 16, 18:17Day 19, 08:30KCl
733Day 21, 12:52Day 23, 22:41KCl
743Day 21, 12:55Day 24, 14:00Calcium
751Day 22, 19:00Day 22, 19:09KCl
761Day 22, 19:00Day 24, 09:35Calcium
772Day 23, 03:29Day 24, 14:40KCl
784Day 24, 06:28Day 24, 14:00KCl
792Day 24, 13:06Day 24, 14:40Calcium
803Day 24, 16:20Day 25, 22:31Calcium
813Day 24, 16:20Day 25, 22:38KCl
825Day 25, 21:15Day 28, 21:54KCl
834Day 25, 22:20Day 26, 15:53Calcium
845Day 26, 23:18Day 28, 20:50Calcium
854Day 28, 00:30Day 29, 20:29Calcium
864Day 28, 00:30Day 29, 20:29KCl
875Day 29, 23:30Day 01, 09:01Calcium
885Day 29, 23:34Day 01, 09:01KCl
896Day 30, 00:45Day 30, 23:00KCl
906Day 13, 18:00Day 18, 17:30Calcium
916Day 13, 18:00Day 18, 17:30KCl
927Day 25, 13:00Day 29, 11:45Calcium
938Day 29, 13:05Day 30, 00:25Calcium
941Day 10, 19:55Day 11, 09:30Calcium
952Day 11, 13:00Day 12, 07:21Calcium
961Day 12, 06:51Day 12, 07:18KCl
973Day 12, 11:51Day 14, 05:48Calcium
982Day 12, 11:51Day 14, 05:56KCl
994Day 14, 07:45Day 15, 05:20Calcium
1003Day 14, 08:03Day 15, 05:20KCl
1014Day 15, 07:00Day 17, 07:30KCl
1025Day 15, 07:00Day 17, 07:31Calcium
1035Day 17, 10:15Day 18, 01:30KCl
1046Day 17, 10:15Day 18, 01:30Calcium
1057Day 18, 03:02Day 18, 04:30Calcium
1068Day 18, 06:00Day 18, 13:07Calcium
1079Day 19, 00:32Day 19, 08:37Calcium
1086Day 19, 03:21Day 19, 07:00KCl
1091Day 08, 23:00Day 09, 05:38KCl
1101Day 08, 23:00Day 09, 06:08Calcium
1111Day 18, 06:15Day 18, 07:00Calcium
1121Day 18, 06:15Day 18, 07:00KCl
1132Day 18, 10:30Day 20, 13:29Calcium
1142Day 18, 10:30Day 20, 14:01KCl
1151Day 19, 21:05Day 22, 20:30Calcium
1161Day 20, 03:51Day 22, 20:30KCl
1172Day 22, 22:10Day 25, 05:00KCl
1182Day 22, 22:10Day 25, 08:56Calcium
1193Day 25, 15:46Day 27, 11:01Calcium
1203Day 25, 18:57Day 27, 11:02KCl
1211Day 30, 00:28Day 30, 09:41Calcium
1221Day 07, 09:20Day 07, 18:10KCl
1231Day 07, 09:20Day 07, 18:10Calcium
1242Day 07, 22:00Day 08, 08:37Calcium
1252Day 07, 22:00Day 08, 10:54KCl
1261Day 12, 22:30Day 16, 00:40Calcium
1271Day 12, 22:38Day 13, 10:07KCl
1282Day 14, 08:14Day 16, 00:24KCl
1291Day 04, 19:15Day 05, 07:11KCl
1301Day 04, 19:15Day 05, 08:43Calcium
1312Day 05, 08:51Day 07, 21:00Calcium
1322Day 05, 08:54Day 07, 23:23KCl
1333Day 09, 17:15Day 10, 04:20KCl
1343Day 09, 17:18Day 10, 12:58Calcium
1351Day 25, 05:35Day 25, 06:00KCl
1361Day 25, 05:35Day 25, 20:23Calcium
1371Day 17, 15:52Day 17, 18:08KCl
1381Day 17, 15:52Day 18, 09:30Calcium
1391Day 01, 18:00Day 02, 19:30Calcium
1401Day 01, 18:00Day 02, 19:30KCl
1412Day 02, 20:00Day 03, 09:31KCl
1422Day 02, 20:00Day 06, 10:15Calcium
1433Day 03, 09:33Day 06, 10:15KCl
1441Day 04, 02:02Day 04, 15:21Calcium
1451Day 04, 02:02Day 04, 15:21KCl
1462Day 04, 21:00Day 05, 19:15Calcium
1472Day 04, 21:00Day 05, 19:15KCl
1483Day 05, 20:35Day 06, 08:33Calcium
1493Day 05, 20:35Day 06, 08:36KCl
1504Day 06, 11:06Day 07, 08:15KCl
1514Day 06, 11:06Day 07, 08:20Calcium
1525Day 07, 11:20Day 09, 09:30KCl
1535Day 07, 11:20Day 09, 09:30Calcium
1541Day 22, 15:00Day 22, 15:20KCl
1551Day 22, 15:00Day 22, 17:01Calcium
1562Day 22, 22:30Day 25, 23:15Calcium
1572Day 23, 00:00Day 25, 23:15KCl
1581Day 23, 20:46Day 24, 11:23KCl
1592Day 24, 12:45Day 25, 11:42KCl
1603Day 25, 19:15Day 26, 08:49KCl
1614Day 29, 19:30Day 29, 20:30KCl
1625Day 30, 16:20Day 01, 03:00KCl
1631Day 30, 21:45Day 01, 03:00Calcium
1642Day 01, 05:30Day 01, 15:59Calcium
1656Day 01, 05:30Day 01, 16:01KCl
1667Day 01, 19:12Day 02, 12:25KCl
1673Day 01, 22:16Day 02, 10:18Calcium
1681Day 04, 17:47Day 04, 18:41Calcium
1691Day 04, 17:50Day 04, 18:42KCl
1702Day 04, 21:11Day 06, 16:01KCl
1712Day 04, 21:11Day 07, 16:02Calcium
1721Day 27, 19:00Day 27, 19:30KCl
1731Day 27, 20:00Day 27, 20:40Calcium
1742Day 27, 23:08Day 27, 23:48Calcium
1753Day 28, 00:19Day 30, 09:23Calcium
1762Day 28, 00:22Day 30, 09:05KCl
1771Day 06, 18:15Day 06, 22:30Calcium
1781Day 06, 18:15Day 06, 22:31KCl
1792Day 07, 01:04Day 09, 10:55Calcium
1802Day 07, 01:04Day 09, 11:00KCl
1813Day 09, 14:00Day 10, 09:00KCl
1823Day 09, 14:00Day 10, 09:00Calcium
1834Day 12, 12:30Day 15, 12:00Calcium
1844Day 12, 23:15Day 15, 12:00KCl
1855Day 21, 16:41Day 22, 04:38Calcium
1865Day 21, 16:42Day 23, 22:00KCl
1876Day 22, 05:45Day 23, 22:00Calcium
1887Day 03, 22:00Day 04, 20:40Calcium
1896Day 03, 22:00Day 06, 21:25KCl
1908Day 04, 20:44Day 05, 10:35Calcium
1919Day 06, 07:00Day 06, 13:30Calcium
19210Day 06, 16:10Day 07, 00:25Calcium
1937Day 06, 21:34Day 07, 00:22KCl
1948Day 07, 02:14Day 07, 21:47KCl
19511Day 07, 02:14Day 08, 07:13Calcium
1969Day 08, 04:24Day 08, 07:14KCl
1971Day 14, 01:00Day 15, 10:30Calcium
1982Day 15, 12:00Day 16, 08:30Calcium
1993Day 16, 13:45Day 16, 14:15Calcium
2004Day 16, 23:54Day 17, 09:15Calcium
2015Day 17, 10:30Day 19, 14:00Calcium
2026Day 19, 19:30Day 20, 10:01Calcium
2031Day 05, 23:30Day 08, 19:58Calcium
2041Day 05, 23:30Day 08, 20:04KCl
2052Day 08, 23:38Day 10, 12:17Calcium
2062Day 09, 09:45Day 10, 01:26KCl
2071Day 03, 22:30Day 04, 04:01KCl
2081Day 03, 22:48Day 04, 04:07Calcium
2092Day 04, 04:22Day 04, 20:50KCl
2102Day 04, 04:23Day 04, 20:50Calcium
2113Day 04, 22:18Day 08, 07:03Calcium
2123Day 04, 22:19Day 08, 07:00KCl
2131Day 02, 21:20Day 05, 21:45KCl
2141Day 02, 21:20Day 05, 21:45Calcium
2152Day 05, 22:20Day 07, 11:59KCl
2162Day 05, 22:20Day 07, 12:01Calcium
2171Day 25, 20:00Day 28, 08:00KCl
2181Day 25, 20:00Day 28, 08:00Calcium
2191Day 25, 15:40Day 27, 19:00Calcium
2201Day 25, 16:02Day 27, 19:00KCl
2212Day 27, 22:53Day 01, 02:00Calcium
2222Day 27, 22:53Day 01, 02:00KCl
2233Day 01, 01:00Day 02, 21:00KCl
2243Day 01, 01:00Day 02, 21:00Calcium
2251Day 18, 16:23Day 20, 12:30Calcium
2261Day 11, 18:52Day 14, 11:45Calcium
2271Day 11, 18:53Day 14, 11:45KCl
2282Day 14, 13:00Day 16, 14:20KCl
2292Day 14, 13:00Day 16, 14:20Calcium
2303Day 16, 15:15Day 20, 04:30Calcium
2313Day 16, 15:15Day 20, 04:30KCl
2324Day 20, 08:50Day 22, 05:01Calcium
2334Day 20, 08:50Day 22, 05:02KCl
2341Day 06, 21:45Day 09, 22:06Calcium
2351Day 06, 21:45Day 09, 22:08KCl
2362Day 10, 00:00Day 11, 17:21KCl
2372Day 10, 00:00Day 11, 17:22Calcium
2383Day 12, 02:00Day 12, 09:59Calcium
2393Day 12, 07:37Day 12, 09:59KCl
2404Day 12, 13:30Day 14, 13:02Calcium
2414Day 12, 13:30Day 14, 13:04KCl
2425Day 15, 21:15Day 17, 10:00KCl
2435Day 15, 21:15Day 17, 10:00Calcium
2441Day 16, 11:45Day 18, 14:00Calcium
2452Day 18, 15:00Day 20, 06:54Calcium
2463Day 22, 22:34Day 23, 23:53Calcium
2474Day 24, 04:28Day 24, 07:02Calcium
2485Day 24, 17:04Day 25, 20:01Calcium
2496Day 25, 21:45Day 26, 09:05Calcium
2507Day 26, 11:25Day 26, 14:25Calcium
2511Day 09, 19:00Day 12, 21:30Calcium
2522Day 12, 23:00Day 13, 04:30Calcium
2531Day 13, 15:30Day 16, 07:49KCl
2541Day 13, 15:30Day 16, 07:52Calcium
2551Day 24, 18:08Day 24, 19:04Calcium
2561Day 24, 18:10Day 24, 19:04KCl
2572Day 25, 13:03Day 26, 15:13Calcium
2582Day 25, 13:03Day 27, 12:45KCl
2593Day 26, 23:25Day 27, 12:45Calcium
2604Day 29, 22:00Day 30, 15:00Calcium
2613Day 30, 01:48Day 30, 15:01KCl
2624Day 30, 17:00Day 01, 18:03KCl
2635Day 30, 17:00Day 01, 18:03Calcium
2641Day 05, 04:40Day 06, 05:23Calcium
2651Day 05, 04:40Day 06, 05:23KCl
2661Day 27, 17:20Day 30, 17:50KCl
2671Day 27, 17:20Day 30, 17:50Calcium
2682Day 30, 18:50Day 01, 14:10KCl
2692Day 30, 18:50Day 01, 14:10Calcium
2701Day 19, 13:30Day 19, 15:17Calcium
2711Day 01, 14:40Day 02, 13:19Calcium
2721Day 01, 14:41Day 02, 11:12KCl
2732Day 02, 20:30Day 04, 18:26Calcium
2742Day 02, 20:30Day 04, 18:32KCl
2751Day 11, 16:30Day 12, 13:18KCl
2761Day 11, 16:30Day 12, 16:51Calcium
2772Day 12, 18:44Day 14, 14:30Calcium
2782Day 12, 18:45Day 14, 14:30KCl
2793Day 14, 18:00Day 15, 02:52KCl
2803Day 14, 18:00Day 15, 03:14Calcium
2814Day 15, 07:00Day 15, 09:06KCl
2824Day 15, 07:00Day 16, 12:09Calcium
2835Day 16, 13:00Day 16, 18:28Calcium
2841Day 18, 19:00Day 19, 16:14Calcium
2851Day 18, 23:30Day 19, 15:14KCl
2862Day 19, 17:15Day 20, 00:30Calcium
2872Day 19, 17:15Day 20, 00:30KCl
2883Day 20, 02:03Day 20, 16:30KCl
2893Day 20, 02:03Day 20, 16:30Calcium
2901Day 21, 02:50Day 21, 16:00Calcium
2911Day 21, 02:50Day 21, 16:01KCl
2922Day 22, 05:03Day 23, 04:05Calcium
2932Day 22, 05:05Day 23, 04:34KCl
2943Day 23, 17:40Day 25, 09:15KCl
2953Day 23, 18:13Day 25, 09:15Calcium
2961Day 02, 21:00Day 04, 01:20Calcium
2972Day 04, 04:02Day 06, 09:00Calcium
2983Day 06, 22:37Day 07, 08:40Calcium
2991Day 07, 05:11Day 07, 08:31KCl
3001Day 20, 16:32Day 20, 19:52KCl
3011Day 20, 16:33Day 20, 22:35Calcium
3022Day 20, 21:19Day 20, 22:37KCl
3032Day 27, 18:53Day 28, 00:50Calcium
3043Day 28, 02:43Day 28, 14:09Calcium
3051Day 27, 02:00Day 27, 21:26Calcium
3061Day 27, 02:00Day 27, 21:27KCl
3072Day 27, 22:05Day 01, 07:12KCl
3082Day 27, 22:05Day 01, 07:12Calcium
3093Day 01, 13:00Day 04, 13:06KCl
3103Day 01, 13:00Day 04, 13:41Calcium
3111Day 29, 17:24Day 29, 18:50Calcium
3121Day 29, 18:15Day 29, 18:50KCl
3132Day 30, 00:05Day 31, 15:23Calcium
3142Day 30, 00:06Day 30, 03:05KCl
3153Day 30, 12:02Day 31, 15:25KCl
3161Day 19, 17:00Day 19, 22:23Calcium
3172Day 20, 00:45Day 20, 05:27Calcium
3181Day 04, 23:55Day 05, 09:30Calcium
3191Day 04, 23:56Day 05, 09:30KCl
3202Day 05, 16:33Day 08, 16:57KCl
3212Day 05, 16:33Day 08, 16:57Calcium
3223Day 08, 18:45Day 09, 09:35Calcium
3233Day 08, 18:45Day 09, 09:38KCl
3244Day 09, 10:15Day 10, 14:20Calcium
3254Day 09, 10:16Day 10, 14:20KCl
3265Day 10, 14:55Day 10, 17:00Calcium
3275Day 10, 14:55Day 10, 17:00KCl
3286Day 10, 18:00Day 10, 18:25Calcium
3296Day 10, 18:00Day 10, 18:25KCl
3307Day 10, 21:00Day 11, 17:29Calcium
3317Day 10, 21:00Day 11, 17:35KCl
3328Day 11, 20:20Day 11, 23:01Calcium
3338Day 11, 20:21Day 11, 23:01KCl
3349Day 11, 23:45Day 12, 22:00Calcium
3359Day 11, 23:45Day 12, 22:00KCl
33610Day 13, 00:00Day 13, 19:22Calcium
33710Day 13, 00:00Day 13, 19:36KCl
33811Day 14, 03:00Day 14, 09:35Calcium
33911Day 14, 03:00Day 14, 09:35KCl
34012Day 14, 18:00Day 16, 03:45Calcium
34112Day 14, 18:42Day 16, 03:45KCl
34213Day 16, 04:30Day 16, 05:15KCl
34313Day 16, 04:30Day 16, 05:15Calcium
34414Day 16, 06:15Day 16, 07:00KCl
34514Day 16, 06:15Day 16, 07:00Calcium
34615Day 16, 10:15Day 17, 16:27Calcium
34715Day 16, 10:15Day 17, 16:34KCl
34816Day 18, 10:30Day 18, 11:45Calcium
34916Day 18, 10:40Day 18, 11:45KCl
35017Day 18, 16:00Day 19, 01:27Calcium
35117Day 18, 16:00Day 19, 01:30KCl
35218Day 19, 03:00Day 19, 12:27Calcium
35318Day 19, 03:00Day 19, 12:30KCl
35419Day 19, 16:00Day 19, 17:45KCl
35519Day 19, 16:00Day 19, 17:45Calcium
35620Day 19, 19:00Day 20, 17:47Calcium
35720Day 19, 19:30Day 20, 17:48KCl
35821Day 20, 18:50Day 22, 11:00Calcium
35921Day 20, 18:50Day 22, 11:02KCl
36022Day 22, 14:01Day 23, 23:00Calcium
36122Day 22, 14:02Day 23, 22:54KCl
36223Day 24, 01:00Day 24, 14:39KCl
36323Day 24, 01:00Day 24, 14:41Calcium
36424Day 24, 18:20Day 25, 08:31Calcium
36524Day 24, 18:34Day 25, 08:31KCl
36625Day 25, 14:20Day 28, 22:45KCl
36725Day 25, 14:20Day 28, 22:45Calcium
36826Day 29, 04:30Day 01, 07:13KCl
36926Day 29, 04:30Day 01, 07:16Calcium
3701Day 29, 21:15Day 30, 15:00Calcium
3711Day 29, 21:15Day 30, 15:00KCl
3722Day 30, 18:00Day 31, 17:54KCl
3732Day 30, 18:00Day 31, 17:58Calcium
37427Day 01, 10:35Day 04, 05:00KCl
37527Day 01, 10:35Day 04, 06:30Calcium
37628Day 04, 13:00Day 07, 06:44Calcium
37728Day 05, 06:30Day 07, 06:51KCl
37829Day 14, 10:14Day 14, 18:58Calcium
37929Day 14, 11:25Day 17, 15:30KCl
38030Day 14, 20:00Day 17, 15:30Calcium
38131Day 17, 21:49Day 18, 12:30Calcium
38230Day 17, 21:49Day 18, 12:31KCl
38332Day 18, 18:23Day 19, 20:20Calcium
38431Day 18, 18:25Day 19, 20:20KCl
38532Day 19, 21:30Day 20, 02:40KCl
38633Day 19, 21:30Day 20, 02:40Calcium
38733Day 20, 04:00Day 21, 05:18KCl
38834Day 20, 04:00Day 21, 05:18Calcium
38935Day 21, 07:51Day 21, 11:00Calcium
39034Day 21, 07:52Day 21, 11:00KCl
39136Day 21, 12:15Day 21, 23:55Calcium
39235Day 21, 12:15Day 21, 23:56KCl
39337Day 22, 01:38Day 22, 03:30Calcium
39436Day 22, 01:38Day 22, 03:30KCl
39537Day 22, 04:20Day 22, 07:10KCl
39638Day 22, 04:20Day 22, 07:13Calcium
39739Day 22, 11:00Day 24, 05:33Calcium
39838Day 22, 11:00Day 24, 05:33KCl
3991Day 16, 21:45Day 17, 06:28Calcium
4002Day 17, 07:45Day 17, 09:46Calcium
4013Day 17, 12:12Day 17, 20:11Calcium
4024Day 19, 16:40Day 19, 19:00Calcium
4035Day 21, 00:30Day 21, 13:02Calcium
4046Day 21, 14:19Day 22, 04:37Calcium
4057Day 22, 08:45Day 23, 13:29Calcium
4068Day 23, 19:00Day 24, 07:55Calcium
4079Day 24, 13:43Day 25, 01:27Calcium
4081Day 24, 20:00Day 25, 01:39KCl
40910Day 25, 18:17Day 25, 18:54Calcium
4102Day 25, 18:17Day 25, 19:00KCl
4113Day 26, 00:10Day 26, 00:30KCl
41211Day 26, 00:10Day 26, 06:30Calcium
41312Day 26, 11:50Day 26, 18:15Calcium
4144Day 26, 11:53Day 26, 14:29KCl
4151Day 03, 02:34Day 03, 17:27KCl
4161Day 03, 02:34Day 03, 17:29Calcium
4171Day 03, 02:00Day 04, 08:10Calcium
4181Day 03, 15:00Day 04, 08:10KCl
4191Day 16, 12:35Day 16, 17:23KCl
4201Day 16, 12:35Day 16, 20:35Calcium
4212Day 17, 10:30Day 18, 13:10Calcium
4222Day 17, 10:30Day 18, 13:11KCl
4233Day 18, 16:20Day 19, 17:00KCl
4243Day 18, 16:20Day 20, 12:38Calcium
4254Day 20, 00:22Day 20, 12:37KCl
4261Day 27, 15:30Day 28, 14:44Calcium
4271Day 07, 20:36Day 07, 23:30Calcium
4281Day 07, 20:40Day 07, 23:37KCl
4292Day 08, 04:30Day 09, 17:33KCl
4302Day 08, 04:30Day 12, 12:57Calcium
4313Day 09, 19:45Day 12, 13:00KCl
4323Day 12, 14:00Day 14, 12:00Calcium
4334Day 12, 14:00Day 14, 12:07KCl
4341Day 25, 22:40Day 26, 02:14Calcium
4351Day 29, 11:44Day 01, 10:45Calcium
4361Day 29, 11:44Day 01, 10:45KCl
4372Day 01, 13:00Day 02, 22:07KCl
4382Day 01, 13:00Day 02, 22:16Calcium
4393Day 04, 16:00Day 05, 12:41KCl
4403Day 04, 21:38Day 05, 12:41Calcium
4414Day 05, 16:04Day 06, 07:45Calcium
4424Day 05, 16:04Day 06, 07:45KCl
4435Day 06, 12:28Day 09, 12:30KCl
4445Day 06, 12:28Day 09, 12:30Calcium
4456Day 09, 16:06Day 12, 16:20Calcium
4466Day 09, 16:06Day 12, 16:20KCl
4471Day 06, 12:18Day 07, 15:45Calcium
4481Day 06, 15:05Day 07, 15:47KCl
4491Day 28, 02:03Day 31, 01:30Calcium
4501Day 30, 19:00Day 31, 01:30KCl
4512Day 31, 02:15Day 31, 02:30KCl
4522Day 31, 02:15Day 31, 02:30Calcium
4533Day 31, 04:00Day 31, 10:15Calcium
4543Day 31, 04:00Day 31, 10:15KCl
4554Day 02, 15:20Day 05, 12:02Calcium
4561Day 23, 00:20Day 24, 05:59KCl
4571Day 23, 00:20Day 26, 04:01Calcium
4582Day 24, 10:30Day 26, 04:00KCl
4592Day 26, 05:30Day 27, 06:55Calcium
4603Day 26, 21:39Day 27, 08:01KCl
4614Day 27, 10:30Day 28, 14:01KCl
4623Day 27, 10:55Day 28, 11:34Calcium
4635Day 28, 17:00Day 29, 23:30KCl
4644Day 28, 17:00Day 29, 23:30Calcium
4655Day 30, 01:12Day 30, 10:09Calcium
4666Day 30, 01:13Day 30, 14:56KCl
4676Day 30, 10:10Day 30, 14:58Calcium
4687Day 30, 15:58Day 01, 14:00KCl
4697Day 30, 16:00Day 01, 14:00Calcium
4708Day 01, 18:07Day 02, 14:21Calcium
4718Day 01, 18:20Day 02, 13:45KCl
4729Day 02, 15:00Day 03, 00:43KCl
4739Day 02, 15:00Day 03, 01:08Calcium
47410Day 03, 03:05Day 04, 01:20KCl
47510Day 03, 03:18Day 03, 22:49Calcium
47611Day 04, 01:25Day 04, 19:02Calcium
47712Day 04, 21:00Day 06, 17:15Calcium
47811Day 06, 05:49Day 06, 17:30KCl
4791Day 16, 12:15Day 17, 03:05KCl
4801Day 16, 12:15Day 17, 03:10Calcium
4812Day 17, 09:30Day 19, 16:35KCl
4822Day 17, 09:30Day 19, 17:02Calcium
4833Day 19, 23:02Day 20, 14:12Calcium
4843Day 19, 23:02Day 20, 14:13KCl
4854Day 20, 15:13Day 22, 14:07Calcium
4864Day 20, 15:15Day 22, 15:00KCl
4871Day 10, 15:00Day 10, 15:40Calcium
4881Day 10, 15:00Day 11, 00:07KCl
4892Day 10, 18:59Day 11, 00:06Calcium
4903Day 11, 04:20Day 11, 18:19Calcium
4912Day 11, 04:20Day 11, 18:19KCl
4924Day 11, 18:37Day 12, 07:51Calcium
4933Day 11, 18:37Day 12, 07:51KCl
4944Day 12, 10:00Day 12, 12:21KCl
4955Day 12, 10:00Day 12, 12:22Calcium
4965Day 12, 13:15Day 13, 10:00KCl
4976Day 12, 13:15Day 13, 13:43Calcium
4981Day 06, 01:04Day 06, 09:16Calcium
4991Day 06, 01:05Day 06, 08:00KCl
5001Day 15, 17:15Day 17, 16:19Calcium
5012Day 17, 19:04Day 19, 09:25Calcium
5021Day 18, 13:45Day 19, 09:25KCl
5032Day 19, 10:30Day 19, 19:00KCl
5043Day 19, 10:30Day 19, 19:00Calcium
5054Day 19, 19:45Day 20, 11:41Calcium
5063Day 19, 19:45Day 20, 11:42KCl
5075Day 20, 12:49Day 21, 08:30Calcium
5084Day 20, 12:50Day 21, 08:33KCl
5095Day 21, 11:36Day 21, 20:30KCl
5106Day 21, 11:36Day 21, 20:30Calcium
5116Day 21, 20:47Day 22, 09:16KCl
5127Day 21, 20:47Day 22, 09:21Calcium
5138Day 22, 11:15Day 23, 02:17Calcium
5141Day 12, 15:30Day 12, 22:35Calcium
5151Day 12, 20:36Day 12, 22:35KCl
5162Day 13, 00:45Day 15, 15:32KCl
5172Day 13, 00:45Day 15, 22:46Calcium
5181Day 25, 19:50Day 27, 11:10Calcium
5191Day 25, 19:52Day 26, 19:00KCl
5202Day 26, 23:39Day 27, 10:44KCl
5213Day 05, 19:00Day 07, 14:39KCl
5222Day 05, 19:00Day 07, 19:00Calcium
5234Day 07, 21:00Day 08, 15:57KCl
5243Day 07, 21:24Day 08, 16:01Calcium
5254Day 08, 17:45Day 10, 23:30Calcium
5265Day 08, 17:45Day 10, 23:30KCl
5271Day 02, 18:58Day 03, 12:45Calcium
5281Day 02, 19:33Day 03, 12:45KCl
5292Day 03, 15:00Day 04, 09:45Calcium
5302Day 03, 15:00Day 04, 09:45KCl
5313Day 04, 11:00Day 05, 17:33Calcium
5323Day 04, 11:00Day 05, 17:36KCl
5334Day 05, 18:30Day 08, 19:59KCl
5344Day 05, 18:30Day 08, 20:00Calcium
5355Day 08, 21:20Day 09, 09:18KCl
5365Day 08, 21:20Day 09, 15:05Calcium
5376Day 09, 11:01Day 09, 15:01KCl
5387Day 09, 16:30Day 12, 14:12KCl
5396Day 09, 16:30Day 12, 14:15Calcium
5401Day 25, 19:45Day 25, 22:35Calcium
5411Day 28, 22:30Day 30, 05:04KCl
5422Day 28, 22:30Day 30, 05:05Calcium
5433Day 30, 06:40Day 30, 15:01Calcium
5442Day 30, 06:40Day 30, 15:02KCl
5451Day 17, 20:30Day 18, 14:49Calcium
5461Day 14, 02:28Day 14, 04:28KCl
5472Day 14, 06:07Day 14, 08:07KCl
5481Day 02, 22:20Day 04, 05:15KCl
5491Day 02, 22:21Day 04, 05:15Calcium
5502Day 04, 06:00Day 06, 11:15KCl
5512Day 04, 06:00Day 06, 11:17Calcium
5521Day 09, 23:50Day 12, 07:11KCl
5531Day 09, 23:50Day 13, 15:45Calcium
5542Day 12, 07:13Day 13, 15:45KCl
5551Day 02, 16:11Day 07, 09:24Calcium
5561Day 02, 16:16Day 07, 09:23KCl
5571Day 01, 03:00Day 06, 05:30Calcium
5581Day 01, 03:00Day 06, 05:30KCl
5592Day 06, 11:30Day 07, 01:20Calcium
5602Day 06, 11:30Day 09, 13:00KCl
5613Day 07, 01:33Day 09, 13:00Calcium
5623Day 09, 15:45Day 09, 20:44KCl
5634Day 09, 15:45Day 09, 20:45Calcium
5645Day 10, 00:07Day 10, 01:58Calcium
5654Day 10, 00:07Day 16, 16:05KCl
5666Day 10, 04:14Day 16, 16:00Calcium
5675Day 16, 17:15Day 17, 05:13KCl
5687Day 16, 17:15Day 17, 05:15Calcium
5698Day 22, 16:45Day 23, 16:30Calcium
5706Day 23, 01:18Day 23, 16:30KCl
5711Day 30, 17:06Day 30, 22:42KCl
5721Day 30, 17:06Day 30, 22:45Calcium
5732Day 30, 23:49Day 31, 07:31KCl
5742Day 30, 23:50Day 31, 07:31Calcium
5753Day 31, 13:09Day 01, 10:01KCl
5763Day 31, 13:09Day 01, 10:02Calcium
5774Day 01, 14:00Day 02, 01:54Calcium
5784Day 01, 14:00Day 02, 01:54KCl
5795Day 02, 04:47Day 02, 15:31Calcium
5805Day 02, 04:47Day 04, 09:00KCl
5816Day 02, 17:03Day 04, 08:59Calcium
5821Day 07, 12:00Day 08, 11:00KCl
5831Day 07, 12:00Day 08, 11:00Calcium
5841Day 27, 20:25Day 28, 08:01Calcium
5851Day 27, 20:27Day 28, 08:02KCl
5861Day 05, 12:00Day 07, 23:10Calcium
5871Day 25, 18:09Day 26, 01:58KCl
5881Day 25, 18:10Day 27, 17:53Calcium
5892Day 26, 02:02Day 27, 17:17KCl
5903Day 27, 19:00Day 28, 08:18KCl
5912Day 27, 19:29Day 29, 20:33Calcium
5923Day 29, 21:33Day 30, 08:30Calcium
5931Day 27, 15:25Day 02, 14:58Calcium
5941Day 27, 22:29Day 28, 17:00KCl
5951Day 29, 00:45Day 30, 08:01Calcium
5961Day 29, 00:45Day 30, 08:05KCl
5972Day 30, 05:00Day 02, 14:56KCl
5982Day 30, 09:45Day 01, 17:15KCl
5992Day 30, 09:45Day 02, 11:15Calcium
6003Day 02, 00:51Day 02, 11:15KCl
6013Day 04, 15:55Day 05, 00:05Calcium
6024Day 05, 01:05Day 05, 13:06Calcium
6034Day 05, 10:09Day 05, 13:06KCl
6045Day 05, 14:55Day 05, 17:45Calcium
6055Day 05, 14:55Day 05, 17:45KCl
6066Day 05, 18:53Day 06, 09:31Calcium
6076Day 05, 18:54Day 06, 04:30KCl
6087Day 06, 11:00Day 06, 14:30Calcium
6097Day 06, 11:28Day 06, 14:30KCl
6108Day 14, 13:00Day 15, 03:00KCl
6118Day 14, 13:00Day 15, 03:00Calcium
6129Day 15, 05:00Day 15, 10:45Calcium
6139Day 15, 05:00Day 15, 10:46KCl
61410Day 21, 12:00Day 22, 13:01KCl
61510Day 21, 12:00Day 22, 13:02Calcium
6161Day 23, 21:00Day 24, 03:15Calcium
6171Day 23, 21:00Day 24, 03:15KCl
6181Day 16, 18:45Day 18, 11:59Calcium
6192Day 18, 18:46Day 20, 16:20Calcium
6203Day 20, 16:45Day 23, 08:17Calcium
6211Day 19, 12:30Day 21, 09:19Calcium
6221Day 19, 12:30Day 21, 09:22KCl
6231Day 23, 15:00Day 24, 09:00Calcium
6242Day 24, 11:00Day 24, 19:00Calcium
6251Day 24, 21:30Day 26, 13:52KCl
6263Day 24, 21:30Day 26, 13:57Calcium
6274Day 01, 23:30Day 02, 11:30Calcium
6282Day 01, 23:30Day 02, 12:01KCl
6293Day 02, 12:16Day 02, 16:20KCl
6305Day 02, 12:18Day 03, 18:15Calcium
6314Day 02, 17:28Day 03, 17:01KCl
6325Day 05, 11:15Day 06, 12:00KCl
6336Day 05, 11:15Day 06, 12:01Calcium
6346Day 06, 15:30Day 07, 17:00KCl
6357Day 06, 15:30Day 07, 17:00Calcium
6361Day 04, 23:30Day 05, 14:28KCl
6371Day 04, 23:30Day 05, 14:34Calcium
6382Day 05, 17:00Day 07, 03:00Calcium
6392Day 05, 17:00Day 07, 03:00KCl
6403Day 07, 04:15Day 07, 16:32KCl
6413Day 07, 04:15Day 07, 16:32Calcium
6424Day 07, 18:50Day 08, 03:00KCl
6434Day 07, 18:50Day 08, 03:00Calcium
6445Day 08, 04:30Day 08, 09:33Calcium
6455Day 08, 04:30Day 08, 09:35KCl
6466Day 08, 11:00Day 08, 20:48KCl
6476Day 08, 11:00Day 08, 22:37Calcium
6487Day 08, 22:21Day 09, 14:28Calcium
6497Day 08, 22:21Day 09, 14:29KCl
6508Day 09, 16:30Day 09, 21:27KCl
6518Day 09, 16:30Day 10, 14:03Calcium
6529Day 10, 02:48Day 10, 14:03KCl
65310Day 10, 15:03Day 11, 06:33KCl
6549Day 10, 15:03Day 11, 06:33Calcium
65510Day 11, 07:33Day 13, 14:47Calcium
65611Day 11, 07:33Day 13, 14:47KCl
65712Day 13, 22:11Day 13, 23:28KCl
65811Day 13, 22:11Day 13, 23:41Calcium
65913Day 14, 09:17Day 14, 18:28KCl
66012Day 14, 09:17Day 16, 20:00Calcium
66114Day 14, 18:26Day 16, 20:00KCl
66213Day 16, 22:00Day 16, 23:30Calcium
66315Day 16, 22:00Day 16, 23:30KCl
66416Day 17, 02:58Day 17, 11:28KCl
66514Day 17, 02:58Day 18, 12:00Calcium
66617Day 17, 11:42Day 18, 12:00KCl
6671Day 24, 18:26Day 27, 17:03KCl
6681Day 24, 18:33Day 27, 17:00Calcium
66918Day 25, 12:00Day 25, 18:37KCl
67015Day 25, 12:30Day 25, 18:27Calcium
67116Day 25, 22:00Day 28, 12:30Calcium
67219Day 25, 22:00Day 28, 12:30KCl
67317Day 28, 17:03Day 30, 02:40Calcium
67420Day 28, 17:04Day 31, 18:02KCl
67518Day 30, 02:45Day 31, 18:04Calcium
67619Day 31, 22:25Day 01, 19:52Calcium
67721Day 31, 22:26Day 01, 19:50KCl
67822Day 02, 00:35Day 02, 13:23KCl
67920Day 02, 00:35Day 07, 14:30Calcium
68023Day 02, 15:30Day 07, 14:30KCl
68124Day 07, 21:30Day 08, 02:26KCl
68221Day 07, 21:30Day 08, 10:02Calcium
68325Day 08, 02:31Day 08, 09:58KCl
68426Day 08, 12:00Day 09, 12:46KCl
68522Day 08, 12:00Day 09, 13:19Calcium
68627Day 09, 16:00Day 10, 03:18KCl
68723Day 09, 16:19Day 12, 03:31Calcium
68828Day 10, 03:48Day 10, 06:25KCl
68929Day 10, 06:44Day 12, 03:07KCl
69030Day 12, 05:00Day 13, 20:04KCl
69124Day 12, 05:00Day 13, 20:05Calcium
69231Day 13, 23:20Day 14, 04:00KCl
69325Day 13, 23:20Day 14, 04:00Calcium
69426Day 14, 16:10Day 19, 11:00Calcium
69532Day 14, 16:10Day 19, 11:00KCl
6961Day 14, 03:23Day 14, 15:10KCl
6971Day 14, 03:23Day 14, 15:10Calcium
6982Day 14, 16:00Day 15, 10:30Calcium
6992Day 14, 16:00Day 15, 10:30KCl
7003Day 15, 11:02Day 15, 13:50Calcium
7013Day 15, 11:02Day 15, 13:54KCl
7024Day 15, 17:55Day 17, 12:30Calcium
7034Day 15, 17:55Day 17, 12:30KCl
7045Day 17, 13:00Day 17, 22:15Calcium
7055Day 17, 13:00Day 17, 22:30KCl
7066Day 18, 03:50Day 18, 05:44KCl
7076Day 18, 03:50Day 18, 06:31Calcium
7081Day 01, 22:40Day 03, 10:02KCl
7091Day 01, 22:40Day 03, 10:04Calcium
7102Day 06, 09:40Day 07, 18:40Calcium
7111Day 16, 19:00Day 17, 05:15Calcium
7121Day 16, 19:00Day 17, 05:15KCl
7132Day 17, 05:45Day 17, 16:29KCl
7142Day 17, 05:45Day 17, 18:13Calcium
7153Day 17, 21:45Day 19, 11:00Calcium
7163Day 17, 23:00Day 19, 11:06KCl
7174Day 19, 18:00Day 20, 01:06KCl
7184Day 19, 18:00Day 20, 10:33Calcium
7195Day 20, 09:26Day 20, 10:35KCl
7206Day 20, 16:30Day 21, 10:29KCl
7215Day 20, 16:33Day 21, 12:00Calcium
7221Day 31, 22:00Day 01, 12:17Calcium
7232Day 01, 16:00Day 01, 20:20Calcium
7243Day 02, 00:01Day 02, 09:34Calcium
7251Day 02, 00:09Day 02, 08:00KCl
7264Day 02, 20:07Day 03, 14:15Calcium
7272Day 02, 20:08Day 03, 14:10KCl
7283Day 03, 17:00Day 03, 21:28KCl
7295Day 03, 17:00Day 03, 21:31Calcium
7304Day 03, 23:30Day 04, 04:57KCl
7316Day 03, 23:31Day 04, 05:00Calcium
7321Day 15, 00:45Day 17, 12:45Calcium
7331Day 15, 08:45Day 15, 09:37KCl
7342Day 15, 16:35Day 15, 21:37KCl
7353Day 16, 04:56Day 16, 10:15KCl
7364Day 17, 03:00Day 17, 12:45KCl
7371Day 19, 19:00Day 20, 21:30Calcium
7381Day 19, 19:00Day 20, 21:30KCl
7392Day 20, 23:00Day 26, 17:49KCl
7402Day 20, 23:00Day 27, 22:01Calcium
7413Day 28, 02:37Day 29, 05:00Calcium
7423Day 28, 05:30Day 28, 07:50KCl
7434Day 29, 10:00Day 29, 12:00Calcium
7445Day 30, 19:03Day 01, 13:45Calcium
7454Day 01, 07:00Day 01, 13:44KCl
7465Day 01, 20:53Day 03, 13:57KCl
7476Day 01, 20:53Day 04, 22:25Calcium
7486Day 03, 18:01Day 04, 22:25KCl
7497Day 04, 23:34Day 05, 23:20Calcium
7507Day 04, 23:35Day 05, 23:20KCl
7518Day 06, 00:30Day 07, 16:00Calcium
7528Day 06, 00:30Day 10, 08:01KCl
7539Day 07, 17:02Day 10, 08:00Calcium
75410Day 11, 13:45Day 11, 16:02Calcium
7559Day 11, 13:45Day 11, 16:02KCl
75611Day 11, 19:28Day 14, 15:30Calcium
75710Day 11, 19:29Day 12, 14:37KCl
75811Day 12, 14:58Day 14, 15:30KCl
7591Day 12, 16:15Day 12, 16:18KCl
7601Day 12, 16:15Day 13, 11:00Calcium
7612Day 12, 16:15Day 13, 11:00KCl
7623Day 13, 12:00Day 14, 08:38KCl
7632Day 13, 12:00Day 17, 20:00Calcium
7643Day 17, 20:45Day 18, 12:01Calcium
7654Day 18, 07:50Day 18, 12:01KCl
7665Day 18, 16:50Day 21, 13:15KCl
7674Day 18, 16:50Day 21, 13:18Calcium
7681Day 23, 13:00Day 23, 17:34KCl
7691Day 23, 13:41Day 26, 09:29Calcium
7702Day 23, 23:00Day 26, 09:26KCl
7713Day 26, 12:00Day 27, 11:29KCl
7722Day 26, 12:00Day 27, 11:32Calcium
7733Day 27, 13:00Day 28, 09:00Calcium
7744Day 27, 13:00Day 28, 09:00KCl
7755Day 02, 12:30Day 03, 11:58KCl
7764Day 02, 12:30Day 03, 12:00Calcium
7776Day 05, 00:00Day 07, 15:35KCl
7785Day 05, 00:00Day 07, 20:26Calcium
7791Day 24, 02:00Day 24, 22:20KCl
7801Day 24, 02:00Day 24, 22:20Calcium
7812Day 24, 23:00Day 25, 01:05Calcium
7822Day 24, 23:00Day 25, 01:05KCl
7833Day 26, 20:46Day 30, 16:04Calcium
7843Day 27, 19:00Day 01, 05:50KCl
7851Day 28, 06:38Day 28, 08:45Calcium
7861Day 28, 06:38Day 28, 08:45KCl
7872Day 28, 10:00Day 28, 21:33KCl
7882Day 28, 10:00Day 30, 13:00Calcium
7893Day 28, 23:00Day 29, 04:00KCl
7904Day 29, 09:14Day 29, 21:00KCl
7915Day 29, 23:56Day 30, 13:02KCl
7924Day 30, 21:04Day 02, 08:50Calcium
7934Day 01, 05:33Day 02, 08:50KCl
7941Day 04, 00:20Day 05, 14:02KCl
7952Day 05, 16:21Day 06, 05:41KCl
7963Day 06, 08:17Day 07, 04:00KCl
7974Day 07, 04:45Day 10, 08:33KCl
7985Day 11, 20:15Day 13, 13:26KCl
7991Day 12, 13:31Day 13, 13:26Calcium
8006Day 13, 18:06Day 13, 19:30KCl
8012Day 13, 18:06Day 14, 17:17Calcium
8023Day 14, 18:04Day 17, 12:00Calcium
8037Day 15, 12:08Day 17, 12:00KCl
8041Day 22, 17:12Day 23, 14:12Calcium
8051Day 22, 17:15Day 23, 14:08KCl
8062Day 23, 19:08Day 26, 13:00KCl
8072Day 23, 19:12Day 26, 13:00Calcium
8083Day 26, 18:00Day 28, 08:36Calcium
8093Day 26, 18:42Day 28, 08:37KCl
8104Day 28, 11:00Day 29, 13:30Calcium
8114Day 28, 11:00Day 29, 13:30KCl
8121Day 26, 13:51Day 28, 17:00Calcium
8131Day 26, 20:27Day 28, 17:00KCl
8141Day 24, 23:15Day 25, 18:15Calcium
8151Day 24, 23:15Day 25, 18:15KCl
8162Day 25, 20:15Day 26, 13:57KCl
8172Day 25, 20:15Day 27, 03:08Calcium
8183Day 26, 19:50Day 27, 18:33KCl
8193Day 27, 06:03Day 27, 19:05Calcium
8204Day 27, 18:40Day 27, 19:05KCl
8215Day 27, 20:50Day 28, 12:00KCl
8224Day 27, 20:50Day 28, 12:00Calcium
8236Day 28, 14:00Day 29, 04:14KCl
8245Day 28, 14:00Day 29, 13:58Calcium
8257Day 29, 04:00Day 29, 05:00KCl
8268Day 29, 04:14Day 29, 13:59KCl
8276Day 29, 16:30Day 02, 15:00Calcium
8289Day 29, 16:31Day 02, 15:00KCl
82910Day 02, 16:00Day 03, 11:55KCl
8307Day 02, 16:00Day 03, 16:17Calcium
83111Day 03, 11:29Day 03, 16:18KCl
8328Day 03, 23:00Day 04, 20:32Calcium
83312Day 03, 23:00Day 04, 20:34KCl
83413Day 04, 23:11Day 05, 16:15KCl
8359Day 04, 23:11Day 05, 16:15Calcium
83614Day 06, 20:30Day 07, 06:25KCl
83710Day 06, 20:30Day 07, 13:45Calcium
83815Day 07, 06:28Day 07, 13:45KCl
83916Day 07, 19:00Day 10, 18:20KCl
84011Day 07, 19:00Day 10, 18:36Calcium
84112Day 10, 18:40Day 10, 18:44Calcium
84213Day 10, 20:22Day 19, 14:59Calcium
84317Day 10, 22:20Day 19, 14:59KCl
84418Day 19, 16:00Day 21, 11:19KCl
84514Day 19, 16:00Day 21, 11:30Calcium
8461Day 04, 22:00Day 07, 11:52KCl
8471Day 04, 22:00Day 07, 11:52Calcium
8482Day 07, 13:00Day 08, 01:52KCl
8492Day 07, 13:00Day 08, 01:52Calcium
8503Day 08, 04:03Day 08, 07:44Calcium
8513Day 08, 04:03Day 08, 07:45KCl
8521Day 09, 00:45Day 09, 05:45Calcium
8532Day 09, 08:57Day 09, 18:30Calcium
8543Day 09, 20:37Day 12, 08:30Calcium
8554Day 12, 12:00Day 13, 01:30Calcium
8565Day 13, 02:43Day 15, 11:07Calcium
8571Day 05, 01:30Day 07, 09:57KCl
8582Day 07, 10:12Day 10, 11:59KCl
8593Day 10, 13:00Day 11, 16:00KCl
8604Day 11, 22:00Day 12, 11:26KCl
8611Day 21, 19:00Day 21, 21:16Calcium
8621Day 21, 19:00Day 21, 21:19KCl
8632Day 21, 22:45Day 24, 23:52Calcium
8642Day 21, 22:45Day 24, 23:52KCl
8651Day 22, 00:00Day 22, 15:37Calcium
8661Day 22, 00:05Day 22, 15:32KCl
8672Day 22, 18:00Day 24, 03:32Calcium
8682Day 23, 17:30Day 24, 03:49KCl
8693Day 24, 12:00Day 26, 11:06Calcium
8703Day 24, 12:00Day 26, 11:06KCl
8713Day 25, 00:43Day 25, 10:30Calcium
8723Day 25, 00:43Day 25, 10:30KCl
8734Day 25, 14:30Day 25, 15:53Calcium
8744Day 25, 15:30Day 25, 16:00KCl
8755Day 25, 17:08Day 26, 13:12KCl
8765Day 25, 17:08Day 26, 13:15Calcium
8776Day 26, 17:00Day 27, 08:06KCl
8786Day 26, 17:00Day 27, 08:10Calcium
8797Day 27, 13:54Day 28, 22:00Calcium
8807Day 27, 13:56Day 28, 22:01KCl
8818Day 28, 23:00Day 29, 03:32Calcium
8828Day 28, 23:01Day 29, 03:38KCl
8839Day 29, 04:32Day 29, 06:00Calcium
8849Day 29, 04:38Day 29, 05:56KCl
8851Day 09, 11:15Day 12, 12:05Calcium
8861Day 07, 03:46Day 07, 04:46Calcium
8871Day 26, 23:45Day 28, 07:16Calcium
8881Day 29, 18:04Day 02, 01:30KCl
8892Day 02, 02:00Day 04, 11:19KCl
8901Day 12, 20:30Day 13, 11:03KCl
8911Day 12, 20:30Day 13, 11:04Calcium
8922Day 13, 23:20Day 15, 23:58KCl
8932Day 13, 23:20Day 17, 05:30Calcium
8943Day 16, 07:00Day 17, 05:27KCl
8954Day 19, 23:45Day 20, 11:31KCl
8963Day 19, 23:45Day 20, 11:33Calcium
8974Day 21, 00:41Day 21, 09:32Calcium
8981Day 05, 19:34Day 07, 09:35Calcium
8991Day 06, 11:00Day 07, 09:36KCl
9002Day 07, 19:12Day 08, 14:51Calcium
9012Day 07, 19:12Day 08, 15:01KCl
9023Day 11, 13:30Day 12, 13:30Calcium
9033Day 11, 13:30Day 12, 13:31KCl
9044Day 14, 18:38Day 15, 07:20Calcium
9054Day 14, 18:42Day 15, 07:20KCl
9065Day 15, 11:15Day 17, 20:17KCl
9075Day 15, 11:15Day 18, 09:01Calcium
9086Day 17, 20:30Day 18, 09:01KCl
9096Day 22, 15:13Day 23, 00:47Calcium
9107Day 22, 15:13Day 23, 07:40KCl
9117Day 23, 06:00Day 23, 07:40Calcium
9121Day 08, 21:15Day 09, 00:00KCl
9131Day 08, 21:15Day 09, 12:30Calcium
9142Day 09, 10:00Day 09, 12:30KCl
9153Day 09, 15:00Day 11, 06:45KCl
9162Day 09, 15:00Day 11, 06:45Calcium
9174Day 11, 19:00Day 13, 19:30KCl
9183Day 11, 19:00Day 13, 19:30Calcium
9191Day 29, 18:33Day 02, 12:02Calcium
9201Day 29, 18:33Day 02, 12:04KCl
9211Day 17, 02:00Day 17, 04:16Calcium
9221Day 17, 02:00Day 17, 04:16KCl
9232Day 17, 05:05Day 17, 06:32Calcium
9242Day 17, 05:05Day 17, 06:33KCl
9251Day 23, 22:00Day 24, 12:45Calcium
9261Day 06, 05:00Day 06, 18:12KCl
9271Day 06, 07:00Day 06, 18:07Calcium
9281Day 27, 19:03Day 29, 10:40Calcium
9291Day 27, 23:07Day 29, 10:40KCl
9302Day 29, 13:30Day 30, 08:23KCl
9312Day 29, 13:30Day 30, 10:00Calcium
9323Day 30, 11:00Day 01, 08:50Calcium
9333Day 30, 11:13Day 01, 04:59KCl
9341Day 12, 03:35Day 13, 10:10Calcium
9352Day 13, 12:20Day 13, 15:50Calcium
9363Day 13, 17:50Day 14, 20:31Calcium
9374Day 15, 04:26Day 16, 08:47Calcium
9385Day 16, 09:33Day 18, 23:02Calcium
9391Day 17, 00:00Day 18, 23:03KCl
9406Day 19, 01:30Day 20, 08:41Calcium
9412Day 19, 01:30Day 20, 08:42KCl
9421Day 06, 12:30Day 06, 16:58Calcium
9431Day 06, 12:30Day 06, 17:00KCl
9442Day 06, 18:00Day 09, 10:50KCl
9452Day 06, 18:00Day 09, 10:58Calcium
9463Day 09, 14:00Day 09, 17:29Calcium
9473Day 09, 14:00Day 09, 17:31KCl
9484Day 09, 19:52Day 10, 04:43KCl
9494Day 09, 19:52Day 10, 04:44Calcium
9505Day 10, 05:40Day 10, 23:31KCl
9515Day 10, 05:40Day 10, 23:36Calcium
9526Day 11, 00:07Day 13, 14:01Calcium
9536Day 11, 00:07Day 13, 14:03KCl
9541Day 11, 04:30Day 11, 05:30Calcium
9552Day 11, 08:30Day 11, 16:30Calcium
9567Day 13, 15:00Day 15, 22:38Calcium
9577Day 13, 15:00Day 15, 22:38KCl
9588Day 16, 00:05Day 16, 03:11Calcium
9598Day 16, 00:05Day 16, 09:01KCl
9609Day 16, 08:00Day 16, 09:02Calcium
96110Day 16, 12:00Day 16, 21:25Calcium
9629Day 16, 12:00Day 16, 21:25KCl
96311Day 16, 21:55Day 17, 00:23Calcium
96410Day 16, 21:55Day 17, 00:23KCl
96511Day 17, 03:34Day 17, 07:16KCl
96612Day 17, 03:34Day 17, 20:30Calcium
96712Day 17, 10:19Day 17, 20:26KCl
9681Day 14, 17:30Day 17, 01:09Calcium
9691Day 14, 20:00Day 16, 16:50KCl
9702Day 16, 23:31Day 17, 01:10KCl
9712Day 17, 04:09Day 17, 09:55Calcium
9723Day 17, 04:10Day 17, 09:59KCl
9733Day 18, 04:00Day 19, 09:04Calcium
9741Day 13, 15:00Day 17, 11:20Calcium
9751Day 13, 15:00Day 17, 11:20KCl
9761Day 05, 19:40Day 05, 23:00KCl
9771Day 05, 19:40Day 07, 02:18Calcium
9782Day 06, 19:38Day 07, 02:18KCl
9791Day 10, 17:30Day 11, 09:00KCl
9801Day 10, 17:30Day 11, 09:00Calcium
9811Day 17, 00:30Day 19, 23:00Calcium
9822Day 20, 00:45Day 20, 04:15Calcium
9833Day 20, 05:45Day 22, 07:45Calcium
9841Day 05, 03:35Day 05, 03:45Calcium
9852Day 05, 05:20Day 07, 13:45Calcium
9861Day 05, 10:50Day 07, 14:09KCl
9871Day 13, 12:24Day 13, 21:30Calcium
9882Day 13, 23:15Day 16, 09:00Calcium
9891Day 14, 08:59Day 15, 18:41KCl
9901Day 24, 18:15Day 24, 22:33Calcium
9911Day 24, 18:32Day 24, 21:52KCl
9922Day 24, 21:49Day 25, 06:32KCl
9932Day 24, 22:30Day 28, 10:15Calcium
9943Day 25, 09:02Day 28, 10:15KCl
9951Day 26, 22:44Day 28, 04:15Calcium
9961Day 26, 22:44Day 28, 04:25KCl
9974Day 28, 10:45Day 29, 01:51KCl
9983Day 28, 10:45Day 29, 01:53Calcium
9992Day 28, 18:00Day 28, 22:55KCl
10002Day 28, 18:00Day 29, 01:03Calcium
10013Day 28, 22:58Day 29, 00:59KCl
10025Day 29, 03:51Day 30, 11:50KCl
10034Day 29, 04:14Day 30, 11:50Calcium
10044Day 29, 19:45Day 30, 22:02KCl
10053Day 29, 19:45Day 30, 22:16Calcium
10066Day 30, 13:30Day 01, 11:37KCl
10075Day 30, 13:30Day 03, 06:30Calcium
10085Day 31, 00:32Day 31, 11:41KCl
10094Day 31, 00:32Day 01, 07:00Calcium
10105Day 01, 09:00Day 01, 11:44Calcium
10117Day 01, 13:13Day 03, 06:30KCl
10126Day 01, 14:00Day 03, 13:30Calcium
10136Day 02, 02:00Day 03, 13:32KCl
10146Day 03, 08:45Day 03, 14:50Calcium
10158Day 03, 08:45Day 03, 15:13KCl
10167Day 03, 15:15Day 03, 18:04Calcium
10177Day 03, 15:15Day 03, 18:08KCl
10189Day 03, 17:51Day 05, 16:44KCl
10197Day 03, 17:52Day 05, 16:50Calcium
10208Day 04, 05:46Day 04, 14:19Calcium
10219Day 04, 16:50Day 06, 09:30Calcium
10228Day 05, 00:00Day 05, 13:21KCl
102310Day 06, 13:00Day 06, 16:00Calcium
102411Day 07, 04:25Day 10, 02:40Calcium
10258Day 07, 06:17Day 08, 15:00Calcium
102610Day 07, 17:00Day 08, 13:18KCl
10279Day 07, 18:00Day 10, 00:00KCl
102811Day 08, 17:00Day 09, 12:15KCl
10299Day 08, 18:20Day 09, 12:00Calcium
103010Day 09, 18:14Day 11, 21:14Calcium
103112Day 09, 18:14Day 11, 21:15KCl
103210Day 10, 01:20Day 13, 03:02KCl
103312Day 10, 04:47Day 13, 03:02Calcium
103411Day 11, 22:04Day 12, 20:28Calcium
103513Day 11, 22:04Day 13, 02:27KCl
103614Day 12, 21:28Day 13, 02:25KCl
103712Day 12, 21:28Day 14, 09:00Calcium
103815Day 13, 02:27Day 14, 09:00KCl
103913Day 13, 03:38Day 13, 08:20Calcium
104011Day 13, 03:39Day 13, 08:20KCl
104114Day 15, 13:08Day 18, 00:35Calcium
104212Day 15, 16:36Day 18, 00:35KCl
104316Day 16, 11:10Day 18, 15:45KCl
104413Day 16, 11:10Day 18, 16:09Calcium
104515Day 18, 01:18Day 18, 16:30Calcium
104613Day 18, 01:18Day 18, 16:30KCl
104714Day 18, 18:17Day 19, 08:50Calcium
104817Day 18, 18:17Day 19, 08:56KCl
104916Day 18, 18:45Day 20, 12:45Calcium
105014Day 18, 18:45Day 20, 12:45KCl
105117Day 20, 14:45Day 21, 14:15Calcium
105215Day 20, 14:45Day 21, 14:15KCl
10531Day 01, 14:36Day 04, 03:00Calcium
10541Day 01, 14:37Day 04, 03:00KCl
10551Day 25, 19:00Day 26, 04:28Calcium
10561Day 25, 19:04Day 26, 04:28KCl
10572Day 26, 05:55Day 26, 13:00KCl
10582Day 26, 05:55Day 26, 13:07Calcium
10593Day 26, 13:36Day 26, 18:57Calcium
10603Day 26, 13:36Day 26, 18:59KCl
10614Day 26, 22:00Day 27, 07:00Calcium
10624Day 26, 22:00Day 27, 07:00KCl
10635Day 27, 09:00Day 29, 01:02Calcium
10645Day 27, 09:00Day 29, 02:06KCl
10651Day 25, 20:50Day 25, 23:29KCl
10661Day 25, 20:50Day 25, 23:30Calcium
10672Day 25, 23:45Day 27, 06:36KCl
10682Day 25, 23:45Day 27, 06:43Calcium
10693Day 27, 11:00Day 28, 15:00Calcium
10703Day 27, 11:00Day 28, 15:38KCl
10711Day 21, 22:00Day 22, 17:07Calcium
10721Day 22, 03:00Day 22, 17:02KCl
10732Day 22, 18:44Day 23, 08:02KCl
10742Day 22, 18:44Day 25, 05:00Calcium
10753Day 23, 07:50Day 25, 05:00KCl
10763Day 25, 06:37Day 25, 08:15Calcium
10774Day 25, 06:37Day 25, 08:15KCl
10784Day 25, 12:00Day 26, 10:30Calcium
10795Day 25, 12:00Day 26, 10:30KCl
10801Day 07, 21:31Day 09, 07:45Calcium
10811Day 03, 18:24Day 03, 23:49Calcium
10822Day 04, 02:29Day 04, 05:00Calcium
10831Day 10, 17:00Day 11, 04:25Calcium
10841Day 10, 17:00Day 11, 06:15KCl
10852Day 11, 17:30Day 13, 12:59Calcium
10862Day 11, 17:30Day 13, 13:01KCl
10873Day 13, 16:45Day 14, 22:11KCl
10883Day 13, 16:45Day 17, 08:45Calcium
10894Day 15, 03:11Day 16, 15:03KCl
10905Day 17, 00:33Day 17, 08:25KCl
10911Day 27, 17:06Day 28, 01:57Calcium
10922Day 28, 03:05Day 28, 10:45Calcium
10933Day 30, 01:42Day 30, 11:00Calcium
10941Day 30, 01:43Day 30, 10:58KCl
10951Day 15, 23:37Day 19, 09:24Calcium
10961Day 15, 23:38Day 19, 09:19KCl
10972Day 19, 12:47Day 20, 05:14KCl
10982Day 19, 12:47Day 20, 13:24Calcium
10991Day 08, 22:00Day 09, 11:29Calcium
11002Day 09, 18:04Day 10, 22:15Calcium
11011Day 06, 04:30Day 06, 07:13Calcium
11022Day 06, 08:30Day 06, 12:13Calcium
11033Day 06, 13:40Day 07, 07:35Calcium
11044Day 07, 17:10Day 08, 08:38Calcium
11055Day 09, 13:48Day 09, 16:36Calcium
11061Day 11, 08:30Day 11, 15:11Calcium
11072Day 11, 20:00Day 12, 23:11Calcium
11083Day 13, 00:15Day 14, 06:15Calcium
11094Day 14, 09:20Day 15, 06:20Calcium
11105Day 15, 12:30Day 16, 08:29Calcium
11116Day 16, 14:52Day 17, 06:00Calcium
11121Day 12, 22:00Day 14, 20:00Calcium
11131Day 12, 22:00Day 14, 22:36KCl
11142Day 15, 05:03Day 15, 05:31Calcium
11151Day 07, 21:00Day 08, 12:35Calcium
11162Day 08, 14:35Day 08, 23:25Calcium
11173Day 09, 00:36Day 09, 23:54Calcium
11184Day 10, 02:56Day 14, 10:02Calcium
11191Day 10, 19:28Day 14, 10:00KCl
11202Day 14, 22:00Day 15, 11:00KCl
11215Day 14, 22:00Day 15, 11:00Calcium
11223Day 15, 12:00Day 17, 16:02KCl
11236Day 15, 12:00Day 17, 16:03Calcium
11244Day 17, 17:40Day 20, 14:30KCl
11257Day 17, 17:45Day 20, 14:30Calcium
11265Day 23, 16:30Day 23, 16:50KCl
11278Day 23, 16:30Day 23, 17:18Calcium
11289Day 23, 20:17Day 27, 03:02Calcium
11296Day 23, 20:18Day 25, 02:58KCl
11307Day 25, 01:51Day 25, 01:53KCl
11318Day 25, 02:58Day 25, 05:10KCl
11329Day 25, 05:05Day 25, 05:12KCl
113310Day 25, 05:10Day 25, 09:59KCl
113411Day 25, 06:57Day 25, 06:58KCl
113512Day 25, 09:59Day 25, 18:29KCl
113613Day 25, 18:32Day 27, 03:03KCl
113714Day 27, 03:30Day 27, 05:25KCl
113810Day 27, 03:30Day 27, 15:45Calcium
113915Day 27, 10:52Day 27, 15:45KCl
11401Day 19, 20:59Day 19, 23:34KCl
11411Day 19, 20:59Day 19, 23:41Calcium
11421Day 08, 23:30Day 09, 15:58Calcium
11431Day 08, 23:30Day 09, 15:59KCl
11441Day 10, 11:13Day 11, 15:05Calcium
11451Day 05, 22:55Day 06, 14:58Calcium
11461Day 05, 22:56Day 06, 00:02KCl
11472Day 06, 02:49Day 06, 14:56KCl
11483Day 06, 16:53Day 11, 11:16KCl
11492Day 06, 16:54Day 11, 11:17Calcium
11503Day 11, 15:08Day 12, 17:57Calcium
11514Day 11, 15:08Day 12, 17:57KCl
11525Day 12, 20:30Day 14, 16:15KCl
11534Day 12, 20:30Day 14, 16:25Calcium
11546Day 14, 18:00Day 15, 14:35KCl
11555Day 14, 19:00Day 15, 14:35Calcium
11567Day 15, 16:07Day 17, 19:06KCl
11576Day 15, 16:07Day 17, 19:06Calcium
11587Day 17, 21:30Day 19, 16:40Calcium
11598Day 17, 21:30Day 19, 16:40KCl
11609Day 19, 17:50Day 21, 07:32KCl
11618Day 19, 17:50Day 21, 07:32Calcium
11629Day 22, 11:52Day 23, 11:00Calcium
116310Day 22, 11:53Day 22, 13:01KCl
116410Day 23, 12:00Day 24, 23:45Calcium
116511Day 23, 22:44Day 24, 06:51KCl
11661Day 16, 01:10Day 16, 07:30Calcium
11671Day 16, 01:10Day 16, 07:30KCl
11682Day 16, 18:00Day 16, 23:00KCl
11692Day 16, 18:00Day 17, 13:15Calcium
11703Day 17, 02:14Day 17, 12:12KCl
11713Day 17, 14:10Day 18, 07:45Calcium
11724Day 17, 22:14Day 18, 07:45KCl
11735Day 18, 10:00Day 19, 18:43KCl
11744Day 18, 10:00Day 19, 18:46Calcium
11755Day 19, 19:45Day 20, 19:52Calcium
11766Day 19, 19:45Day 20, 19:53KCl
11777Day 20, 20:30Day 22, 10:59KCl
11786Day 20, 20:30Day 22, 11:20Calcium
11791Day 14, 00:20Day 14, 10:30Calcium
11801Day 14, 04:30Day 14, 10:30KCl
11811Day 31, 16:10Day 02, 11:30Calcium
11821Day 31, 16:10Day 02, 11:30KCl
11831Day 27, 17:25Day 28, 13:10KCl
11841Day 27, 17:25Day 29, 08:00Calcium
11852Day 28, 18:00Day 29, 07:59KCl
11861Day 16, 21:40Day 18, 05:47Calcium
11871Day 17, 13:45Day 18, 21:00KCl
11882Day 18, 05:50Day 18, 21:00Calcium
11891Day 11, 07:00Day 12, 16:34Calcium
11901Day 11, 07:00Day 13, 17:00KCl
11912Day 12, 11:30Day 12, 11:35Calcium
11923Day 12, 16:34Day 13, 17:00Calcium
11931Day 09, 20:38Day 10, 13:33Calcium
11942Day 10, 17:50Day 13, 14:41Calcium
11953Day 13, 19:15Day 17, 17:00Calcium
11961Day 17, 13:00Day 17, 15:29KCl
11972Day 17, 18:00Day 17, 19:35KCl
11983Day 19, 00:51Day 19, 03:10KCl
11991Day 23, 09:30Day 23, 18:00Calcium
12001Day 23, 09:31Day 23, 18:00KCl
12011Day 28, 22:30Day 01, 08:55KCl
12021Day 28, 22:30Day 01, 08:56Calcium
12032Day 01, 13:00Day 01, 14:30Calcium
12042Day 01, 13:44Day 02, 02:28KCl
12053Day 01, 17:37Day 02, 02:30Calcium
12063Day 02, 11:10Day 04, 08:00KCl
12074Day 02, 11:49Day 04, 08:00Calcium
12085Day 06, 09:15Day 07, 18:01Calcium
12091Day 12, 20:35Day 15, 05:02Calcium
12101Day 13, 00:00Day 15, 05:15KCl
12111Day 21, 13:21Day 25, 13:15KCl
12121Day 05, 00:00Day 05, 19:00Calcium
12131Day 05, 14:05Day 05, 19:00KCl
12142Day 05, 20:59Day 06, 18:41Calcium
12152Day 05, 20:59Day 06, 18:42KCl
12163Day 07, 06:30Day 07, 14:45Calcium
12173Day 07, 06:30Day 07, 14:45KCl
12184Day 07, 21:20Day 08, 10:00KCl
12194Day 07, 21:20Day 09, 08:58Calcium
12205Day 10, 01:33Day 11, 20:45Calcium
12215Day 10, 08:20Day 11, 20:45KCl
12226Day 12, 00:09Day 13, 09:13KCl
12236Day 12, 00:09Day 13, 11:00Calcium
12247Day 13, 13:00Day 13, 20:00Calcium
12257Day 13, 13:00Day 13, 20:00KCl
12268Day 13, 22:00Day 14, 09:23Calcium
12278Day 13, 22:00Day 14, 09:37KCl
12289Day 14, 17:06Day 16, 08:00Calcium
12299Day 14, 17:07Day 16, 07:47KCl
123010Day 16, 09:30Day 20, 02:00Calcium
123110Day 16, 09:30Day 20, 02:03KCl
123211Day 20, 04:00Day 22, 11:30Calcium
123311Day 20, 04:00Day 22, 11:30KCl
123412Day 22, 14:30Day 23, 05:13KCl
123512Day 22, 14:40Day 25, 00:01Calcium
123613Day 24, 06:00Day 27, 00:15KCl
123713Day 25, 06:10Day 25, 15:04Calcium
123814Day 25, 19:05Day 26, 06:19Calcium
123915Day 26, 07:00Day 27, 00:15Calcium
124014Day 27, 00:50Day 27, 15:00KCl
124116Day 27, 00:50Day 27, 15:00Calcium
124215Day 27, 19:30Day 28, 04:15KCl
124317Day 27, 19:30Day 28, 04:15Calcium
12441Day 19, 16:45Day 22, 01:02Calcium
12451Day 19, 16:45Day 22, 01:02KCl
12461Day 02, 14:44Day 06, 16:39Calcium
12472Day 06, 16:42Day 07, 06:02Calcium
12483Day 08, 18:00Day 08, 22:01Calcium
12494Day 09, 04:22Day 13, 01:05Calcium
12501Day 10, 21:28Day 11, 22:01KCl
12511Day 13, 17:30Day 14, 16:01KCl
12521Day 13, 17:30Day 14, 16:02Calcium
12532Day 14, 17:30Day 16, 14:38Calcium
12542Day 14, 17:30Day 16, 14:40KCl
12553Day 16, 15:15Day 17, 20:00Calcium
12563Day 16, 15:15Day 17, 20:00KCl
12571Day 08, 00:51Day 09, 19:30Calcium
12581Day 08, 00:52Day 09, 10:15KCl
12592Day 09, 18:25Day 09, 19:30KCl
12603Day 09, 22:45Day 10, 00:35KCl
12612Day 09, 22:45Day 10, 12:45Calcium
12624Day 10, 08:00Day 10, 12:45KCl
12633Day 10, 17:15Day 11, 13:04Calcium
12644Day 11, 18:00Day 13, 18:00Calcium
12655Day 11, 23:11Day 13, 18:00KCl
12666Day 14, 04:02Day 15, 14:39KCl
12675Day 14, 04:02Day 15, 14:42Calcium
12687Day 15, 21:00Day 15, 23:02KCl
12696Day 15, 22:00Day 19, 13:03Calcium
12708Day 16, 03:07Day 16, 05:00KCl
12719Day 16, 08:31Day 19, 13:03KCl
12721Day 09, 22:38Day 11, 21:57KCl
12731Day 09, 22:38Day 12, 13:00Calcium
12742Day 11, 17:05Day 12, 11:26KCl
12752Day 12, 13:10Day 12, 18:44Calcium
12761Day 16, 15:25Day 17, 06:33Calcium
12771Day 16, 15:25Day 17, 06:57KCl
12782Day 17, 06:48Day 17, 07:00Calcium
12793Day 17, 09:04Day 20, 02:20Calcium
12802Day 17, 09:04Day 20, 02:20KCl
12813Day 23, 22:00Day 25, 20:01KCl
12824Day 23, 22:00Day 25, 20:02Calcium
12834Day 26, 01:03Day 26, 12:04KCl
12845Day 26, 01:03Day 26, 12:04Calcium
12856Day 26, 17:19Day 29, 18:00Calcium
12865Day 26, 17:20Day 29, 18:00KCl
12871Day 27, 11:15Day 27, 15:00KCl
12882Day 27, 15:59Day 28, 19:33KCl
12893Day 29, 01:33Day 30, 22:29KCl
12904Day 30, 23:47Day 01, 08:10KCl
12911Day 26, 22:55Day 27, 00:31Calcium
12921Day 26, 23:02Day 27, 00:28KCl
12932Day 27, 04:22Day 27, 15:33KCl
12942Day 27, 04:22Day 27, 15:34Calcium
12953Day 27, 18:13Day 28, 08:57Calcium
12963Day 27, 18:14Day 28, 08:56KCl
12971Day 13, 21:00Day 14, 09:19KCl
12981Day 13, 21:00Day 14, 12:37Calcium
12991Day 22, 14:09Day 26, 09:05Calcium
13001Day 22, 14:10Day 26, 09:07KCl
13012Day 26, 11:20Day 28, 01:59KCl
13022Day 26, 11:20Day 28, 15:13Calcium
13033Day 28, 18:14Day 29, 13:12Calcium
13043Day 29, 08:09Day 29, 13:09KCl
13054Day 29, 15:00Day 30, 10:39Calcium
13064Day 29, 15:00Day 30, 10:44KCl
13075Day 30, 13:55Day 03, 08:00Calcium
13086Day 03, 12:20Day 05, 09:01Calcium
13097Day 07, 14:50Day 08, 16:52Calcium
13105Day 07, 23:53Day 08, 16:50KCl
13118Day 08, 18:03Day 09, 13:45Calcium
13126Day 08, 18:03Day 09, 13:45KCl
13137Day 09, 21:30Day 10, 17:10KCl
13149Day 09, 21:30Day 13, 10:45Calcium
13158Day 10, 17:07Day 13, 10:45KCl
13169Day 13, 11:45Day 14, 00:55KCl
131710Day 13, 11:45Day 14, 01:58Calcium
131811Day 14, 05:20Day 14, 13:15Calcium
131910Day 14, 05:20Day 14, 13:26KCl
132011Day 14, 17:15Day 14, 20:01KCl
132112Day 14, 17:30Day 15, 16:35Calcium
132212Day 15, 00:00Day 15, 15:00KCl
132313Day 15, 19:40Day 16, 15:37KCl
132413Day 15, 19:40Day 16, 15:37Calcium
132514Day 16, 17:03Day 18, 12:01KCl
132614Day 16, 17:03Day 18, 12:30Calcium
132715Day 18, 20:46Day 19, 13:35Calcium
132815Day 18, 20:46Day 19, 13:35KCl
132916Day 19, 21:38Day 21, 17:37KCl
133016Day 19, 21:38Day 22, 23:02Calcium
133117Day 23, 22:45Day 27, 05:01Calcium
13321Day 24, 21:13Day 25, 23:37Calcium
13331Day 25, 13:40Day 25, 23:40KCl
13342Day 26, 01:00Day 27, 12:00KCl
13352Day 26, 01:00Day 27, 12:35Calcium
13361Day 26, 17:30Day 28, 08:31KCl
13371Day 26, 17:30Day 28, 08:32Calcium
13382Day 28, 11:20Day 29, 18:01Calcium
13392Day 28, 11:20Day 29, 19:52KCl
13403Day 30, 12:00Day 30, 16:43KCl
13413Day 30, 12:00Day 30, 18:35Calcium
13424Day 30, 21:00Day 01, 19:18KCl
13434Day 30, 21:00Day 01, 22:50Calcium
13441Day 29, 22:56Day 30, 03:50KCl
13451Day 29, 22:56Day 30, 09:05Calcium
13462Day 30, 03:56Day 30, 09:00KCl
13473Day 30, 10:30Day 01, 11:13KCl
13482Day 30, 10:30Day 01, 13:46Calcium
13494Day 01, 11:15Day 01, 14:06KCl
13505Day 04, 18:29Day 05, 23:27KCl
13513Day 04, 18:30Day 07, 08:15Calcium
13526Day 05, 23:15Day 07, 08:15KCl
13537Day 07, 10:43Day 08, 21:26KCl
13544Day 07, 12:01Day 08, 16:30Calcium
13551Day 21, 07:00Day 22, 21:18KCl
13561Day 21, 07:00Day 24, 17:02Calcium
13572Day 23, 07:00Day 24, 17:02KCl
13581Day 12, 21:30Day 13, 15:23Calcium
13591Day 12, 21:30Day 13, 15:23KCl
13602Day 13, 18:00Day 13, 19:18KCl
13612Day 13, 18:23Day 16, 16:18Calcium
13623Day 16, 22:00Day 18, 13:28Calcium
13633Day 17, 04:30Day 18, 13:27KCl
13644Day 18, 16:30Day 19, 07:56Calcium
13654Day 18, 16:30Day 19, 08:10KCl
13665Day 19, 10:00Day 20, 00:50Calcium
13675Day 19, 10:00Day 20, 01:23KCl
13686Day 20, 20:19Day 21, 05:02Calcium
13696Day 20, 20:20Day 23, 05:55KCl
13701Day 21, 01:45Day 21, 15:45Calcium
13717Day 21, 10:28Day 25, 08:10Calcium
13722Day 21, 21:47Day 24, 05:04Calcium
13733Day 24, 08:35Day 24, 19:53Calcium
13747Day 24, 11:25Day 25, 08:10KCl
13754Day 24, 23:40Day 26, 13:45Calcium
13761Day 08, 21:41Day 09, 09:41KCl
13771Day 08, 21:41Day 09, 09:44Calcium
13782Day 09, 13:47Day 10, 02:22KCl
13792Day 09, 13:47Day 10, 02:27Calcium
13801Day 10, 01:38Day 10, 09:30Calcium
13811Day 10, 02:57Day 10, 09:29KCl
13823Day 10, 05:06Day 10, 18:23KCl
13833Day 10, 05:06Day 10, 18:23Calcium
13844Day 10, 21:00Day 11, 14:03KCl
13854Day 10, 21:00Day 11, 14:03Calcium
13862Day 10, 23:54Day 11, 01:03Calcium
13872Day 10, 23:57Day 11, 01:02KCl
13883Day 11, 02:00Day 11, 12:00KCl
13893Day 11, 02:00Day 11, 12:02Calcium
13904Day 11, 13:50Day 11, 18:58KCl
13914Day 11, 13:50Day 11, 19:02Calcium
13925Day 11, 16:38Day 12, 10:41Calcium
13935Day 11, 16:38Day 12, 10:41KCl
13945Day 11, 22:22Day 12, 07:19KCl
13955Day 12, 02:21Day 12, 11:00Calcium
13966Day 12, 07:05Day 12, 11:00KCl
13976Day 12, 13:24Day 13, 01:00Calcium
13986Day 12, 13:24Day 13, 09:37KCl
13997Day 12, 16:00Day 12, 23:15KCl
14006Day 12, 16:20Day 12, 23:13Calcium
14017Day 13, 01:01Day 13, 09:27Calcium
14027Day 13, 03:14Day 14, 17:50Calcium
14038Day 13, 03:14Day 14, 17:53KCl
14048Day 13, 14:58Day 14, 09:01Calcium
14057Day 13, 14:58Day 14, 09:03KCl
14068Day 14, 11:15Day 15, 10:03KCl
14079Day 14, 11:15Day 15, 10:04Calcium
14088Day 14, 20:45Day 15, 11:13Calcium
14099Day 14, 20:45Day 15, 11:35KCl
14109Day 15, 12:00Day 15, 23:59KCl
141110Day 15, 12:00Day 15, 23:59Calcium
14121Day 25, 23:30Day 26, 02:47Calcium
14131Day 17, 05:45Day 17, 23:45Calcium
14141Day 17, 05:45Day 18, 06:00KCl
14152Day 18, 00:20Day 18, 06:00Calcium
14163Day 18, 06:59Day 18, 12:15Calcium
14172Day 18, 07:00Day 18, 12:15KCl
14183Day 18, 14:30Day 18, 21:17KCl
14194Day 18, 14:30Day 18, 21:20Calcium
14201Day 04, 14:34Day 07, 08:11Calcium
14211Day 07, 00:00Day 09, 20:30Calcium
14221Day 07, 00:00Day 09, 20:30KCl
14232Day 09, 22:00Day 12, 22:00KCl
14242Day 09, 22:00Day 12, 22:00Calcium
14253Day 13, 00:00Day 13, 15:17KCl
14263Day 13, 00:00Day 13, 15:17Calcium
14274Day 13, 15:10Day 13, 17:30KCl
14284Day 13, 15:10Day 13, 19:00Calcium
14295Day 13, 19:21Day 13, 20:30Calcium
14306Day 13, 23:30Day 14, 03:31Calcium
14315Day 14, 13:00Day 15, 13:30KCl
14327Day 14, 13:00Day 15, 13:30Calcium
14331Day 14, 18:00Day 14, 21:15Calcium
14341Day 14, 19:02Day 14, 21:15KCl
14352Day 14, 23:00Day 15, 15:25KCl
14362Day 14, 23:00Day 15, 15:30Calcium
14378Day 15, 16:56Day 17, 12:50Calcium
14386Day 15, 16:57Day 17, 13:11KCl
14393Day 15, 18:59Day 18, 05:10KCl
14403Day 15, 18:59Day 18, 10:20Calcium
14411Day 12, 18:00Day 13, 16:36Calcium
14421Day 12, 18:00Day 13, 16:37KCl
14431Day 04, 00:59Day 04, 04:31Calcium
14441Day 24, 12:41Day 24, 16:30Calcium
14452Day 24, 18:00Day 25, 00:39Calcium
14461Day 24, 18:56Day 25, 00:42KCl
14472Day 25, 20:00Day 25, 21:24KCl
14483Day 25, 20:00Day 25, 21:28Calcium
14494Day 26, 18:12Day 27, 13:01Calcium
14503Day 26, 18:12Day 27, 13:02KCl
14514Day 27, 14:10Day 27, 22:45KCl
14525Day 27, 14:30Day 27, 22:45Calcium
14536Day 27, 23:45Day 29, 12:08Calcium
14545Day 27, 23:45Day 29, 12:11KCl
14551Day 02, 15:30Day 05, 15:00Calcium
14561Day 02, 15:30Day 05, 15:00KCl
14572Day 05, 15:45Day 08, 16:07KCl
14582Day 05, 15:45Day 08, 16:07Calcium
14597Day 06, 00:00Day 07, 12:58Calcium
14608Day 07, 15:30Day 10, 17:26Calcium
14616Day 08, 11:30Day 11, 04:02KCl
14629Day 11, 00:00Day 11, 04:03Calcium
146310Day 11, 05:00Day 11, 09:50Calcium
14647Day 11, 05:01Day 11, 06:00KCl
14658Day 11, 09:04Day 11, 09:50KCl
146611Day 14, 22:37Day 14, 23:17Calcium
146712Day 15, 00:00Day 16, 13:08Calcium
14689Day 15, 03:11Day 15, 08:00KCl
146910Day 15, 14:00Day 16, 13:04KCl
14701Day 24, 12:00Day 28, 12:00Calcium
14711Day 24, 16:30Day 28, 12:00KCl
14721Day 17, 20:00Day 18, 14:26Calcium
14731Day 18, 09:06Day 20, 04:27KCl
14742Day 18, 17:00Day 20, 04:31Calcium
14752Day 20, 17:00Day 22, 15:57KCl
14763Day 20, 17:00Day 22, 16:01Calcium
14771Day 03, 20:30Day 04, 08:10KCl
14781Day 05, 10:35Day 07, 14:59Calcium
14792Day 05, 10:35Day 07, 14:59KCl
14801Day 24, 15:30Day 27, 11:45Calcium
14811Day 24, 15:30Day 27, 11:45KCl
14822Day 27, 12:30Day 28, 16:30KCl
14832Day 27, 12:30Day 30, 10:00Calcium
14843Day 29, 01:00Day 29, 15:11KCl
14854Day 30, 05:15Day 30, 10:00KCl
14861Day 19, 22:30Day 22, 21:00Calcium
14871Day 19, 22:30Day 22, 21:06KCl
14881Day 10, 11:00Day 10, 18:03KCl
14891Day 10, 11:00Day 12, 10:55Calcium
14902Day 10, 22:42Day 12, 10:56KCl
14913Day 12, 13:30Day 13, 10:59KCl
14922Day 12, 13:30Day 13, 11:01Calcium
14933Day 13, 12:30Day 18, 07:33Calcium
14944Day 13, 12:30Day 18, 07:40KCl
14954Day 19, 02:37Day 20, 12:56Calcium
14965Day 20, 04:15Day 20, 12:58KCl
14971Day 15, 04:39Day 15, 05:39Calcium
14981Day 27, 11:30Day 28, 10:56Calcium
14991Day 25, 23:58Day 26, 00:25Calcium
15001Day 25, 23:59Day 26, 00:29KCl
15012Day 26, 16:10Day 27, 00:47Calcium
15022Day 26, 16:12Day 27, 00:42KCl
15033Day 27, 02:31Day 30, 13:30KCl
15043Day 27, 02:31Day 30, 13:30Calcium
15051Day 05, 04:04Day 06, 12:22Calcium
15061Day 05, 22:57Day 06, 12:25KCl
15072Day 06, 14:04Day 07, 20:08KCl
15082Day 06, 14:04Day 07, 20:22Calcium
15093Day 07, 22:00Day 08, 14:30Calcium
15103Day 07, 22:00Day 08, 14:30KCl
15111Day 16, 13:30Day 17, 21:00KCl
15121Day 16, 13:30Day 17, 21:00Calcium
15132Day 25, 01:00Day 25, 20:00Calcium
15142Day 25, 01:00Day 25, 20:00KCl
15153Day 25, 22:00Day 28, 05:48Calcium
15163Day 25, 22:00Day 28, 05:51KCl
15171Day 30, 04:30Day 30, 06:45Calcium
15181Day 22, 12:08Day 25, 08:07Calcium
15191Day 22, 12:09Day 25, 08:00KCl
15202Day 25, 11:15Day 25, 16:00KCl
15212Day 25, 11:15Day 25, 17:30Calcium
15221Day 11, 16:54Day 11, 23:00KCl
15231Day 11, 16:56Day 11, 23:00Calcium
15242Day 12, 04:00Day 12, 19:00Calcium
15252Day 12, 04:00Day 12, 19:00KCl
15263Day 12, 22:00Day 14, 06:45KCl
15273Day 12, 22:00Day 14, 06:47Calcium
15284Day 14, 14:40Day 16, 00:15KCl
15294Day 14, 20:55Day 16, 12:15Calcium
15305Day 16, 00:30Day 16, 12:15KCl
15315Day 16, 17:30Day 18, 21:10Calcium
15326Day 16, 17:30Day 18, 21:10KCl
15337Day 19, 22:57Day 21, 07:15KCl
15341Day 05, 20:30Day 06, 13:56KCl
15351Day 05, 20:30Day 06, 13:59Calcium
15361Day 18, 23:00Day 19, 21:18Calcium
15371Day 19, 05:15Day 19, 21:38KCl
15381Day 28, 02:25Day 28, 16:31Calcium
15391Day 28, 12:13Day 28, 17:13KCl
15401Day 09, 21:30Day 10, 11:47Calcium
15411Day 09, 23:30Day 10, 11:47KCl
15421Day 22, 04:00Day 22, 05:51KCl
15431Day 22, 04:00Day 22, 05:51Calcium
15442Day 22, 09:30Day 22, 13:25KCl
15452Day 22, 10:11Day 22, 13:25Calcium
15463Day 22, 17:45Day 23, 15:31KCl
15473Day 22, 18:00Day 23, 15:01Calcium
15484Day 23, 19:30Day 23, 21:00Calcium
15494Day 23, 19:30Day 23, 21:00KCl
15505Day 23, 23:00Day 24, 13:21KCl
15515Day 23, 23:00Day 24, 13:30Calcium
15521Day 03, 16:30Day 05, 17:33KCl
15531Day 03, 16:30Day 05, 17:33Calcium
15541Day 03, 16:00Day 06, 14:48Calcium
15551Day 04, 13:00Day 06, 14:52KCl
15562Day 06, 17:00Day 09, 10:19Calcium
15572Day 06, 17:00Day 09, 10:22KCl
15583Day 09, 11:30Day 09, 15:18KCl
15593Day 09, 11:30Day 11, 07:02Calcium
15604Day 09, 19:39Day 11, 07:01KCl
15614Day 12, 14:30Day 12, 15:13Calcium
15625Day 12, 14:30Day 13, 22:30KCl
15635Day 12, 21:52Day 13, 22:30Calcium
15641Day 13, 22:56Day 16, 07:08Calcium
15651Day 13, 22:56Day 16, 07:09KCl
15662Day 16, 13:56Day 17, 04:30Calcium
15672Day 16, 13:57Day 17, 04:30KCl
15683Day 17, 14:30Day 17, 15:45KCl
15693Day 17, 14:30Day 17, 15:45Calcium
15704Day 17, 19:20Day 18, 03:01Calcium
15714Day 17, 19:20Day 18, 03:02KCl
15725Day 18, 04:31Day 18, 15:30KCl
15735Day 18, 04:32Day 18, 15:30Calcium
15746Day 18, 16:15Day 20, 12:30KCl
15756Day 18, 16:15Day 20, 12:30Calcium
15761Day 02, 12:15Day 02, 17:00KCl
15771Day 02, 16:00Day 02, 18:04Calcium
15782Day 02, 21:03Day 03, 00:00Calcium
15791Day 02, 21:15Day 04, 12:02Calcium
15801Day 10, 21:30Day 11, 00:42Calcium
15811Day 10, 21:30Day 11, 00:48KCl
15822Day 13, 11:07Day 13, 21:00Calcium
15832Day 13, 11:08Day 13, 21:02KCl
15843Day 19, 11:00Day 20, 14:22KCl
15853Day 19, 11:00Day 22, 16:12Calcium
15861Day 20, 03:00Day 20, 09:15KCl
15871Day 20, 03:00Day 21, 03:15Calcium
15884Day 20, 14:31Day 22, 16:04KCl
15892Day 20, 15:10Day 21, 03:15KCl
15903Day 21, 11:00Day 21, 22:02KCl
15912Day 21, 11:00Day 24, 15:45Calcium
15924Day 22, 10:00Day 22, 16:01KCl
15934Day 22, 17:34Day 25, 17:08Calcium
15945Day 22, 17:34Day 25, 17:11KCl
15955Day 22, 23:40Day 23, 04:05KCl
15966Day 23, 12:00Day 24, 15:54KCl
15976Day 25, 18:15Day 26, 08:15KCl
15985Day 25, 18:15Day 26, 08:15Calcium
15991Day 07, 19:30Day 08, 21:59KCl
16001Day 07, 19:30Day 09, 05:38Calcium
16012Day 09, 14:43Day 10, 23:02Calcium
16022Day 10, 01:00Day 10, 23:02KCl
16031Day 22, 18:50Day 23, 12:42Calcium
16041Day 22, 23:30Day 23, 06:26KCl
16052Day 23, 12:43Day 23, 18:30Calcium
16063Day 26, 16:00Day 27, 13:33Calcium
16074Day 27, 16:00Day 28, 16:31Calcium
16085Day 28, 22:31Day 02, 11:12Calcium
16096Day 02, 16:20Day 04, 14:14Calcium
16107Day 04, 15:00Day 04, 22:30Calcium
16118Day 05, 04:35Day 05, 17:16Calcium
16129Day 16, 02:00Day 17, 14:20Calcium
161310Day 17, 20:00Day 18, 15:28Calcium
161411Day 18, 18:30Day 20, 01:17Calcium
16151Day 08, 19:00Day 11, 04:00KCl
16161Day 08, 19:00Day 11, 04:00Calcium
16171Day 10, 22:33Day 12, 22:20Calcium
16181Day 10, 22:37Day 11, 05:03KCl
16192Day 11, 05:00Day 12, 22:20KCl
16203Day 13, 01:47Day 13, 10:56KCl
16212Day 13, 01:47Day 13, 14:30Calcium
16223Day 13, 17:00Day 13, 21:20Calcium
16234Day 13, 17:00Day 13, 21:20KCl
16241Day 31, 00:00Day 31, 04:00KCl
16251Day 31, 00:00Day 31, 04:00Calcium
16262Day 31, 04:50Day 31, 07:37Calcium
16272Day 31, 04:50Day 31, 07:43KCl
16283Day 31, 18:00Day 01, 01:18Calcium
16291Day 07, 16:45Day 08, 12:00Calcium
16302Day 08, 12:40Day 09, 11:10Calcium
16313Day 09, 12:00Day 12, 15:00Calcium
16324Day 12, 17:00Day 13, 01:30Calcium
16335Day 13, 13:30Day 14, 16:45Calcium
16341Day 28, 04:30Day 28, 15:37Calcium
16351Day 25, 15:00Day 26, 00:01KCl
16361Day 25, 15:00Day 28, 13:08Calcium
16371Day 25, 18:33Day 26, 18:02KCl
16382Day 26, 19:14Day 28, 13:07KCl
16393Day 28, 16:25Day 29, 21:37KCl
16402Day 28, 16:25Day 29, 21:39Calcium
16414Day 01, 20:00Day 04, 15:49KCl
16423Day 01, 20:00Day 04, 16:00Calcium
16434Day 04, 20:20Day 05, 10:15Calcium
16445Day 04, 20:21Day 05, 10:15KCl
16456Day 06, 13:30Day 08, 18:10KCl
16465Day 06, 13:38Day 08, 18:14Calcium
16471Day 11, 12:54Day 13, 11:30Calcium
16481Day 11, 13:00Day 13, 11:30KCl
16491Day 07, 00:02Day 09, 11:30Calcium
16501Day 07, 02:34Day 07, 06:45KCl
16511Day 21, 01:35Day 21, 09:40KCl
16521Day 21, 01:35Day 21, 09:41Calcium
16531Day 28, 13:16Day 28, 17:08KCl
16541Day 28, 13:36Day 28, 13:40Calcium
16552Day 28, 19:00Day 29, 01:54KCl
16562Day 28, 19:00Day 30, 11:41Calcium
16573Day 29, 19:00Day 29, 23:00KCl
16581Day 15, 15:00Day 17, 12:18Calcium
16591Day 22, 08:00Day 22, 21:01Calcium
16601Day 22, 10:00Day 22, 13:25KCl
16612Day 22, 16:12Day 22, 18:58KCl
16623Day 22, 20:00Day 23, 01:07KCl
16632Day 22, 21:06Day 23, 07:58Calcium
16643Day 23, 10:10Day 25, 08:20Calcium
16654Day 25, 19:00Day 28, 12:18Calcium
16664Day 26, 15:30Day 28, 11:49KCl
16675Day 28, 22:00Day 29, 20:00KCl
16685Day 28, 22:00Day 29, 20:01Calcium
16696Day 29, 20:30Day 29, 20:58KCl
16706Day 29, 20:30Day 29, 21:00Calcium
16717Day 30, 00:00Day 30, 11:25KCl
16727Day 30, 00:00Day 30, 14:48Calcium
16738Day 30, 16:34Day 01, 19:01Calcium
16748Day 30, 16:35Day 02, 17:30KCl
16759Day 01, 19:00Day 02, 17:30Calcium
167610Day 02, 18:30Day 03, 09:34Calcium
16779Day 02, 18:30Day 05, 13:30KCl
167811Day 03, 14:21Day 05, 13:30Calcium
167912Day 05, 20:10Day 08, 13:40Calcium
168010Day 08, 12:45Day 08, 13:40KCl
168113Day 09, 15:21Day 10, 09:00Calcium
168211Day 10, 00:50Day 10, 02:55KCl
168314Day 10, 11:30Day 13, 14:25Calcium
168412Day 10, 14:00Day 13, 14:25KCl
16851Day 12, 20:30Day 13, 18:03KCl
16861Day 12, 20:30Day 13, 19:54Calcium
16872Day 14, 11:10Day 15, 10:45Calcium
16881Day 01, 23:18Day 02, 06:05Calcium
16892Day 02, 07:50Day 02, 20:18Calcium
16901Day 06, 06:14Day 07, 05:00Calcium
16911Day 06, 19:00Day 07, 05:00KCl
16922Day 07, 10:41Day 07, 19:00KCl
16932Day 07, 10:41Day 07, 19:00Calcium
16941Day 13, 13:00Day 14, 09:07KCl
16951Day 13, 17:30Day 14, 13:05Calcium
16962Day 14, 16:30Day 16, 16:20Calcium
16972Day 14, 16:30Day 18, 11:22KCl
16983Day 16, 17:31Day 18, 11:20Calcium
16994Day 19, 12:30Day 20, 10:30Calcium
17003Day 19, 15:00Day 20, 10:30KCl
17015Day 20, 12:45Day 21, 20:37Calcium
17024Day 20, 12:45Day 21, 20:39KCl
17035Day 21, 23:00Day 23, 12:02KCl
17046Day 21, 23:00Day 23, 12:03Calcium
17051Day 24, 13:30Day 25, 08:00Calcium
17061Day 24, 18:40Day 25, 08:05KCl
17072Day 25, 10:30Day 27, 20:00KCl
17082Day 25, 10:30Day 27, 20:00Calcium
17091Day 27, 19:50Day 28, 18:30Calcium
17101Day 27, 23:58Day 28, 10:19KCl
17112Day 28, 16:37Day 28, 18:32KCl
17122Day 28, 21:59Day 29, 11:00Calcium
17133Day 28, 22:01Day 29, 11:00KCl
17144Day 29, 18:33Day 29, 18:45KCl
17153Day 29, 18:33Day 29, 20:50Calcium
17161Day 21, 13:30Day 21, 17:20KCl
17171Day 21, 13:30Day 22, 21:06Calcium
17182Day 22, 05:12Day 22, 21:05KCl
17193Day 23, 16:20Day 24, 09:02KCl
17202Day 23, 16:20Day 24, 09:02Calcium
17214Day 24, 12:30Day 25, 04:00KCl
17223Day 24, 12:30Day 25, 04:00Calcium
17235Day 25, 08:01Day 26, 00:02KCl
17244Day 25, 08:01Day 26, 01:00Calcium
17251Day 21, 20:42Day 21, 22:00Calcium
17261Day 21, 20:43Day 21, 22:02KCl
17272Day 22, 04:15Day 22, 11:30Calcium
17282Day 22, 04:15Day 22, 11:30KCl
17291Day 13, 21:00Day 13, 23:30Calcium
17302Day 14, 02:31Day 14, 04:30Calcium
17311Day 14, 04:10Day 14, 04:30KCl
17321Day 12, 16:00Day 14, 13:29Calcium
17331Day 13, 10:09Day 14, 13:38KCl
17342Day 14, 17:30Day 16, 17:00KCl
17352Day 14, 18:35Day 16, 17:30Calcium
17361Day 24, 20:15Day 25, 10:00Calcium
17371Day 24, 20:15Day 25, 10:01KCl
17382Day 26, 02:30Day 26, 09:12Calcium
17392Day 26, 02:30Day 26, 09:13KCl
17403Day 26, 12:55Day 27, 11:41Calcium
17413Day 26, 12:55Day 27, 11:42KCl
17424Day 27, 14:55Day 28, 03:34KCl
17434Day 27, 14:55Day 28, 13:45Calcium
17445Day 28, 04:00Day 28, 13:44KCl
17455Day 29, 16:45Day 30, 10:00Calcium
17466Day 30, 13:30Day 01, 13:00Calcium
17476Day 30, 13:30Day 01, 13:03KCl
17487Day 01, 16:09Day 02, 11:00Calcium
17497Day 01, 16:10Day 01, 17:39KCl
17508Day 01, 22:00Day 02, 02:19KCl
17518Day 02, 16:00Day 02, 20:04Calcium
17529Day 02, 21:00Day 04, 11:00Calcium
17539Day 03, 20:00Day 04, 05:01KCl
175410Day 04, 18:30Day 05, 11:54Calcium
175510Day 05, 04:10Day 05, 11:54KCl
175611Day 05, 15:15Day 06, 10:48Calcium
175711Day 05, 15:15Day 06, 10:48KCl
17581Day 22, 14:16Day 22, 18:30Calcium
17591Day 22, 14:36Day 22, 18:28KCl
17602Day 22, 22:55Day 23, 02:00Calcium
17612Day 22, 22:55Day 23, 02:00KCl
17623Day 23, 06:08Day 23, 11:00Calcium
17633Day 23, 06:09Day 23, 11:01KCl
17644Day 23, 12:00Day 23, 14:30KCl
17654Day 23, 12:00Day 23, 14:35Calcium
17665Day 23, 18:30Day 24, 01:00Calcium
17675Day 23, 18:30Day 24, 01:00KCl
17686Day 24, 04:00Day 24, 13:30Calcium
17696Day 24, 04:00Day 24, 13:30KCl
17707Day 24, 17:30Day 25, 14:05KCl
17717Day 24, 17:30Day 25, 14:05Calcium
17728Day 25, 16:35Day 26, 11:10Calcium
17738Day 25, 16:35Day 26, 11:10KCl
17741Day 27, 22:15Day 28, 23:59Calcium
17751Day 28, 03:17Day 28, 21:02KCl
17762Day 29, 02:01Day 29, 08:03Calcium
17771Day 21, 11:00Day 21, 17:30Calcium
17782Day 21, 20:59Day 22, 13:41Calcium
17791Day 08, 18:00Day 09, 00:15KCl
17802Day 09, 03:00Day 09, 23:00KCl
17813Day 10, 02:37Day 13, 03:45KCl
17824Day 13, 16:00Day 15, 07:55KCl
17831Day 18, 13:15Day 18, 13:45Calcium
17845Day 18, 13:15Day 18, 14:15KCl
17856Day 18, 21:07Day 18, 21:13KCl
17862Day 18, 21:07Day 18, 21:13Calcium
17871Day 11, 12:15Day 12, 12:15KCl
17881Day 11, 12:15Day 12, 12:15Calcium
17892Day 12, 15:30Day 14, 11:14Calcium
17902Day 12, 15:30Day 14, 11:20KCl
17913Day 14, 12:30Day 15, 03:23KCl
17923Day 14, 12:30Day 15, 12:05Calcium
17934Day 15, 03:27Day 15, 12:03KCl
17945Day 15, 14:00Day 17, 10:59KCl
17954Day 15, 14:00Day 17, 11:32Calcium
17961Day 15, 23:33Day 17, 14:05Calcium
17972Day 17, 16:04Day 18, 09:18Calcium
17981Day 14, 20:30Day 17, 19:28Calcium
17991Day 14, 20:30Day 17, 19:32KCl
18002Day 17, 21:15Day 20, 06:20KCl
18012Day 17, 21:15Day 20, 06:21Calcium
18023Day 20, 22:00Day 21, 07:42KCl
18033Day 20, 22:00Day 22, 19:35Calcium
18044Day 21, 10:34Day 22, 11:07KCl
18055Day 22, 10:34Day 22, 15:34KCl
18061Day 15, 06:45Day 15, 14:57Calcium
18071Day 15, 06:45Day 15, 14:58KCl
18082Day 15, 19:00Day 17, 15:30KCl
18092Day 15, 19:00Day 17, 18:42Calcium
18103Day 17, 20:04Day 18, 01:02KCl
18113Day 17, 20:04Day 18, 11:30Calcium
18121Day 17, 21:12Day 19, 12:00Calcium
18131Day 17, 21:13Day 19, 12:00KCl
18144Day 18, 05:30Day 18, 11:30KCl
18151Day 03, 10:39Day 03, 13:32Calcium
18161Day 03, 10:45Day 03, 13:33KCl
18171Day 12, 13:00Day 13, 01:56Calcium
18181Day 12, 13:00Day 13, 01:58KCl
18192Day 13, 03:02Day 15, 16:30KCl
18202Day 13, 03:03Day 15, 16:30Calcium
18213Day 15, 17:30Day 17, 05:00Calcium
18223Day 15, 17:45Day 17, 05:00KCl
18234Day 17, 06:30Day 20, 09:30KCl
18244Day 17, 06:30Day 20, 09:30Calcium
18255Day 20, 11:30Day 22, 01:00KCl
18265Day 20, 11:30Day 23, 08:45Calcium
18276Day 22, 02:41Day 23, 02:17KCl
18286Day 23, 15:40Day 25, 12:00Calcium
18297Day 23, 15:45Day 25, 12:00KCl
18307Day 25, 14:27Day 26, 10:27Calcium
18318Day 25, 14:30Day 26, 10:20KCl
18328Day 26, 11:30Day 29, 05:26Calcium
18339Day 26, 11:30Day 29, 11:40KCl
18349Day 29, 05:54Day 29, 10:40Calcium
183510Day 29, 13:00Day 02, 12:43KCl
183610Day 29, 13:00Day 02, 12:45Calcium
183711Day 02, 17:00Day 04, 22:46KCl
183811Day 02, 17:00Day 05, 13:38Calcium
183912Day 05, 04:46Day 05, 16:30KCl
184012Day 05, 13:22Day 05, 16:30Calcium
184113Day 05, 18:15Day 08, 18:49KCl
184213Day 05, 18:15Day 08, 18:50Calcium
184314Day 08, 20:50Day 11, 18:30Calcium
184414Day 08, 21:56Day 11, 18:30KCl
184515Day 11, 21:39Day 12, 08:30KCl
184615Day 11, 21:39Day 12, 08:30Calcium
184716Day 12, 09:40Day 12, 14:00Calcium
184816Day 12, 09:40Day 12, 14:00KCl
184917Day 12, 17:40Day 15, 17:36KCl
185017Day 12, 17:40Day 15, 19:15Calcium
185118Day 15, 17:35Day 15, 19:15KCl
185219Day 15, 20:56Day 16, 20:00KCl
185318Day 15, 20:56Day 16, 20:00Calcium
185419Day 18, 13:30Day 20, 14:00Calcium
185520Day 18, 13:30Day 20, 14:00KCl
185621Day 20, 20:20Day 22, 14:58KCl
185720Day 21, 00:04Day 22, 14:58Calcium
185822Day 22, 17:17Day 22, 20:40KCl
185921Day 22, 17:17Day 22, 20:40Calcium
186022Day 22, 21:40Day 23, 06:30Calcium
186123Day 22, 21:40Day 23, 06:30KCl
186224Day 23, 15:50Day 26, 11:34KCl
186323Day 23, 16:30Day 26, 11:30Calcium
186424Day 26, 12:30Day 29, 14:00Calcium
186525Day 26, 12:34Day 29, 14:00KCl
186626Day 29, 17:00Day 30, 05:51KCl
186725Day 29, 17:00Day 01, 10:01Calcium
186827Day 30, 10:20Day 31, 06:00KCl
186928Day 31, 10:30Day 01, 10:02KCl
18701Day 17, 23:01Day 18, 04:38Calcium
18711Day 18, 06:16Day 18, 23:30KCl
18722Day 18, 06:17Day 18, 23:30Calcium
18732Day 19, 01:15Day 20, 12:00KCl
18743Day 19, 01:15Day 20, 12:00Calcium
18751Day 21, 04:01Day 22, 09:12Calcium
18761Day 04, 20:51Day 05, 16:00Calcium
18771Day 04, 21:00Day 05, 16:05KCl
18782Day 05, 21:00Day 06, 05:26Calcium
18792Day 05, 21:05Day 06, 05:25KCl
18803Day 06, 23:00Day 09, 01:00Calcium
18813Day 08, 22:10Day 09, 00:58KCl
18824Day 09, 02:45Day 12, 03:00Calcium
18834Day 09, 02:45Day 12, 03:03KCl
18845Day 12, 03:40Day 13, 11:30Calcium
18855Day 12, 03:40Day 13, 11:30KCl
18866Day 16, 13:15Day 17, 23:07Calcium
18876Day 16, 16:00Day 17, 23:06KCl
18887Day 18, 01:27Day 18, 07:17KCl
18897Day 18, 01:28Day 18, 07:16Calcium
18908Day 18, 12:00Day 19, 17:51Calcium
18918Day 18, 12:00Day 20, 15:37KCl
18921Day 18, 21:00Day 19, 10:24Calcium
18932Day 19, 15:45Day 20, 12:02Calcium
18949Day 19, 18:04Day 20, 13:56Calcium
18951Day 02, 17:30Day 05, 13:34Calcium
18961Day 05, 00:47Day 05, 04:20KCl
18972Day 09, 16:30Day 10, 13:45KCl
18982Day 09, 16:30Day 10, 13:50Calcium
18993Day 10, 14:15Day 11, 11:29KCl
19003Day 10, 14:15Day 11, 11:31Calcium
19014Day 11, 12:50Day 13, 10:15Calcium
19024Day 11, 12:50Day 13, 10:15KCl
19035Day 15, 12:15Day 17, 14:23KCl
19045Day 15, 12:15Day 17, 14:26Calcium
19051Day 06, 19:30Day 07, 12:15Calcium
19061Day 07, 10:45Day 08, 08:01KCl
19072Day 07, 13:59Day 08, 08:00Calcium
19081Day 10, 14:10Day 12, 04:19Calcium
19091Day 11, 04:45Day 11, 18:28KCl
19102Day 12, 07:02Day 12, 08:26Calcium
19112Day 12, 07:02Day 12, 13:30KCl
19123Day 12, 20:49Day 15, 16:02Calcium
19133Day 13, 22:38Day 15, 16:03KCl
19144Day 15, 17:50Day 16, 10:33KCl
19154Day 15, 17:50Day 16, 14:00Calcium
19165Day 16, 17:30Day 17, 03:20KCl
19175Day 16, 18:11Day 18, 11:00Calcium
19186Day 17, 18:30Day 18, 11:04KCl
19196Day 18, 13:30Day 19, 15:00Calcium
19207Day 18, 13:30Day 19, 15:00KCl
19217Day 19, 21:00Day 22, 09:30Calcium
19228Day 20, 04:19Day 22, 09:30KCl
19231Day 01, 12:06Day 03, 14:02KCl
19241Day 01, 12:20Day 03, 14:05Calcium
19251Day 08, 01:15Day 09, 09:05KCl
19261Day 08, 01:15Day 09, 09:11Calcium
19272Day 09, 13:44Day 09, 13:47Calcium
19282Day 09, 13:45Day 10, 16:01KCl
19293Day 09, 17:00Day 10, 16:00Calcium
19301Day 07, 18:00Day 08, 10:57KCl
19311Day 07, 18:00Day 09, 16:30Calcium
19322Day 08, 11:00Day 09, 16:30KCl
19331Day 17, 22:42Day 20, 16:20Calcium
19341Day 17, 22:42Day 20, 16:20KCl
19352Day 20, 18:00Day 22, 17:14KCl
19362Day 20, 18:00Day 22, 17:30Calcium
19373Day 22, 21:19Day 23, 05:13KCl
19383Day 22, 21:19Day 23, 05:13Calcium
19391Day 03, 01:15Day 03, 02:35KCl
19401Day 03, 01:15Day 03, 05:56Calcium
19412Day 03, 09:00Day 03, 14:45KCl
19422Day 03, 12:26Day 03, 14:45Calcium
19433Day 03, 16:30Day 03, 21:02KCl
19443Day 03, 16:30Day 05, 15:15Calcium
19454Day 05, 10:09Day 05, 15:16KCl
19465Day 05, 18:30Day 06, 16:41KCl
19474Day 05, 22:15Day 06, 16:41Calcium
19485Day 06, 18:04Day 07, 14:15Calcium
19496Day 06, 18:05Day 07, 14:15KCl
19507Day 07, 14:55Day 07, 18:02KCl
19516Day 07, 14:55Day 07, 23:54Calcium
19528Day 07, 22:45Day 10, 17:07KCl
19537Day 08, 00:00Day 08, 06:00Calcium
19548Day 08, 10:28Day 10, 17:07Calcium
19559Day 10, 18:18Day 11, 13:00KCl
19569Day 10, 18:18Day 11, 13:00Calcium
195710Day 13, 22:00Day 14, 04:11Calcium
195811Day 14, 06:42Day 14, 10:40Calcium
195910Day 14, 06:42Day 14, 10:40KCl
196012Day 14, 12:51Day 15, 10:02Calcium
196111Day 14, 12:51Day 16, 08:01KCl
196213Day 15, 13:30Day 16, 08:01Calcium
196312Day 16, 15:00Day 17, 22:15KCl
196414Day 16, 15:00Day 17, 22:15Calcium
196513Day 17, 23:15Day 19, 15:03KCl
196615Day 17, 23:15Day 20, 11:30Calcium
196714Day 19, 21:30Day 20, 11:30KCl
196815Day 20, 13:50Day 20, 23:52KCl
196916Day 20, 13:50Day 21, 16:33Calcium
197016Day 21, 00:00Day 24, 10:01KCl
197117Day 21, 16:35Day 24, 16:03Calcium
197217Day 24, 15:14Day 24, 16:00KCl
19731Day 10, 22:00Day 12, 16:45KCl
19741Day 10, 22:00Day 12, 20:23Calcium
19751Day 24, 00:00Day 24, 04:40Calcium
19761Day 11, 21:30Day 12, 02:30KCl
19771Day 11, 23:45Day 12, 20:30Calcium
19782Day 11, 23:45Day 12, 20:30KCl
19792Day 12, 21:30Day 13, 15:54Calcium
19803Day 12, 21:30Day 13, 16:29KCl
19814Day 13, 18:15Day 13, 23:15KCl
19823Day 13, 18:15Day 13, 23:15Calcium
19835Day 14, 15:28Day 16, 16:04KCl
19844Day 14, 15:28Day 16, 16:05Calcium
19851Day 12, 08:15Day 12, 11:31Calcium
19861Day 12, 08:27Day 12, 11:26KCl
19872Day 13, 10:25Day 16, 11:15KCl
19882Day 13, 10:25Day 16, 11:15Calcium
19891Day 26, 22:18Day 29, 23:07Calcium
19901Day 26, 22:20Day 29, 23:06KCl
19912Day 30, 00:50Day 30, 11:00Calcium
19922Day 30, 00:51Day 30, 11:00KCl
19931Day 12, 13:00Day 12, 14:00KCl
19942Day 12, 21:00Day 12, 22:00KCl
19953Day 12, 22:44Day 12, 23:44KCl
19961Day 26, 14:12Day 28, 11:15Calcium
19971Day 28, 01:56Day 28, 11:15KCl
19982Day 28, 15:50Day 28, 17:10KCl
19992Day 28, 15:50Day 28, 17:10Calcium
20003Day 28, 21:19Day 29, 06:45KCl
20013Day 28, 21:19Day 29, 06:45Calcium
20021Day 13, 21:25Day 18, 07:03Calcium
20031Day 14, 08:22Day 14, 18:19KCl
20042Day 14, 14:16Day 20, 09:36KCl
20052Day 18, 10:15Day 20, 20:10Calcium
20063Day 20, 01:30Day 21, 15:45KCl
20073Day 20, 01:30Day 21, 16:23Calcium
20081Day 21, 12:12Day 22, 23:52KCl
20091Day 21, 12:12Day 23, 11:18Calcium
20104Day 22, 17:00Day 23, 12:05Calcium
20114Day 22, 22:15Day 23, 22:15KCl
20122Day 23, 10:51Day 23, 11:15KCl
20135Day 23, 18:00Day 27, 21:15Calcium
20145Day 23, 20:17Day 27, 21:15KCl
20156Day 27, 22:50Day 28, 11:34Calcium
20166Day 27, 22:50Day 28, 11:46KCl
20177Day 28, 13:36Day 29, 14:00KCl
20187Day 28, 13:36Day 29, 14:00Calcium
20191Day 11, 18:00Day 12, 07:40Calcium
20202Day 12, 11:00Day 12, 12:00Calcium
20213Day 12, 16:40Day 13, 21:30Calcium
20221Day 21, 22:00Day 22, 15:00Calcium
20231Day 22, 08:43Day 22, 15:00KCl
20242Day 22, 16:00Day 22, 18:31Calcium
20252Day 22, 16:00Day 22, 18:33KCl
20263Day 23, 01:30Day 23, 17:52Calcium
20273Day 23, 01:30Day 23, 17:53KCl
20284Day 23, 22:00Day 24, 10:30Calcium
20294Day 23, 22:00Day 24, 10:30KCl
20305Day 24, 17:00Day 26, 00:12KCl
20315Day 24, 17:00Day 26, 04:03Calcium
20326Day 26, 01:15Day 26, 05:05KCl
20336Day 26, 06:14Day 28, 09:16Calcium
20347Day 26, 06:15Day 28, 09:20KCl
20357Day 28, 15:31Day 01, 16:02Calcium
20368Day 28, 15:32Day 01, 15:58KCl
20379Day 01, 17:45Day 02, 23:30KCl
20388Day 01, 17:45Day 02, 23:30Calcium
203910Day 03, 01:30Day 06, 07:35KCl
20409Day 03, 01:30Day 09, 16:03Calcium
204111Day 06, 07:32Day 09, 16:02KCl
20421Day 04, 16:00Day 06, 21:30Calcium
20431Day 05, 03:34Day 06, 21:30KCl
20442Day 06, 23:59Day 07, 01:45Calcium
20452Day 06, 23:59Day 07, 01:45KCl
20463Day 07, 01:53Day 07, 01:58KCl
20473Day 07, 02:15Day 08, 08:33Calcium
20484Day 07, 02:15Day 08, 08:35KCl
20494Day 08, 12:59Day 12, 11:49Calcium
20505Day 08, 12:59Day 12, 11:56KCl
20515Day 15, 15:59Day 16, 12:22Calcium
20526Day 16, 10:14Day 16, 12:54KCl
20531Day 01, 00:49Day 01, 06:45Calcium
20541Day 01, 00:59Day 01, 06:45KCl
20552Day 01, 20:00Day 02, 10:38Calcium
20562Day 01, 20:00Day 06, 09:30KCl
20573Day 02, 15:07Day 06, 12:15Calcium
20584Day 07, 00:25Day 08, 00:28Calcium
20593Day 07, 00:26Day 08, 00:38KCl
20605Day 08, 02:26Day 08, 11:06Calcium
20614Day 08, 02:27Day 08, 11:10KCl
20625Day 08, 14:00Day 10, 00:02KCl
20636Day 08, 14:00Day 12, 02:20Calcium
20646Day 10, 05:26Day 11, 20:38KCl
20657Day 11, 20:42Day 12, 02:47KCl
20667Day 12, 04:00Day 12, 16:17Calcium
20678Day 12, 04:00Day 12, 16:30KCl
20681Day 12, 18:00Day 14, 13:54KCl
20691Day 12, 18:00Day 14, 13:57Calcium
20702Day 14, 17:30Day 15, 12:00KCl
20712Day 14, 17:30Day 15, 12:00Calcium
20729Day 14, 23:00Day 15, 05:01KCl
20738Day 14, 23:00Day 15, 05:02Calcium
20749Day 15, 06:02Day 15, 12:45Calcium
207510Day 15, 06:02Day 16, 14:00KCl
207610Day 15, 12:47Day 16, 07:55Calcium
20773Day 15, 15:00Day 16, 07:02KCl
20783Day 15, 15:00Day 16, 15:15Calcium
207911Day 16, 08:00Day 16, 10:54Calcium
208012Day 16, 08:03Day 16, 08:06Calcium
208113Day 16, 10:54Day 16, 14:01Calcium
208211Day 16, 16:00Day 16, 21:15KCl
208314Day 16, 16:01Day 16, 22:27Calcium
20844Day 16, 23:40Day 17, 14:00Calcium
208512Day 17, 02:22Day 18, 06:45KCl
208615Day 17, 02:25Day 18, 05:34Calcium
20874Day 17, 05:28Day 17, 14:00KCl
208813Day 18, 07:30Day 19, 08:45KCl
208916Day 18, 07:34Day 19, 08:45Calcium
20901Day 02, 17:29Day 03, 15:20KCl
20911Day 02, 17:29Day 03, 15:20Calcium
20922Day 03, 17:45Day 04, 15:00KCl
20932Day 03, 17:45Day 04, 15:00Calcium
20943Day 04, 17:15Day 06, 17:25KCl
20953Day 04, 17:15Day 06, 17:25Calcium
20964Day 06, 18:45Day 08, 14:34Calcium
20974Day 06, 18:45Day 08, 14:34KCl
20981Day 10, 03:30Day 10, 04:30KCl
20991Day 01, 13:48Day 02, 17:10Calcium
21001Day 01, 14:02Day 02, 17:08KCl
21012Day 02, 22:51Day 04, 10:43Calcium
21022Day 02, 22:52Day 04, 10:46KCl
21033Day 04, 14:30Day 05, 08:59Calcium
21043Day 04, 14:30Day 05, 09:01KCl
21054Day 05, 10:10Day 05, 15:56Calcium
21064Day 05, 10:13Day 05, 15:58KCl
21071Day 21, 23:30Day 22, 12:04Calcium
21081Day 22, 10:32Day 22, 12:04KCl
21091Day 13, 11:35Day 13, 14:15Calcium
21102Day 13, 17:47Day 14, 07:50Calcium
21111Day 14, 00:17Day 14, 07:50KCl
21121Day 23, 01:50Day 23, 06:50KCl
21131Day 23, 01:50Day 23, 10:04Calcium
21142Day 23, 10:01Day 24, 04:57Calcium
21151Day 05, 21:46Day 08, 09:00Calcium
21161Day 05, 21:47Day 08, 09:00KCl
21172Day 08, 12:00Day 10, 14:05KCl
21182Day 08, 12:00Day 10, 14:07Calcium
21193Day 10, 18:30Day 11, 04:43KCl
21203Day 10, 18:30Day 11, 04:44Calcium
21214Day 11, 06:15Day 11, 08:14KCl
21224Day 11, 06:15Day 11, 08:17Calcium
21235Day 11, 12:45Day 12, 03:40KCl
21245Day 11, 12:45Day 12, 03:46Calcium
21256Day 15, 04:58Day 15, 21:38Calcium
21261Day 17, 22:17Day 21, 13:20Calcium
21271Day 19, 16:14Day 21, 13:20KCl
21281Day 14, 21:45Day 18, 09:35Calcium
21291Day 14, 21:45Day 18, 09:35KCl
21301Day 22, 00:00Day 22, 03:03Calcium
21311Day 22, 00:00Day 22, 03:10KCl
21322Day 24, 12:00Day 27, 14:09Calcium
21333Day 27, 16:41Day 30, 13:30Calcium
21342Day 27, 20:58Day 30, 13:30KCl
21353Day 01, 16:30Day 05, 02:58KCl
21364Day 01, 16:30Day 05, 03:00Calcium
21374Day 05, 04:50Day 05, 14:37KCl
21385Day 05, 04:50Day 05, 14:43Calcium
21395Day 05, 17:00Day 07, 11:01KCl
21406Day 05, 17:00Day 07, 11:01Calcium
21411Day 30, 20:00Day 31, 15:55Calcium
21421Day 06, 20:00Day 08, 02:17Calcium
21431Day 07, 22:02Day 08, 02:18KCl
21442Day 08, 06:14Day 08, 09:19Calcium
21452Day 08, 06:14Day 08, 09:23KCl
21461Day 22, 15:04Day 22, 15:15KCl
21471Day 22, 15:04Day 22, 15:15Calcium
21481Day 12, 23:00Day 13, 00:28KCl
21491Day 12, 23:00Day 13, 04:25Calcium
21501Day 18, 12:00Day 21, 03:04KCl
21511Day 18, 12:00Day 21, 03:05Calcium
21522Day 21, 04:05Day 21, 21:00KCl
21532Day 21, 04:05Day 21, 21:00Calcium
21543Day 11, 08:11Day 11, 11:59KCl
21554Day 11, 12:02Day 12, 09:54KCl
21561Day 12, 23:00Day 14, 14:57KCl
21572Day 14, 18:45Day 14, 22:22KCl
21583Day 15, 00:30Day 15, 10:50KCl
21591Day 04, 21:25Day 05, 08:45KCl
21601Day 04, 21:25Day 05, 08:45Calcium
21612Day 05, 14:30Day 06, 03:14Calcium
21622Day 05, 14:30Day 06, 03:15KCl
21633Day 06, 04:21Day 06, 11:58Calcium
21643Day 06, 04:21Day 06, 12:01KCl
21654Day 07, 06:00Day 07, 18:11KCl
21664Day 07, 06:00Day 10, 14:00Calcium
21675Day 07, 18:14Day 10, 14:11KCl
21685Day 10, 16:15Day 13, 17:30Calcium
21696Day 10, 16:15Day 13, 17:30KCl
21701Day 26, 04:47Day 26, 11:32Calcium
21711Day 26, 05:14Day 26, 11:38KCl
21722Day 26, 15:24Day 28, 08:33Calcium
21732Day 26, 15:25Day 28, 08:30KCl
21743Day 28, 10:32Day 28, 13:16KCl
21753Day 28, 10:32Day 28, 19:53Calcium
21764Day 28, 12:56Day 28, 19:51KCl
21771Day 28, 20:05Day 29, 11:14KCl
21781Day 28, 20:05Day 29, 20:28Calcium
21795Day 28, 23:00Day 29, 14:38KCl
21804Day 28, 23:00Day 29, 14:39Calcium
21812Day 29, 13:39Day 29, 13:44KCl
21822Day 29, 22:38Day 30, 00:51Calcium
21833Day 30, 02:51Day 31, 15:10Calcium
21843Day 30, 12:41Day 31, 15:10KCl
21854Day 31, 18:09Day 02, 00:07Calcium
21864Day 31, 18:10Day 02, 00:07KCl
21871Day 04, 23:00Day 04, 23:57Calcium
21881Day 04, 23:00Day 04, 23:59KCl
21892Day 05, 01:00Day 05, 14:59Calcium
21902Day 05, 01:00Day 05, 15:10KCl
21911Day 27, 00:00Day 29, 12:03KCl
21921Day 27, 00:00Day 29, 12:04Calcium
21931Day 01, 17:00Day 02, 10:10KCl
21941Day 01, 17:00Day 02, 15:00Calcium
21952Day 02, 15:30Day 05, 15:00Calcium
21962Day 02, 17:00Day 05, 15:00KCl
21971Day 12, 15:53Day 14, 05:12Calcium
21982Day 14, 08:30Day 17, 08:36Calcium
21993Day 17, 13:20Day 18, 02:20Calcium
22004Day 18, 04:20Day 20, 09:15Calcium
22015Day 20, 12:45Day 20, 19:00Calcium
22021Day 10, 18:40Day 11, 16:06Calcium
22031Day 10, 20:15Day 11, 16:06KCl
22041Day 30, 19:03Day 31, 14:30Calcium
22051Day 30, 19:03Day 31, 14:54KCl
22061Day 18, 19:10Day 19, 01:14KCl
22071Day 18, 19:10Day 19, 01:15Calcium
22082Day 19, 03:00Day 20, 06:25Calcium
22092Day 19, 03:00Day 20, 06:25KCl
22103Day 20, 09:14Day 22, 10:05KCl
22113Day 20, 09:14Day 22, 10:05Calcium
22124Day 22, 11:15Day 22, 15:10KCl
22134Day 22, 11:15Day 22, 15:10Calcium
22145Day 22, 19:00Day 23, 09:57KCl
22155Day 22, 19:00Day 23, 09:57Calcium
22166Day 23, 12:30Day 24, 13:05Calcium
22176Day 23, 12:30Day 24, 13:07KCl
22187Day 24, 15:00Day 25, 13:00Calcium
22197Day 24, 15:00Day 25, 14:46KCl
22201Day 12, 13:44Day 15, 14:02Calcium
22212Day 15, 15:02Day 16, 16:09Calcium
22221Day 27, 10:00Day 27, 14:46Calcium
22232Day 27, 17:00Day 28, 17:12Calcium
22241Day 28, 00:10Day 28, 21:38KCl
22252Day 29, 07:00Day 29, 09:56KCl
22263Day 29, 08:07Day 29, 09:57Calcium
22274Day 29, 12:00Day 29, 14:27Calcium
22283Day 29, 12:00Day 29, 14:30KCl
22295Day 29, 22:14Day 29, 23:10Calcium
22304Day 29, 22:15Day 29, 23:10KCl
22315Day 31, 01:35Day 31, 08:00KCl
22326Day 31, 01:35Day 31, 08:00Calcium
22336Day 31, 10:24Day 02, 08:25KCl
22347Day 31, 10:24Day 02, 08:25Calcium
22357Day 02, 14:06Day 02, 15:27KCl
22368Day 02, 17:40Day 04, 13:30KCl
22378Day 02, 20:13Day 04, 13:33Calcium
22381Day 13, 21:54Day 16, 00:15KCl
22391Day 13, 21:54Day 16, 14:00Calcium
22402Day 16, 11:35Day 16, 14:00KCl
22411Day 19, 02:30Day 19, 11:30KCl
22421Day 19, 02:30Day 19, 11:30Calcium
22432Day 19, 19:12Day 20, 14:43Calcium
22442Day 19, 19:12Day 20, 14:44KCl
22453Day 20, 17:40Day 21, 12:20KCl
22463Day 20, 17:40Day 21, 12:22Calcium
22474Day 21, 14:30Day 23, 21:00KCl
22484Day 21, 14:30Day 23, 21:01Calcium
22491Day 22, 23:15Day 23, 07:30Calcium
22502Day 23, 08:45Day 23, 13:00Calcium
22511Day 23, 11:15Day 23, 13:00KCl
22522Day 23, 17:00Day 23, 20:39KCl
22533Day 23, 17:00Day 23, 20:41Calcium
22545Day 23, 22:00Day 24, 16:35KCl
22555Day 23, 22:00Day 24, 20:15Calcium
22561Day 20, 22:20Day 21, 05:15Calcium
22571Day 21, 02:17Day 21, 05:02KCl
22582Day 21, 07:30Day 24, 00:20KCl
22592Day 21, 07:30Day 24, 00:20Calcium
22603Day 24, 02:43Day 25, 01:06KCl
22613Day 24, 02:43Day 25, 15:30Calcium
22624Day 25, 04:09Day 25, 15:30KCl
22631Day 30, 14:03Day 01, 20:03Calcium
22642Day 01, 22:03Day 04, 13:35Calcium
22653Day 04, 15:49Day 04, 19:30Calcium
22664Day 04, 20:15Day 06, 01:26Calcium
22675Day 06, 05:40Day 06, 18:30Calcium
22686Day 06, 20:10Day 07, 10:03Calcium
22697Day 07, 16:03Day 07, 21:00Calcium
22708Day 08, 10:16Day 11, 10:02Calcium
22711Day 07, 16:15Day 07, 16:45KCl
22721Day 07, 16:15Day 08, 05:22Calcium
22732Day 07, 16:15Day 08, 14:04KCl
22742Day 07, 16:48Day 07, 16:50Calcium
22753Day 08, 05:22Day 08, 14:03Calcium
22764Day 08, 16:03Day 12, 03:31Calcium
22773Day 08, 16:04Day 12, 03:33KCl
22781Day 05, 14:20Day 05, 16:30KCl
22791Day 05, 14:20Day 05, 16:30Calcium
22802Day 05, 21:30Day 09, 14:58Calcium
22812Day 05, 21:30Day 09, 15:01KCl
22823Day 09, 17:58Day 09, 21:01Calcium
22833Day 09, 18:01Day 12, 15:15KCl
22844Day 10, 01:39Day 12, 15:15Calcium
22851Day 18, 14:15Day 19, 13:54Calcium
22862Day 19, 20:14Day 20, 15:00Calcium
22871Day 19, 20:15Day 20, 15:00KCl
22883Day 20, 23:00Day 21, 19:03Calcium
22892Day 21, 00:05Day 21, 19:04KCl
22901Day 17, 18:08Day 19, 16:46Calcium
22911Day 17, 18:08Day 20, 10:00KCl
22922Day 19, 18:00Day 20, 10:02Calcium
22931Day 25, 14:30Day 27, 11:01Calcium
22941Day 28, 07:30Day 28, 23:30KCl
22952Day 28, 07:30Day 29, 05:30Calcium
22962Day 28, 23:46Day 29, 11:14KCl
22973Day 29, 10:40Day 29, 12:00Calcium
22981Day 31, 12:30Day 31, 22:00KCl
22991Day 31, 12:30Day 31, 22:00Calcium
23001Day 16, 00:41Day 16, 01:41Calcium
23011Day 01, 22:00Day 03, 05:01Calcium
23021Day 11, 16:31Day 12, 01:50KCl
23031Day 11, 16:31Day 12, 02:05Calcium
23042Day 12, 10:00Day 13, 12:00KCl
23052Day 12, 10:00Day 13, 12:00Calcium
23063Day 13, 14:30Day 13, 22:26KCl
23073Day 13, 14:30Day 13, 22:36Calcium
23084Day 14, 00:00Day 15, 16:00Calcium
23094Day 14, 00:00Day 15, 16:00KCl
23101Day 20, 15:31Day 21, 07:32Calcium
23111Day 20, 15:33Day 21, 07:32KCl
23122Day 21, 09:58Day 23, 04:48Calcium
23132Day 21, 09:58Day 23, 04:55KCl
23143Day 23, 06:23Day 27, 16:29Calcium
23153Day 23, 06:23Day 27, 16:34KCl
23161Day 23, 15:40Day 25, 17:41Calcium
23171Day 23, 15:42Day 25, 17:42KCl
23181Day 13, 07:55Day 14, 10:01Calcium
23192Day 14, 11:30Day 15, 04:20Calcium
23201Day 01, 00:28Day 01, 09:40Calcium
23211Day 01, 00:30Day 01, 09:40KCl
23221Day 06, 18:00Day 07, 11:59Calcium
23231Day 10, 18:25Day 11, 07:16Calcium
23241Day 10, 18:25Day 11, 07:17KCl
23252Day 11, 16:25Day 12, 10:01KCl
23262Day 11, 16:25Day 12, 10:02Calcium
23273Day 12, 12:29Day 14, 16:25Calcium
23283Day 12, 12:29Day 14, 16:25KCl
23294Day 14, 18:30Day 16, 12:01KCl
23304Day 14, 18:30Day 16, 12:02Calcium
23315Day 16, 14:40Day 17, 12:41KCl
23325Day 16, 14:40Day 17, 12:43Calcium
23336Day 17, 18:23Day 19, 09:20KCl
23346Day 17, 18:23Day 19, 09:20Calcium
23357Day 19, 10:10Day 22, 00:19KCl
23367Day 19, 10:10Day 22, 00:20Calcium
23378Day 22, 02:35Day 22, 10:58Calcium
23388Day 22, 02:35Day 22, 11:00KCl
23399Day 25, 16:09Day 27, 08:20KCl
23409Day 25, 16:11Day 27, 08:20Calcium
23411Day 30, 15:00Day 30, 19:00Calcium
23422Day 30, 23:20Day 01, 05:52Calcium
23433Day 01, 13:00Day 02, 15:31Calcium
23444Day 02, 17:15Day 04, 12:17Calcium
23455Day 04, 12:15Day 05, 17:27Calcium
23461Day 01, 01:45Day 01, 02:05KCl
23471Day 01, 01:45Day 04, 00:36Calcium
23482Day 01, 05:00Day 04, 00:42KCl
23491Day 19, 15:15Day 21, 09:29Calcium
23502Day 21, 13:30Day 22, 00:00Calcium
23513Day 22, 01:00Day 25, 05:47Calcium
23524Day 25, 19:00Day 26, 08:50Calcium
23535Day 29, 16:45Day 30, 13:13Calcium
23541Day 30, 09:46Day 30, 13:13KCl
23551Day 15, 22:31Day 16, 08:00Calcium
23561Day 15, 22:33Day 16, 08:01KCl
23572Day 16, 09:15Day 16, 12:45KCl
23582Day 16, 09:15Day 16, 12:45Calcium
23593Day 16, 15:00Day 17, 07:30KCl
23603Day 16, 15:00Day 17, 07:30Calcium
23614Day 24, 07:00Day 24, 17:20Calcium
23624Day 24, 07:00Day 24, 17:20KCl
23635Day 24, 18:53Day 25, 15:00Calcium
23645Day 24, 18:53Day 25, 15:00KCl
23651Day 25, 12:30Day 26, 11:54KCl
23661Day 25, 12:30Day 26, 11:55Calcium
23672Day 26, 13:30Day 26, 20:49Calcium
23682Day 26, 13:30Day 26, 20:51KCl
23693Day 27, 01:30Day 27, 14:34KCl
23703Day 27, 01:30Day 28, 00:20Calcium
23714Day 27, 17:13Day 28, 00:19KCl
23721Day 30, 22:48Day 31, 12:45Calcium
23731Day 30, 22:49Day 31, 12:45KCl
23741Day 29, 21:00Day 29, 21:49KCl
23751Day 29, 21:13Day 29, 21:59Calcium
23762Day 29, 23:50Day 30, 01:11KCl
23772Day 29, 23:50Day 30, 01:12Calcium
23783Day 30, 11:46Day 04, 10:47Calcium
23793Day 30, 11:46Day 04, 10:48KCl
23804Day 04, 15:20Day 06, 08:20Calcium
23814Day 04, 15:20Day 06, 08:24KCl
23825Day 06, 20:09Day 08, 03:10Calcium
23835Day 06, 20:11Day 08, 03:10KCl
23846Day 08, 08:00Day 09, 15:30Calcium
23856Day 08, 08:00Day 09, 15:33KCl
23867Day 09, 21:04Day 14, 21:25KCl
23877Day 09, 21:04Day 15, 04:39Calcium
23888Day 15, 06:00Day 16, 08:54Calcium
23899Day 16, 18:46Day 18, 22:45Calcium
23908Day 17, 02:00Day 17, 11:57KCl
23919Day 17, 11:55Day 18, 22:56KCl
239210Day 19, 11:25Day 19, 20:22Calcium
239311Day 19, 21:16Day 20, 12:45Calcium
239412Day 20, 18:25Day 21, 09:03Calcium
239513Day 21, 18:03Day 22, 12:30Calcium
239614Day 22, 15:45Day 22, 20:00Calcium
23971Day 30, 15:00Day 01, 12:23KCl
23981Day 30, 15:03Day 01, 12:30Calcium
23991Day 21, 16:30Day 24, 06:00Calcium
24001Day 26, 10:00Day 27, 06:45KCl
24011Day 26, 10:00Day 27, 06:45Calcium
24022Day 27, 10:00Day 29, 12:43KCl
24032Day 27, 10:00Day 29, 14:04Calcium
24041Day 02, 06:00Day 04, 11:47Calcium
24051Day 03, 00:32Day 03, 13:45KCl
24062Day 03, 18:30Day 04, 11:47KCl
24071Day 14, 21:08Day 17, 21:22Calcium
24081Day 15, 04:42Day 16, 10:12KCl
24092Day 16, 10:17Day 17, 21:23KCl
24103Day 17, 22:15Day 18, 09:32KCl
24112Day 17, 22:15Day 18, 09:34Calcium
24121Day 01, 17:25Day 03, 11:50Calcium
24131Day 01, 22:45Day 03, 11:53KCl
24142Day 03, 12:50Day 04, 12:45KCl
24152Day 03, 12:50Day 04, 12:45Calcium
24161Day 06, 14:22Day 07, 17:45KCl
24171Day 06, 16:09Day 07, 17:45Calcium
24182Day 07, 19:48Day 11, 16:57Calcium
24192Day 07, 19:48Day 11, 16:58KCl
24201Day 22, 08:04Day 23, 13:01KCl
24211Day 22, 08:05Day 23, 11:21Calcium
24222Day 23, 15:16Day 23, 22:32KCl
24232Day 23, 15:16Day 24, 20:49Calcium
24243Day 24, 00:15Day 24, 20:49KCl
24251Day 29, 21:30Day 30, 14:59Calcium
24261Day 29, 21:30Day 30, 14:59KCl
24272Day 30, 16:30Day 02, 01:05KCl
24282Day 30, 16:30Day 02, 09:42Calcium
24293Day 02, 01:08Day 02, 09:36KCl
24301Day 03, 21:30Day 04, 01:01KCl
24311Day 03, 21:30Day 04, 01:49Calcium
24322Day 04, 01:44Day 04, 07:17Calcium
24332Day 04, 03:45Day 04, 07:05KCl
24343Day 05, 17:45Day 06, 01:12Calcium
24354Day 05, 17:45Day 06, 01:14KCl
24361Day 06, 12:12Day 08, 11:57Calcium
24371Day 06, 12:14Day 08, 12:03KCl
24382Day 08, 13:30Day 10, 16:12KCl
24392Day 08, 13:30Day 10, 16:13Calcium
24401Day 29, 12:00Day 30, 09:10KCl
24411Day 29, 12:00Day 01, 08:58Calcium
24422Day 30, 09:11Day 01, 09:00KCl
24432Day 01, 19:15Day 03, 11:00Calcium
24443Day 01, 19:15Day 03, 11:02KCl
24454Day 03, 13:00Day 04, 07:00KCl
24463Day 03, 13:00Day 04, 09:29Calcium
24474Day 05, 11:00Day 06, 06:00Calcium
24485Day 05, 11:00Day 06, 06:57KCl
24496Day 06, 16:30Day 09, 19:39KCl
24505Day 06, 16:30Day 11, 14:03Calcium
24517Day 09, 23:23Day 11, 03:00KCl
24521Day 23, 22:56Day 24, 04:48KCl
24531Day 23, 22:56Day 24, 04:54Calcium
24541Day 09, 22:02Day 11, 08:30Calcium
24551Day 10, 07:57Day 11, 08:27KCl
24562Day 11, 11:45Day 12, 05:31Calcium
24572Day 11, 11:45Day 12, 05:35KCl
24583Day 12, 06:24Day 13, 07:22Calcium
24593Day 12, 06:24Day 13, 07:22KCl
24604Day 13, 11:08Day 14, 21:42KCl
24614Day 13, 11:08Day 14, 23:38Calcium
24625Day 15, 00:38Day 16, 12:00Calcium
24635Day 15, 04:05Day 16, 12:00KCl
24646Day 16, 14:45Day 17, 05:00Calcium
24657Day 17, 06:12Day 18, 15:33Calcium
24666Day 18, 09:12Day 18, 15:33KCl
24678Day 18, 17:33Day 18, 23:40Calcium
24689Day 19, 00:00Day 19, 10:03Calcium
24697Day 19, 04:14Day 19, 10:01KCl
247010Day 20, 00:00Day 21, 09:10Calcium
24711Day 16, 16:30Day 16, 19:00KCl
24721Day 16, 16:30Day 16, 19:02Calcium
24731Day 05, 02:30Day 05, 03:50KCl
24741Day 05, 02:30Day 05, 03:50Calcium
24752Day 05, 06:45Day 05, 19:00Calcium
24762Day 05, 06:45Day 05, 19:02KCl
24771Day 22, 19:10Day 23, 11:00KCl
24781Day 22, 19:10Day 23, 11:00Calcium
24791Day 22, 20:50Day 24, 14:00KCl
24801Day 23, 01:45Day 24, 14:03Calcium
24812Day 23, 12:45Day 23, 16:15KCl
24822Day 23, 12:45Day 23, 16:15Calcium
24831Day 05, 21:30Day 06, 16:09Calcium
24841Day 05, 21:30Day 06, 16:15KCl
24852Day 06, 18:00Day 08, 12:00KCl
24862Day 06, 18:00Day 08, 12:00Calcium
24873Day 08, 12:45Day 09, 09:10Calcium
24883Day 08, 12:45Day 09, 09:10KCl
24891Day 10, 22:00Day 11, 23:03KCl
24901Day 10, 22:00Day 12, 11:03Calcium
24912Day 12, 05:27Day 12, 11:00KCl
24923Day 12, 21:35Day 12, 23:57KCl
24932Day 12, 21:35Day 13, 08:00Calcium
24943Day 14, 00:43Day 14, 10:59Calcium
24951Day 02, 14:21Day 03, 02:31Calcium
24961Day 03, 02:42Day 03, 12:53KCl
24972Day 03, 02:48Day 03, 04:20Calcium
24983Day 03, 05:13Day 03, 15:37Calcium
24991Day 02, 16:45Day 03, 09:00Calcium
25001Day 02, 16:45Day 03, 09:00KCl
25012Day 03, 12:33Day 04, 22:40KCl
25022Day 03, 12:33Day 04, 22:40Calcium
25033Day 04, 23:20Day 06, 16:00KCl
25043Day 04, 23:20Day 06, 16:00Calcium
25051Day 13, 01:00Day 13, 01:45Calcium
25061Day 13, 01:00Day 13, 01:45KCl
25072Day 13, 03:00Day 15, 13:21Calcium
25082Day 13, 03:00Day 15, 14:16KCl
25093Day 15, 16:15Day 16, 02:30KCl
25103Day 15, 16:15Day 16, 02:30Calcium
25114Day 16, 03:28Day 19, 09:42KCl
25124Day 16, 03:29Day 19, 09:42Calcium
25135Day 19, 12:26Day 20, 09:30KCl
25145Day 19, 12:26Day 20, 09:31Calcium
25156Day 20, 17:32Day 21, 07:28Calcium
25166Day 20, 17:33Day 21, 07:29KCl
25177Day 21, 08:30Day 21, 11:50KCl
25187Day 21, 08:30Day 21, 11:50Calcium
25191Day 11, 18:15Day 12, 10:55Calcium
25201Day 11, 18:53Day 12, 11:03KCl
25212Day 12, 15:15Day 13, 09:15Calcium
25222Day 12, 15:15Day 13, 09:15KCl
25231Day 22, 22:50Day 23, 08:24Calcium
25241Day 27, 23:00Day 01, 12:00Calcium
25251Day 28, 23:41Day 31, 01:45Calcium
25261Day 29, 12:00Day 01, 12:00KCl
25272Day 31, 14:00Day 03, 05:58Calcium
25283Day 03, 08:00Day 03, 12:00Calcium
25291Day 14, 14:30Day 17, 04:48KCl
25301Day 14, 14:30Day 17, 16:34Calcium
25312Day 17, 10:01Day 17, 16:43KCl
25322Day 17, 20:15Day 18, 10:41Calcium
25333Day 17, 20:15Day 18, 10:41KCl
25344Day 18, 13:58Day 18, 16:29KCl
25353Day 18, 13:59Day 18, 18:01Calcium
25365Day 18, 19:00Day 18, 21:06KCl
25374Day 18, 19:15Day 20, 20:01Calcium
25386Day 19, 00:44Day 19, 05:05KCl
25397Day 20, 03:38Day 20, 20:00KCl
25405Day 20, 23:19Day 22, 11:42Calcium
25418Day 20, 23:19Day 22, 11:43KCl
25426Day 22, 14:00Day 23, 12:06Calcium
25439Day 22, 14:00Day 23, 12:06KCl
254410Day 23, 16:59Day 23, 17:25KCl
25457Day 23, 17:00Day 23, 17:25Calcium
25468Day 23, 19:30Day 24, 11:30Calcium
254711Day 23, 19:30Day 24, 11:30KCl
25481Day 01, 19:08Day 01, 20:13KCl
25491Day 01, 19:08Day 01, 20:13Calcium
25501Day 31, 22:04Day 01, 06:00Calcium
25511Day 01, 03:17Day 01, 17:00Calcium
25521Day 01, 22:00Day 04, 16:29KCl
25532Day 01, 22:00Day 04, 16:32Calcium
25543Day 04, 19:00Day 05, 07:03Calcium
25552Day 04, 19:00Day 05, 07:03KCl
25563Day 05, 08:48Day 05, 12:54KCl
25574Day 05, 08:48Day 05, 12:54Calcium
25584Day 05, 17:05Day 05, 22:22KCl
25595Day 05, 17:05Day 05, 22:27Calcium
25606Day 05, 22:41Day 08, 15:02Calcium
25615Day 05, 22:41Day 08, 15:04KCl
25627Day 08, 16:16Day 09, 07:05Calcium
25636Day 08, 16:16Day 09, 07:05KCl
25648Day 12, 18:00Day 13, 12:29Calcium
25659Day 13, 15:13Day 14, 17:43Calcium
25667Day 14, 01:54Day 14, 17:45KCl
25671Day 12, 20:00Day 12, 21:00Calcium
25681Day 12, 20:00Day 12, 21:00KCl
25692Day 12, 22:00Day 13, 08:30Calcium
25702Day 12, 22:00Day 13, 08:30KCl
25713Day 13, 18:34Day 15, 17:32Calcium
25723Day 14, 00:16Day 14, 11:12KCl
25731Day 14, 17:54Day 14, 20:00Calcium
25742Day 14, 21:00Day 14, 23:16Calcium
25753Day 15, 02:00Day 15, 11:40Calcium
25764Day 15, 03:02Day 15, 12:53KCl
25775Day 15, 18:51Day 16, 19:48KCl
25784Day 15, 20:59Day 16, 19:49Calcium
25794Day 16, 08:36Day 16, 14:00Calcium
25801Day 16, 11:12Day 16, 14:00KCl
25812Day 16, 17:00Day 17, 13:10KCl
25825Day 16, 17:52Day 17, 13:10Calcium
25835Day 16, 22:00Day 17, 17:45Calcium
25846Day 16, 22:00Day 17, 17:45KCl
25851Day 17, 09:30Day 18, 07:08KCl
25861Day 17, 10:41Day 18, 07:08Calcium
25876Day 17, 16:00Day 19, 00:30Calcium
25883Day 17, 16:10Day 19, 00:11KCl
25892Day 18, 13:29Day 21, 02:00KCl
25902Day 18, 13:29Day 21, 02:00Calcium
25914Day 20, 17:10Day 21, 02:18KCl
25927Day 20, 17:10Day 21, 05:21Calcium
25933Day 21, 02:30Day 23, 01:00Calcium
25943Day 21, 02:30Day 23, 01:01KCl
25954Day 26, 12:39Day 29, 08:25Calcium
25964Day 26, 12:40Day 29, 08:25KCl
25975Day 29, 12:58Day 30, 12:09KCl
25985Day 29, 12:58Day 30, 12:09Calcium
25996Day 31, 15:15Day 02, 22:54KCl
26006Day 31, 15:15Day 03, 10:31Calcium
26011Day 02, 19:00Day 05, 12:30KCl
26021Day 02, 19:00Day 05, 12:31Calcium
26037Day 02, 22:58Day 03, 10:11KCl
26048Day 03, 16:20Day 06, 14:05KCl
26057Day 03, 16:40Day 06, 14:06Calcium
26062Day 05, 14:00Day 05, 22:32Calcium
26072Day 05, 14:00Day 05, 22:34KCl
26083Day 06, 00:00Day 06, 06:30Calcium
26093Day 06, 00:00Day 06, 06:33KCl
26104Day 06, 11:30Day 06, 16:00Calcium
26114Day 06, 11:33Day 06, 16:00KCl
26129Day 06, 15:51Day 07, 14:14KCl
26138Day 06, 15:51Day 07, 14:14Calcium
26145Day 06, 21:00Day 06, 23:00KCl
26155Day 06, 21:00Day 07, 19:20Calcium
26161Day 11, 23:45Day 14, 15:45Calcium
26172Day 14, 17:45Day 16, 18:39Calcium
26181Day 03, 11:45Day 06, 08:45KCl
26191Day 03, 11:45Day 06, 08:50Calcium
26202Day 06, 10:00Day 09, 08:00Calcium
26212Day 06, 10:00Day 09, 08:00KCl
26223Day 09, 09:20Day 09, 18:50KCl
26233Day 09, 09:20Day 09, 18:50Calcium
26244Day 09, 20:45Day 09, 21:00KCl
26254Day 09, 20:45Day 09, 21:00Calcium
26265Day 09, 22:00Day 10, 20:45KCl
26275Day 09, 22:00Day 10, 20:45Calcium
26281Day 23, 17:50Day 25, 16:59KCl
26291Day 23, 17:50Day 25, 17:00Calcium
26301Day 12, 14:30Day 13, 16:45Calcium
26311Day 12, 22:38Day 13, 16:45KCl
26322Day 20, 19:45Day 20, 22:56KCl
26332Day 20, 19:45Day 20, 23:00Calcium
26343Day 21, 03:00Day 21, 03:53Calcium
26353Day 21, 03:00Day 21, 03:53KCl
26364Day 21, 04:45Day 21, 09:19KCl
26374Day 21, 04:45Day 21, 09:34Calcium
26385Day 23, 11:14Day 24, 20:15KCl
26396Day 24, 22:45Day 27, 05:00KCl
26401Day 29, 20:00Day 30, 12:50Calcium
26411Day 29, 20:00Day 30, 12:52KCl
26422Day 31, 13:30Day 02, 16:26Calcium
26432Day 31, 13:30Day 02, 16:26KCl
26447Day 21, 11:15Day 25, 09:49KCl
26455Day 21, 11:15Day 25, 09:50Calcium
26461Day 05, 02:00Day 08, 01:52Calcium
26471Day 08, 21:15Day 10, 03:20Calcium
26481Day 09, 01:45Day 10, 03:20KCl
26492Day 10, 04:45Day 10, 14:36KCl
26502Day 10, 04:53Day 10, 14:36Calcium
26513Day 10, 16:00Day 12, 02:28Calcium
26523Day 10, 16:08Day 11, 06:53KCl
26534Day 11, 06:55Day 12, 02:28KCl
26545Day 12, 04:45Day 12, 05:18KCl
26554Day 12, 04:45Day 12, 05:18Calcium
26566Day 12, 07:20Day 13, 12:33KCl
26575Day 12, 07:20Day 13, 17:48Calcium
26581Day 22, 12:00Day 23, 07:00KCl
26591Day 22, 12:30Day 23, 07:02Calcium
26602Day 23, 11:43Day 26, 12:34Calcium
26612Day 23, 12:54Day 26, 12:32KCl
26623Day 26, 14:00Day 26, 18:19KCl
26633Day 26, 14:00Day 26, 23:33Calcium
26644Day 27, 00:30Day 28, 14:49Calcium
26655Day 28, 18:00Day 30, 02:20Calcium
26666Day 30, 03:20Day 02, 14:00Calcium
26677Day 02, 19:00Day 05, 18:51Calcium
26684Day 04, 13:30Day 05, 09:10KCl
26691Day 31, 12:30Day 31, 12:41KCl
26701Day 31, 12:40Day 31, 23:17Calcium
26712Day 01, 02:28Day 03, 12:20Calcium
26722Day 01, 12:00Day 03, 12:20KCl
26733Day 03, 14:00Day 03, 16:20KCl
26743Day 03, 14:00Day 03, 16:22Calcium
26754Day 03, 19:33Day 04, 12:00KCl
26764Day 03, 19:34Day 04, 12:30Calcium
26775Day 04, 14:00Day 06, 12:14KCl
26785Day 04, 14:00Day 06, 12:14Calcium
26791Day 21, 10:14Day 24, 10:10Calcium
26801Day 25, 16:50Day 27, 03:32Calcium
26811Day 25, 16:51Day 27, 03:34KCl
26822Day 27, 04:25Day 29, 16:04Calcium
26832Day 27, 05:00Day 29, 16:03KCl
26843Day 29, 17:31Day 01, 14:00Calcium
26853Day 29, 17:31Day 01, 14:00KCl
26864Day 01, 15:45Day 03, 10:01KCl
26874Day 01, 15:45Day 03, 10:04Calcium
26881Day 26, 18:00Day 27, 13:24KCl
26891Day 26, 18:00Day 27, 13:32Calcium
26902Day 27, 17:15Day 28, 13:15KCl
26912Day 27, 17:15Day 01, 09:01Calcium
26923Day 28, 14:30Day 30, 19:24KCl
26934Day 01, 04:28Day 01, 09:05KCl
26941Day 22, 14:00Day 24, 07:00KCl
26951Day 22, 14:00Day 24, 07:00Calcium
26962Day 24, 16:00Day 24, 18:56KCl
26972Day 24, 16:00Day 25, 14:00Calcium
26983Day 25, 02:00Day 25, 14:00KCl
26994Day 25, 16:30Day 27, 16:30KCl
27003Day 25, 16:30Day 28, 00:01Calcium
27011Day 31, 02:00Day 02, 11:35Calcium
27021Day 31, 05:00Day 02, 11:28KCl
27032Day 02, 18:00Day 03, 03:20KCl
27042Day 02, 18:00Day 03, 03:20Calcium
27053Day 03, 03:35Day 03, 04:50KCl
27063Day 03, 03:35Day 03, 04:50Calcium
27074Day 03, 05:20Day 05, 03:05KCl
27084Day 03, 05:20Day 05, 03:08Calcium
27091Day 09, 13:15Day 09, 22:45KCl
27101Day 09, 13:15Day 09, 22:45Calcium
27112Day 09, 23:55Day 10, 22:30Calcium
27122Day 09, 23:55Day 11, 12:56KCl
27133Day 11, 03:42Day 11, 12:54Calcium
27141Day 21, 13:45Day 26, 17:51Calcium
27151Day 21, 13:45Day 27, 11:10KCl
27162Day 27, 03:06Day 27, 11:00Calcium
27173Day 28, 12:43Day 28, 16:02Calcium
27182Day 28, 12:45Day 28, 16:03KCl
27193Day 28, 17:50Day 29, 13:30KCl
27204Day 28, 17:50Day 29, 13:30Calcium
27211Day 12, 21:15Day 13, 09:00Calcium
27221Day 13, 18:00Day 14, 18:29KCl
27232Day 13, 18:00Day 14, 18:30Calcium
27241Day 03, 20:45Day 04, 05:00Calcium
27251Day 04, 03:56Day 04, 05:00KCl
27261Day 06, 01:45Day 09, 02:15Calcium
27271Day 06, 07:00Day 09, 02:15KCl
27282Day 09, 03:20Day 10, 09:00Calcium
27292Day 09, 03:20Day 10, 09:00KCl
27303Day 10, 10:45Day 10, 19:38KCl
27313Day 10, 10:45Day 10, 21:01Calcium
27324Day 11, 09:20Day 11, 09:45KCl
27335Day 11, 12:00Day 11, 13:30KCl
27344Day 11, 12:00Day 11, 13:30Calcium
27355Day 11, 14:25Day 14, 16:00Calcium
27366Day 11, 14:25Day 14, 16:00KCl
27376Day 15, 15:09Day 18, 14:50Calcium
27387Day 16, 04:47Day 18, 14:50KCl
27398Day 18, 20:20Day 20, 20:02KCl
27407Day 18, 20:20Day 21, 08:45Calcium
27419Day 21, 05:46Day 21, 08:45KCl
27428Day 23, 20:34Day 26, 04:58Calcium
274310Day 23, 20:37Day 26, 05:04KCl
274411Day 26, 07:00Day 27, 14:25KCl
27459Day 26, 07:00Day 27, 14:25Calcium
27461Day 07, 01:00Day 09, 17:02Calcium
27471Day 01, 16:31Day 05, 04:02Calcium
27481Day 01, 16:33Day 05, 04:00KCl
27491Day 08, 16:00Day 09, 06:56KCl
27501Day 08, 16:00Day 11, 08:06Calcium
27512Day 26, 23:00Day 27, 03:30KCl
27522Day 26, 23:15Day 27, 03:15Calcium
27533Day 27, 03:45Day 27, 05:13KCl
27543Day 27, 03:45Day 27, 21:28Calcium
27554Day 27, 07:00Day 27, 15:02KCl
27564Day 27, 23:05Day 28, 12:45Calcium
27575Day 27, 23:36Day 28, 12:30KCl
27585Day 28, 16:30Day 29, 18:30Calcium
27596Day 28, 16:30Day 29, 18:30KCl
27606Day 29, 20:45Day 30, 09:29Calcium
27617Day 29, 20:45Day 30, 09:31KCl
27628Day 30, 12:45Day 30, 19:17KCl
27637Day 30, 18:46Day 01, 10:30Calcium
27649Day 01, 08:25Day 01, 10:30KCl
27651Day 02, 00:10Day 02, 06:00Calcium
27661Day 02, 00:10Day 02, 06:00KCl
27671Day 20, 00:13Day 21, 12:47Calcium
27681Day 20, 00:14Day 20, 21:33KCl
27692Day 21, 04:03Day 21, 12:40KCl
27701Day 11, 22:13Day 12, 01:41Calcium
27711Day 12, 18:30Day 13, 07:00Calcium
27721Day 12, 18:30Day 13, 07:00KCl
27732Day 13, 08:36Day 14, 18:15Calcium
27742Day 13, 08:39Day 14, 18:14KCl
27753Day 14, 21:30Day 15, 08:57KCl
27763Day 14, 21:30Day 15, 10:34Calcium
27774Day 15, 16:00Day 15, 18:16Calcium
27785Day 15, 21:30Day 16, 00:29Calcium
27796Day 16, 01:30Day 16, 21:00Calcium
27801Day 19, 12:07Day 19, 13:07Calcium
27812Day 19, 13:11Day 19, 14:11Calcium
27821Day 22, 07:01Day 22, 23:17KCl
27831Day 22, 07:03Day 23, 15:39Calcium
27842Day 23, 11:00Day 23, 15:26KCl
27853Day 23, 22:30Day 24, 05:27KCl
27862Day 23, 22:30Day 25, 12:00Calcium
27874Day 24, 11:30Day 25, 12:02KCl
27883Day 25, 15:25Day 28, 08:30Calcium
27895Day 25, 15:25Day 28, 08:31KCl
27904Day 29, 17:00Day 31, 21:38Calcium
27915Day 01, 00:27Day 02, 02:01Calcium
27921Day 01, 20:30Day 02, 15:28KCl
27931Day 01, 20:30Day 02, 15:30Calcium
27946Day 02, 07:00Day 02, 21:59Calcium
27952Day 02, 15:45Day 03, 08:50KCl
27962Day 02, 15:45Day 04, 18:15Calcium
27977Day 03, 01:12Day 04, 16:30Calcium
27983Day 04, 06:26Day 04, 18:15KCl
27998Day 05, 21:21Day 07, 10:00Calcium
28006Day 05, 21:30Day 07, 09:40KCl
28017Day 07, 19:33Day 08, 10:01KCl
28029Day 07, 19:34Day 08, 10:02Calcium
280310Day 08, 12:33Day 09, 14:30Calcium
28048Day 08, 12:33Day 09, 14:31KCl
280511Day 03, 17:54Day 04, 09:00Calcium
280612Day 04, 10:30Day 05, 15:01Calcium
28071Day 26, 22:12Day 27, 10:40Calcium
28081Day 26, 22:13Day 27, 10:40KCl
28092Day 27, 15:15Day 28, 15:59KCl
28102Day 27, 15:15Day 28, 16:00Calcium
28113Day 28, 17:33Day 29, 21:00Calcium
28123Day 28, 17:34Day 29, 21:00KCl
28134Day 29, 23:00Day 30, 04:00KCl
28144Day 29, 23:00Day 31, 04:29Calcium
28155Day 30, 17:15Day 31, 02:50Calcium
28165Day 30, 22:30Day 31, 02:50KCl
28176Day 31, 05:45Day 31, 09:39Calcium
28186Day 31, 05:45Day 31, 09:40KCl
28197Day 31, 12:15Day 03, 13:15KCl
28207Day 31, 12:15Day 03, 13:15Calcium
28218Day 03, 16:00Day 04, 06:14Calcium
28228Day 03, 16:00Day 04, 06:30KCl
28239Day 04, 10:58Day 04, 14:00Calcium
282410Day 04, 16:50Day 05, 13:00Calcium
282511Day 05, 16:51Day 05, 18:00Calcium
282612Day 05, 22:00Day 05, 23:04Calcium
28271Day 11, 23:30Day 13, 08:00KCl
28281Day 11, 23:30Day 13, 08:00Calcium
28291Day 27, 17:55Day 27, 22:30Calcium
28301Day 27, 17:56Day 27, 22:30KCl
28312Day 28, 08:49Day 02, 05:55KCl
28322Day 28, 08:49Day 02, 05:56Calcium
28333Day 02, 10:15Day 02, 15:47Calcium
28344Day 02, 18:48Day 02, 19:30Calcium
28353Day 02, 19:00Day 04, 07:49KCl
28365Day 03, 00:35Day 04, 07:45Calcium
28374Day 04, 08:45Day 04, 18:22KCl
28386Day 04, 08:45Day 06, 12:01Calcium
28395Day 04, 23:27Day 06, 12:00KCl
28407Day 06, 18:54Day 08, 16:34Calcium
28416Day 06, 18:55Day 08, 16:36KCl
28421Day 17, 15:30Day 18, 04:02Calcium
28431Day 17, 15:30Day 18, 04:02KCl
28442Day 18, 12:30Day 19, 00:24Calcium
28452Day 18, 12:30Day 19, 00:24KCl
28463Day 19, 01:52Day 21, 16:26Calcium
28473Day 19, 01:52Day 21, 16:29KCl
28484Day 21, 17:15Day 24, 06:58Calcium
28494Day 21, 17:15Day 24, 07:00KCl
28505Day 24, 11:46Day 25, 10:39Calcium
28515Day 24, 11:47Day 25, 10:39KCl
28526Day 25, 14:48Day 25, 20:15KCl
28536Day 25, 14:48Day 25, 20:15Calcium
28547Day 25, 21:30Day 25, 22:34KCl
28557Day 25, 21:30Day 25, 22:35Calcium
28568Day 26, 16:34Day 29, 13:50Calcium
28578Day 26, 16:35Day 28, 10:37KCl
28589Day 29, 17:33Day 01, 09:37Calcium
28591Day 29, 21:42Day 03, 12:00Calcium
28601Day 29, 21:45Day 03, 12:00KCl
28611Day 31, 14:38Day 04, 03:26Calcium
28629Day 31, 16:21Day 01, 09:44KCl
28631Day 31, 19:47Day 04, 03:27KCl
286410Day 01, 23:31Day 02, 07:30Calcium
28651Day 30, 22:00Day 31, 18:03KCl
28661Day 30, 22:00Day 03, 04:20Calcium
28672Day 31, 19:18Day 01, 08:02KCl
28683Day 01, 12:00Day 03, 04:22KCl
28694Day 05, 20:00Day 06, 16:29KCl
28702Day 05, 20:00Day 06, 16:32Calcium
28713Day 06, 19:00Day 07, 16:00Calcium
28725Day 06, 19:00Day 07, 16:01KCl
28734Day 07, 20:15Day 08, 09:16Calcium
28746Day 07, 20:15Day 08, 09:19KCl
28751Day 02, 20:00Day 03, 03:38Calcium
28762Day 03, 10:38Day 03, 13:32Calcium
28771Day 24, 22:46Day 26, 19:00Calcium
28781Day 24, 22:47Day 26, 19:00KCl
28792Day 27, 02:30Day 27, 12:30KCl
28802Day 27, 07:00Day 27, 12:00Calcium
28813Day 27, 22:29Day 28, 05:33Calcium
28823Day 27, 22:29Day 28, 05:41KCl
28834Day 28, 11:20Day 29, 12:45Calcium
28844Day 28, 11:24Day 29, 13:02KCl
28855Day 29, 17:15Day 29, 23:32Calcium
28865Day 29, 17:15Day 29, 23:34KCl
28876Day 30, 02:15Day 30, 04:30KCl
28886Day 30, 02:15Day 30, 04:32Calcium
28891Day 19, 18:00Day 22, 17:43Calcium
28901Day 21, 04:00Day 21, 14:47KCl
28912Day 22, 22:08Day 25, 10:45Calcium
28921Day 28, 18:25Day 29, 08:36Calcium
28931Day 29, 00:06Day 31, 17:30KCl
28942Day 29, 11:32Day 31, 17:30Calcium
28952Day 31, 20:30Day 01, 14:30KCl
28963Day 31, 20:30Day 01, 14:33Calcium
28973Day 01, 15:06Day 02, 17:41KCl
28984Day 01, 16:00Day 02, 18:00Calcium
28991Day 31, 12:30Day 31, 15:29KCl
29001Day 31, 12:30Day 01, 18:20Calcium
29012Day 31, 15:30Day 01, 18:45KCl
29023Day 01, 22:00Day 02, 01:00KCl
29032Day 01, 22:00Day 02, 01:00Calcium
29044Day 02, 02:00Day 02, 07:00KCl
29053Day 02, 02:00Day 02, 07:02Calcium
29064Day 02, 09:00Day 02, 09:30Calcium
29075Day 02, 09:00Day 02, 09:30KCl
29086Day 02, 15:00Day 03, 17:49KCl
29095Day 02, 15:00Day 06, 13:00Calcium
29107Day 03, 18:04Day 06, 13:00KCl
29118Day 06, 15:45Day 06, 18:58KCl
29126Day 06, 16:01Day 06, 18:58Calcium
29139Day 07, 05:00Day 07, 11:20KCl
29147Day 07, 05:00Day 07, 11:28Calcium
291510Day 07, 12:45Day 08, 01:53KCl
29168Day 07, 12:45Day 08, 10:26Calcium
291711Day 08, 01:56Day 08, 10:27KCl
29181Day 15, 06:00Day 15, 07:00Calcium
29191Day 15, 06:00Day 15, 07:00KCl
29202Day 15, 09:44Day 15, 16:40Calcium
29212Day 15, 09:44Day 15, 16:40KCl
29221Day 20, 18:20Day 23, 07:58Calcium
29231Day 20, 18:20Day 23, 12:51KCl
29241Day 06, 11:00Day 06, 14:48Calcium
29251Day 06, 11:00Day 06, 14:48KCl
29262Day 06, 18:30Day 07, 14:26KCl
29272Day 06, 18:30Day 07, 14:53Calcium
29283Day 07, 21:49Day 08, 07:47KCl
29293Day 07, 21:50Day 08, 07:44Calcium
29304Day 08, 14:30Day 08, 19:30KCl
29314Day 08, 14:30Day 08, 20:03Calcium
29321Day 08, 18:30Day 09, 18:15Calcium
29335Day 11, 00:30Day 13, 16:19Calcium
29345Day 11, 00:30Day 13, 16:25KCl
29356Day 13, 19:05Day 15, 16:00KCl
29366Day 13, 19:05Day 15, 16:00Calcium
29377Day 15, 21:00Day 18, 08:25KCl
29387Day 15, 21:00Day 18, 08:30Calcium
29398Day 18, 12:00Day 19, 10:30Calcium
29408Day 18, 12:00Day 19, 10:30KCl
29412Day 19, 10:20Day 19, 17:01Calcium
29421Day 19, 10:20Day 19, 17:01KCl
29439Day 19, 11:30Day 20, 08:29KCl
29449Day 19, 11:30Day 20, 08:32Calcium
29453Day 19, 19:10Day 20, 14:04Calcium
29462Day 19, 19:10Day 20, 14:06KCl
29474Day 20, 17:00Day 22, 09:59Calcium
294810Day 21, 12:00Day 23, 23:27Calcium
294910Day 22, 01:27Day 23, 21:12KCl
295011Day 24, 11:22Day 25, 13:24KCl
295111Day 24, 13:43Day 25, 13:30Calcium
295212Day 26, 00:11Day 26, 20:09KCl
295312Day 26, 06:45Day 27, 08:42Calcium
295413Day 26, 20:17Day 27, 08:45KCl
295513Day 27, 10:30Day 28, 11:47Calcium
295614Day 27, 10:30Day 28, 11:47KCl
29571Day 26, 12:19Day 27, 15:02Calcium
29581Day 26, 20:42Day 27, 15:04KCl
29592Day 27, 16:15Day 28, 10:29KCl
29602Day 27, 16:15Day 30, 13:41Calcium
29613Day 29, 02:18Day 29, 09:23KCl
29621Day 30, 08:45Day 30, 10:06KCl
29632Day 30, 13:00Day 30, 18:17KCl
29643Day 30, 23:56Day 02, 10:30KCl
29651Day 02, 21:30Day 03, 11:42Calcium
29664Day 02, 21:30Day 03, 21:00KCl
29672Day 03, 11:45Day 03, 21:00Calcium
29683Day 04, 01:45Day 05, 09:50Calcium
29695Day 04, 01:53Day 05, 09:50KCl
29704Day 04, 13:00Day 07, 13:00KCl
29713Day 04, 13:00Day 07, 13:00Calcium
29725Day 07, 15:30Day 09, 15:01KCl
29734Day 07, 15:30Day 09, 15:01Calcium
29741Day 09, 10:30Day 09, 19:09KCl
29751Day 09, 10:30Day 10, 07:30Calcium
29762Day 10, 11:30Day 11, 11:00Calcium
29772Day 10, 18:16Day 11, 11:01KCl
29783Day 11, 12:18Day 12, 10:12Calcium
29793Day 11, 12:18Day 12, 11:09KCl
29804Day 12, 12:00Day 13, 11:30KCl
29814Day 12, 12:15Day 13, 11:30Calcium
29821Day 31, 18:10Day 01, 06:35KCl
29832Day 01, 12:00Day 03, 03:41KCl
29843Day 03, 10:10Day 03, 10:25KCl
29854Day 03, 11:00Day 05, 13:18KCl
29861Day 05, 07:00Day 05, 13:18Calcium
29871Day 17, 20:45Day 18, 01:45Calcium
29881Day 17, 23:00Day 18, 01:45KCl
29892Day 18, 04:00Day 19, 01:30KCl
29902Day 18, 04:00Day 20, 14:00Calcium
29913Day 19, 01:50Day 20, 14:00KCl
29923Day 20, 15:00Day 21, 04:28Calcium
29934Day 20, 15:00Day 21, 04:31KCl
29945Day 21, 05:20Day 22, 20:48KCl
29954Day 21, 05:20Day 22, 20:53Calcium
29965Day 23, 00:14Day 23, 01:01Calcium
29976Day 23, 00:15Day 23, 01:05KCl
29986Day 23, 02:20Day 26, 08:30Calcium
29997Day 23, 02:20Day 26, 08:30KCl
30008Day 21, 22:00Day 25, 06:00KCl
30017Day 21, 22:00Day 25, 06:00Calcium
30029Day 25, 16:50Day 25, 23:00KCl
30038Day 25, 16:50Day 25, 23:00Calcium
30049Day 26, 02:30Day 26, 18:08Calcium
300510Day 26, 02:30Day 26, 18:10KCl
30061Day 29, 16:25Day 30, 10:02Calcium
30071Day 29, 16:33Day 30, 09:59KCl
30082Day 30, 13:00Day 01, 11:30Calcium
30092Day 30, 13:01Day 01, 11:29KCl
30103Day 01, 14:03Day 03, 12:27KCl
30113Day 01, 14:03Day 03, 12:29Calcium
30124Day 07, 20:40Day 09, 19:13Calcium
30134Day 08, 22:03Day 09, 19:15KCl
30145Day 09, 20:20Day 12, 06:00KCl
30155Day 09, 20:20Day 12, 06:02Calcium
30166Day 12, 14:30Day 14, 16:00KCl
30176Day 12, 14:45Day 14, 16:00Calcium
30187Day 28, 20:30Day 30, 04:19Calcium
30197Day 28, 20:30Day 30, 04:19KCl
30208Day 30, 06:00Day 03, 02:10KCl
30218Day 30, 06:00Day 03, 02:10Calcium
30229Day 03, 05:20Day 03, 23:20KCl
30239Day 03, 05:20Day 03, 23:20Calcium
302410Day 04, 00:30Day 04, 14:58Calcium
302510Day 04, 00:30Day 04, 15:01KCl
302611Day 04, 18:11Day 05, 18:01Calcium
302711Day 04, 18:11Day 05, 18:05KCl
30281Day 01, 04:00Day 01, 07:05Calcium
30291Day 09, 13:30Day 12, 14:20KCl
30301Day 09, 13:30Day 12, 14:20Calcium
30312Day 12, 15:00Day 16, 10:23KCl
30322Day 12, 15:00Day 16, 10:27Calcium
30333Day 16, 12:12Day 16, 12:19Calcium
30343Day 16, 12:12Day 18, 05:05KCl
30354Day 16, 15:49Day 18, 05:05Calcium
30365Day 18, 06:05Day 20, 12:41Calcium
30374Day 18, 06:05Day 20, 12:46KCl
30386Day 20, 13:44Day 22, 13:30Calcium
30395Day 20, 13:45Day 20, 20:25KCl
30406Day 20, 20:20Day 22, 13:30KCl
30417Day 23, 12:30Day 24, 04:29Calcium
30427Day 23, 12:30Day 24, 04:32KCl
30438Day 24, 07:00Day 25, 23:00KCl
30448Day 24, 07:00Day 25, 23:00Calcium
30451Day 07, 22:15Day 10, 08:15Calcium
30462Day 10, 15:19Day 13, 19:00Calcium
30473Day 13, 20:30Day 14, 05:02Calcium
30484Day 14, 05:45Day 14, 13:00Calcium
30495Day 14, 15:45Day 15, 19:20Calcium
30506Day 16, 00:25Day 16, 15:58Calcium
30517Day 16, 19:30Day 17, 01:30Calcium
30521Day 11, 16:35Day 12, 08:40Calcium
30531Day 12, 03:00Day 12, 08:40KCl
30541Day 31, 21:03Day 01, 14:00Calcium
30551Day 31, 21:04Day 01, 03:49KCl
30562Day 01, 16:00Day 01, 23:12Calcium
30571Day 01, 22:15Day 03, 22:03Calcium
30583Day 02, 17:32Day 02, 23:00Calcium
30594Day 03, 02:30Day 03, 17:18Calcium
30602Day 04, 11:00Day 05, 08:30Calcium
30613Day 05, 09:20Day 06, 03:03Calcium
30624Day 06, 04:10Day 06, 17:40Calcium
30635Day 06, 18:25Day 07, 08:10Calcium
30646Day 07, 15:30Day 07, 21:50Calcium
30657Day 08, 11:20Day 08, 17:00Calcium
30668Day 08, 18:15Day 09, 08:40Calcium
30679Day 09, 16:15Day 10, 21:00Calcium
30681Day 04, 22:00Day 06, 12:41KCl
30691Day 04, 22:00Day 07, 17:30Calcium
30701Day 23, 18:24Day 23, 20:20Calcium
30711Day 23, 18:26Day 23, 20:20KCl
30721Day 06, 01:05Day 06, 06:45Calcium
30731Day 06, 01:05Day 06, 06:45KCl
30741Day 05, 17:00Day 07, 18:49KCl
30751Day 05, 17:00Day 07, 19:06Calcium
30761Day 30, 23:30Day 02, 08:22Calcium
30771Day 01, 10:20Day 02, 07:00KCl
30781Day 27, 03:00Day 27, 15:59Calcium
30791Day 27, 03:02Day 27, 15:49KCl
30802Day 27, 22:30Day 28, 20:40Calcium
30812Day 27, 22:30Day 28, 20:40KCl
30821Day 11, 15:10Day 11, 17:30Calcium
30831Day 11, 15:10Day 11, 17:34KCl
30842Day 12, 12:00Day 15, 15:00Calcium
30852Day 12, 12:00Day 15, 15:00KCl
30863Day 15, 15:45Day 19, 11:04KCl
30873Day 15, 15:45Day 19, 11:09Calcium
30884Day 19, 12:54Day 22, 19:00Calcium
30894Day 19, 12:54Day 22, 19:04KCl
30901Day 02, 20:22Day 03, 11:55Calcium
30911Day 02, 20:23Day 03, 11:59KCl
30921Day 25, 00:00Day 25, 08:35Calcium
30931Day 25, 01:38Day 25, 08:36KCl
30942Day 25, 10:30Day 28, 01:58Calcium
30952Day 25, 11:00Day 28, 02:02KCl
30963Day 28, 03:00Day 01, 12:37Calcium
30973Day 28, 03:00Day 01, 12:40KCl
30981Day 11, 19:00Day 11, 22:18Calcium
30991Day 11, 19:00Day 11, 22:18KCl
31002Day 12, 00:59Day 12, 02:04Calcium
31012Day 12, 00:59Day 12, 02:08KCl
31023Day 12, 03:30Day 15, 03:00Calcium
31033Day 12, 03:30Day 15, 03:00KCl
31044Day 16, 01:14Day 16, 13:05Calcium
31054Day 16, 03:38Day 16, 13:03KCl
31065Day 16, 15:02Day 19, 11:04Calcium
31075Day 16, 15:02Day 19, 11:06KCl
31086Day 19, 12:00Day 20, 13:40Calcium
31096Day 19, 12:00Day 20, 13:40KCl
31107Day 20, 14:05Day 20, 20:43KCl
31117Day 20, 14:05Day 20, 20:46Calcium
31128Day 20, 22:05Day 21, 03:58Calcium
31138Day 20, 22:05Day 21, 03:58KCl
31149Day 21, 16:00Day 24, 13:30KCl
31159Day 21, 16:00Day 24, 13:30Calcium
311610Day 28, 02:30Day 28, 08:04Calcium
311710Day 28, 02:30Day 28, 08:05KCl
31181Day 13, 01:15Day 16, 02:39Calcium
31191Day 14, 17:00Day 15, 16:25KCl
31202Day 16, 04:10Day 17, 19:30Calcium
31212Day 16, 10:00Day 16, 15:43KCl
31223Day 17, 05:22Day 17, 19:30KCl
31234Day 17, 21:50Day 17, 22:19KCl
31243Day 17, 21:50Day 19, 01:19Calcium
31251Day 18, 16:45Day 20, 16:36Calcium
31261Day 18, 16:45Day 20, 19:30KCl
31272Day 20, 19:00Day 20, 19:30Calcium
31282Day 27, 14:33Day 27, 15:32KCl
31293Day 27, 14:34Day 30, 19:39Calcium
31303Day 27, 22:00Day 30, 19:41KCl
31314Day 30, 20:50Day 31, 10:00KCl
31324Day 30, 20:50Day 31, 10:01Calcium
31335Day 31, 12:30Day 31, 12:45KCl
31345Day 31, 12:34Day 31, 12:45Calcium
31356Day 31, 16:00Day 01, 16:00KCl
31366Day 31, 16:00Day 01, 16:00Calcium
31377Day 01, 18:45Day 02, 13:00Calcium
31387Day 01, 18:45Day 02, 13:01KCl
31391Day 10, 14:45Day 16, 04:30Calcium
31401Day 10, 15:15Day 14, 18:31KCl
31412Day 15, 05:00Day 15, 11:00KCl
31422Day 21, 01:15Day 21, 13:22Calcium
31433Day 21, 01:22Day 21, 11:22KCl
31448Day 21, 13:57Day 22, 05:16Calcium
31451Day 30, 12:00Day 01, 15:16Calcium
31461Day 30, 14:11Day 30, 19:53KCl
31471Day 30, 22:46Day 01, 11:32Calcium
31482Day 31, 03:05Day 31, 22:50KCl
31493Day 31, 23:45Day 01, 15:15KCl
31504Day 01, 17:57Day 02, 11:01KCl
31512Day 01, 18:00Day 04, 18:01Calcium
31525Day 02, 13:07Day 04, 18:00KCl
31531Day 04, 20:00Day 05, 21:18Calcium
31541Day 04, 20:00Day 06, 06:45KCl
31553Day 04, 21:15Day 05, 10:00Calcium
31566Day 04, 21:15Day 05, 10:00KCl
31574Day 05, 11:03Day 07, 20:02Calcium
31587Day 05, 11:04Day 06, 22:06KCl
31592Day 06, 08:30Day 06, 23:05KCl
31602Day 06, 08:30Day 06, 23:05Calcium
31615Day 08, 09:24Day 08, 14:56Calcium
31626Day 08, 20:26Day 09, 02:43Calcium
31638Day 08, 20:26Day 09, 02:43KCl
31647Day 09, 08:59Day 10, 00:30Calcium
31659Day 09, 22:02Day 10, 00:30KCl
31668Day 10, 01:19Day 12, 10:45Calcium
316710Day 10, 01:19Day 12, 12:33KCl
31681Day 10, 10:15Day 11, 18:12Calcium
31691Day 10, 17:18Day 11, 18:08KCl
31702Day 12, 02:00Day 18, 17:27Calcium
31712Day 12, 06:00Day 14, 12:56KCl
31723Day 14, 10:27Day 18, 17:34KCl
31733Day 18, 20:00Day 19, 16:03Calcium
31744Day 18, 20:00Day 20, 15:00KCl
31754Day 19, 16:08Day 20, 15:00Calcium
31765Day 20, 16:30Day 22, 02:31Calcium
31776Day 22, 03:15Day 23, 08:30Calcium
31785Day 22, 05:39Day 23, 08:30KCl
31797Day 24, 11:59Day 25, 10:30Calcium
31801Day 28, 12:14Day 02, 10:15Calcium
31812Day 02, 12:00Day 03, 21:16Calcium
31823Day 04, 00:16Day 05, 13:52Calcium
31831Day 03, 20:22Day 06, 08:35KCl
31841Day 03, 20:23Day 06, 08:35Calcium
31852Day 06, 12:20Day 07, 14:40KCl
31862Day 06, 12:20Day 07, 14:42Calcium
31873Day 08, 02:30Day 08, 12:12Calcium
31881Day 18, 21:00Day 19, 11:02KCl
31891Day 18, 21:49Day 20, 17:04Calcium
31902Day 19, 09:05Day 19, 11:05KCl
31913Day 19, 11:02Day 20, 17:09KCl
31921Day 11, 00:00Day 11, 21:01Calcium
31931Day 18, 18:15Day 20, 03:45KCl
31941Day 18, 18:15Day 20, 03:45Calcium
31952Day 20, 04:45Day 20, 16:30KCl
31962Day 20, 04:45Day 20, 16:30Calcium
31971Day 05, 22:00Day 07, 09:43KCl
31981Day 05, 22:00Day 07, 10:02Calcium
31991Day 15, 18:00Day 17, 07:57Calcium
32001Day 15, 18:00Day 18, 09:00KCl
32012Day 17, 08:00Day 18, 09:00Calcium
32021Day 03, 17:00Day 03, 17:40Calcium
32032Day 03, 21:14Day 06, 12:04Calcium
32041Day 03, 23:32Day 06, 12:04KCl
32051Day 03, 11:00Day 03, 15:47Calcium
32062Day 03, 16:00Day 04, 11:00Calcium
32073Day 04, 14:00Day 04, 18:19Calcium
32084Day 04, 19:52Day 04, 22:10Calcium
32095Day 05, 03:50Day 05, 16:45Calcium
32101Day 12, 18:15Day 14, 16:25Calcium
32111Day 12, 18:15Day 14, 16:25KCl
32122Day 14, 19:25Day 16, 08:43KCl
32132Day 14, 19:25Day 16, 08:56Calcium
32141Day 20, 13:00Day 22, 00:00KCl
32151Day 20, 13:00Day 22, 00:02Calcium
32162Day 22, 01:21Day 22, 14:01KCl
32172Day 22, 01:21Day 22, 14:05Calcium
32183Day 22, 23:14Day 24, 05:30KCl
32193Day 22, 23:14Day 24, 05:30Calcium
32204Day 24, 09:30Day 24, 21:15Calcium
32214Day 24, 09:30Day 24, 21:15KCl
32225Day 25, 00:00Day 25, 10:30Calcium
32235Day 25, 00:00Day 25, 10:30KCl
32246Day 26, 22:00Day 28, 12:45Calcium
32256Day 27, 20:29Day 28, 12:45KCl
32267Day 28, 18:45Day 31, 14:46KCl
32277Day 28, 18:45Day 31, 15:24Calcium
32288Day 31, 17:30Day 02, 07:00Calcium
32298Day 31, 17:35Day 02, 07:00KCl
32309Day 02, 16:55Day 03, 15:15KCl
32319Day 02, 16:55Day 03, 15:15Calcium
323210Day 03, 17:00Day 04, 11:33KCl
323310Day 03, 17:00Day 04, 11:59Calcium
323411Day 13, 01:00Day 13, 11:03Calcium
323511Day 13, 01:00Day 13, 11:05KCl
323612Day 13, 12:03Day 15, 16:29KCl
323712Day 13, 12:03Day 15, 16:29Calcium
32381Day 13, 10:27Day 13, 10:40Calcium
32392Day 13, 14:34Day 14, 08:44Calcium
32403Day 14, 13:53Day 15, 19:01Calcium
32411Day 13, 14:39Day 14, 11:30Calcium
32421Day 14, 15:15Day 17, 15:18KCl
32431Day 14, 15:15Day 17, 15:18Calcium
32442Day 14, 15:30Day 16, 14:01Calcium
32451Day 15, 06:15Day 16, 13:58KCl
32462Day 16, 15:00Day 17, 01:00KCl
32473Day 16, 15:01Day 17, 01:00Calcium
32482Day 17, 16:22Day 20, 16:15KCl
32492Day 17, 16:23Day 20, 16:11Calcium
32503Day 20, 17:41Day 21, 15:01KCl
32513Day 20, 17:41Day 21, 15:04Calcium
32521Day 21, 16:09Day 23, 09:50Calcium
32532Day 24, 05:00Day 24, 17:01Calcium
32541Day 24, 07:00Day 24, 17:02KCl
32552Day 24, 18:00Day 25, 10:48KCl
32563Day 24, 18:00Day 25, 10:48Calcium
32574Day 26, 14:10Day 29, 10:00Calcium
32581Day 03, 21:25Day 05, 13:17Calcium
32591Day 08, 11:00Day 09, 11:56KCl
32601Day 08, 11:00Day 09, 11:57Calcium
32612Day 09, 12:20Day 11, 00:09Calcium
32622Day 09, 12:30Day 11, 00:13KCl
32631Day 12, 02:00Day 13, 04:57KCl
32641Day 12, 02:00Day 13, 05:05Calcium
32652Day 13, 09:00Day 14, 10:30KCl
32662Day 13, 09:00Day 14, 10:30Calcium
32673Day 14, 14:00Day 16, 06:30KCl
32683Day 14, 14:00Day 16, 06:31Calcium
32691Day 23, 15:20Day 24, 11:17Calcium
32701Day 26, 12:30Day 28, 11:10Calcium
32711Day 26, 12:30Day 28, 17:30KCl
32721Day 15, 17:35Day 15, 17:48Calcium
32732Day 15, 21:33Day 18, 13:50Calcium
32743Day 18, 18:15Day 20, 22:00Calcium
32754Day 20, 23:15Day 23, 09:01Calcium
32761Day 05, 18:35Day 07, 15:45KCl
32771Day 05, 18:35Day 07, 15:45Calcium
32781Day 28, 21:30Day 29, 16:51KCl
32791Day 28, 21:30Day 30, 19:57Calcium
32801Day 27, 21:00Day 29, 09:29KCl
32811Day 27, 21:00Day 29, 09:29Calcium
32822Day 29, 12:20Day 30, 08:10Calcium
32832Day 29, 12:40Day 30, 02:03KCl
32843Day 30, 06:17Day 30, 08:10KCl
32854Day 30, 16:00Day 01, 11:33KCl
32863Day 30, 16:00Day 01, 19:26Calcium
32871Day 05, 05:31Day 07, 03:02Calcium
32881Day 05, 05:33Day 07, 03:02KCl
32892Day 07, 04:46Day 10, 16:57KCl
32902Day 07, 04:46Day 10, 16:57Calcium
32911Day 16, 18:10Day 19, 15:00Calcium
32921Day 16, 18:12Day 19, 04:50KCl
32932Day 19, 12:06Day 19, 15:00KCl
32941Day 25, 19:00Day 26, 13:30KCl
32951Day 25, 19:00Day 26, 13:30Calcium
32961Day 30, 21:00Day 01, 08:02Calcium
32972Day 01, 11:33Day 01, 18:31Calcium
32981Day 03, 16:15Day 03, 19:42Calcium
32992Day 03, 20:42Day 03, 23:03Calcium
33001Day 04, 00:00Day 04, 08:30KCl
33013Day 04, 00:00Day 04, 09:00Calcium
33024Day 04, 14:44Day 06, 08:00Calcium
33032Day 04, 14:45Day 06, 07:59KCl
33043Day 06, 13:05Day 09, 04:01KCl
33055Day 06, 13:55Day 09, 03:01Calcium
33064Day 09, 07:00Day 09, 16:26KCl
33076Day 09, 07:00Day 09, 16:30Calcium
33085Day 09, 18:00Day 10, 08:00KCl
33097Day 09, 18:00Day 10, 19:00Calcium
33106Day 10, 08:03Day 10, 19:00KCl
33118Day 10, 23:45Day 11, 08:45Calcium
33127Day 10, 23:45Day 11, 08:45KCl
33139Day 11, 18:50Day 13, 05:45Calcium
33148Day 11, 18:50Day 13, 05:45KCl
331510Day 13, 08:15Day 13, 13:59Calcium
33169Day 13, 08:16Day 13, 14:00KCl
331711Day 13, 18:54Day 16, 21:30Calcium
331810Day 13, 18:54Day 16, 21:49KCl
331912Day 16, 23:30Day 17, 09:15Calcium
332011Day 16, 23:30Day 17, 09:15KCl
332113Day 17, 10:45Day 19, 00:03Calcium
332212Day 17, 10:45Day 19, 00:05KCl
33231Day 23, 18:45Day 25, 11:24KCl
33241Day 23, 18:45Day 25, 11:26Calcium
33252Day 25, 16:30Day 26, 12:27KCl
33262Day 25, 16:30Day 27, 04:32Calcium
33273Day 26, 13:00Day 27, 04:25KCl
33283Day 27, 05:30Day 28, 10:01Calcium
33294Day 27, 05:30Day 28, 10:04KCl
33301Day 21, 19:21Day 23, 08:15Calcium
33311Day 22, 09:23Day 23, 08:15KCl
33322Day 23, 12:55Day 26, 12:00KCl
33332Day 23, 12:55Day 26, 12:00Calcium
33341Day 06, 22:32Day 08, 18:37Calcium
33351Day 06, 22:34Day 08, 18:20KCl
33362Day 08, 21:15Day 09, 00:28KCl
33372Day 08, 21:16Day 09, 18:47Calcium
33383Day 08, 21:34Day 08, 21:36KCl
33394Day 09, 00:28Day 09, 18:53KCl
33401Day 19, 11:30Day 20, 15:03Calcium
33411Day 24, 05:35Day 25, 15:00Calcium
33421Day 04, 02:26Day 04, 08:33Calcium
33431Day 04, 02:34Day 04, 08:27KCl
33442Day 04, 10:54Day 07, 11:20KCl
33452Day 04, 10:54Day 07, 11:28Calcium
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numstarttimeendtime
01Day 10, 15:00Day 11, 01:00
12Day 11, 04:20Day 11, 18:00
23Day 11, 19:00Day 12, 08:00
34Day 12, 10:02Day 13, 10:26
41Day 30, 00:19Day 30, 08:00
51Day 27, 17:30Day 28, 17:14
62Day 28, 21:00Day 29, 11:00
71Day 06, 11:20Day 07, 14:03
81Day 26, 16:10Day 28, 07:00
92Day 28, 11:00Day 29, 21:00
103Day 29, 23:00Day 30, 01:00
114Day 30, 11:40Day 30, 18:22
125Day 30, 21:00Day 01, 19:00
131Day 05, 20:00Day 07, 09:16
142Day 07, 19:00Day 07, 19:16
153Day 07, 19:17Day 08, 13:15
164Day 08, 13:17Day 08, 14:00
175Day 11, 14:00Day 12, 13:00
186Day 14, 18:36Day 15, 07:30
197Day 15, 11:00Day 17, 18:00
208Day 17, 20:00Day 18, 09:00
219Day 22, 14:56Day 23, 07:07
221Day 24, 21:19Day 25, 22:20
232Day 26, 01:00Day 27, 12:00
243Day 28, 15:00Day 29, 13:15
254Day 29, 19:00Day 31, 13:00
261Day 13, 17:00Day 16, 17:37
272Day 16, 19:00Day 19, 07:00
283Day 21, 18:40Day 22, 14:07
294Day 22, 16:20Day 24, 14:00
305Day 25, 21:00Day 28, 21:00
316Day 29, 19:15Day 30, 22:00
321Day 22, 08:08Day 22, 13:25
332Day 22, 16:00Day 23, 07:30
343Day 23, 10:10Day 24, 05:00
354Day 24, 06:00Day 25, 07:00
365Day 25, 18:15Day 28, 12:00
376Day 28, 22:00Day 29, 20:00
387Day 29, 20:30Day 29, 20:45
398Day 30, 00:01Day 30, 11:10
409Day 30, 16:41Day 02, 16:00
4110Day 02, 17:00Day 05, 14:00
4211Day 05, 20:00Day 08, 14:30
4312Day 09, 15:18Day 10, 08:00
4413Day 10, 11:35Day 12, 15:00
4514Day 12, 15:30Day 13, 14:30
461Day 04, 01:13Day 04, 05:00
471Day 04, 17:41Day 04, 17:53
482Day 04, 21:08Day 07, 15:00
491Day 12, 14:00Day 15, 14:00
502Day 15, 15:00Day 16, 15:00
511Day 12, 18:00Day 13, 15:00
521Day 03, 17:00Day 03, 18:00
532Day 03, 21:16Day 06, 13:00
541Day 03, 16:00Day 06, 14:00
552Day 06, 17:00Day 09, 10:00
563Day 09, 11:00Day 11, 07:00
574Day 12, 14:37Day 13, 22:00
581Day 11, 15:10Day 11, 17:30
592Day 12, 12:19Day 15, 15:00
603Day 15, 15:45Day 19, 10:00
614Day 19, 13:00Day 22, 19:00
621Day 05, 18:00Day 05, 19:00
632Day 05, 19:02Day 07, 16:00
641Day 21, 18:30Day 24, 11:00
652Day 24, 12:10Day 25, 01:00
663Day 25, 01:03Day 25, 10:15
674Day 25, 14:30Day 26, 13:00
685Day 26, 17:00Day 27, 08:00
696Day 27, 13:47Day 27, 13:52
707Day 27, 14:00Day 29, 06:00
711Day 25, 20:45Day 25, 23:00
722Day 26, 00:00Day 27, 07:00
733Day 27, 11:00Day 28, 15:00
741Day 03, 21:00Day 04, 03:00
751Day 15, 00:45Day 17, 12:30
761Day 05, 23:20Day 06, 08:00
772Day 06, 09:00Day 08, 20:00
783Day 08, 23:00Day 10, 11:12
791Day 25, 14:12Day 28, 14:30
802Day 28, 15:00Day 01, 09:00
813Day 04, 19:00Day 06, 21:26
824Day 06, 23:00Day 07, 01:00
835Day 07, 02:00Day 07, 02:18
846Day 07, 03:00Day 08, 08:00
857Day 08, 12:46Day 12, 12:00
868Day 15, 15:56Day 16, 12:45
871Day 30, 18:36Day 30, 19:00
882Day 30, 19:32Day 01, 02:54
893Day 01, 03:00Day 01, 15:00
904Day 01, 16:00Day 04, 19:00
915Day 04, 21:00Day 07, 11:00
926Day 07, 16:00Day 08, 14:00
937Day 11, 18:00Day 12, 09:00
948Day 12, 14:00Day 12, 18:00
959Day 13, 17:15Day 13, 17:18
9610Day 13, 19:00Day 15, 02:00
9711Day 15, 05:00Day 16, 08:00
9812Day 16, 11:00Day 17, 11:00
991Day 22, 21:08Day 25, 00:30
1002Day 25, 01:00Day 26, 23:00
1011Day 13, 20:51Day 16, 07:00
1021Day 02, 20:30Day 05, 09:00
1031Day 01, 03:00Day 01, 04:00
1041Day 02, 20:45Day 05, 21:00
1051Day 07, 10:00Day 08, 00:00
1062Day 08, 01:45Day 09, 13:00
1073Day 09, 14:00Day 09, 18:00
1084Day 09, 21:00Day 10, 05:00
1095Day 10, 06:00Day 10, 13:00
1106Day 10, 15:00Day 10, 19:00
1117Day 10, 21:00Day 11, 00:00
1128Day 11, 02:00Day 11, 08:00
1139Day 11, 11:00Day 11, 19:00
11410Day 11, 20:00Day 12, 06:00
11511Day 13, 15:00Day 14, 00:00
11612Day 14, 03:15Day 14, 03:55
11713Day 14, 11:00Day 14, 12:00
11814Day 14, 12:45Day 14, 20:00
11915Day 14, 21:00Day 15, 04:00
12016Day 15, 04:50Day 15, 13:00
1211Day 01, 13:54Day 02, 16:14
1222Day 02, 23:00Day 04, 09:00
1233Day 04, 15:00Day 05, 09:00
1244Day 05, 10:00Day 05, 15:00
1251Day 01, 14:00Day 02, 11:00
1262Day 02, 19:00Day 02, 20:00
1273Day 02, 20:30Day 03, 15:00
1284Day 03, 16:00Day 04, 18:00
1291Day 08, 20:10Day 11, 10:00
1302Day 11, 11:00Day 11, 15:07
1313Day 12, 01:00Day 12, 01:01
1324Day 12, 01:03Day 12, 21:12
1335Day 12, 23:00Day 13, 00:00
1346Day 13, 14:00Day 14, 08:06
1357Day 14, 13:51Day 15, 19:00
1361Day 09, 18:00Day 12, 15:15
1372Day 12, 16:02Day 12, 19:01
1383Day 12, 21:00Day 13, 14:03
1391Day 07, 16:15Day 08, 14:00
1402Day 08, 16:00Day 09, 13:00
1413Day 09, 14:00Day 10, 09:00
1424Day 10, 10:00Day 12, 03:00
1431Day 20, 22:00Day 23, 20:24
1442Day 23, 22:25Day 24, 11:00
1453Day 24, 16:00Day 24, 18:00
1464Day 24, 19:00Day 26, 00:00
1471Day 04, 16:00Day 05, 12:00
1482Day 05, 12:30Day 06, 03:00
1493Day 06, 04:00Day 06, 12:15
1504Day 06, 12:45Day 08, 13:45
1515Day 08, 14:00Day 08, 15:00
1526Day 08, 15:04Day 11, 03:00
1531Day 22, 14:00Day 24, 06:00
1542Day 24, 16:00Day 25, 14:00
1553Day 25, 16:30Day 28, 00:00
1561Day 02, 18:51Day 03, 14:00
1572Day 03, 19:00Day 04, 04:16
1583Day 04, 06:37Day 04, 09:00
1594Day 04, 12:00Day 05, 16:10
1605Day 05, 20:00Day 08, 21:00
1616Day 08, 21:15Day 09, 15:30
1627Day 09, 16:00Day 09, 16:30
1638Day 09, 17:00Day 12, 14:00
1641Day 12, 21:56Day 13, 04:00
1652Day 13, 05:08Day 14, 11:00
1663Day 14, 11:55Day 15, 09:00
1671Day 24, 12:25Day 24, 13:00
1682Day 24, 13:15Day 25, 08:00
1693Day 25, 10:30Day 27, 05:00
1704Day 27, 06:00Day 27, 19:00
1711Day 28, 23:36Day 29, 08:00
1722Day 29, 08:19Day 31, 01:45
1733Day 31, 14:11Day 02, 18:00
1744Day 02, 19:00Day 03, 05:43
1755Day 03, 08:00Day 03, 12:00
1761Day 21, 23:30Day 22, 12:00
1772Day 23, 12:20Day 23, 12:55
1783Day 23, 14:15Day 23, 16:00
1794Day 23, 16:15Day 25, 18:00
1805Day 25, 22:00Day 26, 03:00
1816Day 26, 07:00Day 28, 10:00
1827Day 30, 00:00Day 30, 13:04
1838Day 30, 17:35Day 02, 16:00
1849Day 02, 18:00Day 04, 03:00
18510Day 04, 06:00Day 05, 07:00
1861Day 28, 06:18Day 28, 09:00
1872Day 28, 10:00Day 28, 20:00
1883Day 28, 23:00Day 30, 13:22
1891Day 23, 12:06Day 23, 14:00
1901Day 20, 15:00Day 21, 07:00
1912Day 21, 10:00Day 23, 04:00
1923Day 23, 06:00Day 26, 05:00
1934Day 26, 06:00Day 27, 18:00
1941Day 05, 22:00Day 08, 21:00
1952Day 08, 23:00Day 11, 19:00
1961Day 26, 17:24Day 27, 11:15
1972Day 27, 11:47Day 28, 08:00
1983Day 28, 18:00Day 29, 17:00
1994Day 29, 21:30Day 30, 20:00
2005Day 30, 23:19Day 31, 18:00
2016Day 31, 23:00Day 01, 18:00
2021Day 31, 22:00Day 01, 11:00
2032Day 01, 16:00Day 01, 20:15
2043Day 02, 00:03Day 02, 04:00
2054Day 02, 18:00Day 03, 14:28
2065Day 03, 17:59Day 03, 18:00
2076Day 03, 18:01Day 03, 21:00
2087Day 03, 23:00Day 04, 05:00
2091Day 04, 20:20Day 06, 08:00
2102Day 06, 09:00Day 06, 23:18
2111Day 25, 16:04Day 27, 05:00
2122Day 27, 06:00Day 27, 19:05
2133Day 27, 22:00Day 30, 23:00
2144Day 01, 01:00Day 02, 21:00
2151Day 14, 16:00Day 14, 23:00
2162Day 14, 23:10Day 15, 09:00
2171Day 15, 23:36Day 17, 15:00
2182Day 17, 16:00Day 18, 09:00
2191Day 24, 12:00Day 27, 00:00
2202Day 27, 02:00Day 28, 12:05
2211Day 19, 21:00Day 19, 21:06
2222Day 19, 21:11Day 22, 21:00
2233Day 22, 22:00Day 25, 10:00
2244Day 25, 11:00Day 27, 11:00
2251Day 17, 07:42Day 17, 14:15
2261Day 12, 18:00Day 14, 14:09
2272Day 14, 18:01Day 15, 12:00
2283Day 15, 15:00Day 16, 15:00
2294Day 16, 23:00Day 17, 13:00
2301Day 13, 17:30Day 14, 16:00
2312Day 14, 17:30Day 16, 14:00
2323Day 16, 15:15Day 17, 20:00
2331Day 14, 03:19Day 14, 15:30
2342Day 14, 16:11Day 15, 10:00
2353Day 15, 11:03Day 15, 14:00
2364Day 15, 18:24Day 17, 11:00
2375Day 17, 13:00Day 17, 22:00
2386Day 18, 03:00Day 18, 08:00
2391Day 26, 16:18Day 27, 02:00
2402Day 27, 02:33Day 27, 23:00
2413Day 28, 00:00Day 30, 02:00
2424Day 30, 03:00Day 30, 13:00
2435Day 02, 18:27Day 04, 03:00
2441Day 03, 20:19Day 06, 08:33
2452Day 06, 12:27Day 07, 14:00
2463Day 08, 02:30Day 08, 07:16
2474Day 08, 11:00Day 08, 12:00
2481Day 03, 02:00Day 05, 04:39
2492Day 05, 07:00Day 07, 11:29
2501Day 03, 02:35Day 03, 15:00
2511Day 17, 20:00Day 19, 12:00
2521Day 18, 21:26Day 19, 13:00
2532Day 19, 15:48Day 19, 18:00
2543Day 19, 20:07Day 20, 11:00
2551Day 04, 22:10Day 07, 05:00
2562Day 07, 06:00Day 08, 01:00
2573Day 08, 04:00Day 08, 06:00
2581Day 06, 04:00Day 06, 07:00
2592Day 06, 09:00Day 06, 13:00
2603Day 06, 13:23Day 07, 08:00
2614Day 07, 17:48Day 08, 08:00
2621Day 19, 00:00Day 20, 19:00
2631Day 15, 00:00Day 15, 04:00
2641Day 27, 17:00Day 27, 22:00
2652Day 28, 08:38Day 02, 06:00
2663Day 02, 10:15Day 02, 15:00
2674Day 02, 18:00Day 04, 08:00
2685Day 04, 08:45Day 06, 12:00
2696Day 06, 19:00Day 08, 16:00
2701Day 27, 17:25Day 29, 08:11
2711Day 13, 14:36Day 14, 11:30
2722Day 14, 15:30Day 16, 14:00
2733Day 16, 15:00Day 17, 01:00
2741Day 19, 22:31Day 20, 07:12
2752Day 20, 15:26Day 21, 12:00
2763Day 21, 12:20Day 22, 05:00
2774Day 22, 06:00Day 22, 08:55
2781Day 07, 21:30Day 07, 22:00
2792Day 07, 22:18Day 09, 20:00
2803Day 09, 21:00Day 10, 05:00
2814Day 10, 08:00Day 10, 09:00
2825Day 10, 15:00Day 13, 17:01
2836Day 13, 20:30Day 14, 05:00
2847Day 14, 06:00Day 14, 12:05
2858Day 14, 15:00Day 15, 16:00
2869Day 16, 00:00Day 16, 16:00
28710Day 16, 19:30Day 17, 01:00
2881Day 26, 10:18Day 27, 09:00
2892Day 27, 10:00Day 28, 08:00
2903Day 28, 11:00Day 28, 12:00
2914Day 28, 20:00Day 29, 14:00
2921Day 15, 05:00Day 15, 06:00
2932Day 15, 09:38Day 15, 16:00
2943Day 15, 16:23Day 15, 20:00
2951Day 16, 01:00Day 16, 13:00
2962Day 16, 13:57Day 17, 15:00
2973Day 17, 18:00Day 19, 05:00
2984Day 19, 06:00Day 20, 10:15
2995Day 20, 10:55Day 20, 11:05
3006Day 20, 13:50Day 20, 14:00
3017Day 20, 14:19Day 21, 10:15
3028Day 23, 20:00Day 24, 04:00
3039Day 24, 05:00Day 24, 10:00
30410Day 24, 13:00Day 25, 11:46
30511Day 25, 19:00Day 26, 08:50
30612Day 29, 20:00Day 29, 23:00
30713Day 30, 01:00Day 30, 02:00
30814Day 30, 16:00Day 01, 02:13
30915Day 01, 05:30Day 01, 14:00
31016Day 01, 19:13Day 02, 12:00
3111Day 01, 16:00Day 02, 15:00
3122Day 02, 15:30Day 05, 15:00
3131Day 29, 09:30Day 30, 19:00
3141Day 12, 02:00Day 13, 06:03
3151Day 04, 17:00Day 04, 19:00
3162Day 04, 19:15Day 05, 08:00
3173Day 05, 08:37Day 07, 21:00
3184Day 09, 17:44Day 10, 11:00
3191Day 29, 17:38Day 29, 19:00
3202Day 30, 00:00Day 31, 15:00
3211Day 04, 15:00Day 07, 15:09
3221Day 05, 17:00Day 07, 18:00
3231Day 26, 22:00Day 29, 23:00
3242Day 30, 01:00Day 30, 11:00
3251Day 09, 22:20Day 12, 18:00
3261Day 22, 05:01Day 23, 15:00
3272Day 23, 22:00Day 23, 22:33
3283Day 23, 22:36Day 25, 11:00
3294Day 25, 15:30Day 28, 08:00
3305Day 29, 17:00Day 01, 03:00
3316Day 01, 04:00Day 02, 06:00
3327Day 02, 07:00Day 02, 23:00
3338Day 03, 01:00Day 04, 16:00
3349Day 05, 10:00Day 05, 20:00
33510Day 05, 21:00Day 07, 10:00
33611Day 07, 19:00Day 07, 21:00
33712Day 07, 22:00Day 09, 14:38
33813Day 11, 17:30Day 12, 12:00
33914Day 12, 13:00Day 14, 11:00
34015Day 03, 17:30Day 04, 06:00
34116Day 04, 06:50Day 04, 09:00
34217Day 04, 10:32Day 05, 14:00
3431Day 24, 16:00Day 24, 22:00
3442Day 24, 23:58Day 25, 11:30
3453Day 25, 13:45Day 28, 10:00
3461Day 10, 19:36Day 11, 09:45
3472Day 11, 13:00Day 12, 05:30
3483Day 12, 06:51Day 12, 07:00
3494Day 12, 12:00Day 14, 06:00
3505Day 14, 07:00Day 15, 05:20
3516Day 15, 06:00Day 17, 07:00
3527Day 17, 10:00Day 18, 01:30
3538Day 18, 03:00Day 18, 04:30
3549Day 18, 06:00Day 18, 13:00
35510Day 18, 19:00Day 18, 22:00
35611Day 18, 23:00Day 19, 06:00
3571Day 29, 11:37Day 01, 10:00
3582Day 01, 13:00Day 02, 21:00
3593Day 04, 16:00Day 05, 12:00
3604Day 05, 17:00Day 06, 05:00
3615Day 06, 07:00Day 06, 08:00
3626Day 06, 12:00Day 09, 12:00
3637Day 09, 16:00Day 12, 15:00
3641Day 26, 22:00Day 28, 04:00
3652Day 28, 18:11Day 29, 01:00
3663Day 29, 20:00Day 30, 22:00
3674Day 31, 00:35Day 01, 07:00
3685Day 01, 09:00Day 01, 11:00
3696Day 01, 14:00Day 03, 14:00
3707Day 03, 15:00Day 03, 17:00
3718Day 04, 05:00Day 04, 13:00
3729Day 04, 16:00Day 05, 16:00
37310Day 05, 18:00Day 06, 09:05
37411Day 06, 13:00Day 06, 16:00
37512Day 07, 04:00Day 09, 23:00
37613Day 10, 02:00Day 13, 03:00
37714Day 13, 03:30Day 13, 08:00
37815Day 15, 13:02Day 18, 00:00
37916Day 18, 01:00Day 18, 16:00
38017Day 18, 18:00Day 20, 13:00
38118Day 20, 15:00Day 21, 14:00
3821Day 21, 21:00Day 22, 04:00
3832Day 22, 05:00Day 22, 06:00
3843Day 22, 08:40Day 22, 16:00
3851Day 28, 23:15Day 29, 04:30
3862Day 29, 12:15Day 30, 09:00
3873Day 30, 10:00Day 01, 20:00
3884Day 02, 02:00Day 02, 11:30
3895Day 02, 12:45Day 03, 09:00
3901Day 25, 17:40Day 25, 17:50
3912Day 25, 21:15Day 26, 05:00
3923Day 26, 07:00Day 26, 14:00
3934Day 26, 17:35Day 26, 18:00
3945Day 26, 20:00Day 27, 18:00
3956Day 27, 22:00Day 27, 22:15
3967Day 29, 20:00Day 29, 20:02
3978Day 29, 20:17Day 30, 04:15
3989Day 30, 14:05Day 01, 19:00
39910Day 01, 22:00Day 04, 13:35
40011Day 04, 15:00Day 05, 03:00
40112Day 05, 04:00Day 06, 00:00
40213Day 06, 05:00Day 06, 18:00
40314Day 06, 20:00Day 07, 11:00
40415Day 07, 16:05Day 07, 21:00
40516Day 08, 10:17Day 11, 10:29
4061Day 16, 21:00Day 18, 21:00
4071Day 26, 12:23Day 27, 15:00
4082Day 27, 16:17Day 30, 13:45
4093Day 04, 12:53Day 07, 15:00
4104Day 07, 15:30Day 09, 15:00
4111Day 25, 15:55Day 27, 14:00
4122Day 27, 16:00Day 28, 19:00
4133Day 29, 02:00Day 01, 08:00
4141Day 13, 17:00Day 14, 09:00
4152Day 14, 10:30Day 14, 10:41
4163Day 14, 10:42Day 16, 07:17
4171Day 17, 05:00Day 17, 08:00
4182Day 17, 10:15Day 17, 12:54
4193Day 17, 13:00Day 17, 23:45
4204Day 18, 00:20Day 18, 06:00
4215Day 18, 07:00Day 18, 12:56
4226Day 18, 14:41Day 18, 21:08
4231Day 04, 00:00Day 04, 00:20
4242Day 04, 01:58Day 04, 15:20
4253Day 04, 21:00Day 05, 19:00
4264Day 05, 20:00Day 06, 10:00
4275Day 06, 11:06Day 07, 07:00
4286Day 07, 11:00Day 09, 09:06
4291Day 23, 01:50Day 24, 02:00
4301Day 12, 21:00Day 13, 20:00
4312Day 14, 11:10Day 15, 11:00
4321Day 19, 02:46Day 19, 02:48
4331Day 22, 15:00Day 22, 17:00
4342Day 22, 22:35Day 25, 23:25
4351Day 30, 15:00Day 01, 11:00
4361Day 03, 10:15Day 03, 13:07
4371Day 15, 22:30Day 17, 12:00
4381Day 12, 15:00Day 15, 14:00
4392Day 15, 14:54Day 18, 15:00
4403Day 18, 16:04Day 22, 20:13
4414Day 22, 21:35Day 24, 16:00
4421Day 06, 18:15Day 07, 00:54
4432Day 07, 01:00Day 09, 11:00
4443Day 09, 13:55Day 10, 09:00
4454Day 12, 12:50Day 15, 12:00
4465Day 21, 16:43Day 23, 21:13
4476Day 03, 22:00Day 07, 01:00
4487Day 07, 02:00Day 08, 06:06
4491Day 03, 20:37Day 04, 08:00
4502Day 05, 10:00Day 05, 11:00
4513Day 05, 11:14Day 07, 13:00
4521Day 20, 16:35Day 20, 22:00
4532Day 27, 17:00Day 28, 01:00
4543Day 28, 03:00Day 28, 13:15
4551Day 02, 19:05Day 05, 21:00
4562Day 06, 00:00Day 06, 06:00
4573Day 06, 11:30Day 06, 16:05
4584Day 06, 21:00Day 07, 18:00
4591Day 21, 01:00Day 21, 09:00
4602Day 21, 11:00Day 22, 19:00
4613Day 22, 19:15Day 24, 04:42
4624Day 24, 05:00Day 27, 00:00
4635Day 27, 01:00Day 27, 11:15
4641Day 25, 13:12Day 27, 12:00
4652Day 29, 17:45Day 30, 14:00
4663Day 30, 18:00Day 01, 18:00
4671Day 27, 21:28Day 29, 09:30
4682Day 29, 13:00Day 30, 08:15
4693Day 30, 16:00Day 01, 14:48
4701Day 24, 23:20Day 25, 10:00
4712Day 25, 10:30Day 27, 13:00
4723Day 27, 14:00Day 28, 02:06
4734Day 28, 03:14Day 01, 12:00
4741Day 22, 20:45Day 24, 14:00
4751Day 12, 20:34Day 12, 21:00
4762Day 12, 22:00Day 13, 07:00
4773Day 13, 18:20Day 15, 17:30
4784Day 15, 20:30Day 16, 19:00
4795Day 16, 23:00Day 17, 17:45
4801Day 08, 01:53Day 08, 12:45
4812Day 08, 17:22Day 11, 11:00
4823Day 14, 17:00Day 17, 03:00
4834Day 17, 04:00Day 17, 09:00
4845Day 18, 04:00Day 19, 09:00
4851Day 19, 21:01Day 19, 22:07
4861Day 13, 21:25Day 17, 00:00
4872Day 17, 01:00Day 20, 00:52
4883Day 20, 01:30Day 21, 16:00
4894Day 22, 17:00Day 25, 02:00
4905Day 25, 03:00Day 27, 21:15
4916Day 27, 22:50Day 28, 10:00
4927Day 28, 13:30Day 29, 05:00
4938Day 29, 05:05Day 29, 12:00
4941Day 10, 18:00Day 11, 16:00
4951Day 31, 12:31Day 01, 13:00
4962Day 01, 14:00Day 01, 19:00
4973Day 01, 21:45Day 02, 01:00
4984Day 02, 02:00Day 02, 09:00
4995Day 02, 14:00Day 02, 15:00
5006Day 02, 16:00Day 05, 10:00
5017Day 05, 11:00Day 05, 13:00
5028Day 05, 14:00Day 06, 12:00
5039Day 06, 15:00Day 06, 18:00
50410Day 07, 05:00Day 07, 11:41
50511Day 07, 12:45Day 08, 01:15
50612Day 08, 02:00Day 08, 11:00
5071Day 27, 23:00Day 30, 14:00
5082Day 30, 16:00Day 01, 12:00
5091Day 02, 14:05Day 03, 14:00
5101Day 24, 12:42Day 24, 15:11
5112Day 24, 18:00Day 25, 01:00
5123Day 26, 18:00Day 27, 12:00
5134Day 27, 14:00Day 29, 12:00
5145Day 06, 00:00Day 07, 01:00
5156Day 07, 03:00Day 07, 14:00
5167Day 07, 15:00Day 08, 06:00
5178Day 08, 07:00Day 11, 04:00
5189Day 11, 05:00Day 11, 10:16
51910Day 14, 18:30Day 15, 00:00
52011Day 15, 00:15Day 16, 12:00
5211Day 26, 14:00Day 28, 11:15
5222Day 28, 16:00Day 28, 18:45
5233Day 28, 21:00Day 29, 07:00
5241Day 23, 15:30Day 24, 11:10
5251Day 08, 18:30Day 10, 09:00
5262Day 10, 10:38Day 11, 16:15
5273Day 11, 21:00Day 13, 12:57
5284Day 19, 10:29Day 19, 18:00
5295Day 19, 19:00Day 20, 14:00
5306Day 20, 17:00Day 22, 10:23
5311Day 02, 12:00Day 02, 16:40
5321Day 04, 22:58Day 05, 00:00
5332Day 05, 02:30Day 05, 14:00
5341Day 30, 23:30Day 02, 07:00
5351Day 02, 14:45Day 03, 12:15
5362Day 03, 16:00Day 06, 17:00
5373Day 06, 18:00Day 06, 22:40
5384Day 06, 23:00Day 07, 06:00
5395Day 08, 18:00Day 10, 17:15
5406Day 10, 18:00Day 10, 19:00
5417Day 10, 19:01Day 12, 21:00
5421Day 18, 21:00Day 20, 14:00
5431Day 23, 22:53Day 24, 01:00
5442Day 24, 03:00Day 24, 04:30
5451Day 08, 20:06Day 09, 01:16
5462Day 09, 06:43Day 10, 11:45
5471Day 13, 01:00Day 13, 02:45
5482Day 13, 03:00Day 15, 12:15
5493Day 15, 16:00Day 16, 02:30
5504Day 16, 03:35Day 17, 14:00
5515Day 17, 15:00Day 19, 09:00
5526Day 19, 12:00Day 19, 12:20
5537Day 19, 13:00Day 20, 09:45
5548Day 20, 16:00Day 21, 07:30
5559Day 21, 08:30Day 21, 11:50
5561Day 29, 21:00Day 29, 21:30
5572Day 29, 21:33Day 01, 21:00
5583Day 01, 22:00Day 03, 12:00
5591Day 05, 00:00Day 05, 14:00
5601Day 09, 19:00Day 12, 22:00
5612Day 12, 23:00Day 13, 05:00
5621Day 07, 18:00Day 08, 01:15
5632Day 08, 04:33Day 08, 12:15
5641Day 06, 01:00Day 06, 06:00
5651Day 12, 21:15Day 13, 08:00
5662Day 13, 18:00Day 14, 18:00
5671Day 11, 12:46Day 13, 10:00
5681Day 25, 20:10Day 28, 07:00
5691Day 05, 03:00Day 08, 01:00
5701Day 20, 03:02Day 21, 01:00
5712Day 21, 11:00Day 22, 20:00
5723Day 22, 21:00Day 24, 16:00
5731Day 06, 14:19Day 07, 17:00
5742Day 07, 19:00Day 09, 08:00
5753Day 09, 09:00Day 11, 14:00
5761Day 13, 20:00Day 14, 08:05
5771Day 01, 03:00Day 01, 14:00
5782Day 01, 22:00Day 03, 22:00
5793Day 03, 23:00Day 04, 15:00
5804Day 04, 19:00Day 05, 07:00
5815Day 05, 08:48Day 05, 14:00
5826Day 05, 17:11Day 05, 22:15
5837Day 05, 23:00Day 07, 14:03
5848Day 07, 15:00Day 08, 15:00
5859Day 08, 16:00Day 09, 07:00
58610Day 12, 17:45Day 13, 11:00
58711Day 13, 15:11Day 14, 16:00
5881Day 01, 18:41Day 02, 20:00
5892Day 02, 20:07Day 03, 13:15
5903Day 03, 14:12Day 05, 23:19
5914Day 06, 00:00Day 06, 10:15
5921Day 31, 03:28Day 31, 08:00
5932Day 31, 12:00Day 31, 20:00
5941Day 26, 22:52Day 27, 01:00
5952Day 27, 04:00Day 27, 15:00
5963Day 27, 18:00Day 28, 09:00
5971Day 10, 14:00Day 11, 22:00
5982Day 11, 23:00Day 12, 03:00
5993Day 12, 06:00Day 12, 12:00
6004Day 12, 12:07Day 12, 15:45
6015Day 12, 20:49Day 15, 16:00
6026Day 15, 18:00Day 16, 14:00
6037Day 16, 15:00Day 16, 17:00
6048Day 16, 18:00Day 18, 11:03
6059Day 18, 12:00Day 18, 13:00
60610Day 18, 14:00Day 19, 15:00
60711Day 19, 21:00Day 22, 09:00
6081Day 01, 00:01Day 01, 03:00
6092Day 01, 03:11Day 04, 00:00
6103Day 04, 01:00Day 05, 07:45
6114Day 05, 08:00Day 05, 16:00
6125Day 05, 18:00Day 06, 05:00
6136Day 06, 11:30Day 06, 14:00
6147Day 06, 16:00Day 06, 22:00
6158Day 06, 23:00Day 09, 13:00
6169Day 09, 14:45Day 09, 15:00
61710Day 09, 15:45Day 09, 20:16
61811Day 10, 00:14Day 10, 01:00
61912Day 10, 04:24Day 12, 14:00
62013Day 12, 15:00Day 12, 15:30
62114Day 12, 16:00Day 12, 18:20
62215Day 12, 20:59Day 16, 16:00
62316Day 16, 17:15Day 17, 04:00
62417Day 22, 17:18Day 23, 15:51
62518Day 26, 21:50Day 28, 00:00
6261Day 05, 04:08Day 06, 12:00
6272Day 06, 14:00Day 07, 16:00
6283Day 07, 22:00Day 08, 14:00
6291Day 10, 09:16Day 10, 14:00
6302Day 13, 12:23Day 13, 23:00
6313Day 13, 23:30Day 16, 06:00
6321Day 18, 16:45Day 20, 19:00
6332Day 21, 09:57Day 22, 09:00
6343Day 22, 10:00Day 22, 21:00
6354Day 22, 22:00Day 23, 13:00
6365Day 23, 15:00Day 26, 10:00
6376Day 27, 13:39Day 28, 00:00
6387Day 28, 01:00Day 30, 20:00
6398Day 30, 21:00Day 31, 09:00
6409Day 31, 16:00Day 01, 16:00
64110Day 01, 19:00Day 02, 13:00
64211Day 21, 13:00Day 22, 04:40
6431Day 05, 14:30Day 05, 16:28
6442Day 05, 22:00Day 08, 10:00
6453Day 08, 10:07Day 09, 14:27
6464Day 09, 18:00Day 12, 15:15
6471Day 03, 22:54Day 04, 13:00
6481Day 05, 22:57Day 06, 15:30
6492Day 06, 17:00Day 09, 00:00
6503Day 09, 01:00Day 11, 14:00
6514Day 11, 15:00Day 12, 18:00
6525Day 12, 20:30Day 14, 16:16
6536Day 14, 20:00Day 15, 05:00
6547Day 15, 06:00Day 15, 14:00
6558Day 15, 16:00Day 16, 15:00
6569Day 16, 16:00Day 17, 19:00
65710Day 17, 21:30Day 19, 17:00
65811Day 19, 17:47Day 21, 07:30
65912Day 22, 11:51Day 23, 11:00
66013Day 23, 12:00Day 24, 23:00
6611Day 06, 12:10Day 10, 16:29
6621Day 07, 21:00Day 08, 12:00
6632Day 08, 13:00Day 08, 23:22
6643Day 09, 00:00Day 09, 14:00
6654Day 10, 02:00Day 12, 02:00
6665Day 12, 02:16Day 13, 01:00
6676Day 13, 01:11Day 13, 06:11
6687Day 13, 07:19Day 14, 10:00
6698Day 14, 22:27Day 15, 10:00
6709Day 15, 12:00Day 17, 17:00
67110Day 17, 17:40Day 20, 14:00
67211Day 23, 16:30Day 24, 02:00
67312Day 24, 02:03Day 27, 02:00
67413Day 27, 03:00Day 27, 16:00
67514Day 31, 19:10Day 01, 13:00
67615Day 01, 18:00Day 02, 23:00
67716Day 03, 01:00Day 04, 09:09
67817Day 04, 11:00Day 04, 14:00
6791Day 22, 12:00Day 23, 08:00
6802Day 23, 12:00Day 26, 11:00
6813Day 26, 12:00Day 26, 12:30
6824Day 26, 14:00Day 27, 00:00
6835Day 27, 00:24Day 27, 17:45
6846Day 27, 18:35Day 28, 15:00
6857Day 28, 18:00Day 30, 03:00
6868Day 30, 03:20Day 02, 13:12
6879Day 02, 19:00Day 05, 12:04
6881Day 02, 22:23Day 04, 05:00
6892Day 04, 07:00Day 06, 11:15
6901Day 29, 22:00Day 30, 10:00
6912Day 30, 11:00Day 01, 14:00
6923Day 04, 18:24Day 07, 10:00
6934Day 07, 11:00Day 08, 20:00
6941Day 26, 18:07Day 27, 12:00
6952Day 27, 17:00Day 29, 15:00
6963Day 29, 17:00Day 29, 18:00
6974Day 29, 18:05Day 01, 09:00
6981Day 05, 21:45Day 08, 08:00
6992Day 08, 12:00Day 10, 15:00
7003Day 10, 18:00Day 11, 04:52
7014Day 11, 06:00Day 11, 06:15
7025Day 11, 06:16Day 11, 07:02
7036Day 11, 12:45Day 12, 03:40
7047Day 15, 05:00Day 15, 21:00
7051Day 12, 20:35Day 14, 19:00
7062Day 14, 20:00Day 15, 05:00
7071Day 10, 09:00Day 10, 15:00
7082Day 10, 15:45Day 10, 17:00
7093Day 10, 21:30Day 11, 00:45
7104Day 13, 11:02Day 13, 21:00
7115Day 13, 21:53Day 13, 23:26
7126Day 19, 10:00Day 22, 17:00
7137Day 22, 17:27Day 25, 16:00
7148Day 25, 18:00Day 26, 07:00
7151Day 01, 01:52Day 03, 22:00
7162Day 04, 00:00Day 04, 02:00
7171Day 11, 18:20Day 12, 08:00
7182Day 12, 15:00Day 13, 09:00
7191Day 08, 23:30Day 09, 16:00
7201Day 06, 23:25Day 09, 11:36
7212Day 09, 14:41Day 11, 11:19
7223Day 21, 01:45Day 21, 15:03
7234Day 21, 22:06Day 24, 05:00
7245Day 24, 08:30Day 24, 20:06
7256Day 24, 23:40Day 26, 13:44
7261Day 30, 00:00Day 30, 05:00
7272Day 30, 08:00Day 30, 09:00
7283Day 30, 13:00Day 30, 18:00
7294Day 30, 23:50Day 30, 23:54
7305Day 31, 00:00Day 02, 11:00
7316Day 02, 21:00Day 02, 22:00
7327Day 02, 22:04Day 03, 21:00
7338Day 04, 02:00Day 05, 10:00
7341Day 01, 11:31Day 03, 06:00
7352Day 03, 08:04Day 03, 09:03
7361Day 24, 21:35Day 25, 18:00
7372Day 25, 19:00Day 26, 10:30
7383Day 26, 11:00Day 27, 16:00
7391Day 14, 01:00Day 15, 10:30
7402Day 15, 12:00Day 15, 17:30
7413Day 15, 19:00Day 16, 08:30
7424Day 16, 13:45Day 16, 14:15
7435Day 16, 23:52Day 17, 09:15
7446Day 17, 10:30Day 18, 22:00
7457Day 19, 00:00Day 19, 14:00
7468Day 19, 17:00Day 19, 17:15
7479Day 19, 19:30Day 20, 10:00
7481Day 28, 20:02Day 29, 20:41
7492Day 29, 22:37Day 31, 16:00
7503Day 31, 18:00Day 02, 00:15
7511Day 24, 18:30Day 27, 16:21
7521Day 14, 00:48Day 14, 11:00
7531Day 09, 13:05Day 12, 14:00
7542Day 12, 15:00Day 15, 03:00
7553Day 15, 04:00Day 18, 06:00
7564Day 18, 06:05Day 22, 13:00
7575Day 23, 12:30Day 24, 04:00
7586Day 24, 06:00Day 25, 23:00
7591Day 25, 05:00Day 25, 11:00
7601Day 01, 17:00Day 03, 06:00
7612Day 03, 06:10Day 05, 06:00
7623Day 26, 22:45Day 27, 03:15
7634Day 27, 03:45Day 28, 12:30
7645Day 28, 16:34Day 29, 20:00
7656Day 29, 20:45Day 30, 09:30
7667Day 30, 10:00Day 30, 12:00
7678Day 30, 12:45Day 01, 10:30
7681Day 02, 06:23Day 04, 11:00
7691Day 25, 23:41Day 26, 11:00
7702Day 26, 14:00Day 26, 18:00
7713Day 26, 22:00Day 27, 14:00
7724Day 27, 20:00Day 28, 12:00
7735Day 28, 15:00Day 29, 10:00
7746Day 29, 22:00Day 30, 10:06
7751Day 11, 05:00Day 11, 13:00
7762Day 11, 17:00Day 11, 22:00
7773Day 12, 04:00Day 12, 18:00
7784Day 12, 22:42Day 14, 14:39
7795Day 14, 14:45Day 16, 12:00
7806Day 16, 18:00Day 18, 21:00
7817Day 19, 10:51Day 20, 09:30
7828Day 20, 12:00Day 21, 07:00
7831Day 15, 07:00Day 15, 14:00
7842Day 15, 19:00Day 17, 18:00
7853Day 17, 20:00Day 18, 11:30
7861Day 21, 20:28Day 21, 22:00
7872Day 22, 04:00Day 22, 11:00
7881Day 05, 00:00Day 06, 18:30
7892Day 06, 20:00Day 09, 09:00
7903Day 09, 09:30Day 09, 17:30
7914Day 09, 18:00Day 10, 04:30
7921Day 15, 17:16Day 16, 16:00
7932Day 16, 17:00Day 17, 11:00
7943Day 17, 12:00Day 17, 16:00
7954Day 17, 19:00Day 19, 09:00
7965Day 19, 10:00Day 19, 19:00
7976Day 19, 19:45Day 20, 11:00
7987Day 20, 12:00Day 21, 08:00
7998Day 21, 11:00Day 21, 20:00
8009Day 21, 21:00Day 22, 08:00
80110Day 22, 11:00Day 23, 02:20
8021Day 03, 01:21Day 03, 15:00
8032Day 03, 16:00Day 05, 15:00
8043Day 05, 18:58Day 06, 16:18
8054Day 06, 18:00Day 07, 14:00
8065Day 07, 15:00Day 11, 13:00
8076Day 13, 22:08Day 14, 05:00
8087Day 14, 06:00Day 14, 10:00
8098Day 14, 13:00Day 16, 07:00
8109Day 16, 15:00Day 17, 23:00
81110Day 17, 23:17Day 20, 11:00
81211Day 20, 11:30Day 20, 13:00
81312Day 20, 14:00Day 24, 16:00
8141Day 05, 12:00Day 05, 23:00
8152Day 06, 04:00Day 07, 23:12
8161Day 19, 13:00Day 19, 15:00
8171Day 07, 17:07Day 08, 00:00
8182Day 08, 08:00Day 08, 21:00
8193Day 08, 22:52Day 09, 11:00
8204Day 09, 12:49Day 09, 13:47
8215Day 11, 12:15Day 12, 12:10
8226Day 12, 15:30Day 14, 11:00
8237Day 14, 12:00Day 14, 20:00
8248Day 14, 22:00Day 15, 12:00
8259Day 15, 14:00Day 17, 11:30
8261Day 19, 11:26Day 20, 14:04
8271Day 10, 22:23Day 12, 10:00
8282Day 12, 21:32Day 13, 07:00
8293Day 13, 23:27Day 14, 11:00
8301Day 15, 21:00Day 16, 09:00
8312Day 16, 09:15Day 16, 12:01
8323Day 16, 15:00Day 17, 07:00
8334Day 23, 21:45Day 24, 17:00
8345Day 24, 18:00Day 24, 20:00
8356Day 24, 21:00Day 25, 15:00
8361Day 13, 21:00Day 14, 04:00
8371Day 11, 18:00Day 11, 18:50
8382Day 11, 18:51Day 13, 05:00
8393Day 13, 07:00Day 14, 11:45
8404Day 14, 13:00Day 16, 14:20
8415Day 16, 15:15Day 18, 03:00
8426Day 18, 04:00Day 20, 04:00
8437Day 20, 08:50Day 22, 05:00
8448Day 24, 18:50Day 25, 08:00
8451Day 25, 17:59Day 27, 16:00
8462Day 27, 19:00Day 29, 22:00
8473Day 29, 23:00Day 30, 08:00
8481Day 04, 01:59Day 04, 08:00
8492Day 04, 11:00Day 07, 11:42
8501Day 08, 21:46Day 09, 09:48
8512Day 09, 13:00Day 10, 03:00
8523Day 10, 06:00Day 10, 17:00
8534Day 10, 21:00Day 11, 14:17
8545Day 11, 15:00Day 13, 09:00
8556Day 13, 15:00Day 14, 09:00
8567Day 14, 11:00Day 15, 11:00
8578Day 15, 12:00Day 16, 00:00
8581Day 18, 04:00Day 18, 07:30
8591Day 22, 18:09Day 23, 18:30
8602Day 26, 15:59Day 27, 15:00
8613Day 27, 16:00Day 28, 16:22
8624Day 28, 22:00Day 02, 11:34
8635Day 02, 16:34Day 04, 14:00
8646Day 04, 15:00Day 05, 16:00
8657Day 06, 20:00Day 07, 09:00
8668Day 07, 10:00Day 09, 13:00
8679Day 09, 15:00Day 11, 10:00
86810Day 11, 15:00Day 14, 14:00
86911Day 16, 01:45Day 17, 14:30
87012Day 17, 20:00Day 18, 15:00
87113Day 18, 18:45Day 20, 01:00
8721Day 30, 21:09Day 01, 07:00
8732Day 01, 11:00Day 01, 18:15
8741Day 31, 18:10Day 01, 06:00
8752Day 01, 15:00Day 03, 06:00
8763Day 03, 10:12Day 03, 10:25
8774Day 03, 11:00Day 05, 12:00
8781Day 16, 18:00Day 19, 15:00
8791Day 22, 00:23Day 22, 03:00
8801Day 18, 19:17Day 19, 01:00
8812Day 19, 03:00Day 20, 06:00
8823Day 20, 09:15Day 22, 10:00
8834Day 22, 11:00Day 22, 14:00
8845Day 22, 19:00Day 23, 09:00
8856Day 23, 12:00Day 24, 11:00
8867Day 24, 12:00Day 24, 13:00
8878Day 24, 15:00Day 25, 13:00
8881Day 07, 09:30Day 07, 18:00
8892Day 07, 23:00Day 08, 07:03
8901Day 29, 12:35Day 30, 12:00
8912Day 30, 13:00Day 01, 09:00
8923Day 01, 19:00Day 02, 19:00
8934Day 02, 20:00Day 03, 00:00
8945Day 03, 00:39Day 03, 10:00
8956Day 03, 14:00Day 04, 07:00
8967Day 05, 11:02Day 06, 06:00
8978Day 06, 16:30Day 11, 10:00
8981Day 06, 23:48Day 09, 11:00
8991Day 15, 17:03Day 15, 21:00
9002Day 15, 21:29Day 18, 13:00
9013Day 18, 18:00Day 20, 23:00
9024Day 20, 23:14Day 23, 09:00
9031Day 15, 02:01Day 16, 09:00
9042Day 16, 13:00Day 18, 04:07
9051Day 27, 02:00Day 27, 04:30
9062Day 27, 05:00Day 27, 16:00
9073Day 27, 22:00Day 27, 23:00
9084Day 28, 00:00Day 28, 20:40
9091Day 28, 04:30Day 28, 09:00
9101Day 17, 22:40Day 18, 20:00
9112Day 18, 21:00Day 19, 00:00
9123Day 19, 01:00Day 19, 04:00
9134Day 19, 05:00Day 20, 08:00
9145Day 20, 09:00Day 20, 16:15
9156Day 20, 18:00Day 21, 08:00
9167Day 21, 09:00Day 21, 10:34
9178Day 21, 11:00Day 22, 00:00
9189Day 22, 00:49Day 22, 17:00
91910Day 22, 21:00Day 22, 21:17
92011Day 22, 22:00Day 23, 04:00
9211Day 01, 23:19Day 02, 05:00
9222Day 02, 08:48Day 02, 19:00
9233Day 03, 12:00Day 03, 12:44
9244Day 03, 13:00Day 04, 00:12
9255Day 04, 03:00Day 05, 10:00
9266Day 05, 11:30Day 06, 10:00
9271Day 19, 23:00Day 22, 21:00
9281Day 24, 23:27Day 25, 17:45
9292Day 25, 20:00Day 26, 11:00
9303Day 26, 12:00Day 26, 13:45
9314Day 26, 15:45Day 26, 16:00
9325Day 26, 18:35Day 27, 19:00
9336Day 27, 21:00Day 28, 12:00
9347Day 28, 14:00Day 29, 14:00
9358Day 29, 16:00Day 01, 11:00
9369Day 01, 15:00Day 01, 23:00
93710Day 02, 00:00Day 02, 15:00
93811Day 02, 16:00Day 03, 16:00
93912Day 03, 23:00Day 04, 20:00
94013Day 04, 23:00Day 05, 16:00
94114Day 06, 20:27Day 07, 14:00
94215Day 07, 19:00Day 10, 16:00
94316Day 10, 22:20Day 13, 14:00
94417Day 13, 15:37Day 16, 15:00
94518Day 16, 16:13Day 19, 15:00
94619Day 19, 16:00Day 21, 11:49
9471Day 04, 21:00Day 07, 17:10
9481Day 10, 22:13Day 12, 22:20
9492Day 13, 01:48Day 13, 14:00
9503Day 13, 17:00Day 13, 21:20
9511Day 17, 16:29Day 18, 19:00
9521Day 18, 10:25Day 20, 14:00
9531Day 15, 20:48Day 18, 17:15
9542Day 18, 22:30Day 19, 09:14
9553Day 19, 12:47Day 20, 11:21
9561Day 20, 20:00Day 23, 17:00
9572Day 23, 18:00Day 24, 17:00
9581Day 07, 18:49Day 09, 16:27
9591Day 22, 12:00Day 22, 13:00
9602Day 22, 13:30Day 22, 18:30
9613Day 22, 23:01Day 23, 02:00
9624Day 23, 06:00Day 23, 11:00
9635Day 23, 12:00Day 23, 14:30
9646Day 23, 18:00Day 23, 23:00
9657Day 24, 04:00Day 24, 09:00
9668Day 24, 09:13Day 24, 13:00
9679Day 24, 17:20Day 25, 13:00
96810Day 25, 16:00Day 26, 11:10
9691Day 25, 19:00Day 26, 13:30
9701Day 03, 12:30Day 03, 15:00
9712Day 03, 20:16Day 04, 03:00
9721Day 22, 15:00Day 22, 15:15
9731Day 02, 00:01Day 02, 10:00
9742Day 02, 18:00Day 03, 11:00
9751Day 07, 00:00Day 07, 22:00
9762Day 08, 00:00Day 09, 20:00
9773Day 09, 22:00Day 12, 21:00
9784Day 13, 00:00Day 13, 13:00
9795Day 13, 15:00Day 13, 22:00
9806Day 13, 23:00Day 14, 04:00
9817Day 14, 13:00Day 15, 13:00
9828Day 15, 16:58Day 17, 13:00
9831Day 21, 21:30Day 22, 17:00
9842Day 22, 18:00Day 25, 04:05
9853Day 25, 05:00Day 25, 08:00
9864Day 25, 09:00Day 25, 11:00
9875Day 25, 12:00Day 26, 10:39
9881Day 10, 18:35Day 11, 07:00
9892Day 11, 16:26Day 12, 09:00
9903Day 12, 12:25Day 14, 16:00
9914Day 14, 18:00Day 16, 12:00
9925Day 16, 14:40Day 17, 12:00
9936Day 18, 02:00Day 19, 09:00
9947Day 19, 10:00Day 22, 00:00
9958Day 22, 03:00Day 22, 10:00
9969Day 25, 15:53Day 25, 16:00
99710Day 25, 16:02Day 26, 13:00
99811Day 26, 15:00Day 27, 08:00
9991Day 02, 21:00Day 04, 03:00
10002Day 04, 04:00Day 06, 08:00
10013Day 06, 22:39Day 07, 06:00
10021Day 16, 11:51Day 18, 14:00
10032Day 18, 15:00Day 20, 06:00
10043Day 22, 22:00Day 23, 17:15
10054Day 23, 19:00Day 24, 06:00
10065Day 24, 17:00Day 25, 21:00
10076Day 25, 22:00Day 26, 08:00
10087Day 26, 10:58Day 26, 14:00
10091Day 11, 11:50Day 11, 19:00
10101Day 04, 21:00Day 05, 02:00
10112Day 05, 03:35Day 05, 04:00
10123Day 05, 05:18Day 07, 14:15
10131Day 15, 15:00Day 16, 02:09
10142Day 16, 04:53Day 16, 10:00
10153Day 16, 11:00Day 16, 13:30
10164Day 16, 14:00Day 17, 12:00
10171Day 04, 17:19Day 04, 18:03
10181Day 06, 01:45Day 09, 03:00
10192Day 09, 03:20Day 10, 09:00
10203Day 10, 10:45Day 10, 20:00
10214Day 11, 09:20Day 11, 09:45
10225Day 11, 12:00Day 11, 14:00
10236Day 11, 14:25Day 14, 16:00
10247Day 15, 14:00Day 18, 14:50
10258Day 18, 20:00Day 21, 08:45
10269Day 23, 20:00Day 23, 20:06
102710Day 23, 20:31Day 26, 06:00
102811Day 26, 06:54Day 27, 01:00
102912Day 27, 03:00Day 27, 14:30
10301Day 16, 18:00Day 18, 10:00
10311Day 30, 01:00Day 30, 03:00
10321Day 13, 13:25Day 14, 12:00
10332Day 14, 16:30Day 16, 16:00
10343Day 16, 17:00Day 18, 11:00
10354Day 19, 12:30Day 20, 12:00
10365Day 20, 15:00Day 21, 20:44
10376Day 21, 21:00Day 21, 21:13
10387Day 21, 23:00Day 23, 12:00
10391Day 24, 00:20Day 25, 14:13
10401Day 21, 12:30Day 22, 23:27
10412Day 23, 00:00Day 25, 13:15
10421Day 07, 16:45Day 08, 12:00
10432Day 08, 12:30Day 09, 11:00
10443Day 09, 12:00Day 09, 12:07
10454Day 09, 13:00Day 10, 23:00
10465Day 11, 00:00Day 12, 15:06
10476Day 12, 17:12Day 13, 01:00
10487Day 13, 13:30Day 14, 16:13
10491Day 28, 21:25Day 30, 12:00
10501Day 01, 14:15Day 02, 06:30
10512Day 02, 09:00Day 02, 09:16
10523Day 02, 10:00Day 02, 13:00
10531Day 13, 22:57Day 16, 07:25
10542Day 16, 13:52Day 17, 04:00
10553Day 17, 14:39Day 17, 15:10
10564Day 17, 19:27Day 18, 03:00
10575Day 18, 04:28Day 18, 16:00
10586Day 18, 16:56Day 20, 12:30
10591Day 14, 17:56Day 14, 20:00
10602Day 14, 21:00Day 14, 23:00
10613Day 15, 02:00Day 15, 10:00
10624Day 15, 10:10Day 15, 12:24
10635Day 16, 08:40Day 16, 13:00
10646Day 16, 17:10Day 17, 13:00
10657Day 17, 16:00Day 19, 00:00
10668Day 20, 17:54Day 21, 00:00
10671Day 31, 16:10Day 02, 11:30
10682Day 17, 11:00Day 18, 00:00
10693Day 18, 01:30Day 18, 05:15
10701Day 27, 11:23Day 28, 10:17
10711Day 21, 23:38Day 22, 15:00
10722Day 22, 18:00Day 24, 03:12
10733Day 24, 11:00Day 26, 10:24
10741Day 17, 15:00Day 18, 09:30
10751Day 19, 17:00Day 22, 00:00
10761Day 10, 10:27Day 11, 19:00
10772Day 11, 19:30Day 12, 01:00
10783Day 12, 02:00Day 15, 00:19
10794Day 15, 00:21Day 15, 04:05
10805Day 15, 04:31Day 18, 16:00
10816Day 18, 20:00Day 18, 21:01
10827Day 18, 21:05Day 22, 00:00
10838Day 22, 02:00Day 22, 03:00
10849Day 22, 03:15Day 23, 08:00
108510Day 24, 11:58Day 25, 10:23
10861Day 28, 22:30Day 28, 23:00
10872Day 01, 00:00Day 01, 08:00
10883Day 01, 12:24Day 02, 06:00
10894Day 02, 12:00Day 03, 06:30
10905Day 03, 06:45Day 04, 07:48
10916Day 05, 12:00Day 05, 15:00
10927Day 05, 19:00Day 05, 22:00
10938Day 06, 04:58Day 07, 17:00
10941Day 11, 23:43Day 12, 20:00
10952Day 12, 22:00Day 13, 16:30
10963Day 13, 18:15Day 13, 23:00
10974Day 14, 15:27Day 16, 16:00
10981Day 14, 17:30Day 14, 20:00
10992Day 14, 23:00Day 15, 15:00
11003Day 15, 18:57Day 18, 10:00
11011Day 24, 20:15Day 25, 10:00
11022Day 26, 02:35Day 26, 09:00
11033Day 26, 12:57Day 26, 13:00
11044Day 26, 14:00Day 27, 13:00
11055Day 27, 14:20Day 29, 16:00
11066Day 29, 17:00Day 30, 10:08
11077Day 30, 13:30Day 01, 13:00
11088Day 01, 16:00Day 02, 14:53
11099Day 02, 16:00Day 02, 20:00
111010Day 02, 21:00Day 03, 07:00
111111Day 03, 08:00Day 03, 10:00
111212Day 03, 13:00Day 04, 11:00
111313Day 04, 18:00Day 05, 11:53
111414Day 05, 15:15Day 06, 10:48
11151Day 23, 18:27Day 23, 20:00
11162Day 27, 16:00Day 27, 22:00
11173Day 27, 23:00Day 28, 06:00
11184Day 28, 09:00Day 28, 11:19
11191Day 23, 23:51Day 24, 04:00
11201Day 10, 21:23Day 12, 07:00
11212Day 12, 11:00Day 15, 10:45
11223Day 15, 22:00Day 18, 04:00
11231Day 19, 17:57Day 22, 17:00
11242Day 22, 21:58Day 25, 11:00
11251Day 29, 21:15Day 30, 15:00
11262Day 30, 18:00Day 31, 17:00
11271Day 30, 03:30Day 30, 07:05
11282Day 30, 11:00Day 31, 22:50
11293Day 31, 23:35Day 01, 15:25
11304Day 01, 18:00Day 02, 10:00
11315Day 02, 13:00Day 04, 18:00
11326Day 04, 21:00Day 05, 09:45
11337Day 05, 11:00Day 07, 01:00
11348Day 07, 02:00Day 10, 00:30
11359Day 10, 01:18Day 12, 12:00
11361Day 23, 18:00Day 25, 17:00
11371Day 23, 18:56Day 25, 11:00
11382Day 25, 16:00Day 26, 12:00
11393Day 26, 13:00Day 28, 08:00
11401Day 21, 00:19Day 21, 10:00
11412Day 21, 10:30Day 24, 02:00
11423Day 24, 02:15Day 24, 06:00
11431Day 06, 12:33Day 06, 16:07
11442Day 06, 18:54Day 09, 01:00
11453Day 09, 02:00Day 09, 11:00
11464Day 09, 14:00Day 09, 17:00
11475Day 09, 19:50Day 10, 04:30
11486Day 10, 05:48Day 11, 00:02
11497Day 11, 00:14Day 13, 14:00
11508Day 13, 15:00Day 15, 22:41
11519Day 16, 00:06Day 16, 11:00
115210Day 16, 12:00Day 16, 21:25
115311Day 16, 21:55Day 17, 18:00
11541Day 07, 21:38Day 09, 07:09
11551Day 23, 13:00Day 24, 09:00
11562Day 24, 11:00Day 26, 11:00
11573Day 26, 12:00Day 27, 11:00
11584Day 27, 13:00Day 28, 09:00
11595Day 02, 12:00Day 03, 12:00
11606Day 05, 00:00Day 05, 05:00
11617Day 05, 06:00Day 07, 17:00
11621Day 25, 14:30Day 27, 11:00
11632Day 28, 07:43Day 29, 11:00
11641Day 20, 21:26Day 21, 17:00
11652Day 21, 21:00Day 22, 13:11
11661Day 12, 02:03Day 13, 05:10
11672Day 13, 09:00Day 14, 11:00
11683Day 14, 14:00Day 16, 06:00
11691Day 03, 11:45Day 04, 03:00
11702Day 04, 03:50Day 06, 09:00
11713Day 06, 10:00Day 07, 00:00
11724Day 07, 02:00Day 09, 08:00
11735Day 09, 10:00Day 09, 18:50
11746Day 09, 20:45Day 09, 21:00
11757Day 09, 22:00Day 10, 20:45
11761Day 03, 02:13Day 04, 08:03
11771Day 11, 16:36Day 12, 15:00
11782Day 12, 18:48Day 14, 14:00
11793Day 14, 17:00Day 14, 23:00
11804Day 15, 00:47Day 15, 03:00
11815Day 15, 07:00Day 16, 01:44
11826Day 16, 02:07Day 16, 11:00
11837Day 16, 13:02Day 16, 18:30
11841Day 02, 11:00Day 04, 13:00
11852Day 04, 13:15Day 06, 07:00
11863Day 06, 11:00Day 09, 08:02
11874Day 09, 09:00Day 10, 16:00
11885Day 10, 16:15Day 12, 00:00
11896Day 13, 13:00Day 14, 19:00
11901Day 16, 01:10Day 16, 02:14
11912Day 16, 03:07Day 16, 07:00
11923Day 16, 18:15Day 16, 20:00
11934Day 16, 21:06Day 17, 01:00
11945Day 17, 02:00Day 17, 14:00
11956Day 17, 14:10Day 17, 18:00
11967Day 17, 20:12Day 18, 07:45
11978Day 18, 10:00Day 19, 19:00
11989Day 19, 19:45Day 20, 19:00
119910Day 20, 20:04Day 22, 11:00
12001Day 09, 11:15Day 12, 11:00
12011Day 13, 00:00Day 13, 05:00
12022Day 13, 08:00Day 14, 05:00
12033Day 14, 06:00Day 15, 23:00
12044Day 16, 12:54Day 16, 20:00
12055Day 17, 10:30Day 18, 13:20
12066Day 18, 16:20Day 20, 12:40
12071Day 23, 00:00Day 23, 18:00
12082Day 24, 04:37Day 27, 04:00
12093Day 27, 06:20Day 27, 16:00
12104Day 27, 17:00Day 30, 09:00
12111Day 08, 18:51Day 09, 00:00
12122Day 09, 03:00Day 09, 23:00
12133Day 10, 03:00Day 11, 05:00
12144Day 11, 06:00Day 13, 03:00
12155Day 13, 16:15Day 15, 08:30
12166Day 19, 13:00Day 20, 06:00
12177Day 20, 08:36Day 20, 18:35
12188Day 20, 20:00Day 21, 10:00
12191Day 26, 04:51Day 26, 11:00
12202Day 26, 15:22Day 28, 08:00
12213Day 28, 11:00Day 28, 19:00
12224Day 28, 23:00Day 29, 14:00
12231Day 28, 02:08Day 31, 02:00
12242Day 31, 04:00Day 31, 08:53
12253Day 31, 09:00Day 31, 10:30
12264Day 02, 15:20Day 05, 12:00
12271Day 30, 04:30Day 30, 06:42
12281Day 06, 11:00Day 06, 14:00
12292Day 06, 18:36Day 07, 14:00
12303Day 07, 21:00Day 08, 04:11
12314Day 08, 14:30Day 08, 18:00
12325Day 11, 00:00Day 11, 00:15
12336Day 11, 00:19Day 13, 15:00
12347Day 13, 19:00Day 15, 15:00
12358Day 15, 21:00Day 17, 21:00
12369Day 17, 22:00Day 18, 09:00
123710Day 18, 12:00Day 19, 11:00
123811Day 19, 12:00Day 20, 05:00
123912Day 20, 07:00Day 20, 08:30
124013Day 21, 12:00Day 22, 01:00
124114Day 22, 02:00Day 23, 21:00
124215Day 24, 11:22Day 25, 13:30
124316Day 26, 00:13Day 27, 07:00
124417Day 27, 07:06Day 27, 08:00
124518Day 27, 10:00Day 28, 08:00
12461Day 03, 11:00Day 03, 16:00
12472Day 03, 19:38Day 04, 11:00
12483Day 04, 14:00Day 04, 19:00
12494Day 04, 19:53Day 04, 22:02
12505Day 05, 03:50Day 05, 17:00
12511Day 09, 10:00Day 10, 08:00
12522Day 10, 11:00Day 11, 11:00
12533Day 11, 12:00Day 12, 09:30
12544Day 12, 12:00Day 13, 11:30
12551Day 26, 14:13Day 28, 16:15
12561Day 12, 13:00Day 15, 17:00
12572Day 15, 18:00Day 17, 06:00
12583Day 17, 07:00Day 20, 09:00
12594Day 20, 11:00Day 23, 08:00
12605Day 23, 15:40Day 25, 12:00
12616Day 25, 14:00Day 26, 10:32
12627Day 26, 11:30Day 29, 12:00
12638Day 29, 13:00Day 02, 12:00
12649Day 02, 17:00Day 05, 16:32
126510Day 05, 18:36Day 08, 18:00
126611Day 08, 20:00Day 11, 18:30
126712Day 11, 21:00Day 12, 07:00
126813Day 12, 09:00Day 12, 14:00
126914Day 12, 17:00Day 15, 18:00
127015Day 15, 21:00Day 16, 19:00
127116Day 18, 13:40Day 20, 14:00
127217Day 20, 20:04Day 22, 14:00
127318Day 22, 17:00Day 22, 20:00
127419Day 22, 22:00Day 23, 06:00
127520Day 23, 15:00Day 26, 11:00
127621Day 26, 12:00Day 29, 13:00
127722Day 29, 17:00Day 01, 10:00
12781Day 01, 23:07Day 03, 10:00
12792Day 06, 09:45Day 07, 18:15
12801Day 19, 15:15Day 21, 10:12
12812Day 21, 15:03Day 22, 01:00
12823Day 22, 01:04Day 22, 02:00
12834Day 22, 02:14Day 25, 05:45
12845Day 25, 15:00Day 25, 17:00
12856Day 25, 17:52Day 26, 08:00
12867Day 29, 16:00Day 30, 13:00
12871Day 11, 02:46Day 11, 07:30
12882Day 11, 08:00Day 11, 13:00
12893Day 11, 20:12Day 12, 23:32
12904Day 13, 00:21Day 13, 06:00
12915Day 13, 06:31Day 13, 23:00
12926Day 13, 23:29Day 14, 06:16
12937Day 14, 09:20Day 15, 05:00
12948Day 15, 12:30Day 16, 08:00
12959Day 16, 14:00Day 17, 05:00
12961Day 12, 21:30Day 13, 14:00
12972Day 13, 18:00Day 14, 21:00
12983Day 14, 22:00Day 15, 21:00
12994Day 15, 22:00Day 16, 17:23
13005Day 16, 23:00Day 18, 15:00
13016Day 18, 16:00Day 18, 22:00
13027Day 18, 22:50Day 19, 07:09
13038Day 19, 10:00Day 20, 00:00
13049Day 20, 20:30Day 23, 01:00
130510Day 23, 01:10Day 25, 08:10
13061Day 27, 02:52Day 27, 05:30
13071Day 26, 00:30Day 26, 02:00
13081Day 02, 00:10Day 02, 06:00
13091Day 04, 23:11Day 05, 09:00
13102Day 05, 17:00Day 07, 03:00
13113Day 07, 04:00Day 07, 16:16
13124Day 07, 16:40Day 07, 18:00
13135Day 07, 18:57Day 08, 03:00
13146Day 08, 05:00Day 08, 17:32
13157Day 08, 22:19Day 09, 16:00
13168Day 09, 16:58Day 09, 21:00
13179Day 09, 21:09Day 10, 14:22
131810Day 10, 14:43Day 11, 07:17
131911Day 11, 08:05Day 13, 15:06
132012Day 14, 09:02Day 16, 20:00
132113Day 16, 22:00Day 17, 00:00
132214Day 17, 03:00Day 18, 12:00
132315Day 25, 12:30Day 25, 17:05
132416Day 25, 22:00Day 28, 12:00
132517Day 28, 18:00Day 29, 09:00
132618Day 29, 10:00Day 30, 11:00
132719Day 30, 12:00Day 31, 21:00
132820Day 31, 22:19Day 01, 19:08
132921Day 02, 00:35Day 02, 11:00
133022Day 02, 15:00Day 05, 16:00
133123Day 05, 17:00Day 07, 14:30
133224Day 07, 21:34Day 08, 05:00
133325Day 08, 08:00Day 08, 10:00
133426Day 08, 12:00Day 09, 11:13
133527Day 09, 17:00Day 12, 04:00
133628Day 12, 04:53Day 13, 19:00
133729Day 13, 23:00Day 14, 03:00
133830Day 14, 16:00Day 17, 10:00
133931Day 17, 11:00Day 19, 11:00
13401Day 23, 15:00Day 24, 18:30
13412Day 24, 21:30Day 26, 13:00
13423Day 01, 23:30Day 02, 11:00
13434Day 02, 12:00Day 03, 17:00
13445Day 05, 11:15Day 06, 12:00
13456Day 06, 15:30Day 07, 17:00
13461Day 14, 03:00Day 17, 14:00
13472Day 17, 15:00Day 19, 00:00
13483Day 20, 08:00Day 23, 15:00
13494Day 23, 16:00Day 24, 18:00
13501Day 06, 00:50Day 06, 08:00
13511Day 17, 22:00Day 17, 22:10
13522Day 17, 22:12Day 19, 09:00
13533Day 19, 16:00Day 21, 13:10
13541Day 02, 21:15Day 05, 22:00
13552Day 05, 22:20Day 07, 11:16
13561Day 08, 01:26Day 09, 09:15
13572Day 09, 13:49Day 10, 15:10
13581Day 30, 22:56Day 31, 12:00
13591Day 28, 15:43Day 28, 21:00
13601Day 27, 17:22Day 30, 17:00
13612Day 30, 19:00Day 01, 14:00
13621Day 12, 16:10Day 13, 12:00
13632Day 13, 13:00Day 15, 14:00
13643Day 15, 15:00Day 17, 20:00
13654Day 17, 20:45Day 18, 10:00
13665Day 18, 18:00Day 21, 13:00
13671Day 20, 18:01Day 21, 01:00
13682Day 21, 05:12Day 21, 09:50
13693Day 21, 10:40Day 21, 23:00
13704Day 21, 23:30Day 22, 07:35
13715Day 22, 07:45Day 22, 08:30
13726Day 22, 08:40Day 22, 16:00
13737Day 22, 22:30Day 23, 05:00
13748Day 23, 06:00Day 23, 08:00
13751Day 27, 02:00Day 27, 21:00
13762Day 27, 22:00Day 28, 17:00
13773Day 28, 18:00Day 30, 06:45
13784Day 30, 07:00Day 01, 07:15
13795Day 01, 13:00Day 01, 15:00
13806Day 01, 16:00Day 02, 06:00
13817Day 02, 06:30Day 02, 20:05
13828Day 02, 20:58Day 02, 21:00
13839Day 02, 21:08Day 03, 01:00
138410Day 03, 01:03Day 03, 06:05
138511Day 03, 07:00Day 04, 09:00
13861Day 05, 09:30Day 05, 15:00
13872Day 05, 17:00Day 05, 18:00
13881Day 04, 18:00Day 04, 23:00
13892Day 05, 00:00Day 05, 19:00
13903Day 05, 20:45Day 06, 23:00
13914Day 07, 04:30Day 07, 05:00
13925Day 07, 06:39Day 07, 15:00
13936Day 07, 21:35Day 10, 00:00
13947Day 10, 02:00Day 11, 23:00
13958Day 12, 00:10Day 12, 22:21
13969Day 12, 23:00Day 13, 19:00
139710Day 13, 22:00Day 14, 06:00
139811Day 14, 09:00Day 14, 11:00
139912Day 14, 17:04Day 16, 02:00
140013Day 16, 04:00Day 20, 01:00
140114Day 20, 04:00Day 22, 11:30
140215Day 22, 14:30Day 25, 16:00
140316Day 25, 18:00Day 27, 00:00
140417Day 27, 00:50Day 27, 15:00
140518Day 27, 19:30Day 28, 04:00
14061Day 30, 22:00Day 01, 14:00
14072Day 01, 19:00Day 02, 08:00
14083Day 02, 10:00Day 03, 18:00
14091Day 17, 20:30Day 18, 01:47
14102Day 18, 04:07Day 21, 04:00
14113Day 21, 05:00Day 22, 21:00
14124Day 23, 00:00Day 23, 01:00
14135Day 23, 02:00Day 23, 09:00
14146Day 23, 09:23Day 26, 08:06
14157Day 21, 21:00Day 21, 21:49
14168Day 21, 22:00Day 25, 06:00
14179Day 25, 16:50Day 25, 23:00
141810Day 26, 02:30Day 26, 18:00
14191Day 13, 22:00Day 16, 13:34
14201Day 20, 00:08Day 21, 13:00
14211Day 12, 22:00Day 13, 10:00
14222Day 13, 11:00Day 14, 15:00
14233Day 14, 18:30Day 14, 19:00
14244Day 14, 19:06Day 14, 23:00
14255Day 15, 00:00Day 15, 11:00
14261Day 07, 11:00Day 08, 11:30
14271Day 31, 02:00Day 02, 11:00
14282Day 02, 18:00Day 03, 04:00
14293Day 03, 06:00Day 05, 03:00
14301Day 13, 15:00Day 17, 11:00
14311Day 16, 12:15Day 17, 02:06
14322Day 17, 09:56Day 19, 16:00
14333Day 19, 23:02Day 20, 14:00
14344Day 20, 15:00Day 22, 17:00
14355Day 29, 16:00Day 29, 17:46
14361Day 14, 21:10Day 17, 21:02
14372Day 17, 22:00Day 18, 09:52
14381Day 04, 23:52Day 05, 09:32
14392Day 05, 16:30Day 08, 16:00
14403Day 08, 18:45Day 09, 10:00
14414Day 09, 10:05Day 10, 21:00
14425Day 10, 21:18Day 11, 18:00
14436Day 11, 20:00Day 11, 23:00
14447Day 12, 00:00Day 12, 23:00
14458Day 13, 00:00Day 13, 18:00
14469Day 14, 03:00Day 14, 09:06
144710Day 14, 18:00Day 16, 03:45
144811Day 16, 04:30Day 16, 05:15
144912Day 16, 06:15Day 16, 07:30
145013Day 16, 10:17Day 17, 17:00
145114Day 18, 10:45Day 18, 11:45
145215Day 18, 16:00Day 19, 12:30
145316Day 19, 16:00Day 19, 18:00
145417Day 19, 19:30Day 20, 18:00
145518Day 20, 19:00Day 22, 10:00
145619Day 22, 14:00Day 23, 23:00
145720Day 24, 01:00Day 24, 14:00
145821Day 24, 18:00Day 25, 08:05
145922Day 25, 12:26Day 25, 14:00
146023Day 25, 14:14Day 28, 22:45
146124Day 29, 04:00Day 01, 07:00
146225Day 01, 10:00Day 01, 10:35
146326Day 01, 10:45Day 04, 12:00
146427Day 04, 13:00Day 07, 06:32
146528Day 14, 10:10Day 15, 05:00
146629Day 15, 09:14Day 15, 14:10
146730Day 15, 16:15Day 17, 05:00
146831Day 17, 06:00Day 17, 15:00
146932Day 17, 21:30Day 18, 02:45
147033Day 18, 03:15Day 18, 11:00
147134Day 18, 19:00Day 19, 20:00
147235Day 19, 21:00Day 20, 02:00
147336Day 20, 04:00Day 20, 04:10
147437Day 20, 05:00Day 21, 05:00
147538Day 21, 07:45Day 21, 11:00
147639Day 21, 12:00Day 22, 00:00
147740Day 22, 01:00Day 22, 03:00
147841Day 22, 04:00Day 22, 04:20
147942Day 22, 05:00Day 22, 07:00
148043Day 22, 11:00Day 24, 05:30
14811Day 26, 20:30Day 27, 03:00
14822Day 27, 03:30Day 28, 03:00
14833Day 28, 05:00Day 28, 08:00
14844Day 28, 12:06Day 29, 13:15
14851Day 11, 16:23Day 12, 02:00
14862Day 12, 10:00Day 12, 17:00
14873Day 12, 17:15Day 13, 11:00
14884Day 13, 14:00Day 13, 23:00
14895Day 14, 00:00Day 14, 08:00
14906Day 14, 09:00Day 15, 01:00
14917Day 15, 02:00Day 15, 16:00
14921Day 25, 12:30Day 26, 12:00
14932Day 26, 13:33Day 26, 21:00
14943Day 27, 01:35Day 28, 00:11
14951Day 27, 15:33Day 30, 04:00
14962Day 30, 05:00Day 01, 12:00
14973Day 01, 15:00Day 02, 14:09
14981Day 29, 16:00Day 30, 10:00
14992Day 30, 13:00Day 01, 10:45
15003Day 01, 14:00Day 01, 19:00
15014Day 01, 20:00Day 03, 12:00
15025Day 07, 20:40Day 09, 20:00
15036Day 09, 20:20Day 12, 05:30
15047Day 12, 14:00Day 14, 18:44
15058Day 28, 20:00Day 30, 03:21
15069Day 30, 06:00Day 02, 05:00
150710Day 02, 06:00Day 03, 02:00
150811Day 03, 05:00Day 04, 00:00
150912Day 04, 00:30Day 04, 14:36
151013Day 04, 18:00Day 05, 18:00
15111Day 21, 16:00Day 24, 17:03
15122Day 24, 18:00Day 25, 10:52
15133Day 26, 14:16Day 29, 10:00
15141Day 12, 16:00Day 14, 13:03
15152Day 14, 17:28Day 16, 16:00
15161Day 27, 15:30Day 27, 22:00
15172Day 27, 23:00Day 28, 14:40
15181Day 12, 20:23Day 12, 20:28
15192Day 12, 20:43Day 13, 09:21
15203Day 13, 23:00Day 17, 05:00
15214Day 20, 00:00Day 20, 11:02
15225Day 21, 00:00Day 21, 00:01
15236Day 21, 00:43Day 21, 09:30
15241Day 11, 22:03Day 12, 00:00
15251Day 23, 21:00Day 23, 23:00
15261Day 23, 09:32Day 23, 17:15
15271Day 07, 01:13Day 09, 17:00
15281Day 05, 01:00Day 05, 12:00
15291Day 06, 18:00Day 07, 10:00
15301Day 09, 13:10Day 09, 23:00
15312Day 10, 00:00Day 11, 12:00
15321Day 24, 19:35Day 28, 04:00
15331Day 05, 01:30Day 06, 14:00
15342Day 06, 15:00Day 07, 06:00
15353Day 07, 10:06Day 10, 11:00
15364Day 10, 12:00Day 10, 20:00
15375Day 10, 21:00Day 11, 18:00
15386Day 11, 20:00Day 12, 17:00
15397Day 12, 21:10Day 13, 07:00
15401Day 07, 20:20Day 07, 23:00
15412Day 08, 04:46Day 09, 19:00
15423Day 09, 19:42Day 11, 20:33
15434Day 11, 21:00Day 12, 12:00
15445Day 12, 12:45Day 12, 14:00
15456Day 12, 16:00Day 13, 15:00
15467Day 13, 17:00Day 14, 12:00
15471Day 21, 02:41Day 21, 16:00
15482Day 22, 04:00Day 23, 04:00
15493Day 23, 17:41Day 25, 08:00
15501Day 02, 16:50Day 05, 15:30
15512Day 05, 16:30Day 06, 10:30
15523Day 06, 11:00Day 07, 06:00
15531Day 16, 21:00Day 17, 16:00
15542Day 17, 18:00Day 19, 04:00
15551Day 17, 20:48Day 18, 13:00
15561Day 26, 21:55Day 26, 23:00
15572Day 26, 23:45Day 28, 06:00
15583Day 29, 18:00Day 02, 00:00
15594Day 02, 01:00Day 04, 11:54
15601Day 11, 23:00Day 12, 00:00
15612Day 12, 00:04Day 13, 08:00
15621Day 27, 00:28Day 29, 11:00
15631Day 22, 22:00Day 22, 23:02
15642Day 22, 23:17Day 23, 09:00
15653Day 23, 09:19Day 23, 15:00
15664Day 23, 17:00Day 23, 20:00
15671Day 30, 19:49Day 31, 12:00
15682Day 31, 13:00Day 31, 15:45
15691Day 03, 21:00Day 06, 21:00
15702Day 06, 21:55Day 07, 12:00
15711Day 13, 11:00Day 13, 14:00
15722Day 13, 17:00Day 14, 08:00
15733Day 14, 09:00Day 14, 10:10
15741Day 03, 14:21Day 03, 21:25
15752Day 03, 23:39Day 04, 00:08
15763Day 04, 02:00Day 04, 05:00
15774Day 07, 23:45Day 08, 11:00
15781Day 01, 20:46Day 02, 15:30
15792Day 02, 16:00Day 04, 17:00
15801Day 12, 14:00Day 12, 19:03
15811Day 23, 15:43Day 25, 17:40
15821Day 01, 00:41Day 01, 08:00
15832Day 01, 20:10Day 06, 12:35
15843Day 07, 00:00Day 08, 01:05
15854Day 08, 02:00Day 08, 13:00
15865Day 08, 14:00Day 12, 02:00
15876Day 12, 04:00Day 12, 16:00
15887Day 14, 23:30Day 15, 05:04
15898Day 15, 06:00Day 16, 14:00
15909Day 16, 16:00Day 17, 02:07
159110Day 17, 03:05Day 18, 07:00
159211Day 18, 07:30Day 19, 08:15
15931Day 24, 18:15Day 25, 06:13
15942Day 25, 09:39Day 28, 10:15
15953Day 28, 11:00Day 29, 01:30
15964Day 29, 03:00Day 01, 12:00
15975Day 01, 14:00Day 03, 06:00
15986Day 03, 08:00Day 03, 14:12
15997Day 03, 17:00Day 04, 06:00
16008Day 04, 09:00Day 05, 16:00
16019Day 07, 02:03Day 07, 12:45
160210Day 07, 15:20Day 08, 15:00
160311Day 08, 18:20Day 09, 12:00
160412Day 09, 18:00Day 11, 20:45
160513Day 11, 21:08Day 12, 20:00
160614Day 12, 21:37Day 14, 09:00
160715Day 16, 11:10Day 18, 14:00
160816Day 18, 18:00Day 19, 08:16
16091Day 03, 20:55Day 04, 05:00
16101Day 31, 14:32Day 04, 02:02
16111Day 10, 17:39Day 11, 06:00
16122Day 11, 08:00Day 11, 14:00
16133Day 11, 17:00Day 17, 07:00
16141Day 22, 07:56Day 23, 12:00
16152Day 23, 15:00Day 23, 21:48
16163Day 24, 00:04Day 24, 19:14
16171Day 10, 11:07Day 11, 15:00
16181Day 25, 19:39Day 25, 22:26
16192Day 28, 22:30Day 30, 05:00
16203Day 30, 06:00Day 30, 15:00
16211Day 14, 15:22Day 17, 14:00
16222Day 17, 16:00Day 20, 17:00
16233Day 20, 17:30Day 21, 14:00
16241Day 30, 19:00Day 31, 12:06
16251Day 09, 23:50Day 13, 15:00
16261Day 04, 17:00Day 05, 16:00
16272Day 05, 21:00Day 06, 05:28
16283Day 06, 21:30Day 08, 11:07
16294Day 08, 11:41Day 09, 01:00
16305Day 09, 02:00Day 09, 02:30
16316Day 09, 02:45Day 12, 02:07
16327Day 12, 03:00Day 13, 10:01
16338Day 16, 13:10Day 17, 23:00
16349Day 18, 00:00Day 18, 07:00
163510Day 18, 12:00Day 20, 18:15
16361Day 24, 22:45Day 26, 16:18
16372Day 27, 02:28Day 27, 12:00
16383Day 27, 21:30Day 28, 05:15
16394Day 28, 11:26Day 29, 12:10
16405Day 29, 17:27Day 29, 23:05
16416Day 30, 02:00Day 30, 04:30
16421Day 28, 06:17Day 30, 04:15
16432Day 30, 17:00Day 01, 16:00
16443Day 01, 17:00Day 03, 09:00
16451Day 03, 19:25Day 04, 21:00
16461Day 10, 11:00Day 10, 17:00
16472Day 10, 21:00Day 10, 22:00
16483Day 10, 23:00Day 12, 10:30
16494Day 12, 13:42Day 13, 11:00
16505Day 13, 12:00Day 16, 12:00
16516Day 16, 13:29Day 18, 07:00
16527Day 19, 02:32Day 20, 12:00
16531Day 31, 13:00Day 31, 16:00
16542Day 31, 17:00Day 01, 00:00
16553Day 01, 02:35Day 03, 13:00
16564Day 03, 13:59Day 03, 16:00
16575Day 03, 19:00Day 04, 12:44
16586Day 04, 14:35Day 06, 12:00
16591Day 29, 18:00Day 02, 11:00
16601Day 08, 23:00Day 09, 04:00
16611Day 02, 20:00Day 03, 03:20
16622Day 03, 10:54Day 03, 13:00
16631Day 27, 19:00Day 27, 19:12
16642Day 27, 20:00Day 28, 08:00
16651Day 27, 10:00Day 27, 14:00
16662Day 27, 17:00Day 29, 10:05
16673Day 29, 11:52Day 29, 14:30
16684Day 29, 22:00Day 29, 23:10
16695Day 31, 01:33Day 31, 07:00
16706Day 31, 10:00Day 02, 08:20
16717Day 02, 14:05Day 02, 15:30
16728Day 02, 17:45Day 04, 13:30
16731Day 25, 16:54Day 27, 05:00
16742Day 27, 05:41Day 29, 17:00
16753Day 29, 17:30Day 01, 13:00
16764Day 01, 14:00Day 01, 15:00
16775Day 01, 15:45Day 03, 10:18
16781Day 03, 18:15Day 04, 19:00
16792Day 04, 19:35Day 06, 20:54
16801Day 16, 16:30Day 16, 19:00
16811Day 12, 21:18Day 13, 15:00
16821Day 23, 00:32Day 23, 10:00
16831Day 11, 00:00Day 11, 21:00
16842Day 12, 03:30Day 12, 15:00
16851Day 24, 22:00Day 27, 09:00
16861Day 12, 18:00Day 14, 16:25
16872Day 14, 19:00Day 16, 08:00
16881Day 09, 00:45Day 09, 05:00
16892Day 09, 09:00Day 09, 19:00
16903Day 09, 20:37Day 12, 10:00
16914Day 12, 12:00Day 13, 01:30
16925Day 13, 02:45Day 15, 10:00
16931Day 03, 22:37Day 04, 04:00
16942Day 04, 04:20Day 04, 21:00
16953Day 04, 22:18Day 08, 06:00
16961Day 16, 15:25Day 17, 06:00
16972Day 17, 09:00Day 17, 09:05
16983Day 17, 09:06Day 17, 09:08
16994Day 17, 10:00Day 19, 08:00
17005Day 19, 09:00Day 20, 00:00
17016Day 20, 01:00Day 20, 02:00
17027Day 23, 22:18Day 24, 21:00
17038Day 24, 21:15Day 25, 22:00
17049Day 26, 01:00Day 26, 12:00
170510Day 26, 17:15Day 29, 17:00
170611Day 07, 16:20Day 08, 12:00
170712Day 08, 22:40Day 11, 22:00
170813Day 11, 23:00Day 13, 15:00
170914Day 13, 18:00Day 15, 15:00
17101Day 08, 20:30Day 09, 12:00
17112Day 09, 15:00Day 11, 07:00
17123Day 11, 19:00Day 13, 19:00
17131Day 17, 20:47Day 18, 14:00
17142Day 18, 17:00Day 20, 04:00
17153Day 20, 17:00Day 22, 04:11
17164Day 22, 06:00Day 22, 15:00
17171Day 14, 20:00Day 17, 19:00
17182Day 17, 21:00Day 20, 06:20
17193Day 20, 22:00Day 22, 19:30
17201Day 01, 18:00Day 01, 20:15
17211Day 24, 00:00Day 24, 09:20
17221Day 05, 04:05Day 05, 05:00
17232Day 05, 05:01Day 06, 05:00
17241Day 11, 19:00Day 11, 23:00
17252Day 12, 00:00Day 12, 02:15
17263Day 12, 03:00Day 15, 03:00
17274Day 16, 01:00Day 16, 14:00
17285Day 16, 15:02Day 19, 10:00
17296Day 19, 11:00Day 20, 13:40
17307Day 20, 14:00Day 20, 21:34
17318Day 20, 22:00Day 21, 04:00
17329Day 21, 15:30Day 24, 14:21
173310Day 28, 00:00Day 28, 02:27
173411Day 28, 02:28Day 28, 07:00
17351Day 05, 05:34Day 07, 03:00
17362Day 07, 04:45Day 08, 14:06
17373Day 08, 14:30Day 08, 15:00
17384Day 08, 15:01Day 10, 17:00
17391Day 06, 21:45Day 09, 22:00
17402Day 10, 00:00Day 11, 16:00
17413Day 12, 02:00Day 12, 10:00
17424Day 12, 13:30Day 14, 03:00
17435Day 14, 03:26Day 14, 13:00
17446Day 15, 21:14Day 17, 10:40
17451Day 22, 02:00Day 22, 03:00
17462Day 22, 03:11Day 22, 05:03
17473Day 22, 08:56Day 22, 11:00
17484Day 22, 18:00Day 23, 14:00
17495Day 23, 20:00Day 24, 03:49
17506Day 24, 03:56Day 24, 07:00
17517Day 24, 09:23Day 24, 12:39
17521Day 29, 19:00Day 30, 06:00
17531Day 03, 17:00Day 05, 15:00
17542Day 05, 16:00Day 06, 05:00
17553Day 06, 08:11Day 07, 04:00
17564Day 07, 05:00Day 10, 08:00
17575Day 11, 19:00Day 13, 13:30
17586Day 13, 18:15Day 14, 16:00
17597Day 14, 18:00Day 17, 12:20
17601Day 18, 19:00Day 19, 14:00
17612Day 19, 17:00Day 19, 17:15
17623Day 19, 18:00Day 20, 01:00
17634Day 20, 02:00Day 20, 16:00
17641Day 14, 13:30Day 14, 14:00
17652Day 14, 14:26Day 17, 16:00
17663Day 17, 20:20Day 18, 10:41
17674Day 18, 13:17Day 20, 22:00
17685Day 20, 23:00Day 22, 12:00
17696Day 22, 14:00Day 23, 12:00
17707Day 23, 17:00Day 23, 18:00
17718Day 23, 19:00Day 24, 11:30
17721Day 30, 18:00Day 30, 20:30
17732Day 31, 00:00Day 31, 02:30
17741Day 18, 14:16Day 19, 12:00
17752Day 19, 20:00Day 20, 16:00
17763Day 20, 23:00Day 21, 07:20
17774Day 21, 08:21Day 21, 17:20
17781Day 28, 13:14Day 28, 18:00
17792Day 28, 18:26Day 30, 07:05
17801Day 21, 22:00Day 22, 15:00
17812Day 22, 16:00Day 22, 18:00
17823Day 23, 01:00Day 23, 19:00
17834Day 23, 22:00Day 24, 10:30
17845Day 24, 17:00Day 26, 03:00
17856Day 26, 06:00Day 28, 08:10
17867Day 28, 15:26Day 28, 17:00
17878Day 28, 17:05Day 03, 00:00
17889Day 03, 01:00Day 06, 01:00
178910Day 06, 05:00Day 08, 15:00
179011Day 08, 16:00Day 09, 16:26
17911Day 14, 22:00Day 18, 08:00
17922Day 24, 12:00Day 27, 15:00
17933Day 27, 16:00Day 30, 13:30
17944Day 01, 16:30Day 05, 03:00
17955Day 05, 04:51Day 05, 15:00
17966Day 05, 17:00Day 07, 11:16
17971Day 17, 23:09Day 18, 04:00
17982Day 18, 06:00Day 18, 06:20
17993Day 18, 07:00Day 18, 23:00
18004Day 19, 01:00Day 20, 11:00
18011Day 02, 17:31Day 02, 18:24
18022Day 02, 20:00Day 03, 00:00
18033Day 03, 01:00Day 04, 15:15
18044Day 04, 17:15Day 07, 05:00
18055Day 07, 06:00Day 08, 14:30
18061Day 26, 22:00Day 27, 09:00
18072Day 27, 15:00Day 28, 01:00
18083Day 28, 02:45Day 28, 06:15
18094Day 28, 07:00Day 28, 16:00
18105Day 28, 18:00Day 29, 21:00
18116Day 29, 23:00Day 30, 12:00
18127Day 30, 17:00Day 31, 03:00
18138Day 31, 05:45Day 31, 09:00
18149Day 31, 12:00Day 01, 01:00
181510Day 01, 01:45Day 03, 14:00
181611Day 03, 16:00Day 04, 14:00
181712Day 04, 15:00Day 05, 13:00
181813Day 05, 16:00Day 05, 21:00
181914Day 05, 22:15Day 06, 01:00
18201Day 30, 22:00Day 31, 18:00
18212Day 31, 19:00Day 01, 08:30
18223Day 01, 12:00Day 03, 04:00
18234Day 05, 20:00Day 06, 16:00
18245Day 06, 19:00Day 07, 16:00
18256Day 07, 20:15Day 08, 09:00
18261Day 29, 20:51Day 29, 22:00
18272Day 30, 00:00Day 30, 02:00
18283Day 30, 05:00Day 30, 06:00
18294Day 30, 11:00Day 01, 02:00
18305Day 01, 06:05Day 01, 09:00
18316Day 01, 10:00Day 04, 10:00
18327Day 04, 15:00Day 06, 08:00
18338Day 06, 19:10Day 08, 06:00
18349Day 08, 07:40Day 09, 15:00
183510Day 09, 21:00Day 12, 18:00
183611Day 12, 20:59Day 13, 18:10
183712Day 13, 20:14Day 15, 04:30
183813Day 15, 06:00Day 16, 08:00
183914Day 16, 19:00Day 19, 01:00
184015Day 19, 11:00Day 20, 12:00
184116Day 20, 18:30Day 21, 09:00
184217Day 21, 17:38Day 21, 18:03
184318Day 21, 18:23Day 22, 12:33
184419Day 22, 15:00Day 22, 17:25
18451Day 08, 21:18Day 10, 04:00
18462Day 10, 04:35Day 10, 14:00
18473Day 10, 16:00Day 11, 03:04
18484Day 11, 03:50Day 12, 02:00
18495Day 12, 04:50Day 12, 06:00
18506Day 12, 07:21Day 13, 00:00
18517Day 13, 01:00Day 13, 12:00
18521Day 16, 22:52Day 17, 06:15
18532Day 17, 07:53Day 17, 11:00
18543Day 17, 12:00Day 17, 16:00
18554Day 19, 16:30Day 19, 18:00
18565Day 21, 00:00Day 22, 07:00
18576Day 22, 08:00Day 23, 13:00
18587Day 23, 20:00Day 24, 08:00
18598Day 24, 13:00Day 25, 01:00
18609Day 25, 18:13Day 25, 19:00
186110Day 26, 00:00Day 26, 06:00
186211Day 26, 11:53Day 26, 18:00
186312Day 27, 16:00Day 27, 16:30
186413Day 27, 17:00Day 27, 19:00
186514Day 28, 01:00Day 28, 07:00
186615Day 28, 10:00Day 28, 11:00
186716Day 28, 12:00Day 29, 01:00
186817Day 29, 23:00Day 30, 09:50
186918Day 30, 12:00Day 30, 16:00
187019Day 30, 23:28Day 31, 22:00
18711Day 08, 00:55Day 09, 19:00
18722Day 09, 22:45Day 10, 13:00
18733Day 10, 17:00Day 11, 13:00
18744Day 11, 18:55Day 13, 18:00
18755Day 14, 03:58Day 15, 13:00
18766Day 15, 21:00Day 19, 13:00
18771Day 25, 17:15Day 27, 04:00
18782Day 27, 05:00Day 28, 16:10
18793Day 28, 16:40Day 30, 05:00
18804Day 30, 06:32Day 01, 15:00
18815Day 01, 15:40Day 02, 12:00
18826Day 03, 16:33Day 05, 16:00
18831Day 15, 17:20Day 16, 06:00
18842Day 16, 06:46Day 17, 05:00
18853Day 17, 05:54Day 17, 18:00
18864Day 17, 22:05Day 19, 11:00
18875Day 19, 18:11Day 19, 19:00
18886Day 19, 19:48Day 20, 13:00
18897Day 20, 16:30Day 21, 12:00
18901Day 20, 18:23Day 23, 13:00
18911Day 27, 16:48Day 28, 03:00
18922Day 28, 03:03Day 28, 10:30
18933Day 30, 01:37Day 30, 11:24
18941Day 16, 18:51Day 18, 14:00
18952Day 18, 18:00Day 20, 16:20
18963Day 20, 16:45Day 23, 08:00
18971Day 01, 14:00Day 01, 14:39
18982Day 01, 14:41Day 03, 12:19
18993Day 03, 13:00Day 04, 03:18
19001Day 16, 13:00Day 17, 21:00
19012Day 18, 20:31Day 21, 05:36
19023Day 21, 07:00Day 23, 22:00
19034Day 25, 01:00Day 25, 20:00
19045Day 25, 22:00Day 28, 04:00
19051Day 05, 20:00Day 06, 13:00
19061Day 12, 20:53Day 13, 15:00
19072Day 13, 16:00Day 14, 14:00
19081Day 29, 00:00Day 30, 09:00
19092Day 30, 09:35Day 01, 15:00
19103Day 01, 17:00Day 02, 06:00
19114Day 02, 06:16Day 02, 11:00
19125Day 04, 15:50Day 05, 00:59
19136Day 05, 01:00Day 05, 18:00
19147Day 05, 18:50Day 06, 11:00
19158Day 06, 11:10Day 06, 14:30
19169Day 12, 18:00Day 12, 20:45
191710Day 13, 00:15Day 13, 04:00
191811Day 14, 11:10Day 14, 12:00
191912Day 14, 13:10Day 15, 03:00
192013Day 15, 05:00Day 15, 10:00
192114Day 21, 12:00Day 22, 13:00
19221Day 17, 02:00Day 17, 04:15
19231Day 18, 16:15Day 20, 21:00
19242Day 21, 10:20Day 21, 12:30
19253Day 21, 13:00Day 24, 10:10
19261Day 13, 01:15Day 16, 02:39
19272Day 16, 04:10Day 17, 19:30
19283Day 17, 22:00Day 19, 01:00
19291Day 28, 11:57Day 02, 10:00
19302Day 02, 12:00Day 03, 21:06
19313Day 04, 00:18Day 05, 13:00
19321Day 01, 17:25Day 03, 12:00
19332Day 03, 13:00Day 04, 13:04
19341Day 18, 16:38Day 20, 12:00
19351Day 12, 23:00Day 13, 03:00
19361Day 19, 19:00Day 20, 21:00
19372Day 20, 23:00Day 29, 12:00
19383Day 30, 18:57Day 01, 13:45
19394Day 01, 20:00Day 04, 22:01
19405Day 04, 23:00Day 05, 23:00
19416Day 06, 00:00Day 07, 15:01
19427Day 07, 16:00Day 08, 22:00
19438Day 08, 22:13Day 10, 07:00
19449Day 11, 14:00Day 11, 16:00
194510Day 11, 19:00Day 14, 16:00
19461Day 03, 16:22Day 03, 21:39
19472Day 03, 22:45Day 03, 23:15
19483Day 04, 01:00Day 04, 06:00
19494Day 04, 14:50Day 06, 08:00
19505Day 06, 13:00Day 07, 19:06
19516Day 07, 20:00Day 09, 03:04
19527Day 09, 07:00Day 09, 16:30
19538Day 09, 18:00Day 10, 18:06
19549Day 10, 23:45Day 11, 09:00
195510Day 11, 19:00Day 13, 05:45
195611Day 13, 08:00Day 13, 14:01
195712Day 13, 18:30Day 13, 21:27
195813Day 13, 22:21Day 15, 11:00
195914Day 15, 12:00Day 16, 01:00
196015Day 16, 03:00Day 16, 23:00
196116Day 16, 23:30Day 17, 09:15
196217Day 17, 10:45Day 19, 00:09
19631Day 22, 19:00Day 24, 14:00
19642Day 24, 16:00Day 25, 22:00
19653Day 28, 00:30Day 29, 20:00
19664Day 29, 23:00Day 01, 09:00
19675Day 13, 17:00Day 18, 17:30
19686Day 25, 13:15Day 27, 10:18
19697Day 27, 11:00Day 29, 11:00
19708Day 29, 11:45Day 29, 13:00
19719Day 29, 13:05Day 30, 00:25
19721Day 01, 22:15Day 03, 04:00
19732Day 03, 05:00Day 03, 22:14
19743Day 04, 11:00Day 05, 09:20
19754Day 05, 09:35Day 06, 03:15
19765Day 06, 04:10Day 06, 18:00
19776Day 06, 18:40Day 07, 08:40
19787Day 07, 15:42Day 07, 21:00
19798Day 08, 11:20Day 08, 18:00
19809Day 08, 19:00Day 09, 08:40
198110Day 09, 16:15Day 10, 21:23
19821Day 12, 22:31Day 14, 17:00
19832Day 14, 18:00Day 15, 23:03
19841Day 23, 22:21Day 24, 11:00
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19862Day 03, 02:58Day 03, 21:17
19873Day 03, 23:00Day 04, 16:00
19881Day 03, 05:00Day 03, 18:00
19892Day 03, 22:00Day 05, 13:49
19903Day 08, 16:00Day 08, 20:00
19914Day 08, 22:00Day 11, 08:00
19921Day 21, 13:45Day 24, 04:00
19932Day 24, 05:21Day 27, 04:00
19943Day 27, 05:00Day 28, 00:00
19954Day 28, 12:51Day 29, 05:00
19965Day 29, 06:00Day 29, 13:30
19971Day 04, 21:45Day 07, 17:00
19981Day 29, 21:41Day 02, 10:00
19992Day 05, 17:45Day 05, 18:04
20003Day 05, 20:06Day 05, 21:20
20014Day 05, 22:08Day 06, 01:00
20021Day 28, 21:00Day 30, 05:00
20032Day 02, 15:30Day 05, 15:00
20043Day 05, 15:45Day 08, 16:00
20051Day 29, 20:00Day 30, 13:00
20062Day 31, 13:30Day 02, 16:00
20071Day 22, 13:00Day 24, 13:59
20082Day 24, 17:00Day 26, 08:02
20093Day 26, 10:07Day 28, 14:16
20104Day 28, 18:00Day 29, 12:03
20115Day 29, 15:10Day 30, 10:40
20126Day 30, 13:55Day 02, 15:00
20137Day 02, 17:00Day 03, 07:05
20148Day 03, 12:00Day 05, 09:00
20159Day 07, 14:50Day 08, 17:00
201610Day 08, 18:00Day 09, 14:00
201711Day 09, 18:00Day 09, 19:00
201812Day 09, 21:00Day 13, 11:00
201913Day 13, 11:45Day 14, 02:00
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202115Day 14, 17:15Day 15, 15:00
202216Day 15, 19:00Day 16, 16:00
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202418Day 18, 20:44Day 19, 13:00
202519Day 19, 21:34Day 22, 23:00
202620Day 23, 22:43Day 27, 05:00
20271Day 17, 00:49Day 19, 23:00
20282Day 20, 00:00Day 20, 04:15
20293Day 20, 05:00Day 22, 07:30
20301Day 12, 15:51Day 14, 04:00
20312Day 14, 08:30Day 17, 08:34
20323Day 17, 13:20Day 18, 02:00
20334Day 18, 04:00Day 20, 08:00
20345Day 20, 12:00Day 20, 13:01
20351Day 12, 22:00Day 15, 05:30
20361Day 19, 12:38Day 21, 08:00
20371Day 16, 20:00Day 18, 17:00
20381Day 12, 15:25Day 15, 18:00
20391Day 27, 18:00Day 29, 10:00
20402Day 29, 13:00Day 30, 09:00
20413Day 30, 11:00Day 01, 08:50
20421Day 01, 01:28Day 01, 07:56
20431Day 15, 18:00Day 18, 08:00
20441Day 11, 16:09Day 12, 08:45
20451Day 07, 10:00Day 07, 11:37
20461Day 12, 05:00Day 13, 10:10
20472Day 13, 12:15Day 13, 17:00
20483Day 13, 18:00Day 14, 20:30
20494Day 15, 04:24Day 16, 08:45
20505Day 16, 09:36Day 18, 23:00
20516Day 19, 01:35Day 20, 09:00
20521Day 05, 21:30Day 06, 17:00
20532Day 06, 18:00Day 08, 12:00
20543Day 08, 13:00Day 09, 09:00
20554Day 10, 12:14Day 13, 10:00
20565Day 13, 12:04Day 16, 10:00
20576Day 16, 15:00Day 16, 23:05
20581Day 02, 17:30Day 05, 13:00
20592Day 09, 16:33Day 10, 14:00
20603Day 10, 15:00Day 11, 11:30
20614Day 11, 11:50Day 11, 12:00
20625Day 11, 12:50Day 12, 05:30
20636Day 12, 06:30Day 13, 10:00
20647Day 15, 12:29Day 16, 22:00
20658Day 16, 23:00Day 17, 14:18
20661Day 10, 01:00Day 10, 09:00
20672Day 10, 23:59Day 11, 01:00
20683Day 11, 02:00Day 11, 05:00
20694Day 11, 06:00Day 11, 08:00
20705Day 11, 10:00Day 11, 11:00
20716Day 11, 13:00Day 11, 18:00
20727Day 11, 22:00Day 12, 05:00
20738Day 12, 06:00Day 12, 11:00
20749Day 12, 16:00Day 12, 23:15
207510Day 13, 00:00Day 13, 02:00
207611Day 13, 03:00Day 14, 18:00
207712Day 14, 20:00Day 14, 20:44
207813Day 14, 20:45Day 15, 11:00
20791Day 06, 06:17Day 06, 07:00
20802Day 06, 07:19Day 07, 05:00
20813Day 07, 10:43Day 07, 18:00
20821Day 27, 22:18Day 29, 01:00
20832Day 29, 02:00Day 29, 08:00
20841Day 05, 18:00Day 06, 17:00
20851Day 17, 15:47Day 17, 17:46
20861Day 17, 00:00Day 17, 06:00
20872Day 17, 08:00Day 18, 08:00
20883Day 18, 13:00Day 21, 02:00
20894Day 21, 03:00Day 23, 00:00
20905Day 24, 01:30Day 25, 12:00
20916Day 25, 16:00Day 25, 21:00
20927Day 26, 12:43Day 27, 17:00
20938Day 27, 18:00Day 29, 08:25
20949Day 29, 12:00Day 30, 12:00
209510Day 31, 17:00Day 03, 09:00
209611Day 03, 16:00Day 06, 14:10
209712Day 06, 15:50Day 07, 14:00
20981Day 21, 19:10Day 23, 08:00
20992Day 23, 13:00Day 26, 12:48
21001Day 08, 11:10Day 09, 08:00
21012Day 09, 09:00Day 09, 11:39
21023Day 09, 12:20Day 11, 00:00
21031Day 30, 17:00Day 30, 22:00
21042Day 30, 23:00Day 31, 07:30
21053Day 31, 13:00Day 31, 16:00
21064Day 31, 17:00Day 01, 10:00
21075Day 01, 14:00Day 02, 01:00
21086Day 02, 04:00Day 02, 15:00
21097Day 02, 17:00Day 04, 09:00
21101Day 30, 15:00Day 30, 19:07
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numstarttimeendtime
01Day 10, 15:00Day 11, 00:07
12Day 11, 04:20Day 12, 07:27
23Day 12, 10:00Day 12, 12:22
34Day 12, 13:15Day 13, 10:29
41Day 30, 00:19Day 30, 09:00
51Day 27, 17:20Day 29, 11:00
61Day 06, 11:20Day 07, 15:21
72Day 06, 11:21Day 07, 15:30
81Day 26, 16:10Day 26, 17:00
92Day 26, 17:30Day 28, 08:38
103Day 28, 11:20Day 30, 02:28
114Day 30, 11:40Day 02, 02:48
121Day 05, 19:15Day 08, 14:28
132Day 11, 13:30Day 12, 12:04
143Day 14, 18:35Day 18, 10:07
154Day 22, 14:56Day 23, 13:25
161Day 24, 21:19Day 26, 00:00
172Day 26, 01:00Day 27, 12:32
183Day 28, 15:00Day 29, 13:15
194Day 29, 18:00Day 30, 16:15
205Day 29, 18:00Day 31, 12:00
216Day 31, 12:15Day 31, 13:35
221Day 13, 17:06Day 16, 15:05
232Day 16, 19:22Day 19, 17:21
243Day 21, 12:37Day 26, 10:41
254Day 25, 21:17Day 28, 21:17
265Day 29, 19:15Day 30, 23:02
271Day 22, 08:08Day 25, 07:59
282Day 25, 18:00Day 28, 12:00
293Day 28, 22:00Day 29, 21:02
304Day 30, 00:00Day 30, 23:02
315Day 30, 22:59Day 31, 22:01
326Day 30, 23:00Day 31, 22:02
337Day 31, 22:01Day 01, 21:03
348Day 01, 21:00Day 04, 21:00
359Day 04, 20:00Day 07, 20:00
3610Day 04, 20:54Day 05, 13:30
3711Day 05, 00:49Day 08, 00:49
3812Day 09, 15:10Day 10, 09:00
3913Day 10, 11:30Day 11, 05:20
4014Day 10, 11:30Day 12, 13:30
4115Day 12, 13:03Day 12, 13:04
4216Day 12, 13:15Day 12, 13:16
4317Day 12, 13:15Day 14, 15:15
4418Day 12, 15:33Day 13, 14:30
451Day 04, 01:13Day 05, 04:08
461Day 04, 17:40Day 04, 18:39
472Day 04, 21:07Day 07, 16:00
481Day 12, 13:45Day 16, 15:39
491Day 12, 18:00Day 13, 16:01
501Day 03, 17:04Day 06, 14:04
511Day 03, 16:00Day 12, 07:00
522Day 12, 14:30Day 13, 22:30
531Day 11, 15:10Day 12, 07:06
542Day 12, 12:18Day 15, 15:00
553Day 15, 15:45Day 19, 11:06
564Day 19, 12:54Day 22, 18:41
575Day 19, 12:54Day 23, 08:15
581Day 05, 18:35Day 07, 16:00
591Day 21, 17:33Day 24, 23:50
602Day 25, 00:43Day 27, 08:00
613Day 25, 00:43Day 28, 07:00
624Day 27, 13:45Day 31, 05:59
631Day 25, 20:45Day 25, 22:31
642Day 25, 20:45Day 25, 22:55
653Day 25, 23:45Day 26, 01:55
664Day 25, 23:45Day 28, 14:45
671Day 03, 21:30Day 04, 12:32
681Day 15, 00:45Day 17, 12:30
691Day 05, 23:19Day 08, 21:00
702Day 08, 23:00Day 10, 18:03
711Day 25, 14:00Day 01, 08:50
722Day 03, 10:00Day 04, 08:01
733Day 03, 10:11Day 04, 08:04
744Day 04, 16:00Day 06, 21:30
755Day 06, 23:57Day 07, 01:45
766Day 07, 02:14Day 07, 04:02
777Day 07, 02:14Day 07, 06:14
788Day 07, 03:58Day 07, 03:59
799Day 07, 05:16Day 07, 05:17
8010Day 07, 06:00Day 07, 10:00
8111Day 07, 06:15Day 07, 10:15
8212Day 07, 06:30Day 07, 10:30
8313Day 07, 07:07Day 07, 20:27
8414Day 07, 10:05Day 07, 14:05
8515Day 07, 10:10Day 07, 14:10
8616Day 07, 12:00Day 07, 16:00
8717Day 07, 14:00Day 07, 18:00
8818Day 07, 18:00Day 07, 22:00
8919Day 07, 18:05Day 08, 10:05
9020Day 07, 19:19Day 07, 19:20
9121Day 07, 20:24Day 08, 12:24
9222Day 08, 12:46Day 13, 13:00
9323Day 15, 15:55Day 16, 15:55
9424Day 15, 15:57Day 16, 13:01
951Day 30, 18:36Day 08, 15:00
962Day 11, 16:12Day 12, 18:02
973Day 13, 17:15Day 15, 03:16
984Day 15, 05:00Day 16, 08:18
995Day 16, 11:00Day 17, 12:27
1001Day 22, 21:08Day 26, 23:50
1011Day 13, 20:51Day 16, 07:00
1021Day 02, 20:30Day 05, 08:45
1031Day 02, 20:45Day 05, 23:22
1041Day 07, 10:16Day 08, 00:30
1052Day 07, 10:16Day 09, 11:44
1063Day 07, 10:16Day 12, 06:59
1074Day 08, 01:45Day 08, 15:59
1085Day 08, 01:45Day 09, 12:15
1096Day 09, 14:15Day 09, 14:35
1107Day 09, 14:30Day 09, 18:54
1118Day 09, 14:30Day 09, 19:02
1129Day 09, 21:20Day 09, 21:26
11310Day 09, 21:20Day 10, 01:54
11411Day 09, 21:21Day 10, 01:55
11512Day 09, 21:26Day 10, 05:25
11613Day 10, 06:00Day 10, 13:59
11714Day 10, 06:00Day 10, 14:00
11815Day 10, 15:10Day 10, 19:50
11916Day 10, 21:00Day 11, 00:30
12017Day 11, 02:30Day 11, 10:30
12118Day 11, 11:10Day 12, 07:02
12219Day 13, 14:57Day 14, 03:15
12320Day 14, 12:45Day 15, 01:03
12421Day 15, 03:00Day 15, 13:00
12522Day 15, 08:34Day 15, 20:52
1261Day 01, 13:30Day 02, 17:07
1272Day 02, 00:00Day 02, 16:32
1283Day 02, 00:00Day 02, 16:43
1294Day 02, 22:50Day 05, 21:14
1305Day 02, 22:50Day 16, 20:10
1316Day 02, 22:53Day 02, 23:07
1327Day 02, 22:54Day 02, 22:55
1338Day 02, 22:55Day 02, 22:56
1341Day 01, 14:42Day 05, 16:02
1351Day 08, 19:45Day 11, 10:02
1362Day 11, 11:45Day 11, 15:37
1373Day 12, 01:00Day 12, 04:52
1384Day 12, 07:00Day 12, 10:52
1395Day 12, 07:00Day 12, 23:40
1406Day 12, 10:47Day 12, 13:21
1417Day 12, 10:47Day 12, 14:39
1428Day 12, 13:16Day 12, 17:08
1439Day 12, 13:16Day 13, 05:56
14410Day 13, 10:25Day 13, 10:42
14511Day 13, 14:33Day 14, 08:44
14612Day 14, 13:52Day 15, 19:05
1471Day 09, 18:00Day 13, 15:04
1481Day 07, 16:15Day 12, 03:30
1491Day 20, 22:47Day 24, 13:30
1502Day 24, 16:00Day 25, 22:58
1511Day 04, 15:45Day 06, 12:15
1522Day 06, 12:45Day 08, 13:50
1533Day 08, 14:45Day 10, 15:50
1544Day 08, 14:45Day 11, 03:55
1551Day 22, 14:00Day 24, 07:00
1562Day 24, 15:25Day 26, 08:25
1573Day 24, 16:00Day 25, 14:00
1584Day 24, 16:00Day 26, 09:00
1595Day 25, 16:30Day 26, 14:30
1606Day 26, 14:30Day 27, 12:30
1617Day 27, 12:30Day 28, 00:03
1621Day 02, 18:00Day 03, 12:45
1632Day 03, 15:00Day 04, 09:45
1643Day 04, 11:00Day 05, 05:45
1654Day 05, 05:50Day 06, 00:35
1665Day 05, 06:04Day 06, 00:49
1676Day 06, 00:32Day 06, 19:17
1687Day 06, 00:32Day 12, 14:18
1698Day 06, 00:32Day 26, 00:32
1709Day 06, 02:33Day 06, 02:34
1711Day 24, 13:15Day 27, 20:00
1721Day 28, 23:36Day 31, 01:45
1732Day 31, 14:12Day 03, 13:00
1741Day 23, 12:38Day 28, 08:33
1752Day 30, 00:00Day 06, 09:19
1761Day 28, 06:18Day 30, 12:59
1771Day 20, 15:18Day 27, 18:03
1781Day 05, 22:20Day 15, 21:26
1791Day 26, 17:23Day 27, 08:05
1802Day 27, 11:15Day 28, 01:57
1813Day 27, 11:15Day 28, 09:20
1824Day 28, 18:00Day 29, 16:05
1835Day 28, 18:00Day 29, 18:00
1846Day 29, 16:02Day 30, 14:07
1857Day 29, 16:03Day 31, 08:03
1868Day 29, 21:30Day 01, 18:20
1871Day 31, 22:00Day 02, 01:00
1882Day 01, 23:55Day 02, 04:32
1893Day 02, 07:00Day 02, 09:39
1904Day 02, 17:00Day 04, 05:03
1911Day 04, 20:00Day 06, 23:08
1921Day 25, 15:40Day 27, 05:45
1932Day 27, 06:45Day 28, 20:50
1943Day 28, 21:00Day 30, 11:05
1954Day 30, 11:10Day 01, 00:00
1965Day 01, 01:00Day 02, 15:05
1976Day 02, 14:08Day 04, 04:13
1981Day 14, 16:00Day 15, 09:00
1991Day 15, 23:36Day 18, 09:33
2001Day 24, 12:00Day 28, 12:06
2011Day 19, 21:05Day 27, 14:00
2021Day 17, 07:41Day 17, 19:01
2031Day 12, 18:00Day 14, 13:53
2042Day 14, 17:30Day 15, 12:00
2053Day 15, 15:00Day 17, 10:53
2064Day 17, 10:27Day 19, 06:20
2075Day 17, 10:35Day 17, 14:05
2086Day 17, 10:40Day 19, 06:33
2097Day 17, 12:27Day 17, 12:28
2108Day 17, 12:28Day 17, 12:29
2111Day 13, 17:30Day 18, 20:00
2121Day 14, 03:25Day 14, 15:10
2132Day 14, 16:00Day 15, 14:02
2143Day 15, 17:40Day 16, 15:42
2154Day 16, 15:39Day 17, 11:36
2165Day 16, 17:00Day 17, 15:02
2176Day 17, 13:23Day 17, 14:30
2187Day 17, 14:30Day 17, 15:22
2198Day 17, 16:29Day 17, 16:35
2209Day 17, 16:42Day 17, 22:30
22110Day 17, 19:42Day 17, 19:43
22211Day 18, 03:51Day 18, 09:00
2231Day 25, 23:45Day 26, 00:29
2242Day 26, 16:00Day 30, 02:02
2253Day 30, 03:07Day 30, 13:32
2264Day 02, 18:26Day 04, 04:22
2271Day 03, 20:15Day 06, 08:25
2282Day 06, 12:20Day 07, 14:42
2293Day 08, 02:30Day 08, 12:01
2301Day 03, 02:00Day 05, 04:05
2312Day 05, 06:45Day 07, 08:50
2323Day 05, 06:45Day 07, 19:13
2331Day 03, 02:36Day 03, 18:38
2341Day 17, 20:00Day 19, 12:00
2351Day 18, 21:00Day 20, 19:00
2361Day 04, 22:00Day 08, 07:44
2371Day 06, 04:37Day 06, 07:13
2382Day 06, 08:40Day 06, 12:08
2393Day 06, 13:30Day 07, 07:33
2404Day 07, 17:40Day 08, 09:17
2411Day 19, 00:30Day 20, 19:35
2421Day 15, 00:00Day 15, 04:00
2431Day 27, 17:51Day 27, 22:30
2442Day 28, 08:37Day 28, 13:16
2453Day 28, 13:20Day 02, 06:00
2464Day 02, 10:15Day 02, 15:01
2475Day 02, 18:00Day 02, 22:46
2486Day 02, 18:00Day 04, 07:45
2497Day 02, 18:00Day 06, 12:04
2508Day 04, 08:45Day 22, 19:00
2519Day 06, 18:56Day 10, 19:43
2521Day 27, 17:28Day 29, 08:00
2531Day 13, 14:36Day 17, 01:00
2541Day 19, 22:31Day 20, 01:19
2552Day 20, 01:45Day 20, 04:20
2563Day 20, 01:45Day 20, 07:30
2574Day 20, 04:20Day 20, 04:33
2585Day 20, 05:41Day 20, 05:42
2596Day 20, 15:25Day 21, 11:30
2607Day 21, 12:20Day 22, 08:25
2618Day 22, 08:35Day 22, 11:57
2621Day 07, 22:15Day 09, 20:00
2632Day 09, 21:00Day 11, 18:45
2643Day 11, 18:35Day 13, 11:56
2654Day 13, 13:00Day 13, 17:24
2665Day 13, 20:30Day 14, 05:00
2676Day 14, 05:45Day 14, 13:00
2687Day 14, 15:45Day 15, 19:39
2698Day 16, 00:27Day 16, 11:03
2709Day 16, 10:30Day 16, 16:00
27110Day 16, 16:00Day 17, 01:32
2721Day 26, 10:43Day 27, 06:45
2732Day 27, 10:49Day 28, 06:51
2743Day 28, 06:34Day 29, 02:36
2754Day 28, 08:34Day 28, 08:35
2765Day 29, 02:00Day 29, 22:02
2776Day 29, 06:17Day 29, 06:18
2781Day 15, 06:00Day 15, 07:27
2792Day 15, 09:40Day 15, 16:30
2801Day 16, 01:00Day 16, 12:24
2812Day 16, 13:56Day 17, 01:20
2823Day 17, 01:00Day 17, 01:01
2834Day 17, 01:20Day 17, 12:44
2845Day 17, 01:20Day 20, 11:05
2856Day 17, 01:30Day 17, 12:54
2867Day 20, 13:50Day 21, 10:00
2878Day 29, 19:15Day 30, 00:00
2889Day 01, 19:00Day 02, 12:02
2891Day 01, 16:57Day 05, 15:00
2901Day 29, 09:00Day 30, 19:45
2911Day 12, 02:01Day 13, 06:25
2921Day 26, 06:00Day 26, 06:15
2931Day 04, 19:15Day 05, 07:15
2942Day 05, 08:37Day 07, 21:00
2953Day 09, 17:40Day 10, 03:20
2961Day 29, 17:35Day 29, 18:58
2972Day 29, 23:55Day 29, 23:58
2981Day 04, 14:30Day 07, 14:52
2991Day 05, 17:00Day 07, 19:06
3001Day 26, 22:00Day 30, 11:00
3011Day 09, 22:19Day 12, 18:36
3021Day 22, 05:00Day 23, 22:12
3032Day 22, 05:00Day 25, 12:22
3043Day 23, 23:50Day 25, 12:00
3054Day 25, 15:20Day 28, 22:42
3065Day 25, 15:22Day 25, 17:00
3076Day 29, 17:00Day 04, 20:35
3087Day 05, 10:00Day 08, 08:04
3098Day 05, 21:17Day 08, 10:04
3109Day 08, 12:10Day 09, 14:41
31110Day 11, 17:30Day 14, 12:00
31211Day 03, 17:30Day 05, 15:15
3131Day 24, 16:00Day 28, 10:06
3141Day 10, 19:35Day 12, 05:30
3152Day 12, 06:51Day 12, 08:03
3163Day 12, 12:03Day 13, 20:46
3174Day 13, 20:45Day 15, 06:40
3185Day 13, 20:50Day 15, 06:45
3196Day 13, 20:58Day 13, 20:59
3207Day 15, 03:10Day 15, 03:11
3218Day 15, 07:00Day 16, 16:55
3229Day 16, 16:21Day 18, 02:16
32310Day 16, 16:45Day 18, 02:40
32411Day 16, 18:20Day 16, 18:21
32512Day 18, 02:59Day 18, 12:48
32613Day 18, 19:00Day 19, 19:02
3271Day 29, 11:37Day 01, 10:43
3282Day 01, 13:01Day 02, 22:10
3293Day 04, 16:02Day 05, 12:41
3304Day 05, 15:58Day 06, 07:45
3315Day 06, 12:00Day 09, 12:36
3326Day 06, 12:30Day 06, 17:22
3337Day 09, 16:07Day 12, 16:43
3341Day 26, 22:45Day 28, 17:20
3352Day 28, 18:00Day 29, 00:59
3363Day 29, 19:45Day 13, 08:29
3374Day 15, 13:01Day 18, 16:30
3385Day 15, 13:01Day 21, 14:15
3396Day 18, 18:45Day 19, 15:16
3401Day 21, 21:30Day 22, 18:36
3411Day 28, 23:15Day 29, 04:30
3422Day 29, 12:15Day 01, 20:04
3433Day 02, 01:30Day 02, 11:30
3444Day 02, 12:45Day 03, 10:00
3451Day 25, 17:40Day 27, 09:30
3462Day 27, 11:00Day 27, 12:35
3473Day 27, 14:19Day 27, 18:36
3484Day 27, 22:14Day 27, 23:08
3495Day 29, 20:17Day 30, 07:03
3506Day 30, 14:04Day 05, 03:00
3517Day 05, 04:00Day 06, 00:45
3528Day 05, 04:00Day 09, 16:56
3539Day 06, 05:45Day 06, 18:00
35410Day 06, 05:45Day 07, 21:00
35511Day 06, 20:15Day 07, 04:45
35612Day 08, 10:18Day 11, 10:20
3571Day 16, 21:40Day 18, 21:00
3581Day 26, 12:22Day 30, 13:44
3592Day 04, 12:52Day 09, 15:02
3601Day 25, 15:55Day 28, 19:36
3612Day 25, 15:55Day 28, 19:44
3623Day 29, 01:30Day 29, 02:18
3634Day 29, 01:30Day 29, 02:26
3645Day 29, 01:30Day 01, 08:20
3656Day 29, 02:40Day 01, 07:17
3661Day 13, 15:30Day 16, 08:00
3671Day 17, 05:45Day 17, 23:45
3682Day 18, 00:20Day 18, 12:21
3693Day 18, 14:30Day 18, 21:20
3701Day 04, 00:00Day 04, 00:21
3712Day 04, 02:00Day 04, 02:21
3723Day 04, 02:00Day 07, 08:11
3734Day 07, 11:15Day 09, 09:00
3741Day 12, 20:30Day 13, 20:00
3752Day 14, 11:10Day 15, 10:45
3761Day 22, 15:00Day 22, 17:31
3772Day 22, 22:35Day 23, 01:06
3783Day 22, 22:35Day 25, 22:35
3794Day 22, 22:35Day 25, 23:15
3805Day 22, 23:00Day 25, 23:15
3816Day 25, 23:30Day 26, 02:01
3827Day 25, 23:30Day 28, 23:30
3831Day 30, 15:00Day 01, 12:32
3841Day 03, 10:15Day 03, 13:30
3851Day 15, 22:30Day 17, 22:22
3861Day 06, 18:05Day 06, 22:57
3872Day 07, 00:30Day 07, 05:22
3883Day 07, 05:25Day 07, 19:00
3894Day 07, 05:25Day 09, 11:05
3905Day 09, 13:55Day 10, 10:26
3916Day 12, 12:30Day 15, 11:58
3927Day 21, 16:43Day 23, 21:59
3938Day 03, 22:00Day 08, 14:47
3941Day 03, 20:36Day 04, 08:00
3952Day 05, 10:21Day 08, 15:55
3961Day 20, 16:35Day 20, 22:20
3972Day 27, 16:45Day 28, 14:00
3981Day 02, 18:55Day 06, 06:30
3992Day 06, 11:35Day 07, 18:30
4003Day 06, 21:00Day 07, 18:30
4011Day 21, 01:00Day 27, 11:58
4021Day 24, 18:02Day 24, 19:00
4032Day 25, 13:00Day 27, 12:45
4043Day 29, 17:45Day 30, 15:00
4054Day 30, 17:00Day 31, 14:15
4065Day 31, 14:11Day 01, 11:26
4076Day 01, 11:03Day 01, 18:03
4081Day 27, 21:00Day 29, 09:30
4092Day 29, 12:20Day 30, 08:10
4101Day 22, 13:15Day 22, 18:43
4111Day 24, 23:20Day 24, 23:29
4122Day 24, 23:20Day 25, 08:49
4133Day 25, 10:30Day 01, 12:36
4141Day 12, 20:30Day 12, 21:00
4152Day 12, 22:00Day 12, 22:30
4163Day 12, 22:00Day 13, 08:30
4174Day 13, 18:20Day 15, 17:34
4185Day 15, 20:30Day 16, 20:02
4196Day 16, 22:00Day 17, 17:30
4201Day 14, 17:00Day 17, 12:00
4212Day 18, 04:00Day 20, 22:07
4221Day 19, 21:00Day 20, 02:08
4231Day 13, 21:25Day 21, 18:00
4242Day 22, 17:00Day 27, 21:15
4253Day 27, 22:50Day 29, 14:00
4261Day 10, 18:40Day 11, 16:08
4271Day 31, 12:31Day 01, 18:30
4282Day 01, 21:45Day 02, 01:00
4293Day 02, 02:00Day 02, 09:20
4304Day 02, 15:00Day 03, 20:59
4315Day 02, 21:00Day 04, 02:59
4326Day 03, 03:00Day 04, 08:59
4337Day 03, 15:11Day 06, 19:00
4348Day 03, 20:22Day 03, 20:23
4359Day 03, 21:00Day 05, 02:59
43610Day 03, 22:21Day 03, 22:22
43711Day 05, 03:00Day 06, 08:59
43812Day 05, 03:18Day 05, 03:19
43913Day 07, 04:50Day 08, 10:30
4401Day 27, 23:00Day 28, 07:58
4412Day 28, 09:15Day 28, 18:13
4423Day 28, 20:15Day 29, 05:13
4434Day 29, 05:20Day 29, 14:18
4445Day 29, 05:20Day 01, 12:00
4451Day 02, 14:05Day 04, 01:47
4461Day 24, 12:41Day 24, 16:31
4472Day 24, 18:00Day 24, 21:50
4483Day 25, 17:49Day 25, 18:00
4494Day 26, 18:12Day 29, 12:49
4505Day 05, 00:00Day 07, 13:00
4516Day 07, 15:30Day 10, 04:30
4527Day 07, 15:30Day 11, 09:45
4538Day 10, 04:29Day 12, 17:29
4549Day 14, 18:28Day 14, 22:57
45510Day 15, 00:00Day 15, 04:29
45611Day 15, 04:27Day 15, 08:56
45712Day 15, 04:28Day 15, 08:57
45813Day 15, 04:30Day 16, 05:30
4591Day 26, 14:05Day 28, 08:00
4601Day 23, 15:20Day 24, 13:59
4611Day 08, 18:30Day 11, 19:00
4622Day 11, 21:00Day 13, 13:00
4633Day 19, 10:20Day 19, 17:02
4644Day 19, 19:09Day 20, 01:51
4655Day 20, 01:30Day 20, 08:12
4666Day 20, 01:44Day 20, 08:26
4677Day 20, 02:23Day 20, 02:24
4688Day 20, 02:24Day 20, 09:06
4691Day 02, 06:37Day 02, 08:05
4702Day 02, 12:00Day 02, 13:28
4713Day 02, 12:10Day 02, 13:38
4724Day 02, 12:15Day 02, 18:01
4735Day 02, 13:25Day 02, 14:53
4741Day 04, 23:00Day 05, 15:03
4751Day 30, 23:30Day 31, 09:03
4762Day 31, 10:07Day 31, 19:40
4773Day 31, 10:07Day 02, 08:22
4781Day 02, 14:45Day 08, 07:07
4792Day 08, 17:01Day 13, 01:43
4801Day 19, 11:00Day 19, 11:10
4811Day 18, 21:20Day 20, 15:24
4821Day 23, 22:52Day 24, 01:01
4832Day 24, 02:48Day 24, 04:57
4843Day 24, 04:11Day 24, 04:30
4851Day 08, 18:00Day 10, 20:13
4861Day 13, 01:00Day 13, 01:30
4872Day 13, 03:15Day 16, 02:26
4883Day 15, 16:15Day 16, 02:30
4894Day 16, 02:30Day 16, 12:45
4905Day 16, 03:30Day 19, 02:41
4916Day 16, 12:26Day 16, 22:41
4927Day 19, 02:41Day 19, 09:15
4938Day 19, 03:13Day 19, 03:14
4949Day 19, 12:20Day 20, 09:30
49510Day 20, 16:30Day 21, 11:50
4961Day 29, 21:00Day 03, 23:46
4971Day 05, 00:00Day 05, 14:57
4981Day 09, 19:48Day 12, 21:30
4992Day 12, 23:00Day 13, 04:35
5001Day 07, 18:00Day 08, 01:16
5012Day 08, 04:33Day 08, 11:49
5021Day 06, 01:05Day 06, 06:45
5031Day 12, 21:15Day 13, 09:36
5042Day 13, 18:00Day 14, 19:02
5051Day 11, 12:46Day 13, 11:32
5061Day 25, 20:10Day 28, 08:00
5071Day 05, 03:00Day 08, 02:00
5082Day 05, 03:00Day 25, 03:00
5091Day 20, 02:45Day 21, 03:15
5102Day 21, 11:00Day 22, 11:30
5113Day 22, 11:30Day 23, 12:00
5124Day 23, 12:00Day 24, 12:30
5135Day 23, 12:52Day 23, 12:53
5146Day 24, 12:27Day 24, 16:01
5157Day 24, 12:27Day 25, 12:57
5168Day 24, 12:39Day 24, 12:40
5179Day 24, 12:39Day 24, 12:40
51810Day 24, 12:39Day 24, 12:40
51911Day 24, 12:39Day 25, 13:09
52012Day 24, 13:07Day 24, 13:08
5211Day 06, 14:19Day 11, 16:39
5221Day 13, 20:45Day 14, 08:13
5231Day 01, 03:18Day 01, 14:08
5242Day 01, 22:00Day 04, 16:00
5253Day 04, 19:00Day 05, 06:45
5264Day 04, 19:00Day 08, 15:10
5275Day 05, 09:03Day 05, 13:04
5286Day 05, 13:04Day 05, 13:48
5297Day 05, 17:12Day 05, 22:24
5308Day 05, 22:36Day 07, 18:54
5319Day 08, 16:15Day 09, 07:05
53210Day 12, 17:45Day 13, 11:07
53311Day 13, 15:11Day 14, 08:33
53412Day 13, 15:11Day 14, 16:48
5351Day 01, 18:00Day 02, 19:30
5362Day 02, 20:00Day 06, 10:15
5371Day 31, 03:27Day 31, 21:11
5381Day 26, 22:52Day 27, 00:32
5392Day 27, 04:00Day 27, 05:40
5403Day 27, 04:00Day 28, 08:45
5414Day 27, 06:59Day 27, 07:00
5421Day 10, 14:10Day 12, 21:40
5432Day 12, 19:00Day 15, 09:12
5443Day 15, 09:10Day 16, 14:27
5454Day 15, 09:10Day 18, 11:00
5465Day 16, 17:27Day 18, 02:22
5476Day 18, 13:30Day 19, 15:00
5487Day 19, 21:00Day 22, 09:33
5491Day 01, 00:08Day 01, 02:00
5502Day 01, 03:05Day 01, 04:57
5513Day 01, 05:00Day 04, 00:15
5524Day 04, 01:00Day 05, 15:58
5535Day 04, 01:56Day 11, 00:36
5546Day 05, 17:30Day 06, 05:30
5557Day 06, 11:30Day 09, 21:05
5568Day 10, 00:00Day 10, 01:55
5579Day 10, 04:00Day 16, 16:00
55810Day 16, 17:15Day 17, 04:31
55911Day 22, 16:45Day 23, 16:30
56012Day 26, 21:49Day 28, 00:03
5611Day 05, 03:45Day 06, 12:32
5622Day 06, 14:00Day 07, 17:15
5633Day 07, 22:00Day 08, 14:30
5641Day 13, 12:22Day 13, 21:30
5652Day 13, 23:00Day 14, 08:08
5663Day 14, 08:15Day 14, 17:23
5674Day 14, 18:03Day 16, 09:00
5681Day 18, 16:45Day 20, 16:04
5692Day 20, 19:00Day 20, 19:46
5703Day 21, 09:56Day 26, 10:56
5714Day 22, 10:00Day 26, 10:56
5725Day 27, 13:36Day 31, 09:58
5736Day 31, 12:30Day 31, 12:45
5747Day 31, 16:00Day 02, 13:00
5751Day 05, 14:24Day 05, 16:40
5762Day 05, 21:30Day 05, 23:46
5773Day 05, 21:30Day 09, 14:40
5784Day 09, 18:00Day 12, 15:15
5791Day 03, 22:53Day 04, 18:59
5801Day 05, 22:57Day 11, 11:15
5812Day 05, 22:57Day 11, 11:15
5823Day 11, 15:08Day 12, 17:58
5834Day 12, 20:30Day 13, 23:20
5845Day 12, 20:30Day 15, 14:30
5856Day 15, 16:00Day 21, 07:26
5867Day 22, 11:50Day 23, 11:50
5878Day 22, 11:50Day 24, 23:42
5881Day 06, 12:10Day 11, 07:04
5891Day 07, 21:00Day 08, 12:30
5902Day 08, 14:30Day 09, 06:00
5913Day 09, 06:10Day 09, 16:34
5924Day 10, 02:51Day 10, 18:21
5935Day 10, 18:19Day 11, 09:49
5946Day 10, 18:20Day 11, 09:50
5957Day 10, 19:23Day 10, 19:24
5968Day 11, 09:30Day 12, 01:00
5979Day 11, 10:10Day 11, 10:11
59810Day 12, 01:36Day 14, 10:00
59911Day 14, 22:59Day 20, 14:30
60012Day 23, 16:30Day 24, 01:01
60113Day 23, 16:30Day 27, 15:43
60214Day 24, 02:02Day 24, 10:33
60315Day 24, 10:30Day 24, 19:01
60416Day 24, 11:07Day 24, 11:08
60517Day 31, 19:10Day 04, 10:05
60618Day 04, 11:29Day 04, 16:01
6071Day 22, 12:00Day 23, 07:00
6082Day 23, 12:17Day 23, 12:18
6093Day 23, 12:18Day 24, 07:18
6104Day 23, 12:18Day 26, 12:30
6115Day 26, 14:00Day 29, 14:12
6126Day 26, 14:00Day 02, 13:55
6137Day 02, 18:59Day 05, 11:59
6141Day 02, 22:19Day 06, 11:15
6151Day 29, 22:52Day 03, 18:56
6162Day 04, 18:23Day 08, 21:19
6171Day 26, 16:00Day 29, 16:01
6182Day 26, 18:00Day 29, 15:50
6193Day 29, 16:45Day 29, 16:55
6204Day 29, 16:45Day 01, 09:57
6211Day 05, 21:45Day 10, 14:12
6222Day 10, 18:30Day 11, 04:47
6233Day 11, 06:15Day 12, 03:44
6244Day 15, 04:51Day 15, 23:00
6251Day 12, 20:35Day 15, 05:00
6261Day 10, 21:30Day 11, 01:00
6272Day 13, 11:02Day 13, 22:00
6283Day 19, 10:30Day 28, 17:04
6291Day 01, 01:45Day 04, 00:00
6301Day 11, 18:15Day 12, 08:19
6312Day 12, 15:10Day 13, 05:14
6323Day 12, 15:10Day 13, 09:15
6331Day 08, 23:30Day 09, 16:01
6341Day 06, 23:25Day 11, 12:00
6352Day 21, 01:45Day 21, 15:45
6363Day 21, 21:51Day 22, 11:51
6374Day 22, 11:48Day 23, 01:48
6385Day 23, 01:31Day 23, 16:16
6396Day 23, 16:16Day 23, 16:19
6407Day 23, 16:16Day 24, 07:04
6418Day 24, 07:15Day 24, 19:45
6429Day 24, 23:40Day 26, 15:40
6431Day 30, 00:00Day 30, 05:47
6442Day 30, 08:45Day 30, 10:01
6453Day 30, 13:00Day 30, 18:47
6464Day 30, 23:50Day 05, 09:50
6471Day 01, 11:31Day 03, 14:05
6481Day 24, 21:00Day 26, 09:00
6492Day 26, 10:30Day 27, 21:37
6501Day 16, 23:51Day 20, 10:00
6511Day 28, 20:01Day 30, 01:01
6522Day 30, 02:46Day 31, 07:46
6533Day 31, 07:31Day 31, 15:15
6544Day 31, 07:41Day 01, 12:41
6555Day 31, 07:43Day 01, 12:43
6566Day 31, 10:53Day 31, 10:54
6577Day 31, 10:53Day 31, 10:54
6588Day 31, 10:54Day 01, 15:54
6599Day 31, 17:56Day 01, 15:12
66010Day 01, 15:09Day 02, 20:09
66111Day 01, 16:52Day 01, 16:53
6621Day 24, 18:30Day 27, 17:00
6631Day 14, 00:00Day 14, 10:30
6641Day 09, 13:30Day 12, 14:20
6652Day 12, 15:00Day 15, 15:50
6663Day 12, 15:00Day 18, 05:05
6674Day 18, 06:05Day 22, 13:30
6685Day 18, 06:05Day 23, 20:10
6696Day 23, 12:30Day 25, 23:00
6701Day 25, 05:36Day 25, 21:57
6711Day 01, 16:00Day 04, 16:00
6722Day 01, 16:33Day 04, 16:33
6733Day 26, 22:45Day 27, 21:30
6744Day 27, 23:00Day 28, 21:45
6755Day 27, 23:00Day 02, 00:00
6766Day 28, 21:04Day 28, 21:05
6771Day 02, 06:23Day 04, 11:35
6781Day 25, 23:41Day 26, 11:04
6792Day 26, 14:53Day 26, 19:17
6803Day 26, 22:17Day 27, 05:16
6814Day 27, 05:08Day 27, 14:20
6825Day 27, 08:06Day 27, 08:07
6836Day 27, 21:57Day 28, 00:08
6841Day 11, 05:45Day 11, 23:00
6852Day 12, 04:15Day 12, 19:01
6863Day 12, 22:30Day 13, 15:45
6874Day 13, 14:36Day 14, 07:51
6885Day 13, 16:00Day 14, 06:45
6896Day 14, 14:39Day 15, 05:24
6907Day 15, 05:22Day 15, 20:07
6918Day 15, 05:25Day 15, 20:10
6929Day 15, 05:30Day 15, 20:15
69310Day 15, 12:25Day 15, 12:24
69411Day 15, 19:00Day 16, 09:45
69512Day 15, 20:01Day 16, 10:46
69613Day 15, 20:03Day 16, 10:48
69714Day 15, 20:10Day 16, 10:55
69815Day 15, 20:15Day 16, 11:00
69916Day 15, 20:20Day 16, 11:05
70017Day 15, 21:00Day 16, 11:45
70118Day 19, 10:51Day 21, 08:02
7021Day 15, 06:00Day 18, 11:30
7032Day 15, 06:45Day 15, 14:45
7043Day 15, 19:00Day 16, 03:00
7054Day 16, 03:17Day 16, 11:17
7065Day 16, 11:20Day 16, 19:20
7076Day 16, 12:08Day 16, 20:08
7087Day 16, 15:00Day 16, 23:00
7098Day 16, 19:20Day 17, 03:20
7109Day 16, 21:07Day 16, 21:08
71110Day 17, 02:22Day 17, 10:22
71211Day 17, 03:15Day 17, 11:15
71312Day 17, 03:30Day 17, 11:30
71413Day 17, 04:22Day 17, 04:23
7151Day 21, 20:28Day 21, 22:02
7162Day 22, 04:05Day 22, 05:39
7173Day 22, 04:05Day 22, 11:15
7181Day 04, 23:50Day 06, 18:30
7192Day 06, 20:00Day 09, 07:45
7203Day 09, 09:30Day 09, 17:30
7214Day 09, 18:15Day 10, 04:30
7221Day 03, 01:00Day 03, 07:03
7232Day 03, 08:45Day 03, 14:48
7243Day 03, 16:30Day 03, 22:33
7254Day 13, 22:08Day 16, 08:02
7265Day 16, 15:00Day 24, 15:59
7271Day 19, 13:35Day 19, 15:20
7281Day 07, 17:07Day 08, 00:15
7292Day 08, 08:00Day 08, 21:13
7303Day 08, 22:50Day 09, 12:03
7314Day 11, 12:15Day 15, 11:58
7325Day 15, 14:00Day 17, 11:30
7331Day 19, 11:26Day 20, 15:00
7341Day 10, 22:00Day 12, 11:06
7352Day 12, 21:30Day 14, 11:03
7361Day 15, 21:25Day 16, 08:00
7372Day 16, 09:15Day 16, 19:50
7383Day 16, 19:53Day 17, 06:28
7394Day 17, 08:10Day 17, 08:11
7405Day 23, 21:45Day 26, 15:53
7411Day 13, 21:00Day 14, 04:58
7421Day 11, 18:08Day 14, 11:45
7432Day 11, 18:50Day 14, 11:45
7443Day 14, 13:00Day 16, 14:20
7454Day 16, 15:15Day 17, 06:50
7465Day 17, 06:28Day 19, 23:23
7476Day 17, 06:48Day 19, 23:43
7487Day 17, 06:55Day 19, 23:50
7498Day 20, 00:00Day 20, 04:30
7509Day 20, 08:50Day 22, 05:08
75110Day 24, 18:50Day 25, 08:15
7521Day 25, 17:59Day 27, 16:58
7532Day 27, 18:53Day 27, 18:59
7541Day 04, 01:58Day 07, 11:33
7551Day 08, 21:30Day 09, 09:41
7562Day 09, 13:44Day 10, 01:55
7573Day 10, 02:00Day 10, 02:45
7584Day 10, 05:05Day 13, 09:01
7595Day 13, 15:12Day 14, 09:12
7606Day 14, 11:11Day 16, 00:00
7611Day 18, 03:59Day 18, 08:15
7621Day 22, 18:00Day 23, 16:42
7632Day 22, 18:00Day 23, 16:42
7643Day 26, 15:35Day 27, 13:19
7654Day 27, 16:00Day 28, 14:58
7665Day 27, 16:00Day 28, 15:58
7676Day 27, 16:00Day 28, 16:33
7687Day 28, 22:00Day 01, 22:33
7698Day 28, 22:30Day 01, 23:03
7709Day 01, 22:08Day 02, 11:15
77110Day 01, 22:25Day 02, 22:58
77211Day 01, 23:07Day 02, 23:40
77312Day 02, 00:07Day 03, 00:40
77413Day 02, 16:20Day 03, 16:53
77514Day 03, 16:20Day 04, 13:44
77615Day 03, 16:44Day 04, 17:17
77716Day 03, 17:57Day 03, 17:58
77817Day 04, 00:00Day 06, 21:19
77918Day 04, 15:00Day 04, 18:09
78019Day 04, 19:01Day 04, 19:02
78120Day 06, 20:00Day 11, 10:03
78221Day 11, 15:00Day 14, 16:14
78322Day 16, 01:45Day 18, 14:30
78423Day 16, 06:47Day 20, 20:50
78524Day 18, 18:45Day 20, 02:01
7861Day 30, 20:30Day 01, 08:30
7872Day 01, 11:07Day 01, 18:35
7881Day 31, 18:10Day 01, 06:30
7892Day 01, 12:00Day 03, 06:30
7903Day 03, 10:10Day 05, 13:00
7911Day 16, 18:29Day 19, 15:00
7921Day 22, 00:00Day 22, 03:04
7931Day 18, 19:10Day 20, 06:24
7942Day 20, 09:10Day 22, 15:00
7951Day 07, 09:20Day 07, 18:22
7962Day 07, 22:00Day 07, 22:01
7971Day 29, 12:00Day 01, 09:00
7982Day 01, 19:15Day 03, 11:00
7993Day 03, 13:15Day 04, 07:00
8004Day 05, 11:00Day 06, 06:38
8015Day 06, 16:30Day 11, 10:00
8021Day 06, 23:45Day 09, 11:30
8031Day 15, 17:02Day 23, 09:00
8041Day 15, 02:00Day 15, 23:30
8052Day 16, 01:00Day 16, 09:30
8063Day 16, 01:00Day 18, 04:34
8074Day 16, 13:00Day 17, 02:00
8081Day 27, 02:00Day 27, 04:30
8092Day 27, 05:15Day 27, 16:02
8103Day 27, 22:18Day 28, 00:49
8114Day 27, 22:18Day 28, 20:40
8125Day 27, 22:30Day 27, 23:46
8136Day 28, 00:11Day 28, 01:27
8147Day 28, 00:49Day 28, 09:05
8158Day 28, 01:18Day 28, 02:34
8169Day 28, 09:00Day 28, 19:47
8171Day 28, 04:30Day 28, 15:38
8181Day 17, 22:40Day 21, 23:45
8192Day 22, 00:47Day 22, 17:29
8203Day 22, 21:15Day 23, 05:01
8211Day 01, 23:19Day 02, 05:58
8222Day 01, 23:24Day 02, 20:00
8233Day 03, 12:44Day 04, 00:00
8244Day 04, 03:00Day 06, 10:40
8251Day 19, 22:30Day 22, 21:00
8261Day 24, 23:10Day 25, 18:15
8272Day 25, 20:15Day 26, 15:20
8283Day 26, 15:45Day 26, 16:00
8294Day 26, 18:00Day 05, 16:15
8305Day 26, 18:35Day 27, 13:25
8316Day 06, 20:27Day 07, 13:45
8327Day 07, 19:00Day 10, 16:53
8338Day 10, 22:20Day 10, 22:21
8349Day 10, 22:20Day 13, 20:14
83510Day 13, 20:20Day 16, 18:14
83611Day 13, 20:27Day 13, 20:28
83712Day 13, 20:28Day 13, 20:29
83813Day 16, 18:23Day 19, 16:17
83914Day 19, 16:15Day 22, 14:09
84015Day 19, 16:30Day 21, 11:31
84116Day 19, 16:54Day 19, 16:55
8421Day 04, 21:17Day 07, 17:19
8431Day 10, 22:00Day 12, 22:34
8442Day 13, 01:45Day 13, 21:20
8451Day 17, 16:27Day 19, 08:37
8461Day 18, 06:15Day 18, 07:00
8472Day 18, 10:25Day 18, 10:25
8483Day 18, 10:25Day 18, 11:10
8494Day 18, 10:25Day 20, 14:00
8501Day 15, 20:47Day 18, 17:20
8512Day 19, 12:45Day 21, 07:42
8521Day 20, 20:00Day 24, 17:00
8531Day 07, 18:48Day 09, 16:30
8541Day 22, 13:44Day 24, 01:00
8552Day 24, 04:00Day 25, 14:04
8563Day 25, 16:35Day 25, 17:47
8574Day 25, 17:30Day 27, 04:46
8585Day 25, 17:45Day 26, 11:00
8596Day 25, 20:18Day 27, 07:34
8601Day 25, 19:00Day 26, 13:30
8611Day 03, 20:00Day 04, 03:00
8621Day 22, 13:29Day 22, 13:46
8632Day 22, 15:02Day 22, 15:15
8641Day 02, 00:01Day 02, 10:30
8652Day 02, 17:53Day 03, 04:22
8663Day 03, 04:07Day 03, 04:08
8674Day 03, 04:19Day 03, 14:48
8685Day 03, 04:20Day 03, 04:10
8696Day 03, 04:23Day 03, 11:45
8707Day 03, 04:47Day 03, 04:48
8711Day 07, 00:00Day 07, 22:20
8722Day 08, 00:30Day 13, 13:00
8733Day 13, 15:00Day 13, 20:23
8744Day 13, 23:00Day 14, 04:23
8755Day 13, 23:00Day 14, 04:34
8766Day 14, 13:00Day 17, 13:01
8771Day 21, 16:37Day 21, 18:11
8782Day 21, 21:30Day 22, 16:12
8793Day 22, 18:30Day 23, 13:12
8804Day 22, 18:30Day 25, 05:00
8815Day 25, 06:36Day 25, 08:05
8826Day 25, 06:36Day 25, 08:16
8837Day 25, 06:36Day 27, 17:06
8848Day 25, 12:00Day 25, 13:29
8859Day 25, 12:00Day 26, 10:30
8861Day 10, 18:25Day 11, 07:22
8872Day 11, 16:00Day 14, 16:25
8883Day 11, 16:20Day 12, 10:01
8894Day 12, 12:25Day 13, 06:06
8905Day 14, 18:30Day 16, 12:02
8916Day 16, 14:41Day 17, 12:44
8927Day 17, 18:20Day 19, 09:20
8938Day 19, 10:10Day 21, 01:10
8949Day 25, 15:52Day 27, 08:19
8951Day 02, 21:00Day 04, 01:08
8962Day 04, 04:00Day 05, 08:08
8973Day 04, 04:00Day 07, 08:41
8984Day 06, 22:38Day 07, 08:39
8991Day 16, 11:50Day 20, 06:30
9002Day 22, 22:00Day 23, 16:01
9013Day 23, 19:30Day 24, 06:46
9024Day 24, 17:03Day 25, 04:19
9035Day 24, 17:03Day 25, 20:01
9046Day 25, 04:12Day 25, 15:28
9057Day 25, 04:19Day 25, 15:35
9068Day 25, 04:30Day 25, 15:46
9079Day 25, 07:16Day 25, 07:17
90810Day 25, 14:56Day 25, 14:57
90911Day 25, 15:25Day 25, 15:26
91012Day 25, 15:25Day 25, 15:26
91113Day 25, 15:25Day 26, 02:41
91214Day 25, 15:35Day 26, 02:51
91315Day 25, 15:58Day 25, 15:59
91416Day 25, 15:58Day 25, 15:59
91517Day 25, 21:45Day 26, 07:45
91618Day 26, 11:15Day 26, 14:00
9171Day 11, 11:50Day 11, 19:00
9181Day 04, 21:00Day 05, 01:03
9192Day 05, 03:35Day 05, 03:45
9203Day 05, 05:19Day 07, 14:15
9211Day 15, 15:00Day 16, 13:30
9222Day 16, 14:00Day 17, 12:30
9231Day 04, 17:10Day 04, 22:45
9241Day 06, 01:45Day 09, 02:15
9252Day 09, 03:20Day 10, 09:00
9263Day 09, 03:20Day 14, 16:00
9274Day 10, 10:45Day 11, 09:45
9285Day 11, 12:00Day 12, 07:50
9296Day 15, 14:05Day 18, 14:50
9307Day 18, 20:20Day 21, 09:01
9311Day 16, 17:38Day 18, 16:36
9321Day 30, 00:00Day 30, 02:49
9331Day 13, 13:00Day 23, 18:52
9341Day 24, 00:20Day 25, 15:03
9351Day 09, 12:18Day 13, 01:30
9362Day 13, 13:30Day 14, 16:45
9371Day 28, 21:30Day 30, 19:58
9381Day 01, 14:15Day 02, 06:30
9392Day 02, 09:11Day 02, 13:00
9401Day 13, 22:57Day 17, 04:30
9412Day 17, 14:30Day 17, 15:45
9423Day 17, 19:20Day 18, 15:30
9434Day 18, 16:15Day 19, 12:16
9445Day 19, 11:18Day 20, 07:28
9456Day 19, 12:16Day 19, 12:25
9467Day 19, 12:19Day 20, 08:29
9478Day 19, 12:20Day 20, 08:30
9489Day 19, 12:20Day 20, 12:30
94910Day 19, 13:18Day 20, 09:28
95011Day 19, 16:20Day 20, 12:30
9511Day 14, 17:45Day 14, 20:00
9522Day 14, 21:00Day 14, 23:01
9533Day 14, 22:56Day 14, 23:01
9544Day 15, 02:00Day 15, 06:03
9555Day 15, 06:03Day 15, 06:16
9566Day 15, 06:03Day 15, 10:19
9577Day 15, 06:11Day 15, 10:27
9588Day 15, 06:14Day 15, 06:16
9599Day 15, 06:37Day 15, 10:53
96010Day 15, 06:39Day 15, 10:55
96111Day 15, 10:17Day 15, 16:37
96212Day 15, 10:25Day 15, 12:49
96313Day 16, 08:36Day 16, 14:24
96414Day 16, 17:10Day 17, 13:00
96515Day 17, 16:15Day 19, 00:30
96616Day 19, 11:30Day 19, 11:41
96717Day 20, 17:10Day 21, 07:06
9681Day 31, 16:10Day 02, 16:00
9692Day 17, 10:50Day 18, 05:00
9701Day 27, 11:23Day 28, 10:54
9711Day 21, 23:38Day 24, 03:26
9722Day 24, 11:30Day 26, 11:11
9731Day 17, 15:40Day 18, 16:27
9741Day 19, 16:30Day 22, 01:01
9751Day 10, 10:15Day 23, 08:30
9762Day 24, 12:00Day 25, 10:30
9771Day 28, 23:01Day 01, 08:57
9782Day 01, 13:00Day 02, 02:21
9793Day 02, 11:15Day 04, 10:00
9804Day 05, 09:30Day 05, 10:30
9815Day 05, 12:00Day 05, 15:30
9826Day 05, 18:00Day 05, 21:30
9837Day 05, 21:23Day 06, 00:53
9848Day 05, 21:23Day 06, 00:53
9859Day 05, 21:30Day 05, 22:10
98610Day 05, 21:30Day 05, 23:14
98711Day 05, 22:15Day 06, 01:45
98812Day 06, 04:58Day 06, 07:48
98913Day 06, 05:00Day 09, 01:32
9901Day 11, 23:45Day 12, 20:30
9912Day 12, 21:30Day 13, 23:15
9923Day 14, 15:27Day 16, 16:02
9931Day 14, 17:30Day 15, 15:29
9942Day 15, 18:57Day 18, 11:04
9951Day 24, 09:30Day 25, 11:01
9962Day 26, 02:35Day 27, 04:06
9973Day 27, 07:46Day 28, 09:17
9984Day 28, 09:07Day 28, 15:15
9995Day 28, 09:44Day 29, 11:15
10006Day 28, 14:11Day 28, 14:12
10017Day 29, 13:00Day 02, 11:04
10028Day 02, 16:00Day 02, 20:01
10039Day 02, 21:00Day 06, 12:57
10041Day 23, 18:27Day 23, 20:00
10052Day 28, 09:48Day 28, 11:47
10061Day 23, 23:50Day 25, 08:20
10071Day 10, 21:06Day 15, 11:00
10082Day 15, 22:00Day 18, 04:00
10091Day 19, 17:45Day 22, 21:30
10102Day 22, 22:03Day 25, 10:43
10111Day 29, 21:15Day 30, 15:00
10122Day 30, 18:00Day 31, 11:45
10133Day 31, 12:10Day 01, 05:55
10141Day 30, 03:30Day 30, 07:50
10152Day 30, 09:00Day 12, 12:04
10161Day 23, 17:50Day 25, 15:11
10171Day 23, 18:45Day 30, 22:50
10181Day 21, 00:00Day 24, 06:00
10191Day 06, 12:40Day 09, 11:06
10202Day 09, 14:00Day 12, 12:26
10213Day 12, 19:49Day 13, 14:03
10224Day 12, 19:49Day 15, 18:15
10235Day 13, 15:00Day 16, 09:00
10246Day 16, 12:00Day 17, 19:59
10251Day 07, 21:37Day 09, 07:47
10261Day 23, 12:40Day 28, 09:30
10272Day 02, 12:00Day 03, 11:58
10283Day 05, 00:00Day 07, 20:58
10291Day 25, 14:00Day 27, 11:01
10302Day 28, 07:30Day 29, 11:50
10311Day 20, 21:25Day 21, 17:23
10322Day 21, 20:50Day 22, 14:11
10331Day 12, 02:03Day 16, 06:00
10341Day 03, 11:45Day 10, 20:45
10351Day 03, 02:13Day 04, 08:10
10361Day 11, 16:36Day 16, 19:01
10371Day 02, 11:40Day 06, 07:30
10382Day 06, 11:45Day 09, 08:01
10393Day 06, 11:45Day 10, 07:35
10404Day 06, 11:45Day 10, 07:35
10415Day 09, 09:00Day 12, 01:00
10426Day 13, 13:14Day 14, 19:00
10431Day 16, 01:10Day 16, 07:30
10442Day 16, 18:15Day 17, 13:15
10453Day 17, 14:10Day 18, 07:45
10464Day 18, 10:00Day 22, 11:20
10471Day 09, 11:00Day 12, 10:37
10481Day 13, 00:00Day 15, 22:33
10492Day 16, 12:35Day 16, 20:35
10503Day 17, 10:30Day 20, 12:33
10514Day 18, 16:20Day 20, 12:36
10521Day 23, 00:00Day 23, 18:00
10532Day 24, 04:37Day 04, 17:37
10541Day 08, 17:59Day 13, 03:41
10552Day 13, 16:15Day 15, 08:16
10563Day 18, 13:15Day 18, 14:10
10574Day 18, 21:07Day 18, 21:13
10585Day 19, 12:20Day 20, 06:20
10596Day 20, 08:36Day 21, 18:38
10601Day 26, 04:50Day 29, 14:30
10611Day 28, 02:08Day 31, 10:30
10622Day 02, 15:20Day 06, 09:00
10631Day 30, 04:30Day 30, 06:44
10641Day 06, 11:00Day 06, 14:45
10652Day 06, 18:30Day 06, 22:15
10663Day 06, 22:18Day 07, 15:01
10674Day 07, 21:47Day 09, 00:01
10685Day 08, 14:30Day 08, 20:17
10696Day 11, 00:15Day 13, 15:51
10707Day 11, 00:15Day 18, 08:28
10718Day 13, 19:05Day 13, 19:29
10729Day 18, 12:00Day 19, 10:30
107310Day 19, 11:30Day 20, 08:30
107411Day 21, 12:00Day 22, 02:00
107512Day 22, 02:05Day 23, 23:15
107613Day 24, 11:15Day 25, 13:33
107714Day 26, 00:13Day 28, 11:48
10781Day 03, 11:00Day 04, 11:00
10792Day 04, 13:45Day 04, 18:00
10803Day 04, 19:54Day 05, 00:09
10814Day 05, 03:50Day 05, 08:05
10825Day 05, 07:00Day 05, 17:00
10831Day 09, 10:35Day 10, 07:30
10842Day 10, 11:30Day 11, 08:25
10853Day 10, 11:30Day 11, 11:03
10864Day 11, 12:19Day 12, 11:52
10875Day 12, 12:00Day 13, 11:33
10881Day 26, 13:51Day 27, 22:50
10892Day 27, 23:52Day 27, 23:56
10901Day 12, 13:00Day 15, 16:25
10912Day 12, 13:00Day 17, 05:00
10923Day 15, 17:32Day 18, 21:02
10934Day 15, 17:45Day 15, 17:50
10945Day 15, 17:45Day 18, 21:15
10956Day 15, 18:32Day 15, 18:33
10967Day 17, 06:30Day 20, 09:30
10978Day 17, 06:30Day 21, 22:30
10989Day 20, 11:30Day 23, 14:30
109910Day 20, 11:30Day 02, 12:45
110011Day 02, 17:00Day 05, 16:59
110112Day 05, 18:15Day 08, 18:30
110213Day 08, 20:45Day 11, 18:30
110314Day 11, 21:38Day 12, 08:30
110415Day 12, 09:38Day 12, 14:00
110516Day 12, 17:40Day 16, 20:00
110617Day 18, 13:30Day 20, 13:00
110718Day 20, 20:03Day 23, 06:30
110819Day 23, 15:00Day 29, 14:00
110920Day 29, 17:00Day 01, 10:03
11101Day 01, 22:40Day 03, 10:00
11112Day 06, 09:40Day 07, 18:40
11121Day 19, 15:15Day 21, 09:40
11132Day 21, 15:03Day 22, 00:01
11143Day 22, 01:00Day 25, 05:45
11154Day 25, 15:45Day 26, 08:45
11165Day 29, 16:45Day 30, 13:37
11171Day 11, 02:46Day 11, 14:02
11182Day 11, 20:12Day 15, 19:00
11193Day 13, 00:21Day 15, 19:00
11204Day 13, 06:31Day 16, 08:30
11215Day 16, 14:51Day 17, 06:01
11221Day 12, 21:30Day 13, 15:00
11232Day 13, 18:52Day 18, 13:33
11243Day 18, 16:30Day 20, 01:43
11254Day 20, 20:30Day 25, 08:10
11261Day 27, 02:52Day 27, 05:30
11271Day 26, 00:30Day 26, 03:13
11281Day 02, 00:10Day 02, 06:02
11291Day 04, 23:11Day 07, 03:00
11302Day 07, 04:10Day 07, 16:30
11313Day 07, 18:50Day 08, 03:00
11324Day 08, 04:30Day 08, 09:49
11335Day 08, 11:05Day 08, 18:03
11346Day 08, 22:19Day 09, 06:29
11357Day 09, 06:30Day 09, 14:27
11368Day 09, 06:46Day 09, 06:47
11379Day 09, 16:25Day 10, 00:35
113810Day 10, 00:00Day 10, 08:10
113911Day 10, 08:05Day 10, 13:56
114012Day 10, 08:05Day 10, 16:15
114113Day 10, 11:04Day 10, 19:14
114214Day 10, 14:43Day 11, 06:02
114315Day 11, 07:30Day 13, 14:47
114416Day 13, 21:39Day 13, 23:16
114517Day 14, 09:16Day 16, 20:01
114618Day 16, 22:00Day 16, 23:33
114719Day 17, 02:45Day 18, 12:00
114820Day 25, 12:30Day 25, 18:27
114921Day 25, 21:45Day 31, 18:10
115022Day 31, 22:18Day 01, 20:07
115123Day 02, 00:35Day 07, 15:00
115224Day 07, 21:42Day 08, 10:00
115325Day 08, 12:00Day 09, 12:58
115426Day 09, 16:00Day 13, 20:03
115527Day 13, 23:20Day 14, 04:03
11561Day 23, 15:00Day 26, 13:59
11572Day 01, 23:30Day 04, 07:02
11583Day 05, 11:15Day 07, 17:01
11591Day 10, 15:07Day 10, 15:31
11602Day 13, 15:30Day 13, 17:00
11613Day 14, 03:00Day 28, 18:31
11621Day 06, 00:50Day 06, 10:00
11631Day 17, 22:10Day 19, 09:56
11642Day 19, 16:00Day 21, 03:46
11653Day 21, 05:08Day 21, 13:10
11661Day 02, 21:15Day 07, 12:05
11671Day 08, 01:15Day 09, 08:47
11682Day 09, 13:30Day 10, 16:11
11691Day 30, 22:56Day 31, 22:00
11701Day 28, 15:43Day 28, 20:45
11711Day 27, 17:21Day 01, 14:15
11721Day 12, 16:10Day 18, 11:11
11732Day 18, 16:45Day 24, 11:46
11743Day 18, 18:11Day 21, 14:56
11751Day 20, 18:00Day 21, 02:03
11762Day 21, 05:00Day 21, 13:03
11773Day 22, 08:35Day 22, 15:40
11784Day 22, 22:30Day 23, 18:19
11791Day 27, 01:58Day 04, 13:42
11801Day 05, 09:30Day 07, 14:47
11811Day 07, 21:36Day 28, 07:04
11821Day 30, 22:00Day 03, 18:30
11831Day 17, 20:30Day 18, 01:45
11842Day 18, 04:00Day 22, 20:59
11853Day 23, 00:00Day 23, 01:04
11864Day 23, 02:17Day 26, 08:30
11875Day 21, 22:00Day 26, 18:00
11881Day 13, 21:50Day 16, 14:00
11891Day 20, 00:08Day 21, 16:30
11901Day 12, 22:00Day 14, 15:00
11912Day 14, 18:30Day 14, 22:30
11923Day 15, 00:30Day 16, 13:30
11931Day 07, 11:00Day 08, 11:30
11941Day 31, 02:00Day 31, 16:28
11952Day 31, 17:10Day 01, 07:38
11963Day 01, 07:00Day 01, 21:28
11974Day 01, 21:30Day 02, 11:58
11985Day 01, 21:58Day 01, 21:59
11996Day 02, 18:00Day 03, 08:28
12007Day 03, 08:00Day 03, 22:28
12018Day 03, 08:40Day 03, 23:08
12029Day 03, 11:55Day 03, 11:56
120310Day 04, 02:43Day 04, 17:11
12041Day 13, 15:00Day 19, 00:18
12051Day 16, 12:00Day 22, 17:00
12062Day 16, 12:15Day 17, 05:28
12073Day 19, 23:03Day 20, 14:20
12084Day 20, 15:17Day 21, 06:34
12095Day 21, 06:31Day 21, 21:48
12106Day 21, 06:31Day 21, 21:48
12117Day 21, 06:31Day 21, 21:48
12128Day 21, 21:55Day 22, 14:35
12139Day 21, 22:07Day 21, 22:08
12141Day 14, 21:10Day 17, 21:33
12152Day 17, 22:15Day 18, 09:31
12161Day 04, 23:30Day 05, 09:20
12172Day 05, 16:30Day 06, 02:20
12183Day 06, 02:05Day 06, 11:55
12194Day 06, 02:05Day 10, 14:15
12205Day 10, 14:55Day 10, 17:00
12216Day 10, 18:00Day 10, 18:25
12227Day 10, 21:17Day 11, 01:50
12238Day 11, 02:10Day 11, 09:45
12249Day 11, 10:01Day 11, 17:49
122510Day 11, 20:10Day 11, 22:00
122611Day 11, 23:45Day 12, 22:00
122712Day 13, 00:00Day 13, 15:00
122813Day 13, 16:30Day 13, 19:02
122914Day 14, 03:00Day 14, 09:35
123015Day 14, 18:03Day 17, 18:08
123116Day 16, 04:42Day 16, 04:43
123217Day 18, 10:45Day 18, 11:45
123318Day 18, 16:00Day 19, 12:21
123419Day 19, 16:00Day 19, 17:45
123520Day 19, 16:00Day 20, 12:21
123621Day 19, 19:30Day 19, 21:15
123722Day 19, 19:30Day 22, 11:00
123823Day 22, 13:15Day 24, 14:30
123924Day 24, 18:41Day 25, 08:18
124025Day 25, 12:27Day 25, 13:00
124126Day 25, 14:14Day 27, 15:29
124227Day 25, 14:14Day 28, 22:45
124328Day 29, 04:30Day 01, 07:15
124429Day 01, 10:35Day 04, 06:30
124530Day 04, 13:00Day 07, 07:03
124631Day 14, 10:10Day 17, 15:30
124732Day 17, 21:45Day 18, 11:30
124833Day 18, 18:00Day 19, 20:20
124934Day 19, 21:30Day 20, 02:39
125035Day 20, 04:11Day 21, 05:15
125136Day 21, 07:45Day 21, 11:00
125237Day 21, 12:15Day 21, 23:45
125338Day 22, 01:34Day 22, 03:30
125439Day 22, 04:20Day 22, 07:15
125540Day 22, 11:00Day 24, 05:30
12561Day 26, 20:30Day 28, 08:00
12572Day 28, 12:06Day 29, 13:15
12581Day 11, 16:23Day 12, 02:00
12592Day 12, 10:16Day 12, 16:30
12603Day 12, 17:15Day 17, 04:54
12611Day 25, 12:30Day 28, 00:17
12621Day 27, 15:25Day 03, 02:46
12631Day 29, 16:25Day 03, 12:00
12642Day 07, 20:40Day 09, 19:15
12653Day 09, 20:20Day 11, 18:55
12664Day 11, 18:54Day 12, 05:46
12675Day 12, 14:45Day 14, 16:00
12686Day 28, 20:30Day 03, 23:20
12697Day 28, 20:30Day 17, 20:30
12708Day 04, 00:30Day 04, 15:20
12719Day 04, 18:05Day 10, 06:04
12721Day 21, 14:21Day 24, 17:02
12732Day 21, 15:30Day 25, 10:44
12743Day 24, 18:00Day 25, 10:18
12754Day 26, 14:15Day 01, 06:58
12761Day 12, 16:00Day 16, 17:00
12771Day 27, 15:39Day 28, 20:29
12781Day 12, 20:28Day 13, 11:02
12792Day 13, 23:20Day 17, 05:32
12803Day 19, 23:45Day 20, 11:43
12814Day 21, 00:30Day 21, 09:29
12821Day 11, 22:03Day 12, 01:32
12831Day 23, 21:00Day 24, 03:16
12841Day 23, 09:31Day 23, 18:50
12851Day 07, 01:12Day 10, 06:59
12861Day 05, 01:05Day 05, 15:33
12871Day 09, 13:22Day 11, 12:44
12881Day 24, 19:35Day 28, 04:00
12891Day 07, 20:28Day 07, 23:30
12902Day 08, 04:38Day 08, 07:40
12913Day 08, 04:38Day 09, 19:01
12924Day 08, 07:40Day 08, 10:42
12935Day 08, 10:42Day 08, 13:44
12946Day 08, 13:45Day 08, 16:47
12957Day 08, 16:43Day 08, 19:45
12968Day 08, 16:47Day 08, 19:49
12979Day 08, 19:45Day 08, 22:47
129810Day 08, 19:49Day 08, 22:51
129911Day 08, 20:18Day 08, 20:19
130012Day 08, 22:48Day 09, 01:50
130113Day 09, 00:05Day 09, 00:06
130214Day 09, 01:50Day 09, 04:52
130315Day 09, 01:50Day 09, 04:52
130416Day 09, 04:02Day 09, 04:03
130517Day 09, 04:03Day 09, 04:04
130618Day 09, 04:51Day 09, 07:53
130719Day 09, 19:46Day 11, 10:09
130820Day 09, 19:46Day 11, 20:00
130921Day 09, 19:46Day 11, 20:00
131022Day 09, 19:46Day 14, 21:42
131123Day 11, 10:13Day 12, 12:45
131224Day 11, 20:30Day 11, 20:42
131325Day 12, 14:00Day 13, 01:51
13141Day 21, 02:40Day 23, 04:15
13152Day 23, 17:30Day 25, 09:15
13161Day 02, 16:00Day 07, 09:58
13171Day 16, 21:08Day 19, 12:00
13181Day 17, 20:30Day 18, 15:07
13191Day 26, 23:45Day 28, 06:33
13202Day 29, 18:00Day 04, 11:26
13211Day 11, 23:15Day 13, 08:01
13221Day 27, 00:00Day 29, 16:35
13231Day 22, 23:00Day 23, 13:00
13242Day 23, 17:00Day 23, 20:20
13251Day 30, 19:49Day 31, 18:38
13261Day 03, 21:00Day 06, 20:04
13272Day 03, 21:00Day 07, 11:04
13283Day 06, 20:48Day 06, 20:49
13294Day 06, 22:00Day 09, 21:05
13301Day 13, 11:00Day 13, 14:15
13312Day 13, 17:22Day 13, 20:37
13323Day 13, 17:39Day 13, 20:54
13334Day 13, 17:40Day 14, 07:50
13345Day 13, 18:37Day 13, 21:52
13356Day 13, 18:40Day 13, 18:41
13367Day 14, 09:38Day 14, 10:06
13371Day 03, 14:21Day 05, 07:03
13382Day 07, 23:45Day 08, 17:11
13391Day 01, 20:46Day 05, 16:40
13401Day 12, 13:50Day 13, 03:05
13411Day 23, 15:43Day 25, 17:00
13421Day 01, 00:40Day 01, 06:45
13432Day 01, 20:10Day 06, 12:25
13443Day 06, 23:54Day 08, 00:26
13454Day 08, 02:08Day 08, 11:08
13465Day 08, 14:00Day 12, 21:15
13476Day 14, 23:10Day 16, 14:00
13487Day 16, 16:08Day 16, 20:00
13498Day 17, 02:00Day 18, 07:20
13509Day 18, 07:30Day 19, 08:45
13511Day 24, 16:45Day 30, 11:51
13522Day 30, 13:30Day 05, 08:36
13533Day 05, 08:12Day 05, 08:13
13544Day 05, 08:20Day 11, 03:26
13555Day 05, 08:21Day 11, 03:27
13566Day 05, 08:24Day 05, 16:00
13577Day 05, 12:03Day 11, 07:09
13588Day 07, 02:00Day 14, 09:00
13599Day 16, 11:10Day 19, 08:43
13601Day 03, 20:45Day 04, 05:00
13611Day 31, 14:32Day 04, 03:07
13621Day 13, 16:00Day 17, 08:22
13631Day 22, 07:56Day 24, 20:50
13641Day 10, 11:07Day 11, 15:00
13651Day 25, 19:39Day 26, 06:59
13662Day 28, 22:30Day 01, 12:02
13671Day 14, 15:15Day 21, 15:06
13681Day 30, 18:59Day 31, 13:00
13691Day 09, 23:50Day 13, 22:55
13701Day 04, 17:38Day 05, 16:00
13712Day 05, 21:09Day 06, 05:27
13723Day 06, 21:30Day 13, 11:30
13734Day 16, 13:10Day 18, 07:14
13745Day 18, 12:00Day 20, 06:04
13756Day 20, 06:21Day 22, 00:25
13761Day 24, 22:45Day 26, 16:54
13772Day 27, 02:28Day 28, 20:37
13783Day 28, 20:00Day 29, 12:45
13794Day 28, 20:59Day 30, 15:08
13805Day 28, 21:59Day 30, 16:08
13816Day 29, 17:15Day 29, 23:40
13827Day 30, 02:15Day 30, 04:41
13831Day 28, 06:17Day 03, 09:01
13841Day 03, 19:25Day 04, 17:14
13852Day 04, 18:20Day 04, 23:01
13861Day 10, 11:00Day 20, 13:02
13872Day 19, 02:31Day 20, 12:59
13881Day 31, 13:00Day 06, 12:13
13891Day 29, 18:00Day 02, 12:52
13901Day 08, 23:00Day 09, 06:47
13911Day 02, 20:00Day 09, 00:00
13922Day 03, 10:53Day 08, 17:00
13931Day 27, 19:12Day 30, 18:43
13941Day 27, 10:00Day 27, 14:50
13952Day 27, 17:13Day 27, 22:03
13963Day 27, 17:13Day 29, 10:01
13974Day 29, 11:45Day 29, 14:30
13985Day 29, 22:10Day 29, 23:26
13996Day 31, 01:33Day 02, 08:25
14007Day 02, 14:05Day 02, 15:30
14018Day 02, 17:40Day 04, 13:28
14021Day 25, 16:53Day 01, 14:02
14032Day 01, 15:45Day 03, 10:15
14041Day 03, 18:15Day 07, 03:16
14051Day 16, 16:30Day 16, 19:12
14061Day 12, 21:18Day 13, 15:02
14071Day 23, 00:20Day 23, 21:04
14081Day 11, 00:00Day 11, 21:07
14092Day 12, 03:30Day 12, 15:00
14101Day 24, 22:00Day 27, 09:00
14111Day 12, 18:15Day 14, 16:24
14122Day 14, 19:25Day 14, 19:26
14131Day 09, 00:17Day 15, 11:01
14142Day 09, 00:45Day 09, 05:41
14153Day 09, 08:50Day 09, 18:22
14164Day 09, 08:55Day 09, 08:59
14175Day 09, 20:37Day 09, 20:45
14186Day 09, 20:37Day 10, 06:17
14191Day 03, 22:37Day 08, 06:00
14201Day 16, 15:25Day 17, 06:48
14212Day 17, 09:05Day 19, 08:22
14223Day 19, 08:30Day 20, 00:03
14234Day 19, 08:30Day 22, 08:30
14245Day 20, 01:14Day 20, 02:20
14256Day 23, 22:00Day 24, 20:15
14267Day 24, 21:30Day 25, 19:45
14278Day 25, 18:52Day 25, 18:53
14289Day 26, 01:05Day 26, 12:40
142910Day 26, 17:40Day 27, 04:20
143011Day 27, 04:15Day 28, 02:30
143112Day 27, 04:17Day 28, 02:32
143213Day 28, 02:00Day 29, 00:15
143314Day 28, 02:11Day 29, 00:26
143415Day 28, 02:15Day 29, 00:30
143516Day 28, 02:20Day 29, 00:35
143617Day 28, 02:30Day 29, 00:45
143718Day 29, 00:40Day 29, 22:55
143819Day 29, 00:45Day 29, 18:00
143920Day 29, 00:57Day 29, 23:12
144021Day 29, 07:56Day 30, 06:11
144122Day 07, 16:20Day 08, 11:57
144223Day 08, 22:39Day 13, 13:30
144324Day 13, 18:30Day 15, 16:00
14441Day 08, 20:30Day 09, 12:30
14452Day 09, 15:08Day 10, 07:08
14463Day 09, 15:08Day 11, 07:00
14474Day 10, 07:05Day 10, 23:05
14485Day 10, 07:08Day 10, 23:08
14496Day 10, 09:04Day 11, 01:04
14507Day 11, 18:57Day 13, 19:30
14511Day 18, 17:00Day 20, 04:00
14522Day 20, 17:00Day 22, 16:00
14531Day 14, 20:00Day 20, 07:00
14541Day 01, 19:08Day 01, 20:10
14551Day 26, 02:00Day 26, 02:40
14561Day 23, 23:59Day 24, 09:20
14571Day 05, 05:01Day 07, 05:04
14581Day 11, 18:45Day 15, 02:59
14592Day 16, 01:19Day 21, 04:00
14603Day 21, 15:30Day 24, 13:30
14614Day 28, 00:00Day 28, 00:05
14625Day 28, 02:30Day 28, 02:35
14636Day 28, 02:30Day 28, 22:39
14641Day 05, 05:34Day 10, 17:09
14651Day 06, 21:45Day 11, 17:32
14662Day 14, 03:00Day 14, 03:06
14673Day 14, 03:00Day 14, 08:30
14684Day 14, 03:00Day 14, 13:02
14695Day 15, 21:13Day 17, 10:00
14701Day 22, 02:00Day 22, 14:30
14712Day 22, 08:56Day 22, 13:00
14723Day 22, 18:00Day 24, 13:24
14731Day 29, 19:10Day 03, 18:05
14741Day 03, 19:00Day 10, 09:00
14752Day 11, 20:00Day 17, 10:39
14761Day 18, 19:00Day 20, 15:50
14771Day 14, 14:26Day 23, 12:00
14782Day 23, 16:55Day 23, 17:21
14793Day 23, 19:30Day 24, 11:30
14801Day 30, 14:18Day 30, 15:02
14812Day 30, 18:00Day 30, 18:44
14823Day 30, 18:00Day 30, 20:30
14834Day 31, 00:00Day 31, 02:30
14845Day 31, 01:05Day 31, 02:45
14856Day 31, 01:05Day 31, 02:45
14867Day 14, 06:05Day 15, 12:35
14871Day 18, 14:16Day 19, 12:42
14881Day 28, 13:14Day 28, 17:05
14892Day 28, 18:00Day 30, 14:41
14903Day 28, 18:26Day 28, 22:17
14911Day 21, 22:22Day 22, 17:24
14922Day 23, 01:30Day 24, 10:35
14933Day 24, 10:45Day 25, 19:50
14944Day 24, 17:11Day 26, 02:16
14955Day 26, 06:16Day 27, 15:21
14966Day 27, 15:18Day 01, 00:23
14977Day 27, 15:20Day 01, 00:25
14988Day 27, 15:21Day 28, 09:04
14999Day 28, 15:26Day 02, 23:30
150010Day 03, 01:30Day 10, 07:14
15011Day 14, 21:45Day 18, 09:35
15022Day 24, 12:00Day 30, 13:30
15033Day 01, 16:30Day 07, 11:01
15041Day 17, 22:00Day 20, 12:00
15052Day 17, 22:45Day 18, 04:46
15063Day 18, 06:18Day 18, 23:30
15074Day 19, 01:00Day 19, 18:12
15081Day 02, 17:00Day 03, 15:20
15092Day 03, 17:45Day 04, 15:00
15103Day 04, 17:15Day 05, 14:30
15114Day 05, 14:27Day 06, 11:42
15125Day 05, 14:30Day 06, 11:45
15136Day 05, 14:57Day 06, 12:12
15147Day 06, 11:45Day 06, 17:25
15158Day 06, 18:45Day 08, 17:00
15161Day 26, 22:13Day 27, 10:00
15172Day 27, 15:15Day 28, 01:40
15183Day 27, 15:15Day 30, 12:15
15194Day 28, 02:50Day 28, 04:12
15205Day 30, 17:00Day 31, 02:50
15216Day 31, 05:45Day 31, 09:41
15227Day 31, 05:45Day 01, 00:00
15238Day 31, 12:00Day 31, 17:54
15249Day 01, 01:45Day 03, 13:15
152510Day 03, 16:00Day 04, 05:29
152611Day 04, 05:50Day 04, 06:00
152712Day 04, 08:23Day 04, 10:19
152813Day 04, 10:45Day 04, 14:00
152914Day 04, 15:30Day 05, 13:00
153015Day 05, 16:51Day 05, 18:00
153116Day 05, 22:00Day 05, 23:25
15321Day 30, 22:00Day 03, 05:02
15332Day 05, 20:00Day 08, 10:00
15341Day 29, 20:50Day 29, 22:10
15352Day 29, 23:51Day 30, 00:16
15363Day 30, 11:30Day 04, 10:45
15374Day 04, 15:20Day 06, 08:27
15385Day 06, 19:10Day 08, 03:10
15396Day 08, 07:40Day 16, 15:25
15407Day 16, 18:42Day 18, 22:45
15418Day 19, 10:00Day 21, 14:03
15429Day 21, 18:00Day 22, 19:00
15431Day 08, 21:15Day 13, 11:20
15441Day 16, 22:00Day 17, 18:58
15452Day 19, 16:00Day 19, 19:24
15463Day 21, 00:30Day 21, 12:45
15474Day 21, 14:15Day 22, 02:30
15485Day 22, 02:00Day 22, 14:15
15496Day 22, 02:30Day 22, 04:20
15507Day 22, 08:41Day 22, 20:56
15518Day 22, 19:00Day 23, 07:15
15529Day 22, 21:00Day 23, 09:15
155310Day 23, 01:38Day 23, 01:39
155411Day 23, 09:05Day 23, 13:32
155512Day 23, 11:04Day 23, 11:05
155613Day 23, 19:00Day 24, 02:48
155714Day 24, 03:32Day 24, 07:22
155815Day 24, 13:30Day 25, 01:36
155916Day 25, 18:00Day 25, 18:58
156017Day 26, 00:11Day 26, 06:41
156118Day 26, 11:53Day 26, 18:15
156219Day 27, 16:30Day 28, 07:00
156320Day 28, 10:22Day 28, 10:45
156421Day 28, 12:10Day 29, 02:40
156522Day 29, 17:00Day 30, 09:50
156623Day 30, 12:00Day 30, 16:02
156724Day 30, 23:15Day 31, 16:05
156825Day 31, 15:52Day 31, 15:53
156926Day 31, 15:52Day 31, 15:53
157027Day 31, 16:00Day 01, 08:50
15711Day 08, 00:50Day 09, 19:28
15722Day 09, 22:45Day 10, 12:45
15733Day 09, 22:45Day 11, 17:25
15744Day 09, 22:45Day 11, 17:25
15755Day 10, 17:15Day 11, 07:15
15766Day 10, 21:55Day 11, 11:55
15777Day 11, 07:12Day 11, 07:13
15788Day 11, 07:15Day 11, 21:15
15799Day 11, 21:18Day 12, 11:18
158010Day 11, 21:18Day 15, 14:36
158111Day 15, 21:00Day 16, 05:30
158212Day 15, 21:00Day 19, 13:02
158313Day 16, 07:26Day 19, 16:14
15841Day 25, 17:33Day 27, 03:13
15852Day 27, 04:30Day 28, 14:10
15863Day 28, 14:10Day 29, 23:50
15874Day 30, 00:00Day 30, 05:03
15885Day 30, 06:30Day 01, 11:07
15896Day 01, 11:23Day 02, 21:03
15901Day 15, 17:20Day 16, 05:10
15912Day 16, 06:45Day 16, 06:46
15923Day 16, 06:45Day 17, 19:00
15934Day 17, 21:45Day 20, 11:58
15945Day 19, 18:11Day 20, 11:58
15951Day 20, 18:22Day 23, 13:02
15961Day 27, 16:47Day 28, 10:40
15972Day 30, 01:36Day 31, 07:21
15981Day 16, 18:45Day 23, 18:34
15991Day 01, 14:39Day 04, 03:00
16001Day 16, 13:40Day 17, 21:09
16012Day 18, 20:23Day 23, 22:00
16021Day 05, 20:15Day 06, 14:00
16031Day 12, 20:53Day 15, 09:40
16041Day 29, 00:10Day 02, 11:15
16052Day 04, 15:51Day 05, 00:05
16063Day 05, 01:00Day 05, 09:14
16074Day 05, 01:00Day 06, 14:30
16085Day 12, 17:50Day 12, 20:45
16096Day 13, 00:16Day 13, 03:11
16107Day 13, 00:16Day 13, 04:01
16118Day 13, 09:00Day 13, 09:08
16129Day 14, 11:10Day 14, 11:30
161310Day 14, 13:00Day 15, 03:00
161411Day 15, 05:00Day 15, 11:00
161512Day 21, 12:00Day 22, 13:01
16161Day 17, 02:00Day 17, 04:13
16172Day 17, 05:05Day 17, 07:18
16181Day 18, 16:15Day 20, 21:20
16192Day 21, 10:20Day 24, 10:10
16201Day 13, 01:15Day 17, 19:26
16212Day 17, 21:50Day 19, 01:20
16221Day 28, 11:56Day 02, 10:18
16232Day 02, 12:00Day 03, 21:13
16243Day 04, 00:15Day 05, 14:02
16251Day 01, 17:20Day 04, 13:45
16261Day 18, 16:37Day 20, 12:30
16271Day 12, 23:00Day 13, 03:01
16281Day 19, 19:00Day 29, 12:00
16292Day 30, 18:45Day 01, 13:45
16303Day 01, 20:51Day 02, 15:54
16314Day 02, 15:52Day 03, 10:55
16325Day 02, 16:00Day 03, 11:03
16336Day 02, 16:12Day 02, 16:13
16347Day 03, 11:02Day 03, 10:59
16358Day 03, 11:03Day 04, 06:06
16369Day 03, 11:03Day 04, 22:30
163710Day 04, 06:04Day 04, 06:05
163811Day 04, 06:05Day 04, 06:06
163912Day 04, 06:06Day 05, 01:09
164013Day 04, 23:29Day 07, 07:00
164114Day 07, 07:00Day 08, 21:05
164215Day 08, 22:10Day 10, 09:00
164316Day 11, 13:45Day 11, 16:01
164417Day 11, 19:33Day 11, 21:49
164518Day 11, 19:33Day 14, 15:30
164619Day 11, 19:33Day 17, 11:33
16471Day 03, 16:21Day 03, 19:42
16482Day 03, 20:30Day 03, 21:36
16493Day 03, 22:45Day 03, 23:13
16504Day 04, 01:00Day 04, 04:21
16515Day 04, 04:20Day 04, 07:41
16526Day 04, 06:16Day 04, 06:17
16537Day 04, 14:30Day 04, 17:51
16548Day 04, 17:50Day 04, 21:11
16559Day 04, 17:50Day 05, 17:50
165610Day 05, 17:55Day 06, 08:00
165711Day 05, 18:02Day 06, 18:02
165812Day 05, 18:08Day 06, 18:08
165913Day 06, 13:20Day 07, 19:16
166014Day 07, 19:55Day 09, 01:51
166115Day 09, 01:30Day 10, 07:26
166216Day 09, 01:32Day 10, 07:28
166317Day 09, 01:41Day 09, 16:30
166418Day 09, 02:31Day 09, 02:32
166519Day 09, 02:32Day 09, 02:33
166620Day 09, 18:00Day 10, 09:06
166721Day 10, 09:03Day 11, 14:59
166822Day 10, 09:04Day 11, 15:00
166923Day 10, 09:07Day 11, 08:45
167024Day 10, 09:13Day 10, 09:14
167125Day 11, 18:50Day 13, 05:45
167226Day 13, 08:05Day 13, 13:57
167327Day 13, 18:30Day 13, 21:23
167428Day 13, 22:21Day 14, 01:13
167529Day 13, 22:31Day 16, 01:47
167630Day 14, 01:13Day 14, 01:14
167731Day 16, 03:00Day 16, 21:30
167832Day 16, 03:00Day 19, 03:00
167933Day 16, 03:04Day 16, 03:05
168034Day 16, 23:30Day 19, 00:02
16811Day 22, 19:00Day 25, 22:56
16822Day 26, 00:30Day 29, 20:30
16833Day 28, 00:30Day 29, 20:30
16844Day 29, 23:36Day 29, 23:37
16855Day 29, 23:37Day 05, 12:57
16866Day 29, 23:38Day 01, 09:00
16877Day 13, 17:30Day 18, 17:30
16888Day 25, 13:00Day 30, 14:42
16891Day 01, 22:15Day 03, 21:59
16902Day 04, 11:00Day 05, 08:30
16913Day 05, 09:20Day 06, 06:50
16924Day 05, 09:20Day 06, 17:40
16935Day 06, 18:25Day 07, 08:10
16946Day 07, 15:30Day 07, 21:55
16957Day 08, 11:20Day 08, 17:00
16968Day 08, 18:15Day 09, 08:40
16979Day 09, 16:15Day 10, 21:00
16981Day 12, 19:00Day 16, 00:30
16991Day 23, 22:00Day 24, 12:45
17001Day 02, 21:17Day 03, 01:11
17012Day 03, 02:58Day 04, 16:05
17021Day 03, 04:30Day 05, 13:42
17032Day 08, 16:00Day 11, 08:00
17041Day 21, 13:45Day 27, 23:59
17052Day 28, 12:30Day 28, 15:41
17063Day 28, 17:30Day 28, 20:41
17074Day 28, 17:30Day 29, 13:30
17085Day 28, 21:16Day 28, 21:17
17096Day 29, 07:04Day 29, 10:24
17101Day 04, 21:45Day 07, 17:30
17111Day 29, 21:40Day 30, 15:00
17122Day 30, 15:30Day 01, 08:50
17133Day 30, 16:34Day 01, 09:54
17144Day 01, 09:30Day 02, 02:50
17155Day 01, 09:30Day 02, 02:50
17166Day 01, 09:30Day 02, 09:40
17177Day 01, 11:08Day 02, 04:28
17188Day 05, 17:45Day 05, 21:21
17199Day 05, 21:46Day 06, 01:22
172010Day 05, 21:48Day 06, 01:11
17211Day 02, 15:30Day 08, 16:07
17221Day 29, 20:00Day 30, 12:50
17232Day 31, 13:30Day 03, 07:00
17241Day 22, 13:49Day 29, 13:15
17252Day 29, 15:11Day 05, 09:18
17263Day 07, 14:50Day 18, 12:30
17271Day 17, 00:30Day 22, 07:00
17281Day 12, 15:52Day 14, 05:12
17292Day 14, 08:35Day 15, 21:55
17303Day 15, 21:40Day 17, 11:00
17314Day 15, 21:55Day 17, 08:35
17325Day 15, 21:55Day 20, 13:25
17336Day 17, 13:20Day 17, 16:00
17341Day 19, 12:30Day 21, 09:30
17351Day 16, 20:23Day 18, 17:45
17361Day 12, 15:25Day 15, 22:47
17371Day 27, 18:00Day 01, 08:50
17381Day 01, 01:28Day 01, 09:45
17391Day 15, 18:00Day 18, 09:05
17401Day 11, 15:30Day 14, 00:53
17411Day 12, 02:00Day 14, 20:28
17422Day 15, 04:25Day 17, 22:53
17433Day 15, 04:25Day 20, 09:21
17441Day 05, 21:30Day 16, 21:37
17452Day 10, 12:13Day 16, 21:39
17461Day 02, 17:30Day 05, 13:28
17472Day 09, 16:33Day 12, 05:30
17483Day 12, 06:30Day 13, 10:05
17494Day 15, 12:15Day 17, 14:25
17501Day 10, 23:59Day 12, 11:28
17512Day 12, 16:00Day 14, 03:29
17523Day 14, 03:12Day 15, 14:41
17534Day 14, 03:26Day 15, 14:55
17545Day 14, 03:33Day 14, 18:03
17556Day 14, 03:43Day 15, 15:12
17567Day 14, 04:12Day 14, 04:13
17578Day 14, 20:42Day 15, 11:47
17581Day 06, 06:16Day 07, 05:02
17592Day 07, 10:35Day 08, 05:43
17601Day 27, 22:15Day 29, 00:13
17612Day 29, 02:00Day 29, 08:05
17621Day 17, 00:00Day 23, 01:00
17632Day 24, 01:30Day 25, 21:00
17643Day 26, 12:43Day 30, 12:00
17654Day 31, 15:15Day 07, 03:28
17661Day 21, 19:09Day 23, 08:15
17672Day 23, 13:00Day 25, 02:06
17683Day 25, 05:30Day 25, 05:31
17694Day 25, 09:08Day 26, 12:00
17705Day 25, 09:08Day 26, 22:14
17711Day 30, 17:00Day 02, 01:42
17722Day 02, 04:42Day 04, 09:02
17731Day 30, 15:00Day 30, 19:00
17742Day 30, 22:45Day 01, 02:45
17753Day 01, 02:45Day 01, 05:30
17764Day 01, 04:17Day 01, 04:18
17775Day 01, 13:00Day 02, 05:00
17786Day 01, 13:00Day 05, 17:25
17791Day 30, 18:15Day 30, 19:36
17802Day 31, 00:39Day 31, 00:40
17813Day 31, 00:39Day 31, 00:54
17824Day 31, 00:40Day 31, 04:00
17835Day 31, 05:09Day 31, 08:29
17846Day 31, 17:00Day 31, 21:42
17851Day 21, 04:04Day 21, 09:06
17862Day 21, 04:04Day 21, 20:44
17873Day 21, 04:04Day 22, 09:02
17881Day 02, 23:12Day 03, 03:31
17891Day 22, 16:05Day 28, 08:45
17902Day 28, 11:00Day 29, 13:30
17911Day 19, 02:30Day 19, 11:00
17922Day 19, 19:25Day 24, 19:45
17931Day 01, 22:00Day 03, 10:32
17941Day 22, 19:10Day 23, 11:00
17952Day 23, 12:45Day 23, 16:15
17963Day 23, 12:45Day 24, 04:35
17971Day 02, 21:05Day 04, 12:00
17981Day 06, 23:17Day 07, 14:30
17992Day 07, 17:15Day 08, 08:28
18003Day 08, 12:30Day 09, 03:43
18014Day 09, 03:42Day 09, 18:55
18025Day 09, 03:45Day 09, 12:30
18036Day 09, 16:15Day 10, 07:28
18047Day 10, 01:50Day 10, 11:56
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" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(\"Durations from INPUTEVENTS...\")\n", + "ie = pd.read_sql_query(query_inputevents,con)\n", + "display_df(ie)\n", + "\n", + "print(\"Durations from CHARTEVENTS...\")\n", + "ce = pd.read_sql_query(query_chartevents,con)\n", + "display_df(ce)\n", + "\n", + "print(\"Durations from PROCEDUREEVENTS...\")\n", + "pe = pd.read_sql_query(query_procedureevents,con)\n", + "display_df(pe)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compare durations\n", + "\n", + "We now need to merge together the above durations into a single, master set of CRRT administrations." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
numstarttimeendtimesource
01Day 09, 18:10Day 13, 15:44inputevents_ca
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1372Day 12, 16:02Day 12, 19:01chartevents
1383Day 12, 21:00Day 13, 14:03chartevents
1471Day 09, 18:00Day 13, 15:04procedureevents
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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# how many PROCEDUREEVENTS_MV dialysis events encapsulate CHARTEVENTS/INPUTEVENTS_MV?\n", + "# vice-versa?\n", + "iid = 205508\n", + "\n", + "# compare the above durations\n", + "ce['source'] = 'chartevents'\n", + "ie['source'] = 'inputevents_kcl'\n", + "ie.loc[ie['label']=='Calcium','source'] = 'inputevents_ca' \n", + "pe['source'] = 'procedureevents'\n", + "df = pd.concat([ie[['icustay_id','num','starttime','endtime','source']], ce, pe])\n", + "idxDisplay = df['icustay_id'] == iid\n", + "display_df(df.loc[idxDisplay, :])\n", + "\n", + "# 2) how many have no overlap whatsoever?\n", + "\n", + "\n", + "col_dict = {'chartevents': [247,129,191],\n", + " 'inputevents_kcl': [255,127,0],\n", + " 'inputevents_ca': [228,26,28],\n", + " 'procedureevents': [55,126,184]}\n", + "\n", + "for c in col_dict:\n", + " col_dict[c] = [x/256.0 for x in col_dict[c]]\n", + "\n", + "\n", + "fig, ax = plt.subplots(figsize=[16,10])\n", + "m = 0.\n", + "M = np.sum(idxDisplay)\n", + "\n", + "# dummy plots for legend\n", + "legend_handle = list()\n", + "for c in col_dict:\n", + " legend_handle.append(mlines.Line2D([], [], color=col_dict[c], marker='o',\n", + " markersize=15, label=c))\n", + "\n", + "for row in df.loc[idxDisplay,:].iterrows():\n", + " # row is a tuple: [index, actual_data], so we use row[1]\n", + " plt.plot([row[1]['starttime'].to_pydatetime(), row[1]['endtime'].to_pydatetime()], [0+m/M,0+m/M],\n", + " 'o-',color=col_dict[row[1]['source']],\n", + " markersize=15, linewidth=2)\n", + " m=m+1\n", + " \n", + "ax.xaxis.set_minor_locator(dates.HourLocator(byhour=[0,12],interval=1))\n", + "ax.xaxis.set_minor_formatter(dates.DateFormatter('%H:%M'))\n", + "ax.xaxis.grid(True, which=\"minor\")\n", + "ax.xaxis.set_major_locator(dates.DayLocator(interval=1))\n", + "ax.xaxis.set_major_formatter(dates.DateFormatter('\\n%d\\n%a'))\n", + "\n", + "ax.set_ylim([-0.1,1.0])\n", + "\n", + "plt.legend(handles=legend_handle,loc='best')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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ajVh6Jc8VsIZhMGNMAYs37knofv4sy2DGmMKE7+P3+Xw89thjbb63adOmNl+f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\nu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PfzzVajVve9vb8tBDD+W3v/1tkjh8FwAAGGJKpaw75Lzs+tBVW/W1MLVKS9Ye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\nAAAAOIZQCgAAAABwDKEUAAAAAOAYt9M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5uvbaayVJf/nLX3T66afr2GOPlSQVFRX16PcFAAAAgFQZG14/KzrwZHm2IyuJ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\nsB7NKwAAAADAejSvAAAAAADr0bwCAAAAAKxH8wo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j8uXLdfjwYS1YsEDz589XPB7X4sWLdemll6q0tFSPPfZYp8fBcRwtXrxY//7v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\nAQAAAACeoSkFAAAAAHiGphQAAAAA4BmaUgAAAACAZ2hKAQA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WvE4AAIB5mjGcttvt3HDDDXnXu96VD3/4w/n617+ehx566EnbjY+P57bbbsvT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\n4erqyuTJk7nnnnsAWL16NUePHsXT05Ply5dXWvP8+fOsXr2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Lf+fk5Mi27ZbHhmEc9d+WZclxHEnxy1+j0ba3o1mwYIFGjx591J+PGjVKb731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\nFYb8+C4AAAAAAMlCKAUAAAAABIZQCgAAAAAIDKE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nDZEMw+3qEtcZ+s3oNVzq0JN+A4B2gGAKAEBbsMPK2P6y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\no0ePKjMzU48++miTsWSja4uUjV27dmnVqlUKBAJKTEzU9On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pyk5StpOimSpJkTJpDOWVVXfZNFqpGs2UI2M5tu2GTG29Kmm06v5XADVTSgEA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\nR3h4eJnvc8mJ24+rfLh+xZ1SisLCwjLrXrp0CavVSosWLdDpdHTt2tX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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# print out the above for 10 examples\n", + "\n", + "# compare the above durations\n", + "ce['source'] = 'chartevents'\n", + "ie['source'] = 'inputevents_kcl'\n", + "ie.loc[ie['label']=='Calcium','source'] = 'inputevents_ca' \n", + "pe['source'] = 'procedureevents'\n", + "df = pd.concat([ie[['icustay_id','num','starttime','endtime','source']], ce, pe])\n", + "\n", + "for iid in np.sort(df.icustay_id.unique()[0:10]):\n", + " iid = int(iid)\n", + " # how many PROCEDUREEVENTS_MV dialysis events encapsulate CHARTEVENTS/INPUTEVENTS_MV?\n", + " # vice-versa?\n", + " idxDisplay = df['icustay_id'] == iid\n", + " \n", + " # no need to display here\n", + " #display_df(df.loc[idxDisplay, :])\n", + " \n", + " # 2) how many have no overlap whatsoever?\n", + " col_dict = {'chartevents': [247,129,191],\n", + " 'inputevents_kcl': [255,127,0],\n", + " 'inputevents_ca': [228,26,28],\n", + " 'procedureevents': [55,126,184]}\n", + "\n", + " for c in col_dict:\n", + " col_dict[c] = [x/256.0 for x in col_dict[c]]\n", + "\n", + "\n", + " fig, ax = plt.subplots(figsize=[16,10])\n", + " m = 0.\n", + " M = np.sum(idxDisplay)\n", + "\n", + " # dummy plots for legend\n", + " legend_handle = list()\n", + " for c in col_dict:\n", + " legend_handle.append(mlines.Line2D([], [], color=col_dict[c], marker='o',\n", + " markersize=15, label=c))\n", + "\n", + " for row in df.loc[idxDisplay,:].iterrows():\n", + " # row is a tuple: [index, actual_data], so we use row[1]\n", + " plt.plot([row[1]['starttime'].to_pydatetime(), row[1]['endtime'].to_pydatetime()], [0+m/M,0+m/M],\n", + " 'o-',color=col_dict[row[1]['source']],\n", + " markersize=15, linewidth=2)\n", + " m=m+1\n", + "\n", + " ax.xaxis.set_minor_locator(dates.HourLocator(byhour=[0,6,12,18],interval=1))\n", + " ax.xaxis.set_minor_formatter(dates.DateFormatter('%H:%M'))\n", + " ax.xaxis.grid(True, which=\"minor\")\n", + " ax.xaxis.set_major_locator(dates.DayLocator(interval=1))\n", + " ax.xaxis.set_major_formatter(dates.DateFormatter('\\n%d-%m-%Y'))\n", + "\n", + " ax.set_ylim([-0.1,1.0])\n", + "\n", + " plt.legend(handles=legend_handle,loc='best')\n", + " \n", + " # if you want to save the figures, uncomment the line below\n", + " #plt.savefig('crrt_' + str(iid) + '.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Examining the plots, it appears that durations from INPUTEVENTS and PROCEDUREEVENTS_MV are redundant to that in CHARTEVENTS. Furthermore, the durations from CHARTEVENTS appear to better reflect small interruptions due to clotted lines and paused treatment. As a result, we only use the durations from the CHARTEVENTS. The `concepts/durations/crrt-durations.sql` query contains the final duration query which additionally has `itemid` from CareVue. These `itemid` were determined in a similar manner to the above." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/notebooks/elective-surgery.ipynb b/notebooks/elective-surgery.ipynb deleted file mode 100644 index f4b8929..0000000 --- a/notebooks/elective-surgery.ipynb +++ /dev/null @@ -1,450 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Elective surgery notebook\n", - "\n", - "This notebook examines three definitions of surgery:\n", - "\n", - "1. CPT codes\n", - "2. ICD-9 procedure codes\n", - "3. Service type\n", - "\n", - "The aim of this is to facilitate defining elective surgery." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# Import libraries\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import psycopg2\n", - "\n", - "# used to calculate AUROC/accuracy\n", - "from sklearn import metrics\n", - "\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# create a database connection\n", - "sqluser = 'postgres'\n", - "dbname = 'mimic'\n", - "schema_name = 'mimiciii'\n", - "\n", - "# Connect to local postgres version of mimic\n", - "con = psycopg2.connect(dbname=dbname, user=sqluser)\n", - "cur = con.cursor()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Service type\n", - "\n", - "First we examine if an ICU stay ever occurs *before* the first service." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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42844591020052177-03-29 00:49:002177-04-04 05:49:002177-04-10 06:47:0012 days 05:58:006 days 00:58:00
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" - ], - "text/plain": [ - " icustay_id hadm_id admittime intime \\\n", - "0 209779 147891 2118-10-24 23:34:00 2118-10-25 11:38:16 \n", - "1 212553 161640 2147-07-25 15:52:00 2147-07-26 17:02:42 \n", - "2 225953 140615 2150-07-07 16:13:00 2150-07-09 11:43:25 \n", - "3 247562 168715 2106-10-08 20:55:00 2106-10-14 19:44:19 \n", - "4 284459 102005 2177-03-29 00:49:00 2177-04-04 05:49:00 \n", - "\n", - " firstservice deltaadmit deltain \n", - "0 2118-10-31 07:29:24 6 days 07:55:24 5 days 19:51:08 \n", - "1 2147-07-27 11:09:39 1 days 19:17:39 0 days 18:06:57 \n", - "2 2150-07-14 10:31:11 6 days 18:18:11 4 days 22:47:46 \n", - "3 2106-10-16 04:37:30 7 days 07:42:30 1 days 08:53:11 \n", - "4 2177-04-10 06:47:00 12 days 05:58:00 6 days 00:58:00 " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cur.execute('SET search_path to ' + schema_name)\n", - "query = \\\n", - "\"\"\"\n", - "select ie.icustay_id, ie.hadm_id\n", - " , adm.admittime\n", - " , ie.intime\n", - " , min(transfertime) as FirstService\n", - " , min(transfertime) - adm.admittime as DeltaAdmit\n", - " , min(transfertime) - ie.intime as DeltaIn\n", - "from icustays ie \n", - "inner join admissions adm\n", - " on ie.hadm_id = adm.hadm_id\n", - "left join services se \n", - " on ie.hadm_id = se.hadm_id\n", - "group by ie.icustay_id, ie.hadm_id, ie.intime, adm.admittime\n", - "having (min(transfertime)) > intime;\n", - "\"\"\"\n", - "\n", - "data = pd.read_sql_query(query,con)\n", - "data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Looks like we have 5 cases where the first service occurs between 1-6 days after the first ICU admission." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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01234
subject_id2905231189866452643813786
hadm_id185131144019164444187342190267
icustay_id231712286281234312286085223101
admission_typeEMERGENCYURGENTEMERGENCYNEWBORNNEWBORN
surgical_cpt001NaNNaN
surgical_icd01111
surgical_service01100
\n", - "
" - ], - "text/plain": [ - " 0 1 2 3 4\n", - "subject_id 29052 31189 86645 26438 13786\n", - "hadm_id 185131 144019 164444 187342 190267\n", - "icustay_id 231712 286281 234312 286085 223101\n", - "admission_type EMERGENCY URGENT EMERGENCY NEWBORN NEWBORN\n", - "surgical_cpt 0 0 1 NaN NaN\n", - "surgical_icd 0 1 1 1 1\n", - "surgical_service 0 1 1 0 0" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# extract surgery definitions using the three methods\n", - "cur.execute('SET search_path to ' + schema_name)\n", - "query = \\\n", - "\"\"\"\n", - "WITH serv as\n", - "(\n", - " -- note, 80% of patients only ever have one service\n", - " -- also note, it is possible a patient's trajectory is like this:\n", - " -- 1. elective admission\n", - " -- 2. medical service\n", - " -- 3. ICU admission\n", - " -- 4. change to surgical service\n", - " -- we do *not* treat this as an elective surgery\n", - "\n", - " select ie.icustay_id\n", - " , se.curr_service\n", - " , case when lower(curr_service) like '%surg%' then 1 else 0 end as surgical\n", - " , ROW_NUMBER() over\n", - " (\n", - " PARTITION BY ICUSTAY_ID\n", - " ORDER BY TRANSFERTIME\n", - " ) as serviceOrder\n", - " from icustays ie\n", - " left join services se\n", - " on ie.hadm_id = se.hadm_id\n", - ")\n", - ", cpt as\n", - "(\n", - " select cpt.hadm_id\n", - " , max(case when surgeryflag = 2 then 1 else 0 end) as surgical\n", - " from cptevents cpt\n", - " left join surgeryflag_cpt cptflg\n", - " on cpt.cpt_number between cptflg.cptstart and cptflg.cptstop\n", - " group by cpt.hadm_id\n", - ")\n", - ", icd as\n", - "(\n", - " select icd.hadm_id\n", - " , max(case when surgeryflag = 2 then 1 else 0 end) as surgical\n", - " from procedures_icd icd\n", - " left join surgeryflag_icd9 flg\n", - " on icd.icd9_code = flg.icd9_code\n", - " group by icd.hadm_id\n", - ")\n", - "select\n", - " ie.subject_id, ie.hadm_id, ie.icustay_id\n", - "\n", - " , adm.ADMISSION_TYPE\n", - "\n", - " , cpt.surgical as surgical_cpt\n", - " , icd.surgical as surgical_icd\n", - " , serv.surgical as surgical_service\n", - "from icustays ie\n", - "-- it's useful to get admission type\n", - "inner join admissions adm\n", - " on ie.hadm_id = adm.hadm_id\n", - "-- get CPT codes associated with surgery\n", - "left join cpt\n", - " on ie.hadm_id = cpt.hadm_id\n", - "\n", - "-- get ICD codes associated with surgery\n", - "left join icd\n", - " on ie.hadm_id = icd.hadm_id\n", - "\n", - "-- get first service\n", - "left join serv\n", - " on ie.icustay_id = serv.icustay_id and serv.serviceOrder = 1\n", - "\"\"\" \n", - "\n", - "\n", - "data = pd.read_sql_query(query,con)\n", - "data.head().T" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "cur.close()\n", - "con.close()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(61532, 7)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "% surgical status: 21.43% - CPT.\n", - "% surgical status: 52.58% - ICD.\n", - "% surgical status: 24.64% - SERVICE.\n" - ] - } - ], - "source": [ - "print('% surgical status: {:2.2f}% - CPT.').format(100*data.surgical_cpt.mean())\n", - "print('% surgical status: {:2.2f}% - ICD.').format(100*data.surgical_icd.mean())\n", - "print('% surgical status: {:2.2f}% - SERVICE.').format(100*data.surgical_service.mean())" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/explore-items.html b/notebooks/explore-items.html deleted file mode 100644 index 689ce1d..0000000 --- a/notebooks/explore-items.html +++ /dev/null @@ -1,43030 +0,0 @@ - - - - - - - - - - - - - - - -explore-items - - - - - - - - - - - - - - - - - - - - - - - - -
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Exploring data in MIMIC-III.

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We use a slightly incorrect heuristic in comparing Careview and Metavision data, namely that patients registered in those systems may be recognized by whether the SUBJECT_ID < 40000. This is wrong for patients with data in Metavision if the patient had been previously registered under Careview.

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-

D_ITEMS

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## Loading required package: DBI
-

D_ITEMS have a category and a label. We first examine the distribution of the distinct labels assigned to each category, along with their number.

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item.summary <- dbGetQuery(con, "select category, count(*) c, group_concat(label separator ', ') from d_items where category is not null group by category")
-
-kable(item.summary)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
categorycgroup_concat(label separator ‘,’)
1-Intubation/Extubation6Intubation, Percutaneous Tracheostomy, Unplanned Extubation (patient-initiated), Unplanned Extubation (non-patient initiated), Open Tracheostomy, Extubation
2-Ventilation2Invasive Ventilation, Non-invasive Ventilation
3-Significant Events20Transferred to Floor, Fluoroscopy, Nuclear Medicine, Ventricular Drain, Cardioversion/Defibrillation, Chest Pain, Cardiac Arrest, Chest Opened, OR Received, OR Sent, Pneumothorax, Fall, Respiratory Arrest, Unplanned Line/Catheter Removal (Non-Patient initated), Operation, Peritoneal Fluid, Brain Death, C-Spine Clearance, Unplanned Line/Catheter Removal (Patient Initiated), TLS Clearance
4-Procedures29EEG, Lumbar Puncture, Bronchoscopy, EKG, Blakemore D/C, Blakemore Inserted, Cardiac Cath, Chest Tube Placed, Colonoscopy, Endoscopy, Hemodialysis, Liver Biopsy, Paracentesis, PEG Insertion, Percutaneous Drain Insertion, Pericardiocentesis, Radiation Therapy, Thoracentesis, Temporary Pacemaker Wires Discontinued, ICP Bolt Inserted, Intraventricular Drain Inserted, Esophogeal Balloon, Temporary Pacemaker Wires Inserted, ERCP, Plasma Pheresis., Drain Removed, Chest Tube Removed, Epidural Placement, Pericardial Drain Removed
5-Imaging14CT scan, X-ray, Ultrasound, Venogram, Trans Esophageal Echo, Magnetic Resonance Imaging, Angiography, Transthoracic Echo, Abdominal X-Ray, Chest X-Ray, Cervical Spine, Pelvis, Interventional Radiology, TLS Spine
6-Cultures11Blood Cultured, CSF Culture, Pan Culture, Sputum Culture, Urine Culture, Stool Culture, Wound Culture, BAL Fluid Culture, Pleural Fluid Culture, Nasal Swab, Rectal Swab
7-Communication8Family meeting attempted, unable, Family meeting held, Family met with Case Manager, Family met with Social Worker, Family updated by MD, Family updated by RN, NEOB notified, Family notified of transfer
ABG8Arterial Base Excess, Arterial CO2(Calc), Arterial PaCO2, Arterial PaO2, Arterial pH, HCO3, Art.pH, pH (Art)
ABG’s15SaO2, zzzpH, ABG Chloride, ABG Potassium, ABG Sodium, ABG CHLOIRDE, ABG POTASSIUM, ABG SODIUM, BE, Hematocrit (35-51), Ion Calcium, PCO2, PO2, pCO2, pO2
Access Lines - Invasive312PICC Line Cap Change, PICC Line Insertion Date, PICC Line Change over Wire Date, PICC Line Dressing Change, PICC Line Tubing Change, PICC Line Site Appear, Multi Lumen Cap Change, Multi Lumen Change over Wire Date, Multi Lumen, PICC Line, Cordis/Introducer, Trauma line, CCO PAC, Dialysis Catheter, IABP line, Presep Catheter, Multi Lumen Dressing Change, Multi Lumen Insertion Date, Multi Lumen Site Appear, Multi Lumen Tubing Change, Multi Lumen Waveform Appear, Arterial line Cap Change, Arterial line Change over Wire Date, Arterial Line Dressing Change, Arterial line Insertion Date, Arterial line Site Appear, Arterial line Tubing Change, Arterial line Waveform Appear, Cordis/Introducer Cap Change, Cordis/Introducer Change over Wire Date, Cordis/Introducer Dressing Change, Cordis/Introducer Insertion Date, Cordis/Introducer Site Appear, Cordis/Introducer Tubing Change, PA Catheter, CCO PAC Site Appear, CCO PAC Tubing Change, CCO PAC Waveform Appear, CCO PAC Zero/Calibrate, Tunneled (Hickman) Line, CCO PAC Inser
Access Lines - Peripheral5322 Gauge, 20 Gauge, 16 Gauge, 18 Gauge, 22 Gauge Site Appear, 22 Gauge Reason Discontinued, 14 Gauge, 20 Gauge Reason Discontinued, 20 Gauge Site Appear, 18 Gauge Reason Discontinued, 18 Gauge Site Appear, 16 Gauge Reason Discontinued, 16 Gauge Site Appear, 14 Gauge Reason Discontinued, 14 Gauge Site Appear, RIC, 22 Gauge Insertion Date, 20 Gauge Insertion Date, 18 Gauge Insertion Date, 16 Gauge Insertion Date, 14 Gauge Insertion Date, RIC Insertion Date, RIC Reason Discontinued, RIC Site Appear, 14 Gauge placed in outside facility, 16 Gauge placed in outside facility, 18 Gauge placed in outside facility, 20 Gauge placed in outside facility, 22 Gauge placed in outside facility, RIC placed in outside facility, 22 Gauge Dressing Occlusive, RIC Dressing Occlusive, 14 Gauge Dressing Occlusive, 16 Gauge Dressing Occlusive, 18 Gauge Dressing Occlusive, 20 Gauge Dressing Occlusive, 14 G Phlebitis Scale, 14 G Infiltration Scale, 16 G Phlebitis Scale, 16 G Infiltration Scale, 18 G Phlebitis Scale, 18 G Infiltration Sc
Adm History/FHPA50Date of Admission to Hospital, Past medical history, Is the spokesperson the Health Care Proxy, Unable to assess psychological, Living situation, Any fear in relationships, Emotional / physical / sexual harm by partner or close relation, Social work consult, Pregnant, Pregnancy due date, Post menopausal, Unable to assess cognitive / perceptual, Visual / hearing deficit, Interpreter, Unable to assess activity / mobility, Self ADL, History of slips / falls, Balance, Judgement, Use of assistive devices, Intravenous / IV access prior to admission, Unable to assess habits, ETOH, Tobacco use, Recreational drug use, Unable to assess pain, Currently experiencing pain, Unable to assess nutrition / education, Difficulty swallowing, Appetite, Special diet, Unintentional weight loss >10 lbs., Dialysis patient, Last dialysis, Unable to assess teaching / learning needs, Teaching directed toward, Discharge needs, Consults, Patient valuables, Money given to hospital cashier, See chart for initial patient assessment, Cash am
ADT11ICU Admission date, Date of Birth, Discharge Date/Time, Expected Discharge Date, Religion, Language, Race, Gender, Marital Status, Hospital Admit Date, Patient Location
Alarms32Heart rate Alarm - High, Heart Rate Alarm - Low, Arterial Blood Pressure Alarm - Low, Arterial Blood Pressure Alarm - High, Pulmonary Artery Pressure Alarm - High, Pulmonary Artery Pressure Alarm - Low, Central Venous Pressure Alarm - High, Central Venous Pressure Alarm - Low, Minute Volume Alarm - Low, Minute Volume Alarm - High, Non-Invasive Blood Pressure Alarm - Low, Intra Cranial Pressure Alarm - High, Intra Cranial Pressure Alarm - Low, O2 Saturation Pulseoxymetry Alarm - High, O2 Saturation Pulseoxymetry Alarm - Low, Non-Invasive Blood Pressure Alarm - High, Resp Alarm - High, Resp Alarm - Low, Parameters Checked, Alarms On, SpO2 Desat Limit, ABP Alarm Source, NBP Alarm Source, PAP Alarm Source, ART Blood Pressure Alarm - High, ART Blood Pressure Alarm - Low, ART Blood Pressure Alarm Source, Cerebral Perfusion Pressure Alarm - High, Cerebral Perfusion Pressure Alarm - Low, Intra Cranial Pressure #2 Alarm - High, Intra Cranial Pressure #2 Alarm - Low, ST Segment Monitoring On
ANTIBACTERIUM31PENICILLIN, PENICILLIN G, AMPICILLIN, CEFAZOLIN, ERYTHROMYCIN, CLINDAMYCIN, TRIMETHOPRIM/SULFA, CHLORAMPHENICOL, NITROFURANTOIN, TETRACYCLINE, GENTAMICIN, TOBRAMYCIN, AMIKACIN, VANCOMYCIN, OXACILLIN, CEFTAZIDIME, CEFTRIAXONE, CIPROFLOXACIN, IMIPENEM, PIPERACILLIN, AMPICILLIN/SULBACTAM, CEFUROXIME, CEFPODOXIME, LEVOFLOXACIN, PIPERACILLIN/TAZO, RIFAMPIN, CEFEPIME, MEROPENEM, DAPTOMYCIN, LINEZOLID
Antibiotics51Vancomycin, Acyclovir, Ambisome, Amikacin, Ampicillin, Ampicillin/Sulbactam (Unasyn), Atovaquone, Azithromycin, Aztreonam, Caspofungin, Cefazolin, Cefepime, Ceftazidime, Ceftriaxone, Chloroquine, Ciprofloxacin, Clindamycin, Colistin, Daptomycin, Doxycycline, Erythromycin, Ethambutol, Fluconazole, Foscarnet, Gancyclovir, Gentamicin, Imipenem/Cilastatin, Isoniazid, Levofloxacin, Linezolid, Mefloquine, Meropenem, Metronidazole, Micafungin, Moxifloxacin, Nafcillin, Oxacillin, Penicillin G potassium, Piperacillin, Piperacillin/Tazobactam (Zosyn), Pyrazinamide, Quinine, Ribavirin, Rifampin, Bactrim (SMX/TMP), Dalfopristin/Quinupristin (Synercid), Tobramycin, Valgancyclovir, Voriconazole, Keflex, Tamiflu
ApacheII Parameters3NonOperative Diagnoses, Elective Postoperative Diagnoses, Emergency Postoperative Diagnoses
ApacheIV Parameters2APACHE IV diagnoses, APACHE IV diagnoses-2
Arterial Line Insertion33Side (A-Line), Insertion site (A-Line), Replaced previous Arterial line over wire, Insertion kit used (A-Line), Hand cleansing prior to procedure (A-Line), Site cleaned with ChloraPrep(TM) (A-Line), Number of attempts required to place line (A-Line), Transparent dressing applied and dated (A-Line), Arterial Line ordered on POE, Location (A-Line), Guidewire (A-Line), Ultrasound (A-Line), Number of operators (A-Line), Acuity (A-Line), Sterile field maintained throughout procedure (A-Line), Arterial line sutured, Barrier precautions in place (A-Line), Complications (A-Line), Indications (A-Line), Patient identified correctly (A-Line), Timeout performed by (A-Line), Verification of (A-Line), Insertion kit used (A-Line), Hand cleansing prior to procedure (A-Line), Site cleaned with ChloraPrep(TM) (A-Line), Replaced previous Arterial line over wire, Arterial line sutured, Sterile field maintained throughout procedure (A-Line), Transparent dressing applied and dated (A-Line)_New, Arterial Line ordered on POE, The At
Blood Gases2Mixed Venous O2, Mixed Venous O2% Sat
Blood Products/Colloids26Albumin 25%, Albumin 5%, Fresh Frozen Plasma, Factor VIII, Packed Red Blood Cells, Platelets, Cryoprecipitate, Cell Saver, Hetastarch (Hespan) 6%, Dextran 40, Dextran 70, Factor VIIa, Profilnine, OR Colloid Intake, OR FFP Intake, OR Packed RBC Intake, OR Platelet Intake, OR Autologous Blood Intake, OR Cryoprecipitate Intake, OR Cell Saver Intake, PACU Colloid Intake, PACU Packed RBC Intake, PACU Platelet Intake, PACU FFP Intake, IV Immune Globulin (IVIG), Plasma Pheresis
Bronchoscopy47Patient (Bronch), Indications (Bronch), Route (Bronch), Type (Bronch), Location (Bronch), Services (Bronch), Complications (Bronch), Bronchoalveolar lavage (BAL) / washing (Bronch), Endobronchial ultrasound (EBUS) (Bronch), Brushings (Bronch), Transbronchial biopsy (Bronch), Transbronchial needle aspirate (Bronch), Conscious sedation used (Bronch), Photodynamic therapy PDT (Bronch), Autofluorescence AF (Bronch), Hand cleansing prior to procedure (Bronch), Barrier precautions in place (Bronch), Timeout performed by (Bronch), Patient identified correctly (Bronch), Verification of (Bronch), Hand cleansing prior to procedure (Bronch), Topical Lidocaine used (Bronch), Topical Cetacaine used (Bronch), Bronchoalveolar lavage (BAL) / washing (Bronch, Endobronchial ultrasound (EBUS) (Bronch), Brushings (Bronch), Transbronchial biopsy (Bronch), Transbronchial needle aspirate (Bronch), Conscious sedation used (Bronch), Photodynamic therapy PDT (Bronch), Autofluorescence AF (Bronch), The Attending/Supervisor is the Clini
Cardiovascular35Graft/Flap Pulse, Pericardial Drain Status, Pericardial Drain Drainage, Pericardial Drain Site, Angio Site # 1, Angio Appearance # 1, Angio Dressing # 1, Angio Site # 2, Angio Appearance # 2, Angio Dressing # 2, RUE Color, RLE Color, LUE Color, LLE Color, RUE Temp, LUE Temp, RLE Temp, LLE Temp, Heart Sounds, AV Fistula R Bruit, AV Fistula R Thrill, AV Fistula L Bruit, AV Fistula L Thrill, Pericardial Drain Aspiration, Pericardial Drain Flush, Compression device # 1, Compression device # 2, Angio Appearance # 3, Angio Appearance # 4, Angio Dressing # 3, Angio Dressing # 4, Angio Site # 3, Angio Site # 4, Compression device # 3, Compression device # 4
Cardiovascular (Pacer Data)25Temporary AV interval, Temporary Pacemaker Type, Temporary Pacemaker Mode, Temporary Venticular Sens Threshold mV, Temporary Venticular Stim Threshold mA, Temporary Pacemaker Wire Condition, Temporary Pacemaker Wires Venticular, Temporary Pacemaker Wires Ground, Transcutaneous Pacer, Transcutaneous Pacer Placement, Transcutaneous Pacer Rate, Temporary Pacemaker Rate, Temporary Atrial Sens Threshold mV, Temporary Atrial Sens Setting mV, Temporary Atrial Stim Threshold mA, Temporary Atrial Stim Setting mA, Temporary Atrial Sens, Temporary Ventricular Sens Setting mV, Temporary Ventricular Stim Setting mA, Temporary Pacemaker Wires Atrial, Permanent Pacemaker Mode, Permanent Pacemaker Rate, Temporary Atrial Capture, Temporary Ventricular Sens, Temporary Ventricular Capture
Cardiovascular (Pulses)18Heat Sounds, Dorsal PedPulse R, PostTib. Pulses R, Radial Pulse R, Ulnar Pulse R, Brachial Pulse R, Femoral Pulse R, Popliteal Pulse R, Dorsal PedPulse L, Capillary Refill L, Brachial Pulse L, Femoral Pulse L, Popliteal Pulse L, PostTib. Pulses L, Radial Pulse L, Ulnar Pulse L, Capillary Refill R, Pacer Rate
Case Management17VNA / home infusion, Acute rehab, Patient on vent, Skilled nursing facility, Home without services, Home with services, Long term care custodial non-Medicare certified, Assisted living, VA hospital, Hospice inpatient, Hospice home, DME, Transportation, Free care pharmacy , Patient/family/team understand & agree with plan, Case Management discharge plan, Required documentation
Chemistry56Albumin (>3.2), Ammonia, BUN (6-20), Calcium (8.4-10.2), Carbon Dioxide, Chloride (100-112), Cholesterol (<200), Creatinine (0-1.3), Direct Bili (0-0.3), Fingerstick Glucose, Glucose (70-105), Ionized Calcium, Lactic Acid(0.5-2.0), Magnesium (1.6-2.6), Phosphorous(2.7-4.5), Potassium (3.5-5.3), Serum Osmolality, Sodium (135-148), Total Bili (0-1.5), Total Protein(6.5-8), Triglyceride (0-200), Uric Acid (2.7-7.0), Albumin (3.9-4.8), Alkaline Phosphatase, Anion Gap (8-20), BUN (6-20), Blood Glucose, Calcium (8.8-10.8), Chloride (100-112), Cholesterol (0-199), Creatinine (0-0.7), Indirect Bili(0-1.0), Potassium (3.5-5.3), SGOT, SGPT, Sodium (135-148), T. Protein (5-7.5), TCO2 (21-30), Total CO2, Triglyceride (0-250), Albumin, Calcium, Chloride, Cholesterol, Creatinine, Direct Bili, Glucose, Lactic Acid, Magnesium, Phosphorous, Potassium, Sodium, Total Bili, Total Protein, Triglyceride, Uric Acid
Coags14ACT (102-142), Bleeding Time, D-Dimer (0-500), Fibrinogen (150-400), INR (2-4 ref. range), PT(11-13.5), PTT(22-35), Thrombin (16-21), ACT, D-Dimer, Fibrinogen, INR, PTT, Thrombin
CSF3Basos, Monos, Polys
CVL Insertion38Type of catheter (CVL), Insertion site (CVL), Guidewire exchange (CVL), Side (CVL), Acuity (CVL), Barrier precautions in place (CVL), Timeout performed by (CVL), Patient identified correctly (CVL), Verification of (CVL), Number of attempts required to place line (CVL), Ultrasound (CVL), Number of operators (CVL), Indications (CVL), Is alternative vascular access present (CVL), Location (CVL), Immediate complications (CVL), Transparent dressing applied and dated (CVL), Follow-up chest X-RAY ordered (CVL), Site cleaned with ChloraPrep(TM) (CVL), Hand cleansing prior to procedure (CVL), Insertion kit used (CVL), Central Line ordered on POE, Sterile field maintained throughout procedure (CVL), Attending/Supervisor is Clinician Operator (CVL), Present duirng key moments and agree with checklist (CVL), Catheter insertion length (CVL), Is alternative vascular access present (CVL), Insertion kit used (CVL), Hand cleansing prior to procedure (CVL), Site cleaned with ChloraPrep(TM) (CVL), Sterile field maintained throu
Dialysis46Hourly Patient Fluid Removal, Dialysis Access Site, Dialysis Site Appearance, Blood Flow (ml/min), Heparin Dose (per hour), System Integrity, Access Pressure, Filter Pressure, Effluent Pressure, Return Pressure, Replacement Rate, Dialysate Rate, ART Lumen Volume, VEN Lumen Volume, Current Goal, CRRT Filter Change, Dialysis - CRRT, Dialysis - CVVHD, Peritoneal Dialysis, Volume In (PD), Volume Out (PD), Dialysis - CVVHDF, Dwell Time (Peritoneal Dialysis), Medication Added Amount #1 (Peritoneal Dialysis), Medication Added Units #1 (Peritoneal Dialysis), Peritoneal Dialysis Catheter Type, Peritoneal Dialysis Catheter Status, Replacement Fluid, Dialysate Fluid, Peritoneal Dialysis Fluid Appearance, Medication Added #1 (Peritoneal Dialysis), Solution (Peritoneal Dialysis), Dialysis Access Type, Dialysis - SCUF, Reason for CRRT Filter Change, Heparin Concentration (units/mL), Hemodialysis Output, Ultrafiltrate Output, CRRT mode, Volume not removed, Medication Added #2 (Peritoneal Dialysis), Medication Added Amount
Drains28Penrose #1, Penrose #2, Tap, JP Medial, JP Lateral, Jackson Pratt #1, Jackson Pratt #2, Jackson Pratt #3, Jackson Pratt #4, T Tube, Hemovac #1, Hemovac #2, Cerebral Ventricular #1, Cerebral Ventricular #2, Cerebral Subdural #1, Cerebral Subdural #2, Lumbar, Pericardial, Wound Vac #1, Wound Vac #2, Anderson, Sump #1, Sump #2, Pigtail #1, Pigtail #2, Davol, Red Rubber, Drainage Bag
Drug Level27Cyclosporin, Digoxin, Dilantin, Ethanol, FK506, Gentamycin/Peak, Gentamycin/Random, Gentamycin/Trough, Lidocaine, Phenobarbital, Procan, Procan Napa, Theophylline, Tobramycin/Peak, Tobramycin/Random, Tobramycin/Trough, Tylenol, Vancomycin/Peak, Vancomycin/Random, Vancomycin/Trough, Digoxin (1-2ng/ml), Gentamicin Post 5-10, Gentamicin Pre (0-2), Phenobarbitol(15-40), Phenytoin (10-20), Vancomycin Post, Vancomycin Pre
Enzymes9ALT, AST, Alk. Phosphate, Amylase, CPK, CPK/MB, LDH, Lipase, Troponin
Family Mtg Note19FM Pt Preferences, FM CM Consult, FM Concerns elicited, FM Condition Rev, FM Prehuddle, FM Date/Time, FM Discord noted, FM Dx reviewed, FM ICU-Hosp duration, FM Intepreter present, FM Location, FM Measures, FM Prognosis reviewed, FM Pt present, FM Spirit/cultural, FM SW consult, FM Sx controlled, FM Time spent, FM Tx/goals
Fluids - Other (Not In Use)84Albumin (Human) 20%, Albumin (Human) 4%, Aquadestila, Darrow, Dextrose 20.%, Ringers, Saline 0,9%, Ringers Lactate, Ringers Acetate, Saline 0,255%, Saline 0,3%, Saline 0,45%, Saline 0,65%, Saline 3%, Dextrose 4,3% / Saline 0,18%, Dextrose 5% / Saline 0,9%, Dextrose 5% / Saline 0,45%, Dextrose 2,5% / Saline 0,45%, Dextrose 5% / Ringers Lactate, Dextrose 10% / Ringers Lactate, Filtered erytrocytes, ESDEP (Solvent / Detergent Virus-Inactivated Plasma), Octoplas, Gelofusine, Pentastarch 6% (HAES, Hemohes), Pentastarch 10% (HAES, Hemohes), Hetastarch 6% (Saline 0,9%), Hextend (Hetastarch 6% in Lactate), Intralipid 10%, Intralipid 20%, Intralipid 30%, Mannitol 5%, Mannitol 10%, Mannitol 15%, Mannitol 20%, Mannitol 25%, Packed Red Cells, Dextran 40 / Dextrose 5% (Gentran - Rheomacrodex), Dextran 40 / Saline 0,9% (Gentran - Rheomacrodex), Dextran 70 / Dextrose 5% (Gentran - Macrodex - RescueFlow), Dextran 70 / Saline 0,9% (Gentran - Macrodex - RescueFlow), Thrombocyte suspension, Vamin 14 Electrolyte free, Vamin 18 E
Fluids/Intake37Dextrose 5%, Dextrose 10%, Dextrose 50%, NaCl 0.9%, NaCl 0.45%, NaCl 3% (Hypertonic Saline), Prismasate K2, Prismasate K4, Trisodium Citrate 0.4%, Bicarbonate Base, Free Water, Gastric Meds, D5 1/2NS, D5NS, D5LR, LR, Multivitamins, Piggyback, D5 1/4NS, Solution, Sterile Water, Pre-Admission Intake, ZGastric/TF Residual Intake, Cath Lab Intake, OR Crystalloid Intake, PACU Crystalloid Intake, PACU PO Intake, GU Irrigant - Normal Saline, GU Irrigant - Sterile Water, GU Irrigant - Amphotericin B, PO Intake, GT Flush, Sodium Bicarbonate 8.4% (Amp), Dextrose 20%, Dextrose 30%, Dextrose 40%, NaCl 23.4%
Free Form Intake2420sodium phosphate, THAM, Mannitol 20%, K PHOS, protonics, Bolus, free H2O, epidural-marcaine, pancreatic drain, readi - cat, IV MEDS, kcl 20 meq ivpb, FREE H2O, procedure, urine flush, ALBUMIN, protonix, PANTOPRAZOLE, bicarb push, cisatricurium, t tube flush, Cath Lab Output, Meperidine cc/hr, readi-ct, na phosphate, K-PHOS, NA BICARB, NS w/ 40K, kayexalate PR, magnesium, JT bile, demerol, natracor, DRAIN FLUSH, K+ PHOS, Pantoprazole, free water infusion, free H20, po contrast, supra pubic flush, epidural, protonix infusion, foley irrigation, PROTONIX GTT, PD dialysate in, Na Phosphate, ns bladder, IGG, Kphos 30mmol, MANNITOL, DEXTRAN, NS bolus, fluid bolus, plueral fluid, THERMODILUTION FLUID, versed drip, talc in ct, bumex, IVF AT OSH, PROTONIX MG/HR, 1/2 ns bolus, octreotide, Pamidronate l, iv protonix, enema-lactulose, thermoldilution, dextrose gtt, cardiac output fluid, golytely, Water bolus, free h20 bolus, Chest tube flush, gall drain flush, Dextrose 15%, Rifampin, ER, dialysate in, Bannana Bag,
General25Code Status, Precautions: Standard +, Health Care Proxy, Daily Weight, Service, Called Out, Estimated Energy Needs/Kg, Estimated Protein Needs/Kg, ICU Consent Signed, Admission Weight (Kg), Admission Weight (lbs.), Organ Bank Notified, ZOn Pressors, Mechanically Ventilated, Blood Transfusion Consent, Height, Height (cm), Admit from, Discharge to, Feeding Weight, Legal Guardian, Re-admit < 48 hours, SOFA Score, Print Job, Printer
Generic Proc Note21Location (Gen Proc), Indication (Gen Proc), Patient (Gen Proc), Acuity (Gen Proc), Timeout Performed By (Gen Proc), Patient Identified Correctly (Gen Proc), Verification (Gen Proc), Hand Cleansing (Gen Proc), Site Cleansed With ChloraPrep (Gen Proc), Barrier Precautions (Gen Proc), Conscious Sedation (Gen Proc), Patient Position (Gen Proc), Side (Gen Proc), With Ultrasound (Gen Proc), Number of Attempts (Gen Proc), Number of Operators (Gen Proc), Dressing Applied (Gen Proc), Follow Up Xray (Gen Proc), Complications (Gen Proc), Clinical Operator (Gen Proc), Present and Agree (Gen Proc)
GI/GU53Oral Cavity, Nares R, Diet Type, Abdominal Assessment, Bowel Sounds, Flatus, Emesis Appearance, GI #1 Intub Site, GI #1 Tube Drainage, GI #1 Tube Place Check, Stool Consistency, Stool Management, Stool Guaiac, Urine Source, Urine Color, GU Catheter Size, Bladder Irrigation, GI #1 Tube Type, GI #1 Tube Status, GI #2 Tube Type, GI #2 Tube Status, GI #3 Tube Type, GI #3 Tube Status, Stool Color, GI #2 Intub Site, GI #3 Intub Site, GI #2 Tube Place Check, GI #3 Tube Place Check, GI #2 Tube Drainage, GI #3 Tube Drainage, Urine Appearance, GU Catheter Insertion Date, Nares L, Ostomy, Ostomy Appearance, GI #1 Tube Place Method, GI #2 Tube Place Method, GI #3 Tube Place Method, GU Catheter D/C Date, GI Guaiac, GI pH, GI #1 Tube Place Method, GI #2 Tube Place Method, GI #3 Tube Place Method, Stool Guaiac QC, GI Guaiac QC, All Medications Tolerated without Adverse Side Effects, GI #1 Tube Place Check, GI #1 Tube Place Method, GI #2 Tube Place Check, GI #2 Tube Place Method, GI #3 Tube Place Check, GI #3 Tube Place Meth
Hematology16Blood Products, Differential-Atyps, Differential-Bands, Differential-Basos, Differential-Eos, Differential-Lymphs, Differential-Monos, Differential-Polys, Hematocrit, Hemoglobin, Platelets, RBC, Sed Rate, WBC (4-11,000), WBC (4-11,000), WBC
Heme/Coag19RBC(3.6-6.2), Retic count (.0-1.5), WBC 4.0-11.0, BANDS, BASOs, Bands, Eosinophils, FIBRINOGEN, HGB (10.8-15.8), LYMPHS, Lymphs, MONOs, NEUTS, Platelet (150-440), ProTime, Ptt, RETIC Count(0.5-1.5), Red Blood C(3.6-6.2), WhiteBloodC 4.0-11.0
Hemodynamics28Intra Cranial Pressure, Pulmonary Artery Pressure systolic, Pulmonary Artery Pressure diastolic, Pulmonary Artery Pressure mean, Left Artrial Pressure, Central Venous Pressure, Cardiac Output (thermodilution), Left Ventricular Assit Device Flow, Right Ventricular Assist Device Flow, PCWP, SvO2, PA Line cm Mark, VAD Beat Rate R, VAD Beat Rate L, Vacuum Assist, ECMO, Cardiac Output (CCO), EF (CCO), Cerebral Perfusion Pressure, SvO2 Calibrated, ScvO2 (Presep) Calibrated, CO (Arterial), SVV (Arterial), SV (Arterial), ScvO2 (Presep), SvO2 SQI, ScvO2 (Presep) SQI, Intra Cranial Pressure #2
IABP12Intra Aortic Ballon Pump Setting, Assisted Systole, Augmented Diastole, BAEDP, ABI Brachial BP L, ABI Ankle BP R, IABP Mean, Unassisted Systole, PAEDP, Intra Aortic Balloon Pump Setting, ABI Ankle BP L, ABI Brachial BP R
Impella12ABI Brachial BP R (Impella), ABI Ankle BP L (Impella), ABI Ankle BP R (Impella), ABI Brachial BP L (Impella), Aortic Pressure Signal - Diastolic, Aortic Pressure Signal - Systolic, Dual Signal Screen, Impella 2.5 Flow Rate, Performance Level, Placement Monitoring Screen, Purge Pressure, Purge Solution Flow Rate
Intubation48Mallampati classification (Intubation), Difficult to Intubate, Induction/Drugs/Doses (Intubation), Indication (Intubation), Unable to complete Airway Assessment (Intubation), Mouth Opening (Intubation), Thyromental Distance (Intubation), Mandibular Prognatism (Intubation), Teeth/Dentition (Intubation), Neck ROM (Intubation), No Drugs Administered (Intubation), RSI (Intubation), Pre-Oxygentated (Intubation), Mask Ventilation (Intubation), Blade / Equipment (Intubation), Number of Attempts (Intubation), Oral ETT, Nasal ETT, ETT Depth (Intubation), Stylette (Intubation), Bougie (Intubation), Tube Secured (Intubation), Tracheal Confirmation (Intubation), ETT Size (Intubation), Best Larynx View Obtained (Intubation), No Complications (Intubation), Timeout Performed by (Intubation), Patient identified correctly by 2 means (Intubation), Hand Cleansing prior to procedure (Intubation), Barrier precautions in place (Intubation), ETT Type (Intubation), Complications (Intubation), Verification of (Intubation), Drugs (Int
Labs148Activated Clotting Time, ZFibrinogen, Hematocrit (serum), WBC, ZProthrombin time, ZINR, ZPTT, ZAlbumin, Ammonia, Amylase, AST, Chloride (serum), Cholesterol, ZCortisol, ZC Reactive Protein (CRP), Creatinine, Glucose (serum), HDL, LDH, Magnesium, ZPotassium (serum), ALT, Sodium (serum), Total Protein, PH (dipstick), ZSpecific Gravity (urine), Arterial O2 pressure, Arterial O2 Saturation, Hemoglobin, Arterial CO2 Pressure, PH (Venous), PH (Arterial), TCO2 (calc) Venous, ZHCO3 (serum), Arterial Base Excess, Alkaline Phosphate, ZBrain Natiuretic Peptide (BNP), BUN, Calcium non-ionized, CK-MB fraction (%), ZCK-MB, CK (CPK), D-Dimer, Differential-Atyps, Differential-Bands, Differential-Basos, Differential-Eos, Differential-Lymphs, Differential-Monos, Differential-Neuts, ZDigoxin, ZPhenytoin (Dilantin), Direct Bilirubin, ZFK506, Freeform Chemistry, Freeform Coags, Freeform Drug Level, Freeform Enzymes, Freeform Hematology, ZGentamicin (Peak), Glucose finger stick, Ionized Calcium, Lactic Acid, ZLDL measured, LDL cal
Lumbar Puncture23Indication (LP), Site cleansed with ChloraPrep(TM) (LP), Lidocaine 1% (LP), Patient position (LP), Entry site (LP), Amount of fluid removed (LP), Fluid removed (LP), Timeout Performed by (LP), Patient identified correctly (LP), Verification of (LP), Insertion kit used (LP), Hand cleansing prior to procedure (LP), Barrier precautions in place (LP), Dressing applied and dated (LP), Complications (LP), Hand cleansing prior to procedure (LP), Site cleansed with Providone Iodine (LP), Number of attempts to perform (LP), Number of operators (LP), Dressing applied and dated (LP), The Attending/Supervisor is the Clinician Operator (LP), I was present during the above procedure and agree with the checklist (LP), Barrier precautions in place (LP)
Medications98Sodium Bicarbonate 8.4%, Abciximab (Reopro), Adenosine, Epinephrine, Alteplase (TPA), Aminophylline, Amiodarone, Lorazepam (Ativan), Atropine, Esmolol, Calcium Gluconate, Diltiazem, Cisatracurium, Diazepam (Valium), Dobutamine, Dopamine, Midazolam (Versed), Drotrecogin (Xigris), Ketamine, Fentanyl, Phenylephrine, Furosemide (Lasix), Haloperidol (Haldol), Hydralazine, Hydromorphone (Dilaudid), Lepirudin, Norepinephrine, Milrinone, Magnesium Sulfate, Naloxone (Narcan), Nesiritide, Nicardipine, Nitroprusside, Nitroglycerin, Vecuronium, Procainamide, Propofol, Vasopressin, Verapamil, Insulin - 70/30, Insulin - Regular, Insulin - NPH, Insulin - Glargine, Insulin - Humalog 75/25, Insulin - Humalog, Argatroban, Bivalirudin (Angiomax), Dexmedetomidine (Precedex), Eptifibatide (Integrilin), Heparin Sodium, Labetalol, Morphine Sulfate, Octreotide, Pentobarbital, Tirofiban (Aggrastat), Potassium Chloride, Hydrochloric Acid - HCL, Folic Acid, Thiamine, K Phos, Na Phos, Enoxaparin (Lovenox), Famotidine (Pepcid), Fondapari
Mixed VBGs5Mix Venous CO2(calc), Mix Venous PCO2, Mix Venous PO2, Mix Venous Sat, Mix Venous pH
Mixed Venous Gases2SvO2, Venous PvO2
Neurological69GCS - Eye Opening, Risk for Falls, Orientation, GCS - Verbal Response, GCS - Motor Response, Speech, Communication, Gag Reflex, Cough Reflex, Pupil Size Right, Follows Commands, Spontaneous Movement, Response to Stimuli (Type), RU Strength/Movement, RL Strength/Movement, LU Strength/Movement, LL Strength/Movement, Neuro Symptoms, Seizure Activity, RLE Sensation, Seizure Duration, Neuro Drain #1 Type, Neuro Drain #1 Level, Neuro Drain #1 Status, Neuro Drain #1 Drainage, Corneal Reflex Right, Corneal Reflex Left, Pupil Size Left, Response, RUE Sensation, LUE Sensation, LLE Sensation, Neuro Drain Location, Neuro Drain Head of Bed, Neuro Waveform Appearance, Level of Consciousness, Behavior, Pupil Response Right, Neuro Drain Landmark, Pupil Response Left, Neuro Drain Landmark, Neuro Drain #2 Type, Neuro Drain #2 Status, Neuro Drain #2 Drainage, Neuro Drain #2 Level, Neuro Drain #1 Landmark, Neuro Drain #2 Landmark, Pronator drift present, Orientation to Person, Orientation to Place, Orientation to Time, Commands
NG Feeding1Sterile Water NG
NICOM15Cardiac Index (CI NICOM), Cardiac Output (CO NICOM), CO / CI Change, DO2I (NICOM), Leg Raise Result (NICOM), Passive Leg Raise Performed, Stroke Volume (SV NICOM), Stroke Volume Index (SVI NICOM), Stroke Volume Variation (SVV NICOM), SVI Change, TFCd (NICOM), TFCd0 (NICOM), Thoracic Fluid Content (TFC) (NICOM), Total Peripheral Resistance (TPR) (NICOM), Total Peripheral Resistance Index (TPRI) (NICOM)
Nutrition - Enteral103Nutren Renal (Full), Impact (Full), Beneprotein, Nutren 2.0 (1/4), Nutren 2.0 (2/3), Nutren 2.0 (3/4), Nutren 2.0 (1/2), Impact (1/4), Impact (2/3), Impact (3/4), Impact (1/2), Impact with Fiber (1/4), Impact with Fiber (2/3), Impact with Fiber (3/4), Impact with Fiber (1/2), Nutren Renal (1/4), Nutren Renal (2/3), Nutren Renal (3/4), Nutren Renal (1/2), Peptamen 1.5 (1/4), Peptamen 1.5 (2/3), Peptamen 1.5 (3/4), Peptamen 1.5 (1/2), ProBalance (1/4), ProBalance (2/3), ProBalance (3/4), ProBalance (1/2), Replete (1/4), Replete (2/3), Replete (3/4), Replete (1/2), Replete with Fiber (1/4), Replete with Fiber (2/3), Replete with Fiber (3/4), Replete with Fiber (1/2), Vivonex (1/4), Vivonex (2/3), Vivonex (3/4), Vivonex (1/2), Impact with Fiber (Full), ProBalance (Full), Peptamen 1.5 (Full), Nutren 2.0 (Full), Vivonex (Full), Replete (Full), Replete with Fiber (Full), Ensure (Full), Enteral Nutriton Residuals, Ensure (1/4), Ensure Plus (1/4), Ensure (3/4), Ensure (1/2), Ensure Plus (Full), Ensure Plus (3/4), Ensu
Nutrition - Parenteral9Ranitidine, Lipids 20%, TPN w/ Lipids, Lipids (additive), TPN without Lipids, Peripheral Parenteral Nutrition, Dextrose PN, Amino Acids, Lipids 10%
Nutrition - Supplements17Potassium ACEtate, Resource Fruit Beverage, Optisource, Mighty Shake (Vanilla/Strawberry), Mighty Shake (Chocolate), Mighty Shake (no sugar added), Vanilla Scandi Shake (mixed), Chocolate Scandi Shake (mixed), Vanilla Lactose Free Scandi Shake (mixed), Aminosyn 3.5%, D5 Aminosyn 3.5%, Hepatamine, Nephramine, Folate, Hydrochloric Acid, Potassium Phosphate, Sodium ACEtate
OB-GYN14Breast Feeding, Breast Feeding - L, Breast Feeding - R, Breast Feed - L (Minutes), Breast Feed - R (Minutes), Clot Size (cm), Clots, Fundus Character, Fundus Location (Breadth), Lochia Character, Lochia Estimate, Pad Change/Hour, Reflexes LE, Reflexes UE
ORGANISM312ESCHERICHIA COLI, GRAM NEGATIVE COCCI, KLEBSIELLA PNEUMONIAE, KLEBSIELLA OXYTOCA, KLEBSIELLA OZAENAE, ENTEROBACTER AEROGENES, ENTEROBACTER CLOACAE, HAFNIA ALVEI, ENTEROBACTER AGGLOMERANS, ENTEROBACTER GERGOVIAE, ENTEROBACTER SAKAZAKII, SERRATIA LIQUEFACIENS, SERRATIA MARCESCENS, SERRATIA RUBIDAEA, PROTEUS VULGARIS, PROTEUS MIRABILIS, MORGANELLA MORGANII, PROVIDENCIA STUARTII, PROVIDENCIA RETTGERI, SHIGELLA FLEXNERI, SALMONELLA ENTERITIDIS, STAPH AUREUS COAG +, STAPHYLOCOCCUS EPIDERMIDIS, CITROBACTER AMALONATICUS, PSEUDOMONAS AERUGINOSA, PSEUDOMONAS PUTIDA , BURKHOLDERIA (PSEUDOMONAS) CEPACIA, PSEUDOMONAS SPECIES, BORDETELLA BRONCHISEPTICA, MORAXELLA SPECIES, AEROMONAS HYDROPHILA, VIBRIO SPECIES, PASTEURELLA MULTOCIDA, EIKENELLA CORRODENS, ANAEROBIC GRAM NEGATIVE ROD(S), GRAM VARIABLE RODS, CLOSTRIDIUM PERFRINGENS, CLOSTRIDIUM SPECIES, NEISSERIA GONORRHOEAE, NEISSERIA MENINGITIDIS, NEISSERIA SPECIES, HAEMOPHILUS PARAINFLUENZAE, HAEMOPHILUS SP, BETA STREPTOCOCCUS GROUP B, BETA STREPTOCOCCUS GROUP C, BETA STREPT
OT Notes47Referral Date, Self Feeding, Grooming, UE Bathing, LE Bathing, UE Dressing, LE Dressing, Toileting, Home Management, Money Management, Community Integration, Recovery RR - Aerobic Capacity, Recovery O2 Sat - Aerobic Capacity, Pain (0-10), Patient agrees with the above goals and is willing to participate in the rehabilitation program, Date - Student, Date - Therapist, Position - Rest - Aerobic Activity Response, Rest HR - Aerobic Activity Response, Rest RR - Aerobic Activity Response, Rest O2 Sat - Aerobic Activity Response, Position - Activity - Aerobic Activity Response, Activity HR - Aerobic Activity Response, Activity RR - Aerobic Activity Response, Activity O2 sat - Aerobic Activity Response, Position - Recovery - Aerobic Activity Response, Recovery HR - Aerobic Activity Response, Recovery RR - Aerobic Activity Response, Recovery O2 sat - Aerobic Activity Response, Anticipated Discharge, Rolling, Supine / Side-lying to Sit, Sit to Stand, Transfer, Toilet, Commode, Shower / Tub, Other (Activity); Specify,
Other ABGs10Base Excess (cap), TCO2 (cap), pCO2 (cap), pH (cap), pO2 (cap), Base Excess (other), TCO2 (other), pCO2 (other), pO2 (other), ph (other)
Output46R Ureteral Stent, L Ureteral Stent, Foley, Void, Condom Cath, Suprapubic, R Nephrostomy, L Nephrostomy, Urine and GU Irrigant Out, Straight Cath, Anderson (gastric), Blakemore, Emesis, Ewald, Gastric Tube, Jejunostomy, Nasogastric, Oral Gastric, Stool, Fecal Bag, Ostomy (output), Rectal Tube, Ileoconduit, OR EBL, OR Urine, PACU Drains, PACU EBL, PACU Gastric, PACU Urine, Cath Lab, Pre-Admission, Incontinent (estimate), Stool Estimate, Chest Tube #1, Chest Tube #2, L Pleural #1, L Pleural #2, Mediastinal, R Pleural #1, R Pleural #2, GU Irrigant Type, GU Irrigant Volume In, GU Irrigant/Urine Volume Out, TF Residual, TF Residual Output, Stool containment device placed
PA Line Insertion52Medications / anesthetic (PA line), Indications (PA line), Insertion site (PA line), Type of catheter (PA line), Complications (PA line), Thermodilution (PA line), Fick (PA line), Timeout performed by (PA line), Patient identified correctly (PA line), Verification of (PA line), Barrier precautions in place (PA line), Hand cleansing prior to procedure (PA Line), Insertion kit used (PA line), Site cleaned with ChloraPrep(TM) (PA line), Location (PA line), Number of attempts require to place line (PA line), Guidewire (PA Line), Ultrasound (PA line), Side (PA line), Acuity (PA line), Transparent dressing applied and dated (PA line), PA line ordered on POE, Follow-up X-RAYordered (PA Line), Sterile field maintained throughout procedure (PA line), Number of operators (PA line), RA (mean) pressure (PA Line), RV systolic pressure(PA Line), RV diastolic pressure(PA Line), PA systolic pressure(PA Line), PA diastolic pressure(PA Line), PCWP (v wave) (PA Line), PCWP (mean) (PA Line), PA mean pressure (PA Line), CO pressu
Pain/Sedation62Riker-SAS Scale, Daily Wake Up Deferred, Pain Present, Pain Type, Pain Location, Pain Cause, Pain Level, Pain Management, Pain Level Acceptable, Pain Assessment Method, Untoward Effect, Sensory Level, Epidural Appearance, NMB Medication, Nerve Stimulated, TOF Response/Twitch, PCA medication, PCA dose, PCA lockout (min), PCA 1 hour limit, PCA basal rate (mL/hour), PCA inject, Epidural Infusion Rate (mL/hr), Epidural Total Dose (mL), Epidural Medication, PCA cleared, Pain Level Response, PCA bolus, Epidural Bolus (mL), TOF Response, TOF Twitch, Baseline Current/mA, Current Used/mA, PCA attempt, PCA total dose, PCA dose units, PCA 1 hour limit units, PCA bolus units, PCA total dose units, Epidural Location, Motor Deficit, Daily Wake Up, Medication Infusion Rate - Adjunctive Pain Management, Medication Bolus - Adjunctive Pain Management, Site Appearance - Adjunctive Pain Management, Richmond-RAS Scale, PCA concentrations, Daily Wake Up Deferred, Daily Wake Up, Goal Richmond-RAS Scale, CAM-ICU MS change, CAM-ICU
Paracentesis25Indication (PACEN), Ultrasound (PACEN), Insertion site prepped and draped (PACEN), Lidocaine 1% (PACEN), Entry site (PACEN), Fluid removed (PACEN), Fluid removed description (PACEN), Timeout performed by (PACEN), Patient identified correctly by 2 means (PACEN), Verification of (PACEN), Insertion kit used (PACEN), Hand cleansing prior to procedure (PACEN), Site cleaned with ChloraPrep (PACEN), Barrier precautions in place (PACEN), Number of operators (PACEN), Number of attempts (PACEN), Dressing applied and dated (PACEN), Complications (PACEN), Insertion kit used (PACEN), Hand cleansing prior to procedure (PACEN), Site cleaned with ChloraPrep (PACEN), Dressing applied and dated (PACEN), The Attending/Supervisor is the Clinical Operator (PACEN), I was present during the above procedure and agree with the checklist (PACEN), Barrier precautions in place (PACEN)
Pastoral Care Note9PC Assessment of spiritual issues, PC Date, PC Interpreter present, PC Ministries offered, PC Spiritual interventions, PC Sacrament of the Sick, PC Reason for visit, PC Source of assessment data, PC Time of visit
PICC Line Insertion36Location (PICC), Type of catheter (PICC), Insertion site (PICC), Timeout performed by (PICC), Patient identified correctly (PICC), Barrier precautions in place (PICC), Guidewire exchange (PICC), Side (PICC), Ultrasound (PICC), Verification of (PICC), Number of attempts required to place line (PICC), Acuity (PICC), Indications (PICC), Hand Cleansing Prior to Procedure (PICC), PICC Ordered on POE, Catheter repositioned (PICC), Transparent dressing applied and dated (PICC), Follow-up X-RAY Ordered (PICC), Attending/Supervisor is Clinician Operator (PICC), Present during key moments and agree with checklist (PICC), Site cleaned with ChloraPrep(TM) (PICC), Insertion kit used (PICC Line), Immediate complications (PICC), Sterile field maintained throughout procedure (PICC), Number of operators (PICC), Catheter insertion length (PICC), Insertion kit used (PICC Line), Hand Cleansing Prior to Procedure (PICC), Site cleaned with ChloraPrep(TM) (PICC), Sterile field maintained throughout procedure (PICC), Transparent dre
PiCCO11CI (PiCCO), CO (PiCCO), ELWI (PiCCO), GEDI (PiCCO), ITBVI (PiCCO), SVI (PiCCO), SVRI (PiCCO)_OLD_1, SVV (PiCCO), SVRI (PiCCO), Calibrated (PiCCO), CFI (PiCCO)
Pulmonary40LUL Lung Sounds, LLL Lung Sounds, Respiratory Effort, Cough Effort, Cough Type, Chest Tube Site # 1, CT #1 Suction Amount, CT #1 Fluctuate, CT #1 Drainage, CT #1 Dressing, CT #1 Crepitus, Respiratory Pattern, RUL Lung Sounds, RLL Lung Sounds, CT #1 Leak, CT #2 Leak, CT #3 Leak, CT #4 Leak, CT #2 Crepitus, CT #2 Drainage, CT #2 Dressing, CT #2 Fluctuate, CT #2 Suction Amount, CT #3 Crepitus, CT #4 Crepitus, CT #3 Drainage, CT #4 Drainage, CT #3 Dressing, CT #4 Dressing, CT #3 Fluctuate, CT #4 Fluctuate, CT #3 Suction Amount, CT #4 Suction Amount, Chest Tube Site # 2, Chest Tube Site #3, Chest Tube Site #4, CT #1 Suction Type, CT #2 Suction Type, CT #3 Suction Type, CT #4 Suction Type
Quick Admit1BloodGlucose
Research Enrollment Note1Date signed
Respiratory134Respiratory Rate, Vital Capacity, CO2 production, O2 saturation pulseoxymetry, Resistance, PEEP set, O2 Flow, Inspired O2 Fraction, Airway Type, ETT Size (ID), ETT Location, ETT Re-taped, Passey Muir Valve in use, Ventilator Type, Ventilator Mode, Rate, Additional Settings, Wean, Physio, Inspired Gas Temp., Paw High, Vti High, Fspn High, Apnea Interval, Intubation - Details, MDI #1 Puff, MDI #1 Drug, MDI #2 Puff, MDI #2 Drug, MDI #3 Puff, MDI #3 Drug, Small Volume Neb Dose #2, Small Volume Neb Drug #1, Sputum Consistency, Sputum Color, Cuff Volume/units, Tidal Volume (set), Tidal Volume (observed), Tidal Volume (spontaneous), Minute Volume, Respiratory Rate (Set), Respiratory Rate (spontaneous), Respiratory Rate (Total), Flow Rate (L/min), Flow Pattern, Peak Insp. Pressure, Plateau Pressure, Mean Airway Pressure, ZAuto Peep Level, Total PEEP Level, PSV Level, PCV Level (Avea), ZATC Tube #, ATC %, P High (APRV), P Low (APRV), T High (APRV), T Low (APRV), Recruitment Press, Recruitment Duration, Recruitment Mod
Restraint/Support Systems84Support Systems, Family Communication, Spiritual Support, Reason for Restraint (Behavioral)_V1, Less Restrictive Measures (Acute Med/Surgical), Restraint Type, Restraint Location_V1, Circulation/Skin Integrity, Position Change, Range of Motion, Restraints Evaluated_V1, Side Rails_V1, Sitter, Education Learner, Education Topic, Education Readiness, Education Barrier, Education Method, Education Handout, Education Response, Family Meeting, Reason for Restraint (Acute Medical/Surgical)_V1, Patient/Family Informed_V1, Restraints Ordered, Less Restrictive Measures (Behavioral), Patient Behavior During Application, Status and Comfort (Behavioral), History of falling (within 3 mnths), Secondary diagnosis, Ambulatory aid, IV/Saline lock, Gait/Transferring, Mental status, Low risk (25-50) interventions, High risk (>51) interventions, Patient/Family Informed_V2, Immobilizer, Immobilizer Device, Immobilizer Evaluated, Immobilizer Location, Reason for Immobilizer, Side Rails_V2, Side Rails (Restraint), Violent Restraints
Routine Vital Signs48Heart Rate, Heart Rhythm, Arterial Blood Pressure systolic, Arterial Blood Pressure diastolic, Arterial Blood Pressure mean, Non Invasive Blood Pressure systolic, Non Invasive Blood Pressure diastolic, Non Invasive Blood Pressure mean, Temperature Fahrenheit, Temperature Celsius, Bladder Pressure, Orthostatic HR lying, Orthostatic HR sitting, Orthostatic BPs standing, Doppler BP, Manual Blood Pressure Systolic Left, Pulsus Paradoxus, QTc, Temperature Site, Manual Blood Pressure Diastolic Left, Orthostatic BPs lying, Orthostatic BPs sitting, Orthostatic HR standing, Ectopy Type 1, Ectopy Frequency 1, ART BP Systolic, ART BP Diastolic, ART BP mean, Orthostatic BPd standing, Orthostatic BPd lying, Orthostatic BPd sitting, Blood Temperature CCO (C), Ectopy Type 2, Ectopy Frequency 2, Manual Blood Pressure Diastolic Right, Manual Blood Pressure Systolic Right, Arctic Sun Temp #1 Location, Arctic Sun Temp #2 Location, Arctic Sun/Alsius Temp #1 C, Arctic Sun/Alsius Temp #2 C, PAR-Activity, PAR-Circulation, PAR-Consc
Scores - APACHE II44AaDO2ApacheIIValue, AgeApacheIIScore, AgeApacheIIValue, APACHE II Diagnosistic weight factors - Medical, APACHE II Diagnosistic weight factors - Surgical emergency, APACHE II Diagnosistic weight factors - Surgical, APACHE II, APACHE II PDR - Adjusted, APACHE II Predecited Death Rate, ApacheII chronic health, APACHEII-Chronic health points, APACHEII-Renal failure, ChpApacheIIScore, CreatinineApacheIIScore, CreatinineApacheIIValue, DswfApacheScore, FiO2ApacheIIValue, GcsApacheIIScore, GCSEyeApacheIIValue, GCSMotorApacheIIValue, GCSVerbalApacheIIValue, HCO3ApacheIIValue, HCO3Score, HematocritApacheIIScore, HematocritApacheIIValue, HrApacheIIScore, HrApacheIIValue, MapApacheIIScore, MapApacheIIValue, OxygenApacheIIScore, PhApacheIIScore, PHApacheIIValue, PO2ApacheIIValue, PotassiumApacheIIScore, PotassiumApacheIIValue, RrApacheIIScore, RRApacheIIValue, SodiumApacheIIScore, SodiumApacheIIValue, TempApacheIIScore, TempApacheIIValue, WbcApacheIIScore, WBCApacheIIValue, GCSVerbalApacheIIValue (intubated)
Scores - APACHE IV28APACHE IV diagnosis choice 1, APACHE IV diagnosis choice 2, APACHE IV diagnosis, APACHE IV main groups non-operative, APACHE IV main groups operative, Non-Operative Cardiovascular (CARDIOVASC), Non-Operative Gastrointestinal (GI), Non-Operative Genitourinary (GENITOURIN), Non-Operative Hematology (HEMATO), Non-Operative Metabolic/Endocrine (METAB/ENDO), Non-Operative Musculoskeletal/Skin (MUSKELSKIN), Non-Operative Neurologic (NEUROLOGIC), Non-Operative Respiratory (RESPIRAT), Non-Operative Transplant (TRANSPLANT), Non-Operative Trauma (TRAUMA), Post-Operative Cardiovascular (CARDIOVASC), Post-Operative Gastrointestinal (GI), Post-Operative Genitourinary (GENITOURIN), Post-Operative Hematology (HEMATO), Post-Operative Metabolic/Endocrine (METAB/ENDO), Post-Operative Musculoskeletal/Skin (MUSKELSKIN), Post-Operative Neurologic (NEUROLOGIC), Post-Operative Respiratory (RESPIRAT), Post-Operative Transplant (TRANSPLANT), Post-Operative Trauma (TRAUMA), Thrombolytic therapy, Type of admission, Ventilated at any ti
Scores - APACHE IV (2)60Chronic Dilaysis, Coefficient Hospital Mortality, Coefficient ICU length of stay, Admission location, AgeScore_ApacheIV, Albumin_ApacheIV, AlbuminScore_ApacheIV, Apache IV A-aDO2, Apache IV Age, Apache IV PaFiRatio, APACHEIII, ApacheIV_LOS, ApacheIV_Mortality prediction, ApacheIV_Natural antilog, APS, ARF, Bilirubin_ApacheIV, BiliScore_ApacheIV, BUN_ApacheIV, BunScore_ApacheIV, CABG Patient, Chronic health on admission, ChronicScore_ApacheIV, Creatinine_ApacheIV, CreatScore_ApacheIV, Diabetes, Ejection Fraction, FiO2_ApacheIV_old, FiO2_ApacheIV, GCSEye_ApacheIV, GCSMotor_ApacheIV, GcsScore_ApacheIV, GCSVerbal_ApacheIV, Glucose_ApacheIV, GlucoseScore_ApacheIV, Hematocrit_ApacheIV, HR_ApacheIV, HrScore_ApacheIV, HtScore_ApacheIV, Intubated_ApacheIV, LOS pre-ICU admission, MAP_ApacheIV, MapScore_ApacheIV, OxygenScore_ApacheIV, PCO2_ApacheIV, PH_ApacheIV, PHPaCO2Score_ApacheIV, PO2_ApacheIV, RR_ApacheIV, RRScore_ApacheIV, Sodium_ApacheIV, SodiumScore_ApacheIV, TemperatureF_ApacheIV, TempScore_ApacheIV, UrineScore
Skin - Assessment10Skin Integrity, Skin Temperature, Skin Color, Braden Sensory Perception, Braden Moisture, Braden Activity, Braden Mobility, Braden Nutrition, Braden Friction/Shear, Skin Condition
Skin - Impairment271Impaired Skin Site #1, Impaired Skin Cleanse #1, Impaired Skin Wound Base #1, Impaired Skin Drainage #1, Impaired Skin Treatment #1, Impaired Skin Type #1, Impaired Skin Length #1, Impaired Skin Depth #1, Impaired Skin Odor #1, Surrounding Tissue #1, Pressure Ulcer Stage #1, Tunneling Present #1, Undermining Present #1, Impaired Skin Width #1, Impaired Skin Drainage Amount #1, Impaired Skin Type #4, Impaired Skin Type #5, Impaired Skin Type #6, Impaired Skin Type #7, Impaired Skin Type #8, Impaired Skin Width #2, Impaired Skin Width #3, Impaired Skin Width #4, Impaired Skin Width #5, Impaired Skin Width #6, Impaired Skin Width #7, Impaired Skin Width #8, Impaired Skin Wound Base #2, Impaired Skin Wound Base #3, Impaired Skin Wound Base #4, Impaired Skin Wound Base #5, Impaired Skin Wound Base #6, Impaired Skin Wound Base #7, Impaired Skin Wound Base #8, Pressure Ulcer Stage #2, Pressure Ulcer Stage #3, Pressure Ulcer Stage #4, Pressure Ulcer Stage #5, Pressure Ulcer Stage #6, Pressure Ulcer Stage #7, Pressure
Skin - Incisions83Edema Location, Incision Closure #1, Incision Drainage #1, Incision Dressing #1, Incision Cleansing #1, Incision Packing #1, Incision Appearance #1, Incision Drainage Amount #1, Dressing Status #1, Edema Amount, Incision Appearance #2, Incision Appearance #3, Incision Appearance #4, Incision Appearance #5, Incision Appearance #6, Incision Cleansing #2, Incision Cleansing #3, Incision Cleansing #4, Incision Cleansing #5, Incision Cleansing #6, Incision Closure #6, Incision Closure #2, Incision Closure #3, Incision Closure #4, Incision Closure #5, Incision Drainage #2, Incision Drainage #3, Incision Drainage #4, Incision Drainage #5, Incision Drainage #6, Incision Drainage Amount #2, Incision Drainage Amount #3, Incision Drainage Amount #4, Incision Drainage Amount #5, Incision Drainage Amount #6, Incision Dressing #2, Incision Dressing #3, Incision Dressing #4, Incision Dressing #5, Incision Dressing #6, Dressing Status #2, Dressing Status #3, Dressing Status #4, Dressing Status #5, Dressing Status #6, Incisio
SPECIMEN93THROAT FOR STREP, ABSCESS, ARTHROPOD, ASPIRATE, ANORECTAL/VAGINAL CULTURE, BLOOD BAG FLUID, BRONCHIAL BRUSH - PROTECTED, BILE, BIOPSY, BLOOD CULTURE ( MYCO/F LYTIC BOTTLE), BLOOD CULTURE, FLUID RECEIVED IN BLOOD CULTURE BOTTLES, BLOOD CULTURE - NEONATE, BLOOD, BLOOD CULTURE (POST-MORTEM), SEROLOGY/BLOOD, BONE MARROW, BONE MARROW - CYTOGENETICS, BRONCHIAL BRUSH, BRONCHOALVEOLAR LAVAGE, BRONCHIAL WASHINGS, CATHETER TIP-IV, VIRAL CULTURE: R/O CYTOMEGALOVIRUS, CRE Screen, CSF;SPINAL FLUID, CORNEAL EYE SCRAPINGS, Direct Antigen Test for Herpes Simplex Virus Types 1 & 2, DIALYSIS FLUID, DIRECT ANTIGEN TEST FOR VARICELLA-ZOSTER VIRUS, EAR, Blood (EBV), EYE, FOREIGN BODY, FLUID,OTHER, FLUID WOUND, FOOT CULTURE, FECAL SWAB, GASTRIC ASPIRATE, SWAB, VIRAL CULTURE:R/O HERPES SIMPLEX VIRUS, Influenza A/B by DFA, Influenza A/B by DFA - Bronch Lavage, Influenza A/B by DFA - Bronch Wash, Immunology (CMV), IMMUNOLOGY, JOINT FLUID, NAIL SCRAPINGS, NEOPLASTIC BLOOD, NOSE, FLUID,OTHER, PERIPHERAL BLOOD LYMPHOCYTES, PERITONEAL FL
Tandem Heart14Access Site Observed (Tandem Heart), Cannulas Visible, Change in Blood Color, CO-Tandem Heart Flow, Infusate Drops/Minute, Infusion Pressure of Line, Oxygenator Sweep Rate, Oxygenator/ECMO, Peripheral Pulses (Tandem Heart), Pump Speed, Return Site Observed (Tandem Heart), Tandem Heart Flow, Type (Tandem Heart), Vibration in Line
Thoracentesis29Patient (THCEN), With ultrasound (THCEN), Lidocaine 1% (THCEN), Conscious sedation (THCEN), Indication (THCEN), Side (THCEN), Patient position (THCEN), Fluid Removed (THCEN), Fluid Removed Description (THCEN), Complications (THCEN), Patient identified correctly by 2 means (THCEN), Verification of (THCEN), Insertion kit used (THCEN), Hand cleansing prior to procedure (THCEN), Barrier precautions in place (THCEN), Follow-up X-RAY Ordered (THCEN), Site cleansed with ChloraPrep (THCEN), Timeout Performed by (THCEN), Dressing applied and dated (THCEN), Insertion kit used (THCEN), Hand cleansing prior to procedure (THCEN), Site cleansed with ChloraPrep (THCEN), Number of attempts (THCEN), Number of operators (THCEN), Dressing applied and dated (THCEN), Follow-up X-RAY Ordered (THCEN), The Attending/Supervisor is the Clinician Operator (THCEN), I was present during the above procedure and agree with the checklist (THCEN), Barrier precautions in place (THCEN)
Toxicology28Nausea and Vomiting (CIWA), Tremor (CIWA), Paroxysmal Sweats, Anxiety, Auditory Disturbance, Agitation, Tactile Disturbances, Visual Disturbances, Headache, Orient/Clouding Sensory, Insomnia, Seizure, Sustained Nystagmus, Ataxia, Slurred Speech, Drowsiness, Restlessness, Lacrimation, Pupils, Yawning, Abdominal Changes: Observation, Changes in Temperature, Muscle Aches, Nausea and Vomiting (CINA), Tremor (CINA), Nasal Congestion, Goose Flesh, Sweating
Treatments73Head of Bed, Turn, Activity, Assistance Device, Activity Tolerance, Therapeutic Bed, Pressure Reducing Device, Anti Embolic Device, Knee Immobilizer Status, Continuous Pressure Machine Status, Range of Motion Location, Position, Chest PT R/L, Warming Device, Cooling Device, Cervical Collar Type, Cervical Collar Status, Traction/Immobile #1, Traction/Immobile Location #1, PT Splint Location #1, Traction/Immobile #2, Traction/Immobile #3, Traction/Immobile #4, Traction/Immobile Location #2, Traction/Immobile Location #3, Traction/Immobile Location #4, PT Splint Location #4, PT Splint Location #2, PT Splint Location #3, Bed Bath, Knee Location - Immobilizer, Warming Device Status, Cooling Device Status, Anti Embolic Device Status, Range of Motion Status, PT Splint Status #1, PT Splint Status #2, Eye Care, Skin Care, Back Care, Cough/Deep Breath, Incentive Spirometry, Pin Care, Ostomy Care, Collar Care, PT Splint Status #3, PT Splint Status #4, Oral Care, Subglottal Suctioning, Head of Bead Measurement, Traction/
Urine5Urine Glucose, Urine Heme, Urine Ketones, Urine Leukocytes, Urine Protein
VBG’S3Venous CO2(Calc), Venous PvCO2, Venous pH
Venous ABG4Venous Base Excess, Venous CO2, Venous O2, Venous TCO2
ZIntake5ZCath Lab Intake, ZOR Intake, ZPACU Intake, ZFree Form Intake, ZGU Irrigant Intake
-

We next investigate how many chartevents exist for each of the D_ITEMs, how many distinct patients have such values, and whether these patients’ data came from Careview (I believe SUBJECT_ID < 40000) or Metavision (SUBJECT_ID >= 40000).

-
if (exists("chart.items")) {
-} else if (file.exists("chart-items.csv")) {
-  chart.items = read.csv("chart-items.csv", row.names=1)
-  chart.items$category = as.character(chart.items$category)
-  chart.items$label = as.character(chart.items$label)
-  print("chart-items.csv read from file.")
-} else {
-  print("chart.items must be imported from database; this will take a long time...")
-  chart.items <- dbGetQuery(con, "select itemid, category, label from d_items")
-  chart.items.freq <- dbGetQuery(con, "select itemid, count(*) count from chartevents group by itemid")
-  chart.items.pat <- dbGetQuery(con, "select itemid, count(distinct subject_id) n_pat from chartevents group by itemid")
-  chart.items.pat.cv <- dbGetQuery(con, "select itemid, count(distinct subject_id) cv_pat from chartevents where subject_id < 40000 group by itemid")
-  chart.items.pat.mv <- dbGetQuery(con, "select itemid, count(distinct subject_id) mv_pat from chartevents where subject_id >= 40000 group by itemid")
-  chart.items = merge(chart.items, chart.items.freq, by="itemid", all=TRUE)
-  chart.items = merge(chart.items, chart.items.pat.cv, by="itemid", all=TRUE)
-  chart.items = merge(chart.items, chart.items.pat.mv, by="itemid", all=TRUE)
-  chart.items = merge(chart.items, chart.items.pat, by="itemid", all=TRUE)
-  chart.items = chart.items[order(chart.items$category, chart.items$label),]
-  print("chart.items has been read.")
-}
-
## [1] "chart-items.csv read from file."
-
if (!file.exists("chart-items.csv")) {
-  write.csv(chart.items, "chart-items.csv")
-  print("chart-items.csv written.")
-}
-chart.items.both = subset(chart.items, !is.na(cv_pat) & !is.na(mv_pat))
-

Of the 12478 distinct D_ITEMs, there are only 5561 that are recorded for any of the patients in CHARTEVENTS, of which only 620 occur in both Careview and Metavision patients. Careview seems to use many more of the items (5526) than Metavision (655).

-

From previous examination of the data, we know that in the move from Careview to Metavision, some similar items have been coded with different ITEMIDs. We see whether these matching IDs can be recovered by textual identity of their labels.

-
item.identical <- dbGetQuery(con, "select x.itemid as itemid1, y.itemid as itemid2, x.label, x.category as cat1, y.category as cat2 from d_items x join d_items y on x.label=y.label where x.itemid<y.itemid order by x.itemid")
-
-item.identical.pat = merge(item.identical, chart.items, by.x="itemid1", by.y="itemid")
-item.identical.pat = merge(item.identical.pat, chart.items, by.x="itemid2", by.y="itemid")
-item.identical.pat = item.identical.pat[order(item.identical.pat$cat1, item.identical.pat$label.x), c("itemid1", "itemid2", "count.x", "count.y", "cv_pat.x", "cv_pat.y", "mv_pat.x", "mv_pat.y", "label.x", "cat1", "cat2")]
-item.identical.pat = subset(item.identical.pat, !is.na(count.x) | !is.na(count.y))
-names(item.identical.pat) = c("itemid1", "itemid2", "count1", "count2", "cv.pat.n1", "cv.pat.n2", "mv.pat.n1", "mv.pat.n2", "label", "cat1", "cat2")
-for (i in 1:8) item.identical.pat[,i] = as.integer(item.identical.pat[,i])
-kable(item.identical.pat)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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219199644862182NA41NANANABLADDER PRESSURENAFree Form Intake
278099622376318243784189NA685BLADDER PRESSURENARoutine Vital Signs
39389424222NANAblood culturesNANA
43893833334506124423NANAblood culturesNANA
43994233332506124423NANABLOOD CULTURESNANA
18903334436061NA1NANANABlood OutNANA
19173334437621NA1NANANABlood OutNANA
2847802240041138048NA26244NANANABowel SoundsNAGI/GU
286182224056345309NA22187NANANABraden ActivityNASkin - Assessment
286284224057345105NA22188NANANABraden MobilityNASkin - Assessment
286085224055345316NA22187NANANABraden MoistureNASkin - Assessment
286386224058344990NA22186NANANABraden NutritionNASkin - Assessment
107422686963164516NANABREAST BINDERNANA
89457376219NA7NA1NANAbreast pumpNANA
24646219462607NA1NANANABreast PumpNANA
1207708481091307011NANABroviac DsgNANA
10825260708415213011NANABROVIAC DSGNANA
1208526081091527011NANABROVIAC DSGNANA
30195876225624115247513899NA15413bunNALabs
785116258761525391173581NANABUNNANA
30201162225624152539152475173583899NA15413BUNNALabs
164225034236529NA1NANANAbupivacaineNAFree Form Intake
256525034660929NA1NANANAbupivacaineNAFree Form Intake
27220132503502911NANABupivacaineNANA
164320134236550NA1NANANABupivacaineNAFree Form Intake
256620134660950NA1NANANABupivacaineNAFree Form Intake
13428048404522NA1NANANAC.O.NANA
14218048409082NA1NANANAC.O.NAFree Form Intake
16746470425042NA1NANANACA GLUCONATENAFree Form Intake
23616470456992NA1NANANACA GLUCONATENAFree Form Intake
24696470462912NA1NANANACA GLUCONATENAFree Form Intake
1826321443070324NA1NANANACa++ GluconateNAFree Form Intake
4953346444621721148461NANACaffeineNANA
62444125051425405NANAcaffeine bolusNANA
85044126085421401NANAcaffeine bolusNANA
4644239427829182517NANACaffeine bolusNANA
4884239441229422540NANACaffeine bolusNANA
62342395051295255NANACaffeine bolusNANA
84942396085291251NANACaffeine bolusNANA
4894278441218421740NANACaffeine BolusNANA
62542785051185175NANACaffeine BolusNANA
85242786085181171NANACaffeine BolusNANA
851505160855151NANACAFFEINE BOLUSNANA
63243055106649825NANACaffeine CitrateNANA
898611962257171NANAcaffeine loadNANA
866540461193737NANACAFFEINE LOADNANA
897540462253131NANACAFFEINE LOADNANA
63448435113308333NANAcalciferolNANA
126257533002272NA3NANANACalcium ChlorideNANA
65751145258513221NANACalcium glubionateNANA
63546965114495122NANACalcium GlubionateNANA
65646965258493221NANACalcium GlubionateNANA
44132193347841351317NANAcalcium gluconateNANA
126632193002384NA13NANANAcalcium gluconateNANA
2723321922145684NA13NANANAcalcium gluconateNAMedications
242166321851386663NANACalcium gluconateNANA
4211663321913884613NANACalcium gluconateNANA
44016633347138135617NANACalcium gluconateNANA
1264166330023138NA6NANANACalcium gluconateNANA
27251663221456138NA6NANANACalcium gluconateNAMedications
1265334730023135NA17NANANACalcium GluconateNANA
27243347221456135NA17NANANACalcium GluconateNAMedications
420218532196684313NANACALCIUM GLUCONATENANA
4422185334766135317NANACALCIUM GLUCONATENANA
126321853002366NA3NANANACALCIUM GLUCONATENANA
2726218522145666NA3NANANACALCIUM GLUCONATENAMedications
892619761982221NANAcap gasNANA
901619762372121NANAcap gasNANA
900619862372111NANACAP GASNANA
64746695192125115NANACardiology consultNANA
27324932516726633NANAcariporideNANA
163924934236172NA3NANANAcariporideNAFree Form Intake
164724934237272NA3NANANAcariporideNAFree Form Intake
166724934248072NA3NANANAcariporideNAFree Form Intake
164025164236166NA3NANANACARIPORIDENAFree Form Intake
164525164237266NA3NANANACARIPORIDENAFree Form Intake
166525164248066NA3NANANACARIPORIDENAFree Form Intake
1632249842325169NA4NANANACARIPORIDE CC/HRNAFree Form Intake
1635246042343221NA5NANANACARIPORIDE ML/HRNAFree Form Intake
162624564229824NA1NANANACARIPROSIDENAFree Form Intake
461367137141678145NANACCONANA
650455752042412101NANAcefazolinNANA
266245579000524NA10NANANAcefazolinNAANTIBACTERIUM
3084455722585024NA10NANANAcefazolinNAAntibiotics
5154426455789241810NANACefazolinNANA
651442652048912181NANACefazolinNANA
266444269000589NA18NANANACefazolinNAANTIBACTERIUM
3083442622585089NA18NANANACefazolinNAAntibiotics
266352049000512NA1NANANACEFAZOLINNAANTIBACTERIUM
3085520422585012NA1NANANACEFAZOLINNAAntibiotics
4604215425614212801282NANAcefotaximeNANA
4794215436414264123NANAcefotaximeNANA
47842564364128064823NANACefotaximeNANA
64050865153415925NANAceftazadimeNANA
490422044152413581220NANAceftazidimeNANA
2674422090017241NA12NANANAceftazidimeNAANTIBACTERIUM
30884220225853241NA12NANANAceftazidimeNAAntibiotics
2673441590017358NA20NANANACeftazidimeNAANTIBACTERIUM
30894415225853358NA20NANANACeftazidimeNAAntibiotics
288611922410959610NA2121NANANACervical Collar TypeNATreatments
3461225296323711NANAcheck bruit l armNANA
120978188141402311NANAChest DressingNANA
23910562150311883NANAchest ptNANA
28110562585311682NANAchest ptNANA
129441056223198NANAChest PTNANA
2409442150221893NANAChest PTNANA
2829442585221692NANAChest PTNANA
28321502585181632NANACHEST PTNANA
6725311545632322NANAChest tubeNANA
14345311410033NA2NANANAChest tubeNAFree Form Intake
143554564100323NA2NANANAChest TubeNAFree Form Intake
84352476066261711NANAChest tube dsgNANA
192452474385726NA1NANANAChest tube dsgNANA
192560664385717NA1NANANAChest tube DSGNANA
158671414210117NA1NANANAChest tube siteNANA
292812422443913339NA466NANANAChest Tube Site #3NAPulmonary
29291252244402778NA58NANANAChest Tube Site #4NAPulmonary
301333662254594456NA1685NANANAChest X-RayNA5-Imaging
63846485123164144NANAchloral hydrateNANA
5354349464836162814NANAChloral HydrateNANA
63743495123364284NANAChloral HydrateNANA
723451456998181NANAChromosomesNANA
5394585466372188544NANAcircNANA
5444585470772110885454NANAcircNANA
1921458543764721NA54NANANAcircNANA
19194707437641088NA54NANANACircNANA
54546634707881088454NANACIRCNANA
192046634376488NA4NANANACIRCNANA
49143554436317173208NANAcirc careNANA
56843554791317108206NANAcirc careNANA
5694436479117310886NANACirc careNANA
4764204435549317520NANACIRC CARENANA
492420444364917358NANACIRC CARENANA
570420447914910856NANACIRC CARENANA
588456848621314158827NANAcirc siteNANA
117845687840131419822NANAcirc siteNANA
5094370453415293189320NANACirc siteNANA
51843704568152913149382NANACirc siteNANA
587437048621529158937NANACirc siteNANA
117743707840152919932NANACirc siteNANA
5174534456831813142082NANACirc SiteNANA
58945344862318158207NANACirc SiteNANA
11794534784031819202NANACirc SiteNANA
1180486278401581972NANACIRC SITENANA
525455546131564373NANACirc. siteNANA
512443545559115667NANACirc. SiteNANA
52444354613914363NANACirc. SiteNANA
4813367437787823433120NANACircumcisionNANA
52742284623202241128NANAcircumcision siteNANA
558422847432022941212NANAcircumcision siteNANA
10844228708620237121NANAcircumcision siteNANA
55746234743241294812NANACircumcision siteNANA
1083462370862413781NANACircumcision siteNANA
10854743708629437121NANACircumcision SiteNANA
17042684426082NA1NANANACisat mcg/kg/hrNAFree Form Intake
165626854241441NA1NANANACisat mcg/kg/minNAFree Form Intake
270102324541741312NANAcisatra mcg/kg/minNANA
1297185830114351NA7NANANAcisatracuriumNANA
27311858221555351NA7NANANAcisatracuriumNAMedications
1481028185820835187NANACisatracuriumNANA
1298102830114208NA8NANANACisatracuriumNANA
27291028221555208NA8NANANACisatracuriumNAMedications
16492511423854NA1NANANACISATRACURIUM GTTNAFree Form Intake
134100017718776131NANAcisatricuriumNANA
1356100040552877NA3NANANAcisatricuriumNAFree Form Intake
1602100042180877NA3NANANAcisatricuriumNAFree Form Intake
135717714055261NA1NANANACisatricuriumNAFree Form Intake
160117714218061NA1NANANACisatricuriumNAFree Form Intake
1638261842353107NA1NANANACistracuriumNAFree Form Intake
1501847186910431NANACIWA scaleNANA
140140718471051093NANACIWA SCALENANA
14914071869105491NANACIWA SCALENANA
26673369900071341NA78NANANAClindamycinNAANTIBACTERIUM
309333692258601341NA78NANANAClindamycinNAAntibiotics
6931093556069541NANAcoccyx drsg changeNANA
2776128223758322500293174217111591NA10680Code StatusNAGeneral
2992129225192307695089194749NA945Collar CareNATreatments
4974310446110721203NANAcombiventNANA
467337643103551072620NANACombiventNANA
4963376446135521263NANACombiventNANA
66742465361513142NANACombivent puffsNANA
116542467790512641NANACombivent puffsNANA
116453617790312621NANACombivent PuffsNANA
2817130223903244104NA4821NANANACommunicationNANeurological
431359136912141NANAcomplete bathNANA
171327464265760NA1NANANAcorlopam mcgs/kg/minNAFree Form Intake
31961638227463629113414NA1114cortisolNALabs
2838136223991113825NA3117NANANACough EffortNAPulmonary
2819137223905261904NA4984NANANACough ReflexNANeurological
283913822399280643NA2638NANANACough TypeNAPulmonary
2984282522518811768482856NA6802Cough/Deep BreathNATreatments
33113928521134451272592NANACough/Deep BreatheNANA
16814571914101142NANACPAPNANA
25792231485576425381NANACPPNANA
101246766628352632NANACritic-aidNANA
520454945955396013645NANAcriticaidNANA
802454959535396362NANAcriticaidNANA
801459559536016452NANACriticaidNANA
28439322399845611NA1492NANANACT #1 CrepitusNAPulmonary
284194223996105778NA5879NANANACT #1 DrainageNAPulmonary
284295223997125810NA5885NANANACT #1 DressingNAPulmonary
284097223994115194NA5863NANANACT #1 Suction AmountNAPulmonary
29169822442317903NA591NANANACT #2 CrepitusNAPulmonary
29179922442434269NA1623NANANACT #2 DrainageNAPulmonary
291810022442537582NA1594NANANACT #2 DressingNAPulmonary
291910222442737777NA1600NANANACT #2 Suction AmountNAPulmonary
29201032244285190NA162NANANACT #3 CrepitusNAPulmonary
29221042244309059NA334NANANACT #3 DrainageNAPulmonary
292410522443210032NA329NANANACT #3 DressingNAPulmonary
29261072244369810NA331NANANACT #3 Suction AmountNAPulmonary
29211082244291473NA31NANANACT #4 CrepitusNAPulmonary
29231092244312216NA49NANANACT #4 DrainageNAPulmonary
29251102244332255NA47NANANACT #4 DressingNAPulmonary
29271122244372300NA46NANANACT #4 Suction AmountNAPulmonary
271362402212141NA1NANANACT scanNA5-Imaging
90233436240531481NANACT ScanNANA
2712334322121453NA48NANANACT ScanNA5-Imaging
200480804413116NA1NANANACT siteNANA
71810035668241131NANAcuff pressureNANA
932100363462427137NANAcuff pressureNANA
29121003224417244052313554NA5458cuff pressureNARespiratory
2911634622441727405237554NA5458Cuff PressureNARespiratory
9335668634612717NANACUFF PRESSURENANA
291356682244171405231554NA5458CUFF PRESSURENARespiratory
16731424249158045NA476NANANACurrent GoalNANA
297714222518358045NA476NANANACurrent GoalNADialysis
21113110312003082110752NANACVPNANA
125785118548173646504481336154NANACVP Alarm [High]NANA
76933455814175416561711386681NANACVP Alarm [Low]NANA
5924875488963762745NANAcyclopentolateNANA
803441459572410127NANAcyclopentolate 1%NANA
144965534109347NA1NANANAcyclosporinNAFree Form Intake
177065534281347NA1NANANAcyclosporinNAFree Form Intake
189865534367747NA1NANANAcyclosporinNAFree Form Intake
146056334119328NA1NANANAcyclosporineNAFree Form Intake
235656334566128NA1NANANAcyclosporineNAFree Form Intake
3214563322753428NA1NANANAcyclosporineNAMedications
13283258301872NA1NANANAd10wNANA
18425392432282NA1NANANAD10W bolusNAFree Form Intake
18575392434182NA1NANANAD10W bolusNAFree Form Intake
18595392434232NA1NANANAD10W bolusNAFree Form Intake
18805392435862NA1NANANAD10W bolusNAFree Form Intake
126018483001329NA2NANANAD5WNANA
19125659437332NA1NANANAD5W & 40Gm CaGlucNAFree Form Intake
14391552410651NA1NANANAd5w 10gm ca+ gluconNAFree Form Intake
31821425227375355804NA17256NANANADaily Wake UpNAPain/Sedation
33231425228298355804NA17256NANANADaily Wake UpNAPain/Sedation
29337632246394797946452165531081NA9071Daily WeightNAGeneral
3611871317321943NANAdemerolNANA
451187137032541NANAdemerolNANA
136411874066732NA4NANANAdemerolNAFree Form Intake
141711874090232NA4NANANAdemerolNAFree Form Intake
13631370406675NA1NANANADemerolNAFree Form Intake
14191370409025NA1NANANADemerolNAFree Form Intake
441317137019531NANADEMEROLNANA
136213174066719NA3NANANADEMEROLNAFree Form Intake
142013174090219NA3NANANADEMEROLNAFree Form Intake
53343724641160211688052NANADesitinNANA
480423643721901602780NANADESITINNANA
532423646411901168752NANADESITINNANA
82158836002532812NANAdesitin prnNANA
114449507500251151NANADexamethasone dropsNANA
7595356577011111NANADexamethasone eye dsNANA
1205561280704422132NANADexamethasone gttsNANA
121420918192371321NANAdexmedetomidineNANA
183920914313637NA2NANANAdexmedetomidineNAFree Form Intake
247220914630137NA2NANANAdexmedetomidineNAFree Form Intake
183881924313613NA1NANANADexmedetomidineNAFree Form Intake
247481924630113NA1NANANADexmedetomidineNAFree Form Intake
133569374003326NA1NANANADextranNAFree Form Intake
180369374294426NA1NANANADextranNAFree Form Intake
259569374672926NA1NANANADextranNAFree Form Intake
288814822413558935NA543NANANADialysis Access SiteNADialysis
313314922595458636NA554NANANADialysis Access TypeNADialysis
6094850495128145104NANAdiaper rashNANA
124248058331973712NANADIAZOXIDENANA
2845154224001820776NA19473NANANADiet TypeNAGI/GU
13149813016326NA1NANANAdilaudidNANA
781211658481111NANADisconnectNANA
791590160718621NANAdisconnect alarmNANA
3791590306418721NANAdisconnect alarmNANA
12241590826818121NANAdisconnect alarmNANA
380160730646711NANADisconnect alarmNANA
1226160782686111NANADisconnect alarmNANA
1225306482687111NANADISCONNECT ALARMNANA
1008339465628620112991NANADiurilNANA
12805747300421NA1NANANAdobutamineNANA
273257472216531NA1NANANAdobutamineNAMedications
12815329300431NA1NANANADopamineNANA
273553292216621NA1NANANADopamineNAMedications
13314501303076NA1NANANADOPAMINE DRIPNANA
289015522416617570397NA22Doppler BPNARoutine Vital Signs
136526314068911NA1NANANADRAIN FLUSHNAFree Form Intake
142926314097511NA1NANANADRAIN FLUSHNAFree Form Intake
155426314184911NA1NANANADRAIN FLUSHNAFree Form Intake
206526314435311NA1NANANADRAIN FLUSHNAFree Form Intake
68511885548332083NANAdressing changeNANA
119517317968161811NANAdrive P.NANA
11145764726383571NANAdriving pressNANA
75132355764578327NANADRIVING PRESSNANA
11153235726357521NANADRIVING PRESSNANA
205165820599046866125NANAdriving pressureNANA
232165821299045466137NANAdriving pressureNANA
703165856309044186117NANAdriving pressureNANA
231205921296865462537NANADriving pressureNANA
701205956306864182517NANADriving pressureNANA
702212956305464183717NANADriving PressureNANA
2810142622382910278NA719NANANADrowsinessNAToxicology
224475424505724NA1NANANADSG SITENAFree Form Intake
2938675822466082131NANA5ecmoNAHemodynamics
10365931675822821NANAECMONANA
29375931224660222132NANA5ECMONAHemodynamics
1134326574492873131NANAecmo flowNANA
4322957326512128743NANAECMO FLOWNANA
1133295774491213141NANAECMO FLOWNANA
31481602264809459NA388NANANAEctopy Frequency 2NARoutine Vital Signs
314716222647910957NA397NANANAEctopy Type 2NARoutine Vital Signs
7171057566639621NANAedemaNANA
97810576471396122NANAedemaNANA
9795666647166112NANAEDEMANANA
2874165224076137351NA14387NANANAEducation BarrierNARestraint/Support Systems
2876166224078124039NA14036NANANAEducation HandoutNARestraint/Support Systems
2872167224072147596NA15065NANANAEducation LearnerNARestraint/Support Systems
2875168224077139978NA14815NANANAEducation MethodNARestraint/Support Systems
2873169224075143709NA14938NANANAEducation ReadinessNARestraint/Support Systems
2877170224079135441NA14636NANANAEducation ResponseNARestraint/Support Systems
3162716278333021122NANAEDVNANA
2382139214612317173917928NANAEDVINANA
27163397221223628NA67NANANAEEGNA4-Procedures
299896822540226NA24NANANAEKGNA4-Procedures
15086093415751NA1NANANAenemaNAFree Form Intake
20106093441631NA1NANANAenemaNAFree Form Intake
20436093442651NA1NANANAenemaNAFree Form Intake
3542378298761012NANAepiduralNANA
13722378407266NA1NANANAepiduralNAFree Form Intake
17242378426736NA1NANANAepiduralNANA
17542378427826NA1NANANAepiduralNAFree Form Intake
137329874072610NA2NANANAEpiduralNAFree Form Intake
172329874267310NA2NANANAEpiduralNANA
175829874278210NA2NANANAEpiduralNAFree Form Intake
263219823788611NANAEPIDURALNANA
3552198298781012NANAEPIDURALNANA
13712198407268NA1NANANAEPIDURALNAFree Form Intake
17262198426738NA1NANANAEPIDURALNANA
17552198427828NA1NANANAEPIDURALNAFree Form Intake
27961772238006829NA598NANANAEpidural AppearanceNAPain/Sedation
317617922711911033NA529NANANAEpidural LocationNAPain/Sedation
290318022436812874NA566NANANAEpidural MedicationNAPain/Sedation
107524646964185911NANAepidural ml/hrNANA
96130086437194511NANAEpidural rateNANA
8285066603438651NANAEpogenNANA
100449726536495133NANAerythromycinNANA
266649729000649NA3NANANAerythromycinNAANTIBACTERIUM
3096497222586649NA3NANANAerythromycinNAAntibiotics
266565369000651NA3NANANAErythromycinNAANTIBACTERIUM
3095653622586651NA3NANANAErythromycinNAAntibiotics
71251415658772061NANAErythromycin ointNANA
335318172286406NA2NANANAETCO2NARoutine Vital Signs
176129084280472NA1NANANAETOH Grams/hourNAFree Form Intake
290715822439221348NA6424NANANAETT Position ChangeNARespiratory
32441582278092134857236424219NA2160ETT Position ChangeNARespiratory
298071662251841812558111556NA11485Eye careNATreatments
241183217324800312114841NANAEye CareNANA
1092183716624800318114841NANAEye CareNANA
2979183225184248003125581114841556NA11485Eye CareNATreatments
109321737166121811NANAEYE CARENANA
298121732251841212558111556NA11485EYE CARENATreatments
45642094230157768644NANAeye examNANA
4584209423815753586280NANAeye examNANA
48542094388157158610NANAeye examNANA
457423042387653544280NANAEye examNANA
4874230438876154410NANAEye examNANA
486423843885351528010NANAEye ExamNANA
3343280722839926NA1NANANAfacial droopNANeurological
32019982807172611NANAFacial DroopNANA
3342199822839917NA1NANANAFacial DroopNANeurological
2856187224024246991NA20056NANANAFamily CommunicationNARestraint/Support Systems
331934232281251727NA1213NANANAFamily Meeting heldNA7-Communication
33023682831117224NANAfem abpNANA
78223085854353822NANAFEM ALINENANA
77925435845121611NANAFEM ARTNANA
8732968614565831NANAFEM ART BPNANA
38323693067435433NANAfem mapNANA
33528332868221511NANAFemoral A-LineNANA
30926422731282011NANAFEMORAL ABPNANA
1016573166533611NANAFEMORAL ABP MEANNANA
170727394263875NA3NANANAFENOLDOPAMNAFree Form Intake
173727394270575NA3NANANAFENOLDOPAMNAFree Form Intake
174427394273275NA3NANANAFENOLDOPAMNAFree Form Intake
329259128295804411NANAFENOLDOPAM MCG/KG/MNNANA
444134234321956218414NANAfentanylNANA
129913423011819NA8NANANAfentanylNANA
2742134222174419NA8NANANAfentanylNAMedications
13003432301185621NA414NANANAFentanylNANA
274034322217445621NA414NANANAFentanylNAMedications
67352615464171212NANAFentanyl BolusNANA
56135514123211NANAFentanyl IVNANA
126753963002435NA4NANANAferrous sulfateNANA
910268162781814NANAFINGERSTICKNANA
44329813420667372923087NANAFiO2NANA
114819075705736062142481NANAFiO2 SetNANA
1055200268681492332NANAfistulaNANA
2053200244291149NA3NANANAfistulaNANA
205468684429123NA2NANANAFistulaNANA
284819322400510614NA770NANANAFlatusNAGI/GU
1431213240976227NA2NANANAflolanNAFree Form Intake
1482213241410227NA2NANANAflolanNAFree Form Intake
1560213241859227NA2NANANAflolanNAFree Form Intake
2351837213219522732NANAFlolanNANA
1432183740976195NA3NANANAFlolanNAFree Form Intake
1483183741410195NA3NANANAFlolanNAFree Form Intake
1559183741859195NA3NANANAFlolanNAFree Form Intake
1381419183721619523NANAFLOLANNANA
2361419213221622722NANAFLOLANNANA
1433141940976216NA2NANANAFLOLANNAFree Form Intake
1481141941410216NA2NANANAFLOLANNAFree Form Intake
1561141941859216NA2NANANAFLOLANNAFree Form Intake
1210800181594127012NANAFlolan ng/kg/minNANA
91144626280415532NANAFloventNANA
179262584289931NA2NANANAfluconazoleNAFree Form Intake
3099625822586931NA2NANANAfluconazoleNAAntibiotics
9034905625813531112NANAFluconazoleNANA
1791490542899135NA11NANANAFluconazoleNAFree Form Intake
30984905225869135NA11NANANAFluconazoleNAAntibiotics
129151103006813NA1NANANAfolateNANA
3121511022592313NA1NANANAfolateNANutrition - Supplements
63314745110691361NANAFolateNANA
129214743006869NA6NANANAFolateNANA
3122147422592369NA6NANANAFolateNANutrition - Supplements
242476724596723NA1NANANAFoleyNANA
3153767222655923NA1NANANAFoleyNAOutput
3063506722583231NA2NANANAFolic AcidNAMedications
2820197223910478477NA8976NANANAFollows CommandsNANeurological
3226429122769013NA2NANANAFosphenytoinNAMedications
24045704458582NA1NANANAg-tube flushNAFree Form Intake
123872368320677812NANAg-tube siteNANA
110766677236206711NANAG-Tube SiteNANA
123766678320207812NANAG-Tube SiteNANA
2818208223904254609NA5010NANANAGag ReflexNANeurological
6064689494113383NANAGenetics consultNANA
114652067520212121NANAGent eye ointmentNANA
266834459001213866NA3942NANANAGentamicinNAANTIBACTERIUM
3102344522587513866NA3942NANANAGentamicinNAAntibiotics
5754391480692036NANAgentamycinNANA
12461698849230778629959882288193NANAGI Tube #1 [Site]NANA
1247169984935398953277773756NANAGI Tube #2 [Site]NANA
2371987214053242NANAglucagonNANA
153519874171053NA4NANANAglucagonNAFree Form Intake
15342140417102NA2NANANAGlucagonNAFree Form Intake
228778854533341NA1NANANAGlucagon infusionNAFree Form Intake
119121417928115011NANAGlucagon mg/hrNANA
4684299431137442020NANAGlycerinNANA
530424346323712229NANAglycerin suppNANA
4594237424364373222NANAGlycerin suppNANA
529423746326412329NANAGlycerin suppNANA
51142964553333152NANAGlycerin supp.NANA
5964296491033161513NANAGlycerin supp.NANA
59745534910316213NANAGlycerin Supp.NANA
620501250353539913146NANAglycerin suppositoryNANA
6164525501220351213NANAGlycerin suppositoryNANA
619452550352039912146NANAGlycerin suppositoryNANA
47042404321167134NANAglycerineNANA
665425153402010124NANAglycerine suppNANA
65342105248141337NANAGlycerine Suppos.NANA
28302092239427610NA171NANANAGraft/Flap PulseNACardiovascular
8761920615013712NANAGT CareNANA
119378237951562221NANAGT siteNANA
117348037823625612NANAGT SiteNANA
119248037951622211NANAGT SiteNANA
285220722401786350NA2008NANANAGU Catheter SizeNAGI/GU
3711732303321228NANAHair washedNANA
112107717329232NANAHAIR WASHEDNANA
3721077303391238NANAHAIR WASHEDNANA
700467256285811NANAHead Circ.NANA
2878345322408043247NA353NANANAHead of BedNATreatments
2805142822382017535NA1049NANANAHeadacheNAToxicology
277717032237601177300NA8897NANANAHealth Care ProxyNAGeneral
4734320434729192719NANAhearing screenNANA
5144320455629312731NANAhearing screenNANA
5134347455619311931NANAHearing screenNANA
268021122004551906832762225299982069NA15645Heart RateNARoutine Vital Signs
26812122200483312070NA22431NANANAHeart RhythmNARoutine Vital Signs
29062132243891166NA172NANANAHeart SoundsNACardiovascular
1571233189155351110NANAhelioxNANA
3242123322780855NA11NANANAhelioxNARespiratory
3243189122780835NA10NANANAHelioxNARespiratory
1181779978503111NANAHeliox tank psiNANA
11696586779915321NANAHeliox Tank PSINANA
11826586785015121NANAHeliox Tank PSINANA
5004466446711191118NANAhep BNANA
4984419446666116511NANAHep BNANA
4994419446766196518NANAHep BNANA
6054911491812111211NANAhep B vaccineNANA
5984340491167126712NANAHep B vaccineNANA
6044340491867116711NANAHep B vaccineNANA
4724326434065676567NANAHep B VaccineNANA
5994326491165126512NANAHep B VaccineNANA
6034326491865116511NANAHep B VaccineNANA
5794353482040461465NANAhep flushNANA
1885435343597404NA46NANANAhep flushNAFree Form Intake
474427943534934045246NANAHep flushNANA
5804279482049361525NANAHep flushNANA
1888427943597493NA52NANANAHep flushNAFree Form Intake
465421742794604934952NANAHep FlushNANA
475421743534604044946NANAHep FlushNANA
5814217482046061495NANAHep FlushNANA
1887421743597460NA49NANANAHep FlushNAFree Form Intake
188648204359761NA5NANANAHEP FLUSHNAFree Form Intake
8476053607520711NANAHep flush 10U/CCNANA
8786053615720211NANAHep flush 10U/CCNANA
877607561577211NANAHep Flush 10U/ccNANA
90857456262111421NANAHep flush 10units/ccNANA
81816025997803171NANAhepa filterNANA
126845273002515NA4NANANAheparinNANA
50822894527131514NANAHEPARINNANA
126922893002513NA1NANANAHEPARINNANA
5054267450410511211134162NANAheparin flushNANA
4624225426710081051161134NANAHeparin flushNANA
5064225450410081211161162NANAHeparin flushNANA
455421242256710087161NANAHEPARIN FLUSHNANA
463421242676710517134NANAHEPARIN FLUSHNANA
507421245046712117162NANAHEPARIN FLUSHNANA
7765541583110121NANAHeparin flush 10u/ccNANA
12156311819311411NANAheparin lock flushNANA
12286311827011611NANAheparin lock flushNANA
122781938270141611NANAHeparin Lock FlushNANA
71546545663577577NANAHepatitis B vaccineNANA
5384599465463576357NANAHepatitis B VaccineNANA
71645995663637637NANAHepatitis B VaccineNANA
664460653251182052NANAherniaNANA
2245460645058118NA5NANANAherniaNANA
224653254505820NA2NANANAHerniaNANA
124934558519661253056765NANAHernia [Left]NANA
125034568520647653176359NANAHernia [Right]NANA
73946525730461311NANAHernia RepairNANA
1111487172946011151NANAhi min ventNANA
872148761304606151NANAhi min ventNANA
890148761854601151NANAhi min ventNANA
888613061856111NANAHi min ventNANA
8711729613011611NANAHi Min VentNANA
8891729618511111NANAHi Min VentNANA
817196559942321141NANAhi min volNANA
1271513175846627135NANAHi Min VolNANA
172151319654662321314NANAHi Min VolNANA
815151359944661131NANAHi Min VolNANA
1731758196527232514NANAHI MIN VOLNANA
8161758599427151NANAHI MIN VOLNANA
3952046311975411NANAhi minute volNANA
40421053161333921NANAhi minute volumeNANA
424210532363310021NANAhi minute volumeNANA
423316132363910011NANAHi Minute VolumeNANA
734309857274991109722NANAhi mvNANA
38817463098493499177NANAhi MVNANA
7361746572749311091722NANAhi MVNANA
126168517466984932017NANAHI MVNANA
38716853098698499207NANAHI MVNANA
7351685572769811092022NANAHI MVNANA
174150019661021011NANAHI MV ALARMNANA
72151415701762296NANAHi Tidal VolNANA
2092049206924611NANAHI TIDAL VOLUMENANA
883604161687621NANAhi VeNANA
831572660412787202NANAHi VeNANA
881572661682786201NANAHi VeNANA
7332058572638278220NANAHi VENANA
8302058604138722NANAHi VENANA
8802058616838621NANAHi VENANA
2041694205843822NANAHI VENANA
732169457264278220NANAHI VENANA
832169460414722NANAHI VENANA
882169461684621NANAHI VENANA
6892126555813211NANAHi/Minute/VolNANA
2242090212650111NANAHI/minute/VolNANA
68820905558503211NANAHI/minute/VolNANA
211173720901765041NANAHI/Minute/volNANA
22717372126176141NANAHI/Minute/volNANA
692173755581763241NANAHI/Minute/volNANA
7415781594144577495NANAHI/Minute/VolNANA
114157817371445176494NANAHI/Minute/VolNANA
21215782090144550491NANAHI/Minute/VolNANA
2261578212614451491NANAHI/Minute/VolNANA
69015785558144532491NANAHI/Minute/VolNANA
115159417377717654NANAHI/MINUTE/VOLNANA
21015942090775051NANAHI/MINUTE/VOLNANA
2251594212677151NANAHI/MINUTE/VOLNANA
69115945558773251NANAHI/MINUTE/VOLNANA
11103210551796111NANAHi/minute/vol/alarmNANA
221032110417978114NANAHi/minute/vol/alarmNANA
26103211211795501136NANAHi/minute/vol/alarmNANA
601032146817974112NANAHi/minute/vol/alarmNANA
6710321508179201116NANAHi/minute/vol/alarmNANA
131103217611793761121NANAHi/minute/vol/alarmNANA
289103225901799111NANAHi/minute/vol/alarmNANA
231055110467814NANAHi/Minute/vol/alarmNANA
27105511216550136NANAHi/Minute/vol/alarmNANA
611055146867412NANAHi/Minute/vol/alarmNANA
6510551508620116NANAHi/Minute/vol/alarmNANA
130105517616376121NANAHi/Minute/vol/alarmNANA
284105525906911NANAHi/Minute/vol/alarmNANA
13215081761201376621NANAHi/Minute/Vol/AlarmNANA
29015082590201961NANAHi/Minute/Vol/AlarmNANA
251104112178550436NANAHI/Minute/vol/alarmNANA
5811041468787442NANAHI/Minute/vol/alarmNANA
69110415087820146NANAHI/Minute/vol/alarmNANA
1281104176178376421NANAHI/Minute/vol/alarmNANA
2881104259078941NANAHI/Minute/vol/alarmNANA
68146815087420126NANAHI/Minute/Vol/alarmNANA
1291468176174376221NANAHI/Minute/Vol/alarmNANA
2871468259074921NANAHI/Minute/Vol/alarmNANA
591121146855074362NANAHI/Minute/Vol/AlarmNANA
6611211508550201366NANAHI/Minute/Vol/AlarmNANA
133112117615503763621NANAHI/Minute/Vol/AlarmNANA
285112125905509361NANAHI/Minute/Vol/AlarmNANA
286176125903769211NANAHI/MINUTE/VOL/ALARMNANA
1071720172355935031NANAhigh exhale MVNANA
2211720212355912434NANAhigh exhale MVNANA
2281720212755921134NANAhigh exhale MVNANA
2292123212712421144NANAHigh exhale MVNANA
2221723212335012414NANAHIGH EXHALE MVNANA
2301723212735021114NANAHIGH EXHALE MVNANA
3011021223162318716NANAhigh exhaled min volNANA
331102131316215879NANAhigh exhaled min volNANA
741110257441628471NANAhigh exhaled min volNANA
20101011023204162897NANAHigh exhaled min volNANA
311010122332043188916NANAHigh exhaled min volNANA
34101013133204158899NANAHigh exhaled min volNANA
74310105744320484891NANAHigh exhaled min volNANA
740131357441588491NANAHigh Exhaled min volNANA
3512231313318158169NANAHIGH EXHALED MIN VOLNANA
7421223574431884161NANAHIGH EXHALED MIN VOLNANA
49132313801110813NANAhigh exhaled min.volNANA
2591323234011913NANAhigh exhaled min.volNANA
26013802340108933NANAHigh exhaled min.volNANA
881568164840255113NANAhigh meNANA
1101659172673586362NANAhigh min ventNANA
95156616629303563318NANAhigh min volNANA
9815661665930168336NANAhigh min volNANA
18415662003930398337NANAhigh min volNANA
9716621665356168186NANAHigh min volNANA
18216622003356398187NANAHigh min volNANA
1831665200316839867NANAHigh Min VolNANA
18715692019141331NANAhigh min vol.NANA
82015696000143031NANAhigh min vol.NANA
81920196000133011NANAHigh min vol.NANA
186200620183721251NANAhigh min volumeNANA
19516792034543431NANAhigh min. vent.NANA
10014961679825433NANAHIGH MIN. VENT.NANA
19414962034823431NANAHIGH MIN. VENT.NANA
7114991556525251201NANAhigh min. volNANA
40614993168525219207NANAhigh min. volNANA
4111499318952515201NANAhigh min. volNANA
4051556316825121917NANAHigh min. volNANA
410155631892511511NANAHigh min. volNANA
409316831892191571NANAHIGH MIN. VOLNANA
1701506196042881122NANAhigh min. vol.NANA
1801506199642834126NANAhigh min. vol.NANA
17919601996813426NANAHigh Min. Vol.NANA
871504164121512NANAHigh min. volumeNANA
3943051310517231NANAhigh min.vol.NANA
4281990325433321NANAHigh minute vent.NANA
178171819901513352NANAHigh Minute Vent.NANA
43017183254151351NANAHigh Minute Vent.NANA
105159917181245151465NANAHIGH MINUTE VENT.NANA
17715991990124533462NANAHIGH MINUTE VENT.NANA
4291599325412453461NANAHIGH MINUTE VENT.NANA
1031507168715514455NANAHIGH MINUTE VOLNANA
73138315751613484NANAhigh minute vol.NANA
621340148635914921139149NANAhigh minute volumeNANA
78134016003591179313936NANAhigh minute volumeNANA
216134021033591481397NANAhigh minute volumeNANA
39013403101359181391NANAhigh minute volumeNANA
841134060653591921391NANAhigh minute volumeNANA
3932103310148871NANAHigh minute volumeNANA
83921036065489271NANAHigh minute volumeNANA
8383101606589211NANAHigh Minute volumeNANA
77148616004921179314936NANAHigh Minute VolumeNANA
215148621034921481497NANAHigh Minute VolumeNANA
39114863101492181491NANAHigh Minute VolumeNANA
842148660654921921491NANAHigh Minute VolumeNANA
21416002103179348367NANAHIGH MINUTE VOLUMENANA
3921600310117938361NANAHIGH MINUTE VOLUMENANA
84016006065179392361NANAHIGH MINUTE VOLUMENANA
349165029737440111952NANAhigh mvNANA
75416505766744011951NANAhigh mvNANA
7615511596567406027166NANAhigh MVNANA
8515511639567609227226NANAhigh MVNANA
9015511650567744027195NANAhigh MVNANA
3501551297356711272NANAhigh MVNANA
756155157665671271NANAhigh MVNANA
7522973576611121NANAHigh MvNANA
911639165060927440226195NANAHigh MVNANA
348163929736092112262NANAHigh MVNANA
75316395766609212261NANAHigh MVNANA
861596163940606092166226NANAHIGH MVNANA
891596165040607440166195NANAHIGH MVNANA
351159629734060111662NANAHIGH MVNANA
75515965766406011661NANAHIGH MVNANA
176149219821332473275NANAHigh MV AlarmNANA
21914922109133222273NANAHigh MV AlarmNANA
218198221094732253NANAHIGH MV ALARMNANA
403156531602640981113NANAHigh MV LimitNANA
720168156755323234NANAhigh MV.NANA
10116161681112953373NANAHigh MV.NANA
721161656751129232374NANAHigh MV.NANA
1026626466885211NANAhigh NO alarmNANA
1202208180603911NANAhigh NO2NANA
12042081806731611NANAhigh NO2NANA
12038060806791611NANAHigh NO2NANA
1027626566895211NANAhigh NO2 alarmNANA
899610562363111NANAHigh PIPNANA
76620255786161211NANAHigh pressureNANA
8583142610677331NANAhigh rrNANA
3561618299051721NANAhigh tidal volNANA
3841618308256924NANAhigh tidal volNANA
38529903082176914NANAHigh Tidal VolNANA
20616602065101111NANAHigh Tidal volumeNANA
9416511660371061NANAHigh Tidal VolumeNANA
20716512065371161NANAHigh Tidal VolumeNANA
921546165423848910611NANAhigh veNANA
191154620332384621063NANAhigh veNANA
2001546205323841171063NANAhigh veNANA
7071546565123841631069NANAhigh veNANA
7015101546615238418106NANAhigh VeNANA
9315101654615891811NANAhigh VeNANA
1931510203361562183NANAhigh VeNANA
20215102053615117183NANAhigh VeNANA
70915105651615163189NANAhigh VeNANA
7062053565111716339NANAhigh VENANA
201203320536211733NANAHigh VENANA
708203356516216339NANAHigh VENANA
192165420338962113NANAHIGH VENANA
2031654205389117113NANAHIGH VENANA
7101654565189163119NANAHIGH VENANA
8116211631549104121NANAHigh/Min/VentNANA
82162116335492761212NANAHigh/Min/VentNANA
1181621173854911161227NANAHigh/Min/VentNANA
8316311633104276112NANAHigh/MIN/VENTNANA
117163117381041116127NANAHigh/MIN/VENTNANA
1161633173827611161227NANAHIGH/MIN/VENTNANA
7723121582463512NANAhimvNANA
12588518854916876551810587521914496NANAHR Alarm [High]NANA
7703450581516879261812919521914498NANAHR Alarm [Low]NANA
2744457922182817NA4NANANAHydralazineNAMedications
11964856798316263624NANAhydroceleNANA
4693463431244512423NANAHydrocortisoneNANA
682478955214343NANAhyperoxiaNANA
8851611617963121NANAI timeNANA
289522422432230020844877921NA221IABP MeanNAIABP
33425152865511411NANAIABP-BPNANA
78322658569920575531NANAICPNANA
310204527457023212NANAicp leftNANA
1379324540751127NA2NANANAileostomyNANA
829496060371382791NANAIlexNANA
27110292484211123NANAINCENTIVE SPIROMETERNANA
32628232826242166NANAincentive spirometryNANA
2985282322518924394286390NA4546incentive spirometryNATreatments
2988282622518921394286390NA4546Incentive spirometryNATreatments
3224102815646931963544NANAIncentive SpirometryNANA
3244102823646932463546NANAIncentive SpirometryNANA
3284102826646932163546NANAIncentive SpirometryNANA
298641022518964693394286354390NA4546Incentive SpirometryNATreatments
32328152823192446NANAINCENTIVE SPIROMETRYNANA
32728152826192146NANAINCENTIVE SPIROMETRYNANA
2987281522518919394284390NA4546INCENTIVE SPIROMETRYNATreatments
103757136762201411NANAincision sitesNANA
11527081758592613NANAInExsufflatorNANA
8121942598081411NANAINO Tank PressNANA
2806142922382310846NA751NANANAInsomniaNAToxicology
9233271631582823NANAinsp timeNANA
435326832714812NANAINSP TIMENANA
9223268631542813NANAINSP TIMENANA
11211655733793131NANAInsp. TimeNANA
295220002247381438702223541201890NA7934Inspiratory TimeNARespiratory
501243244751817811NANAinsulinNANA
128224323004518NA8NANANAinsulinNANA
128344753004517NA11NANANAInsulinNANA
2120687244547211NA1NANANAintrathecal catheterNANA
1213135081771211NANAionized calciumNANA
1240135083251NA1NANANAionized calciumNANA
3031135022566718012612000NA7894ionized calciumNALabs
1241817783252NA1NANANAIonized calciumNANA
3029817722566728012612000NA7894Ionized calciumNALabs
30308325225667NA80126NA2000NA7894IONIZED CALCIUMNALabs
31718402784262521NANAIRRIGATE PIGTAILNANA
252782054652162NA2NANANAisoproterenolNAFree Form Intake
121972778205156232NANAIsoproterenolNANA
252672774652115NA3NANANAIsoproterenolNAFree Form Intake
12866921300469NA1NANANAisuprelNANA
322969212276929NA1NANANAisuprelNAMedications
106155656891375832NANAIsuprelNANA
10665565692137931NANAIsuprelNANA
128455653004637NA3NANANAIsuprelNANA
3230556522769237NA3NANANAIsuprelNAMedications
10656891692158921NANAISUPRELNANA
128568913004658NA2NANANAISUPRELNANA
3228689122769258NA2NANANAISUPRELNAMedications
12513465852135910735486240743917NANAIV #1 [Location]NANA
12523466852215627015473011431128NANAIV #2 [Location]NANA
1253346785239025289496626616NANAIV #3 [Location]NANA
1254346885243526734975249242NANAIV #4 [Location]NANA
125534698525847083787573NANAIV #5 [Location]NANA
132748173018412NA6NANANAIVIGNANA
227778264529224NA1NANANAIVPPINAFree Form Intake
19166062437542NA1NANANAJ-tubeNANA
190857054370642NA1NANANAj-tube flushNAFree Form Intake
252457054652042NA1NANANAj-tube flushNAFree Form Intake
137481204072814NA1NANANAJP DRAINNANA
138581204077914NA1NANANAJP DRAINNANA
195881204400414NA1NANANAJP DRAINNANA
1338508040385100NA4NANANAK phosNAFree Form Intake
1395508040863100NA4NANANAK phosNAFree Form Intake
1443508041067100NA4NANANAK phosNAFree Form Intake
1966508044009100NA4NANANAK phosNAFree Form Intake
30685080225834100NA4NANANAK phosNAMedications
629472050801410014NANAK PhosNANA
133747204038514NA1NANANAK PhosNAFree Form Intake
139747204086314NA1NANANAK PhosNAFree Form Intake
144147204106714NA1NANANAK PhosNAFree Form Intake
197047204400914NA1NANANAK PhosNAFree Form Intake
3070472022583414NA1NANANAK PhosNAMedications
234444864564111NA1NANANAk phosphateNAFree Form Intake
50425674486421121NANAK PhosphateNANA
234325674564142NA2NANANAK PhosphateNAFree Form Intake
127024663002622NA5NANANAKCLNANA
63142475098998511NANAKCL suppsNANA
32274732227691144NA11NANANAKeflexNAAntibiotics
1312653730151146NA4NANANAKetamineNANA
27386537221712146NA4NANANAKetamineNAMedications
100527566537114614NANAKETAMINENANA
13132756301511NA1NANANAKETAMINENANA
273627562217121NA1NANANAKETAMINENAMedications
2330803745573153NA1NANANAKetamine 100mg/50mlNAFree Form Intake
171882624266915NA1NANANAketamine mg/hrNAFree Form Intake
1223275782621431531NANAKETAMINE MG/HRNANA
1717275742669143NA3NANANAKETAMINE MG/HRNAFree Form Intake
140051864086413NA1NANANAKphosNAFree Form Intake
140651864088013NA1NANANAKphosNAFree Form Intake
147151864135313NA1NANANAKphosNAFree Form Intake
173051864269113NA1NANANAKphosNAFree Form Intake
64546835186221311NANAKPhosNANA
139846834086422NA1NANANAKPhosNAFree Form Intake
140846834088022NA1NANANAKPhosNAFree Form Intake
147246834135322NA1NANANAKPhosNAFree Form Intake
172946834269122NA1NANANAKPhosNAFree Form Intake
54246794683242211NANAKPHOSNANA
64646795186241311NANAKPHOSNANA
139946794086424NA1NANANAKPHOSNAFree Form Intake
140546794088024NA1NANANAKPHOSNAFree Form Intake
146946794135324NA1NANANAKPHOSNAFree Form Intake
173146794269124NA1NANANAKPHOSNAFree Form Intake
81356005981131111NANAL fem art bpNANA
97464286457132611NANAL fem art lineNANA
765578157842111NANALab resultsNANA
88645266180298726NANAlacrilubeNANA
121678358199181011NANALacrilube ointmentNANA
1176614378353541811NANALacrilube OintmentNANA
1217614381993541011NANALacrilube OintmentNANA
12611634300211NA1NANANAlactated ringersNANA
11624888778022991NANAlasixNANA
130248883012322NA9NANANAlasixNANA
591421948886822199NANALasixNANA
116342197780689191NANALasixNANA
130142193012368NA19NANANALasixNANA
13037780301239NA1NANANALASIXNANA
84149316366111NANALAVAGENANA
112298373391185321NANALEECH THERAPYNANA
722524456934121NANALeft lateral decubNANA
80112216191347242NANAlepirudinNANA
30511222693134141NANAlepirudinNANA
1323112230177134NA4NANANAlepirudinNANA
27461122221892134NA4NANANAlepirudinNAMedications
3061619269372121NANALepirudinNANA
132416193017772NA2NANANALepirudinNANA
2745161922189272NA2NANANALepirudinNAMedications
13222693301771NA1NANANALEPIRUDINNANA
274726932218921NA1NANANALEPIRUDINNAMedications
303324122256721976611074NA2999lipaseNALabs
14441549410685NA1NANANAliperudinNAFree Form Intake
185183820142111NANALithium levelNANA
2825423223920363760NA6924NANANALL Strength/MovementNANeurological
2836425223989785810NA22367NANANALLL Lung SoundsNAPulmonary
9643416643912815132293NANALow Exhaled Min VolNANA
105431436864302624NANAlow insp pressureNANA
1094436717689617328371NANALow Insp. PressureNANA
3472943297212211NANAlow min volNANA
1060577868731911NANAlow mvNANA
389205031006432NANALow MVNANA
764205057786131NANALow MVNANA
1058205068736931NANALow MVNANA
763310057784121NANALOW MVNANA
1059310068734921NANALOW MVNANA
10674376943416261201NANALow PeepNANA
11714377803416251201NANALow PeepNANA
1170694378036511NANALOW PEEPNANA
10706417694432631NANAlow pressureNANA
8592027610717321NANALow pressureNANA
95520276417173223NANALow pressureNANA
10692027694417621NANALow pressureNANA
9566107641733213NANALow PressureNANA
1068610769443611NANALow PressureNANA
1105694572296111NANALOW VTNANA
206929714436721NA2NANANALRNAFree Form Intake
3059297122582821NA2NANANALRNAFluids/Intake
2824426223919364518NA6911NANANALU Strength/MovementNANeurological
2835428223988788599NA22367NANANALUL Lung SoundsNAPulmonary
8625939611410416922NANALumbar drainNANA
1156593976111046221NANALumbar drainNANA
2017593944201104NA2NANANALumbar drainNANA
2146593944637104NA2NANANALumbar drainNANA
201676114420162NA1NANANALumbar DrainNANA
214576114463762NA1NANANALumbar DrainNANA
1157611476111696221NANALUMBAR DRAINNANA
2018611444201169NA2NANANALUMBAR DRAINNANA
2144611444637169NA2NANANALUMBAR DRAINNANA
167761634252329NA1NANANALumbar drain outputNANA
29973496225399499NA326NANANALumbar PunctureNA4-Procedures
6584227526252243016NANAlytesNANA
71442275660528303NANAlytesNANA
71352625660248163NANALytesNANA
879546961655452NANAlytes and biliNANA
790543459017342NANAlytes,biliNANA
807543459717642NANAlytes,biliNANA
808590159713622NANALytes,BiliNANA
12716133300274NA1NANANAMagnesium SulfateNANA
275161332220114NA1NANANAMagnesium SulfateNAMedications
31925792789372102347NANAmammary supportNANA
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314492522638142452NA22901NANANAMarital StatusNAADT
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267750609002985NA2NANANAmeropenemNAANTIBACTERIUM
3108506022588385NA2NANANAmeropenemNAAntibiotics
626458750601248562NANAMeropenemNANA
628458750621246061NANAMeropenemNANA
2678458790029124NA6NANANAMeropenemNAANTIBACTERIUM
31054587225883124NA6NANANAMeropenemNAAntibiotics
267950629002960NA1NANANAMEROPENEMNAANTIBACTERIUM
3106506222588360NA1NANANAMEROPENEMNAAntibiotics
2056187444306100NA1NANANAmethadoneNAFree Form Intake
2382187445764100NA1NANANAmethadoneNAFree Form Intake
2589187446725100NA1NANANAmethadoneNAFree Form Intake
145416054111124NA1NANANAMETHYLPRED 450MG/HRNAFree Form Intake
114018787469584424NANAmethylprednisoloneNANA
171218784265558NA2NANANAmethylprednisoloneNAFree Form Intake
213318784458558NA2NANANAmethylprednisoloneNAFree Form Intake
223118784502358NA2NANANAmethylprednisoloneNAFree Form Intake
171174694265544NA4NANANAMethylprednisoloneNAFree Form Intake
213474694458544NA4NANANAMethylprednisoloneNAFree Form Intake
223074694502344NA4NANANAMethylprednisoloneNAFree Form Intake
5484385472315517332NANAmetoclopramideNANA
14455708410763NA1NANANAMgSO4NAFree Form Intake
551462247292661712420NANAmiconazole powderNANA
69646225605266104248NANAmiconazole powderNANA
12204622824026621242NANAmiconazole powderNANA
69747295605171104208NANAMiconazole powderNANA
12214729824017121202NANAMiconazole powderNANA
1222560582401042182NANAMiconazole PowderNANA
88442186177322243NANAmiconozole powderNANA
1304760230124122NA3NANANAMidazolamNANA
82449546025151041NANAMineral OilNANA
29464482246873645020411940NA8259Minute VolumeNARespiratory
1050288968174211NANAMIXED VENOUSNANA
13051813301261660NA105NANANAMorphine SulfateNANA
296318132251541660NA105NANANAMorphine SulfateNAMedications
22011082110374461NANAmucomystNANA
155211084184837NA6NANANAmucomystNAFree Form Intake
190311084368337NA6NANANAmucomystNAFree Form Intake
244011084606537NA6NANANAmucomystNAFree Form Intake
155321104184844NA1NANANAMucomystNAFree Form Intake
190421104368344NA1NANANAMucomystNAFree Form Intake
244121104606544NA1NANANAMucomystNAFree Form Intake
1159680676541383011NANAMucomyst mg/hrNANA
1923575743840159NA4NANANAmucous fistulaNANA
2006575744133159NA4NANANAmucous fistulaNANA
2274575745283159NA4NANANAmucous fistulaNANA
107918397031892612NANAMulti podis bootNANA
71116035655731421NANAmulti podis bootsNANA
95316036408737422NANAmulti podis bootsNANA
95256556408147412NANAMulti podis BootsNANA
10575648687185931NANAmulti-podis bootsNANA
70511335648348533NANAMulti-podis bootsNANA
10561133687134931NANAMulti-podis bootsNANA
16018701900691852NANAmultipodis bootsNANA
100918706593691151NANAmultipodis bootsNANA
101019006593181121NANAMULTIPODIS BOOTSNANA
97521436458844041NANAmultipodusNANA
249111822091812161NANAmultipodus bootNANA
1198111880351812161NANAmultipodus bootNANA
119922098035212111NANAMULTIPODUS BOOTNANA
1131418173566796338NANAmultipodus bootsNANA
25214182236667183332NANAmultipodus bootsNANA
7751418582866759335NANAmultipodus bootsNANA
251173522369618382NANAMultipodus bootsNANA
77317355828965985NANAMultipodus bootsNANA
774223658281835925NANAMULTIPODUS BOOTSNANA
75713875769213113NANAMultipodus SplintsNANA
501384138723211NANAMultiPodus SplintsNANA
758138457692313113NANAMultiPodus SplintsNANA
150777854156818NA1NANANAMultivitaminNAFree Form Intake
182277854300718NA1NANANAMultivitaminNAFree Form Intake
176280104280518NA2NANANAmultivitaminsNAFree Form Intake
3061801022583018NA2NANANAmultivitaminsNAFluids/Intake
20745800443797NA1NANANAmushroom catheterNANA
22155800449347NA1NANANAmushroom catheterNANA
84832596078106021NANAmvNANA
780575858478511NANAMV hiNANA
845575860738613NANAMV hiNANA
844584760735613NANAMV HiNANA
127248983002820NA2NANANAMVINANA
10712265694773352NANAMVO2NANA
13584297406113NA2NANANANa bicarbNAFree Form Intake
22364297450293NA2NANANANa bicarbNAFree Form Intake
22904297453503NA2NANANANa bicarbNAFree Form Intake
23314297455843NA2NANANANa bicarbNAFree Form Intake
68152205515155012NANANa SuppsNANA
124342318344311821NANANaClNANA
1077521370091062631NANANaCl suppNANA
65250995229707711NANANaCl SuppsNANA
157282354198316NA1NANANAnaHCO3NAFree Form Intake
214182354460216NA1NANANAnaHCO3NAFree Form Intake
13104477301483NA3NANANAnarcanNANA
502442044777373NANANarcanNANA
13114420301487NA7NANANANarcanNANA
7924490592749421NANANAS ScoreNANA
29413532224666591NA23NANANANasal CongestionNAToxicology
131811943017233NA1NANANAnatrecorNANA
846605460741412NANANeonatal biliNANA
113250577424191111NANANEONATAL MORPHINENANA
104264146782192313NANANephrostomyNANA
145521374117710NA1NANANANephrostomy DrainNAFree Form Intake
27984602238029257NA434NANANANerve StimulatedNAPain/Sedation
1516185741611223NA1NANANAnesiritideNAFree Form Intake
27521857222037223NA1NANANAnesiritideNAMedications
14711241857122311NANANESIRITIDENANA
15171124416111NA1NANANANESIRITIDENAFree Form Intake
275411242220371NA1NANANANESIRITIDENAMedications
5954262490211785NANANeuro consultNANA
2826465223921146837NA3555NANANANeuro SymptomsNANeurological
17767614428371NA1NANANAngt residualNANA
19337614438921NA1NANANAngt residualNANA
21037614444751NA1NANANAngt residualNANA
132531863017853NA2NANANAnicardipineNANA
2757318622204253NA2NANANAnicardipineNAMedications
408205231864245332NANANicardipineNANA
1326205230178424NA3NANANANicardipineNANA
27552052222042424NA3NANANANicardipineNAMedications
1811307042967149NA1NANANANicardipine (mg/h)NAFree Form Intake
42520703240143711NANAnicardipine gttNANA
154720704182314NA1NANANAnicardipine gttNAFree Form Intake
182720704308614NA1NANANAnicardipine gttNAFree Form Intake
154632404182337NA1NANANANicardipine gttNAFree Form Intake
182832404308637NA1NANANANicardipine gttNAFree Form Intake
182517144303759NA1NANANANicardipine HClNAFree Form Intake
145821014118981NA2NANANAnicardipine mg/hrNAFree Form Intake
21317152101418112NANANicardipine mg/hrNANA
145917154118941NA1NANANANicardipine mg/hrNAFree Form Intake
188132420221532512NANAnifNANA
156726704191638NA1NANANAnimbexNAFree Form Intake
170226704259038NA1NANANAnimbexNAFree Form Intake
157922854204585NA1NANANAnimbex mcg/kg/minNAFree Form Intake
2782164254824310331NANANIMBEX MG/KG/HRNANA
195135404397312319NA220NANANANipple TypeNAFree Form Intake
195335404397412319NA220NANANANipple TypeNAFree Form Intake
2956595222474913143154NA39nitric oxideNARespiratory
308213127171211743NANANitric oxideNANA
7982131595212113145NANANitric oxideNANA
2953213122474912143144NA39Nitric oxideNARespiratory
2342056213111712164NANANitric OxideNANA
307205627171171763NANANitric OxideNANA
7992056595211713165NANANitric OxideNANA
2955205622474911743164NA39Nitric OxideNARespiratory
800271759521713135NANANITRIC OXIDENANA
295427172247491743134NA39NITRIC OXIDENARespiratory
96220786438191312NANANitric Oxide ppmNANA
10482078679419411NANANitric Oxide ppmNANA
106220786916194612NANANitric Oxide ppmNANA
10476438679413421NANANitric Oxide PPMNANA
106364386916134622NANANitric Oxide PPMNANA
10646794691644612NANANITRIC OXIDE PPMNANA
9122057628310311NANAnitric tank pressureNANA
9172057629110311NANAnitric tank pressureNANA
96320576439101212NANAnitric tank pressureNANA
916628362913311NANANitric Tank PressureNANA
9646283643931212NANANitric Tank PressureNANA
9656291643931212NANANITRIC TANK PRESSURENANA
12112079817339421NANANitric tank psiNANA
27974572238019462NA390NANANANMB MedicationNAPain/Sedation
29825042665618022NANAnoNANA
797206259504912335NANANO ppmNANA
2332063213040221NANANO tank psiNANA
12352063831040621NANANO tank psiNANA
1236213083102611NANANO tank PSINANA
90925056267125116NANAno2NANA
19824647440538NA3NANANAnormal saline bolusNAFree Form Intake
20864647444408NA3NANANAnormal saline bolusNAFree Form Intake
13925321408508NA3NANANAns bolusNAFree Form Intake
14045321408658NA3NANANAns bolusNAFree Form Intake
14905321414908NA3NANANAns bolusNAFree Form Intake
16895321425488NA3NANANAns bolusNAFree Form Intake
648497051997463NANANS bolusNANA
663497053217863NANANS bolusNANA
13934970408507NA6NANANANS bolusNAFree Form Intake
14024970408657NA6NANANANS bolusNAFree Form Intake
14924970414907NA6NANANANS bolusNAFree Form Intake
16874970425487NA6NANANANS bolusNAFree Form Intake
662519953214833NANANS BolusNANA
13945199408504NA3NANANANS BolusNAFree Form Intake
14015199408654NA3NANANANS BolusNAFree Form Intake
14935199414904NA3NANANANS BolusNAFree Form Intake
16865199425484NA3NANANANS BolusNAFree Form Intake
69848135606153133NANAnutrition labsNANA
5594539474471713382NANANystatinNANA
572453947957172163813NANANystatinNANA
5734744479513216213NANANYSTATINNANA
607479349471281941215NANAnystatin creamNANA
57143904793110128912NANANystatin CreamNANA
60843904947110194915NANANystatin CreamNANA
671479754501142963NANANystatin OintNANA
118549367890442323NANANystatin Oint.NANA
528454846251945841440NANAnystatin ointmentNANA
584454848441942321418NANAnystatin ointmentNANA
863454861181946141NANAnystatin ointmentNANA
583462548445842324018NANANystatin ointmentNANA
865462561185846401NANANystatin ointmentNANA
864484461182326181NANANystatin OintmentNANA
6764564548620418131NANANystatin oralNANA
112642347380261021NANAnystatin oral suspenNANA
6794677548712511121NANAnystatin powderNANA
526438946192131392012NANANystatin powderNANA
540438946772131252012NANANystatin powderNANA
6784389548721311201NANANystatin powderNANA
541461946771391251212NANANystatin PowderNANA
6774619548713911121NANANystatin PowderNANA
1410703340889130NA4NANANAoctreotideNAFree Form Intake
1503703341531130NA4NANANAoctreotideNAFree Form Intake
2026703344223130NA4NANANAoctreotideNAFree Form Intake
29687033225155130NA4NANANAoctreotideNAMedications
10802095703312613044NANAOctreotideNANA
1411209540889126NA4NANANAOctreotideNAFree Form Intake
1505209541531126NA4NANANAOctreotideNAFree Form Intake
2025209544223126NA4NANANAOctreotideNAFree Form Intake
29672095225155126NA4NANANAOctreotideNAMedications
21536423446536NA2NANANAOctreotide mcg/hrNAFree Form Intake
1161725277457119125NANAomeprazoleNANA
1611490190311912NANAoob to chairNANA
66852055384289912NANAop siteNANA
3140477226168457046NA17436NANANAOral CareNATreatments
2844478223999776172NA22373NANANAOral CavityNAGI/GU
102152816681813842NANAoral nystatinNANA
66049005281588134NANAOral NystatinNANA
102249006681583832NANAOral NystatinNANA
2815479223898920279NA22382NANANAOrientationNANeurological
278348522376431416021425NA115Orthostatic HR lyingNARoutine Vital Signs
2442586446071218NA2NANANAostomyNANA
29585864224796218NA2NANANAostomyNAGI/GU
1118711473286511421NANAOstomy bagNANA
2991486225191155616118820NA72Ostomy CareNATreatments
166473754247118NA1NANANAotherNANA
177773754283918NA1NANANAotherNANA
267173629001614NA2NANANAoxacillinNAANTIBACTERIUM
3110736222588914NA2NANANAoxacillinNAAntibiotics
1123355473621834141372NANAOxacillinNANA
26723554900161834NA137NANANAOxacillinNAANTIBACTERIUM
310935542258891834NA137NANANAOxacillinNAAntibiotics
822569660112211NANAp HiNANA
9365696636621912NANAp HiNANA
982569664802111NANAp HiNANA
9376011636621912NANAP HiNANA
980601164802111NANAP HiNANA
9816366648019121NANAP HINANA
37330283036111816NANAp highNANA
104158216895916267NANAP highNANA
3641582302859161NANAP highNANA
374158230365911866NANAP highNANA
36316893028162171NANAP HighNANA
3751689303616211876NANAP HighNANA
931319563423911NANAP inspNANA
9381583636751811NANAP loNANA
967158364485111NANAP loNANA
983158364815111NANAP loNANA
985644864811111NANAP LoNANA
9666367644818111NANAP LONANA
9846367648118111NANAP LONANA
41330293209811627NANAp lowNANA
342169029355416277NANAP lowNANA
3651690302954872NANAP lowNANA
414169032095411677NANAP lowNANA
36629353029162872NANAP LowNANA
4152935320916211677NANAP LowNANA
1091609971611417537NANAP-HINANA
9765582646520121NANAp-highNANA
99955826520202022NANAp-highNANA
10006465652012012NANAP-HighNANA
853601061001731583NANAP-LoNANA
10016466652112012NANAP-LowNANA
27881704223773225758236903384117NA2050PA line cm MarkNAHemodynamics
283151722395774351NA4432NANANAPacer RateNACardiovascular (Pulses)
28113411357NA2NANANApad changeNANA
13761134407447NA2NANANApad changeNANA
21291134445837NA2NANANApad changeNANA
2792522223784147906NA15568NANANAPain CauseNAPain/Sedation
27931044223791143010NA15059NANANAPain LevelNAPain/Sedation
2791525223783559257NA21263NANANAPain LocationNAPain/Sedation
27891046223781806030NA21685NANANAPain PresentNAPain/Sedation
2790527223782256778NA18432NANANAPain TypeNAPain/Sedation
3141898277820020996NANApantoprazoleNANA
1125189873722001191NANApantoprazoleNANA
1353189840550200NA9NANANApantoprazoleNAFree Form Intake
1368189840700200NA9NANANApantoprazoleNAFree Form Intake
1514189841583200NA9NANANApantoprazoleNAFree Form Intake
1124277873722091161NANAPantoprazoleNANA
1354277840550209NA6NANANAPantoprazoleNAFree Form Intake
1370277840700209NA6NANANAPantoprazoleNAFree Form Intake
1511277841583209NA6NANANAPantoprazoleNAFree Form Intake
135573724055011NA1NANANAPANTOPRAZOLENAFree Form Intake
136973724070011NA1NANANAPANTOPRAZOLENAFree Form Intake
151273724158311NA1NANANAPANTOPRAZOLENAFree Form Intake
2552122646565148NA6NANANAPantoprazole mg/hrNAFree Form Intake
28911623224168513562507114164992048NA15614Parameters CheckedNAAlarms
2800143222381017992NA1048NANANAParoxysmal SweatsNAToxicology
164121919073311NANAPASatNANA
241115111717930225NANApassey muir valveNANA
1191115173917937221NANApassey muir valveNANA
123111517451798222NANApassey muir valveNANA
1251739174537812NANAPassey Muir ValveNANA
12011171739303751NANAPASSEY MUIR VALVENANA
1241117174530852NANAPASSEY MUIR VALVENANA
797210154113193526NANApassy muir valveNANA
11749727831411114351NANApassy muir valveNANA
117510157831319114261NANAPASSY MUIR VALVENANA
2581051231692953NANApassy-muir valveNANA
26810512451926456NANApassy-muir valveNANA
29110512616924653NANApassy-muir valveNANA
2692316245196436NANAPassy-Muir valveNANA
2932316261694633NANAPassy-Muir valveNANA
29224512616644663NANAPassy-Muir ValveNANA
8912229618611111NANAPAWNANA
29104952244104358351777076NA553PCA BolusNAPain/Sedation
289749622432839013231701699141NA1216PCA DoseNAPain/Sedation
289849822432938953230441693139NA1212PCA Lockout (Min)NAPain/Sedation
289649922432739009NA1694NANANAPCA MedicationNAPain/Sedation
294050022466520044139961568126NA1142PCA Total DoseNAPain/Sedation
92931146336689834NANAPCV Driving PressureNANA
278450422377126562602350825NA155PCWPNAHemodynamics
29515993224728412217NA30Peak exp flow rateNARespiratory
1089530713556368137021NANAPeak FlowNANA
294753522469523615545419212769923NA8215Peak Insp. PressureNARespiratory
875181861491712NANAPECO2NANA
8346049605122892284NANApediarixNANA
268450622033942753648056313269946NA8281PEEP SetNARespiratory
14116401849271851NANAPEG careNANA
8361640605527151NANAPEG careNANA
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9691666645136146815NANAphighNANA
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205225144428941NA1NANANApigtail flushNAFree Form Intake
205925144431141NA1NANANApigtail flushNAFree Form Intake
299032022251901839239NA65pin careNATreatments
41254232021878181473NANAPin CareNANA
298954222519018783921479NA65Pin CareNATreatments
747562557561666299NANAPinpsNANA
950210763844113345NANApinspNANA
2172072210723741194NANAPinspNANA
94920726384237133195NANAPinspNANA
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29485432246961754827537612314748NA7035Plateau PressureNARespiratory
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970204064536128216NANAPLOWNANA
1099204072256922NANAPLOWNANA
10151632664554944585NANAPMVNANA
1153109475892221521NANAPODUS BOOTNANA
53442504643323032NANAPoly Vi SolNANA
56446044773191322NANApoly-vi-solNANA
52144064604471962NANAPoly-vi-solNANA
56544064773471362NANAPoly-vi-solNANA
466424142812001253225NANApolyvisolNANA
2884184422409330NA2NANANApositionNATreatments
139547184485473730218502NANAPositionNANA
2885547224093854737NA21850NANANAPositionNATreatments
2865548224066427556NA14118NANANAPosition ChangeNARestraint/Support Systems
3252548227952427556NA14118NANANAPosition ChangeNARestraint/Support Systems
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1172541078115211NANAPost ductal satNANA
1188541079105111NANAPost ductal satNANA
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2972357822516611981NA120NANANAPotassium ChlorideNAMedications
3321821285628131NANApotassium phosphateNANA
48318214381282633NANApotassium phosphateNANA
64318215167282331NANApotassium phosphateNANA
127418213002928NA3NANANApotassium phosphateNANA
3125182122592528NA3NANANApotassium phosphateNANutrition - Supplements
127351673002923NA1NANANApotassium PhosphateNANA
3126516722592523NA1NANANApotassium PhosphateNANutrition - Supplements
4822856438112613NANAPotassium phosphateNANA
6422856516712311NANAPotassium phosphateNANA
12752856300291NA1NANANAPotassium phosphateNANA
312828562259251NA1NANANAPotassium phosphateNANutrition - Supplements
64443815167262331NANAPotassium PhosphateNANA
127643813002926NA3NANANAPotassium PhosphateNANA
3124438122592526NA3NANANAPotassium PhosphateNANutrition - Supplements
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744564957511211NANAPRBCSNANA
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247219522011714514NANAprecedexNANA
131721953016717NA1NANANAprecedexNANA
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1316219630167119NA3NANANAPRECEDEXNANA
15832296420628NA1NANANAPrecedex (mcg/kg/hr)NAFree Form Intake
27621882547273311NANAprecedex mcg/kg/hrNANA
165321884240727NA1NANANAprecedex mcg/kg/hrNAFree Form Intake
165425474240733NA1NANANAPrecedex mcg/kg/hrNAFree Form Intake
243216621881232721NANAPRECEDEX MCG/KG/HRNANA
277216625471233321NANAPRECEDEX MCG/KG/HRNANA
1655216642407123NA2NANANAPRECEDEX MCG/KG/HRNAFree Form Intake
615492950031234151NANAPrednisoloneNANA
123265568279616571NANAPrednisolone dropsNANA
123463968309782362NANAPrednisolone gttsNANA
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11206339733211211NANAPRESSURE SUPPORTNANA
5824810482336244182NANAPrilosecNANA
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1450105441101716NA23NANANAprotonixNAFree Form Intake
1965105444008716NA23NANANAprotonixNAFree Form Intake
13521336405491146NA32NANANAProtonixNAFree Form Intake
14521336411011146NA32NANANAProtonixNAFree Form Intake
19621336440081146NA32NANANAProtonixNAFree Form Intake
391101133631811461432NANAPROTONIXNANA
1351110140549318NA14NANANAPROTONIXNAFree Form Intake
1451110141101318NA14NANANAPROTONIXNAFree Form Intake
1964110144008318NA14NANANAPROTONIXNAFree Form Intake
223473894502434NA1NANANAprotonix 80mg/250ccNAFree Form Intake
246173894621234NA1NANANAprotonix 80mg/250ccNAFree Form Intake
162224434228559NA1NANANAPROTONIX DRIPNAFree Form Intake
200224434412559NA1NANANAPROTONIX DRIPNAFree Form Intake
225824434516959NA1NANANAPROTONIX DRIPNAFree Form Intake
1382238240777102NA3NANANAProtonix gttNAFree Form Intake
1609238242204102NA3NANANAProtonix gttNAFree Form Intake
2181238244849102NA3NANANAProtonix gttNAFree Form Intake
2376238245755102NA3NANANAProtonix gttNAFree Form Intake
1531853187628120892NANAprotonix mg/hrNANA
1389185340796281NA9NANANAprotonix mg/hrNANA
1500185341525281NA9NANANAprotonix mg/hrNAFree Form Intake
1421331185323128169NANAProtonix mg/hrNANA
1521331187623120862NANAProtonix mg/hrNANA
1388133140796231NA6NANANAProtonix mg/hrNANA
1498133141525231NA6NANANAProtonix mg/hrNAFree Form Intake
1390187640796208NA2NANANAPROTONIX MG/HRNANA
1497187641525208NA2NANANAPROTONIX MG/HRNAFree Form Intake
6024739491535735201NANAprune juiceNANA
47742714362124846733NANAPrune juiceNANA
55542714739124357720NANAPrune juiceNANA
600427149151243571NANAPrune juiceNANA
556436247398463573320NANAPrune JuiceNANA
6014362491584635331NANAPrune JuiceNANA
41133213412411NANApulseNANA
1091332172522515NANApulseNANA
1081341172542515NANAPULSENANA
290059782243592967156NA376qtcNARoutine Vital Signs
809190459789241NANAQtcNANA
289919042243599967456NA376QtcNARoutine Vital Signs
121586174230475386111NANAQTcNANA
1625861904304798614NANAQTcNANA
8115865978304728611NANAQTcNANA
2902586224359304796786156NA376QTcNARoutine Vital Signs
16317421904539114NANAQTCNANA
81017425978532111NANAQTCNANA
29011742224359539671156NA376QTCNARoutine Vital Signs
146718164135176NA1NANANAQUINIDINENAFree Form Intake
8675671612441211NANAr fem art lineNANA
9045671626042511NANAr fem art lineNANA
90661246260122511NANAR fem art lineNANA
7192913567161431NANAR FEM ART LINENANA
86829136124611231NANAR FEM ART LINENANA
90529136260612531NANAR FEM ART LINENANA
76720205799411421NANAr leg elevatedNANA
315617442265641NA1NANANAR NEPHROSTOMYNAOutput
381221132251111NANARAD. ALINENANA
2867679222406790NA1NANANArange of motionNARestraint/Support Systems
3257679222795390NA1NANANArange of motionNARestraint/Support Systems
6411047515940022137135111NANARange of MotionNANA
10461047679240022190135111NANARange of MotionNANA
28661047224067400221NA13511NANANARange of MotionNARestraint/Support Systems
32541047227953400221NA13511NANANARange of MotionNARestraint/Support Systems
104551596792379011NANARANGE OF MOTIONNANA
2868515922406737NA1NANANARANGE OF MOTIONNARestraint/Support Systems
3256515922795337NA1NANANARANGE OF MOTIONNARestraint/Support Systems
554438647382005743NANAranitidineNANA
27644386222190200NA4NANANAranitidineNANutrition - Parenteral
4844272438651520094NANARanitidineNANA
553427247385155793NANARanitidineNANA
27664272222190515NA9NANANARanitidineNANutrition - Parenteral
2765473822219057NA3NANANARANITIDINENANutrition - Parenteral
57647694807482541NANArashNANA
56143424769784854NANARashNANA
57743424807782551NANARashNANA
331092422811142452NA22901NANANAReadmissionNAScores - APACHE IV (2)
148728544143315NA2NANANArectal tubeNANA
185628544340815NA2NANANArectal tubeNANA
3167285422658315NA2NANANArectal tubeNAOutput
315192622654350706NA30762NANANAReligionNAADT
887609618413444122191NANARemote AlarmNANA
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3258623227959428027NA13934NANANARestraints EvaluatedNARestraint/Support Systems
959173064279NA1NANANARIBAVIRIN RXNANA
151566104160917NA2NANANArifampinNAFree Form Intake
267666109002717NA2NANANArifampinNAANTIBACTERIUM
3114661022589817NA2NANANArifampinNAAntibiotics
2369659945728214NA1NANANAright armNANA
614489350006214111NANAright hydroceleNANA
109528197179271311NANARIGHT LEG DRSGNANA
277413372237531086624NA19760NANANARiker-SAS ScaleNAPain/Sedation
277514842237542266018NA15514NANANARisk for FallsNANeurological
21948342448776NA1NANANArituximabNAFree Form Intake
22078342449186NA1NANANArituximabNAFree Form Intake
24098342458766NA1NANANArituximabNAFree Form Intake
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105266216851323803612NANAROTATIONNANA
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1128683573911624011NANArotation on/offNANA
1051328568355116211NANAROTATION ON/OFFNANA
112732857391514011NANAROTATION ON/OFFNANA
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334610692284132NA1NANANAseizure activityNANeurological
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751119159515616103NANAseizure padsNANA
12301119827215653101NANAseizure padsNANA
32241119227626156NA10NANANAseizure padsNATreatments
3223827222762653NA1NANANASeizure padsNATreatments
122915958272165331NANASeizure PadsNANA
3222159522762616NA3NANANASeizure PadsNATreatments
27956322237972248NA279NANANASensory LevelNAPain/Sedation
293491922464042452NA22901NANANAServiceNAGeneral
3251070282415994NANAshampooNANA
4001070313115895NANAshampooNANA
401282431319845NANASHAMPOONANA
151106618721251234246NANAshaveNANA
35210662976125674222NANAshaveNANA
35318722976123674622NANAShaveNANA
108830587095232858NANAshavedNANA
3247637227944956324NA21636NANANASide RailsNARestraint/Support Systems
96059436433361121NANAsigh breathNANA
12006985805316111NANASigh PEEP levelNANA
124569858345161011NANASigh PEEP levelNANA
12448053834511011NANASIGH PEEP LEVELNANA
1206638808723347151NANASigh RateNANA
5674540479013320273NANASimethiconeNANA
2869292422407013NA4NANANAsitterNARestraint/Support Systems
1716411963190134110084NANASitterNANA
3406412924190131310084NANASitterNANA
287064122407019013NA1008NANANASitterNARestraint/Support Systems
33919632924411344NANASITTERNANA
2871196322407041NA4NANANASITTERNARestraint/Support Systems
3132641277474831NANASJO2NANA
59345714901220372NANAskinNANA
519431945713122027NANASkinNANA
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2858644224026593508NA22325NANANASkin IntegrityNASkin - Assessment
39921253126585465NANAslopeNANA
3138212522597858NA6NANANAslopeNARespiratory
22321192125635846NANASlopeNANA
39821193126635445NANASlopeNANA
3136211922597863NA4NANANASlopeNARespiratory
3137312622597854NA5NANANASLOPENARespiratory
2809143422382810137NA716NANANASlurred SpeechNAToxicology
178262994284812NA5NANANAsodium bicarbNAFree Form Intake
181962994299912NA5NANANAsodium bicarbNAFree Form Intake
208162994442412NA5NANANAsodium bicarbNAFree Form Intake
91931096299321295NANASodium BicarbNANA
178331094284832NA9NANANASodium BicarbNAFree Form Intake
181831094299932NA9NANANASodium BicarbNAFree Form Intake
208231094442432NA9NANANASodium BicarbNAFree Form Intake
12776146300301NA1NANANAsodium bicarbonateNANA
13293624302965347NA126NANANASodium ChlorideNANA
129432863006956NA4NANANAsolumedrolNANA
2481803220318565104NANASolumedrolNANA
4371803328618556104NANASolumedrolNANA
1293180330069185NA10NANANASolumedrolNANA
43622033286655644NANASOLUMEDROLNANA
129522033006965NA4NANANASOLUMEDROLNANA
73724825728251811NANASOLUMEDROL MG/KG/HRNANA
11542657759251611NANASOLUMEDROL CC/HNANA
159567404216317NA1NANANAsolumedrol dripNAFree Form Intake
161367404224017NA1NANANAsolumedrol dripNAFree Form Intake
102926296740311721NANASOLUMEDROL DRIPNANA
159426294216331NA2NANANASOLUMEDROL DRIPNAFree Form Intake
161426294224031NA2NANANASOLUMEDROL DRIPNAFree Form Intake
167525684251425NA1NANANASOLUMEDROL DRIP.NAFree Form Intake
297235526561054162NANAsolumedrol mg/kg/hrNANA
3602355300110516164NANAsolumedrol mg/kg/hrNANA
1693235542563105NA6NANANAsolumedrol mg/kg/hrNAFree Form Intake
1691300142563161NA4NANANASolumedrol mg/kg/hrNAFree Form Intake
361265630014116124NANASOLUMEDROL MG/KG/HRNANA
169226564256341NA2NANANASOLUMEDROL MG/KG/HRNAFree Form Intake
2816648223902246129NA5014NANANASpeechNANeurological
3349648228414246129NA5014NANANASpeechNANeurological
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184147124317214NA1NANANASPITNANA
185147124339614NA1NANANASPITNANA
186747124349114NA1NANANASPITNANA
1845362643245331379NA3219NANANASpitsNANA
1847362643323331379NA3219NANANASpitsNANA
1878362643554331379NA3219NANANASpitsNAFree Form Intake
386305030831054251NANAspont tidal volumesNANA
3772094304514321NANASpont VTNANA
2914209422442114164612572NA4464Spont VTNARespiratory
291530452244213164611572NA4464SPONT VTNARespiratory
2821655223911426434NA7767NANANASpontaneous MovementNANeurological
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36225663004691221NANASpontaneous VTNANA
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2850660224013109999NA14358NANANAStool GuaiacNAGI/GU
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155025514184634NA1NANANAstudy drugNAFree Form Intake
163025514230834NA1NANANAstudy drugNAFree Form Intake
2792538255173411NANASTUDY DRUGNANA
15492538418467NA1NANANASTUDY DRUGNAFree Form Intake
16292538423087NA1NANANASTUDY DRUGNAFree Form Intake
31594627802623101NANASTVNANA
18944189436561028NA208NANANASucroseNAFree Form Intake
2855663224023775701NA21123NANANASupport SystemsNARestraint/Support Systems
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2992194266913469113NANASVO2NANA
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4762613732029991658991NANASVRNANA
2943495522466860NA1NANANAsweatingNAToxicology
610364449554460131NANASweatingNANA
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1145599675161111NANAT disconNANA
1186763979019721NANAT disconnectNANA
9391584636871922NANAT hiNANA
986158464827121NANAT hiNANA
9876368648219121NANAT HINANA
72630315706210927NANAt highNANA
34316912936781375NANAT highNANA
3681691303178272NANAT highNANA
728169157067810977NANAT highNANA
3672936303113252NANAT HighNANA
727293657061310957NANAT HighNANA
9401585636951912NANAT loNANA
989158564835111NANAT loNANA
9886369648319121NANAT LONANA
72930325707210727NANAt lowNANA
344169229378813105NANAT lowNANA
36916923032882102NANAT lowNANA
7301692570788107107NANAT lowNANA
3702937303213252NANAT LowNANA
731293757071310757NANAT LowNANA
1194790879616131NANAT-disconnectNANA
854601261011561483NANAT-HiNANA
1142601274931562081NANAT-HiNANA
114361017493142031NANAT-HINANA
9775584646718121NANAt-highNANA
100355846522182123NANAt-highNANA
10026467652212113NANAT-HighNANA
855601361021731483NANAT-LoNANA
11065581723413621NANAT-LowNANA
181657874298740NA1NANANAt-tubeNANA
182357874301540NA1NANANAt-tubeNANA
2803143622381817539NA1047NANANATactile DisturbancesNAToxicology
89562136221742031NANATANK ANANA
89662156222732031NANATANK BNANA
1039557567681811NANATank pressureNANA
6942951557521121NANATANK PRESSURENANA
10382951676821821NANATANK PRESSURENANA
771322058211111NANAtbiliNANA
1190172779194121NANATDISCONNECTNANA
107363086960942644NANATEDSNANA
11381675746144731NANATHNANA
133630374028310NA2NANANATHAMNAFree Form Intake
163630374234410NA2NANANATHAMNAFree Form Intake
2883680224087741704NA16067NANANATherapeutic BedNATreatments
19816682041626112NANAthighNANA
97216686454621021110NANAthighNANA
1101166872266220113NANAthighNANA
11006454722610220103NANAThighNANA
973204164546102210NANATHIGHNANA
11022041722662023NANATHIGHNANA
777436958346151NANAthoracentesisNANA
13414369404036NA5NANANAthoracentesisNANA
14164369408936NA5NANANAthoracentesisNANA
14244369409426NA5NANANAthoracentesisNANA
301543692254796NA5NANANAthoracentesisNA4-Procedures
13405834404031NA1NANANAThoracentesisNANA
14145834408931NA1NANANAThoracentesisNANA
14235834409421NA1NANANAThoracentesisNANA
301658342254791NA1NANANAThoracentesisNA4-Procedures
265681242014710261NANATidal VolumeNANA
274681253414748261NANATidal VolumeNANA
27524202534104811NANATIDAL VOLUMENANA
294568322468423072320950612736843NA7744Tidal Volume (Set)NARespiratory
7461678575531123NANATime disconnectNANA
8703211612827541NANAtime highNANA
41616143211152734NANATime HighNANA
8691614612815531NANATime HighNANA
41716153212142434NANATime LowNANA
11391676746241731NANATLNANA
199166920425911103NANAtlowNANA
1104166972275910102NANAtlowNANA
110320427227111032NANATLOWNANA
53145424639518252NANATobradexNANA
58542444852281712NANAtobradex ointmentNANA
266950639001318NA2NANANATobramycinNAANTIBACTERIUM
3116506322590218NA2NANANATobramycinNAAntibiotics
63044845089422311NANAToesNANA
10811734704413068121NANATOFNANA
279967322380410032NA471NANANATOF Response/TwitchNAPain/Sedation
40228083136266111NANAtongue midlineNANA
2950686224700454544275983543NA5340Total PEEP LevelNARespiratory
77821455841575924NANAtpaNANA
130821453013557NA2NANANAtpaNANA
130758413013559NA4NANANATPANANA
12331717828561711NANAtPA mg/hrNANA
254558924656130NA2NANANAtpa mg/hrNAFree Form Intake
786555658922703072NANATPA mg/hrNANA
2543555646561270NA7NANANATPA mg/hrNAFree Form Intake
687286155561027017NANATPA mg/HRNANA
78728615892103012NANATPA mg/HRNANA
254228614656110NA1NANANATPA mg/HRNAFree Form Intake
33323442861831051NANATPA MG/HRNANA
686234455568327057NANATPA MG/HRNANA
78823445892833052NANATPA MG/HRNANA
254423444656183NA5NANANATPA MG/HRNAFree Form Intake
127865383003278NA2NANANAtpnNANA
100624386538127812NANATPNNANA
127924383003212NA1NANANATPNNANA
59409702454100619763NANAtrach careNANA
99401022245471019768NANAtrach careNANA
14494018552454131972NANAtrach careNANA
3180940227130245416219197160NA916trach careNARespiratory
1451022185571013682NANATrach careNANA
317910222271307101621968160NA916Trach careNARespiratory
26879401433924541123197NANATrach CareNANA
4687970143391006112363NANATrach CareNANA
10687102214339710112368NANATrach CareNANA
1466871855143391311232NANATrach CareNANA
317868722713014339162191123160NA916Trach CareNARespiratory
3181185522713013162192160NA916TRACH careNARespiratory
8970102210067106368NANATRACH CARENANA
1439701855100613632NANATRACH CARENANA
317797022713010061621963160NA916TRACH CARENARespiratory
11477474752810111NANAtrach markNANA
11167118728612932NANATracheNANA
9422993637568103115NANAtrache careNANA
35819482993968311NANATrache CareNANA
94319486375910335NANATrache CareNANA
169977194843963NANATRACHE CARENANA
35797729934368611NANATRACHE CARENANA
94197763754310365NANATRACHE CARENANA
288769722411210857NA333NANANATraction/Immobile #1NATreatments
323569722777110857NA333NANANATraction/Immobile #1NATreatments
29306982244492053NA54NANANATraction/Immobile #2NATreatments
32376982277722053NA54NANANATraction/Immobile #2NATreatments
2931699224450783NA8NANANATraction/Immobile #3NATreatments
3238699227773783NA8NANANATraction/Immobile #3NATreatments
2932700224451204NA2NANANATraction/Immobile #4NATreatments
3241700227774204NA2NANANATraction/Immobile #4NATreatments
9516351640249921NANATrain of FourNANA
6393668514561684451NANATremorsNANA
69548725603171914NANATRI VI SOLNANA
10416288677845825NANAtri-vi-solNANA
9135036628823472NANATri-Vi-SolNANA
104050366778235875NANATri-Vi-SolNANA
107867207029171112NANATrip antibiotic ointNANA
64945515200131814NANATriple ABX creamNANA
114146817471395922NANAtriple abx ointNANA
7945024593389982NANAtriple abx ointmentNANA
110950247259896185NANAtriple abx ointmentNANA
112950247414894385NANAtriple abx ointmentNANA
11085933725996125NANATriple Abx ointmentNANA
11315933741494325NANATriple Abx ointmentNANA
113072597414614355NANATriple Abx OintmentNANA
118344787853861541NANAtriple antibioticNANA
12186274820411411NANATriple AntibxNANA
1049589968078921NANATriple Antibx OintNANA
725504857011217283NANAtrivisolNANA
10245048668612122283NANAtrivisolNANA
622486750482051214928NANATrivisolNANA
724486757012057493NANATrivisolNANA
10254867668620522493NANATrivisolNANA
10235701668672233NANATriViSolNANA
2879704224082935324NA22301NANANATurnNATreatments
1090463671382315421NANAUmb HerniaNANA
5624708477041930463NANAumbi herniaNANA
6124708495641969968NANAumbi herniaNANA
1871470843493419NA6NANANAumbi herniaNAFree Form Intake
1870495643493699NA8NANANAUmbi herniaNAFree Form Intake
6114770495630469938NANAUmbi HerniaNANA
1872477043493304NA3NANANAUmbi HerniaNAFree Form Intake
5464543470816341936NANAUMBI HERNIANANA
5634543477016330433NANAUMBI HERNIANANA
6134543495616369938NANAUMBI HERNIANANA
1869454343493163NA3NANANAUMBI HERNIANAFree Form Intake
5164410456076354798NANAumbilical herniaNANA
5374410465076345994NANAumbilical herniaNANA
5364560465054745984NANAUmbilical herniaNANA
29352633224652118360120NA222unassisted systoleNAIABP
279470522379620793NA4502NANANAUntoward EffectNAPain/Sedation
30396058225695116561129NA437uric acidNALabs
250191922241111NANAUrine creatNANA
58694148559886NANAurine cultureNANA
30109412254549NA8NANANAurine cultureNA6-Cultures
300948552254548NA6NANANAUrine cultureNA6-Cultures
789222558983222NANAURINE NANANA
304191826901111NANAUrine OsmNANA
155149518808219564344NANAurine phNANA
103514956754821544NANAurine phNANA
11111495726282341NANAurine phNANA
6313521495368254NANAurine pHNANA
156135218803619565344NANAurine pHNANA
103313526754361554NANAurine pHNANA
11101352726236351NANAurine pHNANA
11126754726215341NANAurine PHNANA
1034188067541956153444NANAUrine pHNANA
111318807262195633441NANAUrine pHNANA
2851707224015939323NA22357NANANAUrine SourceNAGI/GU
293679672246594596261NANA2vacuum assistNAHemodynamics
874257061485743NANAvalproic acidNANA
907227362613513NANAvanco levelNANA
26703679900154539NA354NANANAVancomycinNAANTIBACTERIUM
304636792257984539NA354NANANAVancomycinNAAntibiotics
2911361222852093NANAvasopressinNANA
26611362445853393NANAvasopressinNANA
129011363005185NA9NANANAvasopressinNANA
2770113622231585NA9NANANAvasopressinNAMedications
128824453005133NA3NANANAVasopressinNANA
2767244522231533NA3NANANAVasopressinNAMedications
26712222445203333NANAVASOPRESSINNANA
128912223005120NA3NANANAVASOPRESSINNANA
2768122222231520NA3NANANAVASOPRESSINNAMedications
2802334256119111NANAvasopressin u/hrNANA
25313272248791121NANAvasopressin unit/minNANA
161713274227379NA2NANANAvasopressin unit/minNAFree Form Intake
161822484227311NA1NANANAVASOPRESSIN UNIT/MINNAFree Form Intake
29097102244067851NA75NANANAVEN Lumen VolumeNADialysis
1117602472931111NANAvenous o2 satNANA
29526342646285611NANAVENT CPPNANA
19352281438961NA1NANANAVENT DRAINNANA
23942281458221NA1NANANAVENT DRAINNANA
2813720223849421200NA13273NANANAVentilator ModeNARespiratory
2812722223848489005NA13258NANANAVentilator TypeNARespiratory
152919684169366NA1NANANAVerapamilNAFree Form Intake
251819684648466NA1NANANAVerapamilNAFree Form Intake
2773196822231866NA1NANANAVerapamilNAMedications
7045004563417211NANAvidalinNANA
83356266042701672NANAVidalynNANA
94756266379701776NANAVidalynNANA
94660426379161726NANAVIDALYNNANA
61750155017631991335NANAvidaylinNANA
945501563786326134NANAvidaylinNANA
9445017637819926354NANAVidaylinNANA
2804143922381917511NA1049NANANAVisual DisturbancesNAToxicology
85655006103392343NANAvit aNANA
99355006510391942NANAvit aNANA
99461036510231932NANAvit ANANA
680548555009339104NANAVit ANANA
857548561039323103NANAVit ANANA
995548565109319102NANAVit ANANA
59047024871985732NANAVit DNANA
80456175960461241NANAvitamin aNANA
99056176509461141NANAvitamin aNANA
99259606509121111NANAvitamin ANANA
699549256171462461344NANAVitamin ANANA
805549259601462121341NANAVitamin ANANA
991549265091462111341NANAVitamin ANANA
1848368643332405998NA5135NANANAVoidNANA
31543686226560405998NA5135NANANAVoidNAOutput
35923112998185571NANAVTNANA
37826743052696722NANAWAFFLE BOOTNANA
10615911722457298217NANAwaffle bootsNANA
25515912307457264219NANAwaffle bootsNANA
7611591577345723211NANAwaffle bootsNANA
2561722230729826479NANAWaffle bootsNANA
762172257732982371NANAWaffle bootsNANA
760230757732642391NANAWAFFLE BOOTSNANA
82773560334525171504NANAWound CareNANA
10965711722011511NANAx-rayNANA
271557112212161NA1NANANAx-rayNA5-Imaging
2714722022121615NA1NANANAX-RayNA5-Imaging
18918792029287392710NANAxigrisNANA
1333187930384287NA7NANANAxigrisNANA
1334202930384392NA10NANANAXigrisNANA
1541311187928328767NANAXIGRISNANA
19013112029283392610NANAXIGRISNANA
1332131130384283NA6NANANAXIGRISNANA
1837275543125181NA2NANANAXIGRIS CC/HRNAFree Form Intake
1810280942965114NA2NANANAxigris mcg/kg/hrNAFree Form Intake
3211586280936011452NANAXigris mcg/kg/hrNANA
1809158642965360NA5NANANAXigris mcg/kg/hrNAFree Form Intake
4331587326912810932NANAxigris mg/hrNANA
152161104165157NA1NANANAxygrisNAFree Form Intake
152561104166957NA1NANANAxygrisNAFree Form Intake
180061104293357NA1NANANAxygrisNAFree Form Intake
3761955303851121NANAXygrisNANA
86019556110515721NANAXygrisNANA
152019554165151NA2NANANAXygrisNAFree Form Intake
152419554166951NA2NANANAXygrisNAFree Form Intake
179819554293351NA2NANANAXygrisNAFree Form Intake
8613038611015711NANAXYGRISNANA
15223038416511NA1NANANAXYGRISNAFree Form Intake
15273038416691NA1NANANAXYGRISNAFree Form Intake
17963038429331NA1NANANAXYGRISNAFree Form Intake
2944369522467247NA13NANANAYawningNAToxicology
5494480472648911162NANAzantacNANA
5034273448012664893316NANAZantacNANA
55042734726126611332NANAZantacNANA
9484590638173957639NANAzosynNANA
-
ex = item.identical.pat[1,]
-

There are 3359 pairs of ITEMIDs with the same label. Of these, only 2184 are associated with any patients.

-

The way to read these rows (taking the first as an example) is as follows:

-

776, 224828, 270221, 140198, 15167, 2401, NA, 9200, Arterial Base Excess, ABG, Labs

-

There are two ITEMIDs for Arterial Base Excess: 776 and 224828, appearing 270221 and 140198 times, respectively. Item 776 occurs in 15167 patients’ records in CareView and 0 in MetaVision. Item 224828 occurs in 2401 patients’ records in CareView and 9200 in MetaVision. The categories assigned to each item/label may also differ. In this example, they are ABG and Labs.

-

The 50 most commonly occurring pairs of ITEMIDs are shown next:

-
#hist(item.identical.pat$cv.pat.n1,breaks=100)
-#hist(log(item.identical.pat$cv.pat.n1),breaks=100)
-iip.common = item.identical.pat[order(item.identical.pat$count1 + item.identical.pat$count2, decreasing = TRUE),]
-iip.common$skew = pmax(iip.common$count1 / iip.common$count2, iip.common$count2 / iip.common$count1)
-iip.skewed = subset(iip.common, !is.na(skew) & (count1+count2 >= 100))
-iip.skewed = iip.skewed[order(iip.skewed$skew),]
-kable(head(iip.common, n=50))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
itemid1itemid2count1count2cv.pat.n1cv.pat.n2mv.pat.n1mv.pat.n2labelcat1cat2skew
268021122004551906832762225299982069NA15645Heart RateNARoutine Vital Signs1.879167e+00
268261822021033959982737105224452067NA15640Respiratory RateNARespiratory1.240726e+00
7703450581516879261812919521914498NANAHR Alarm [Low]NANA1.074051e+00
12588518854916876551810587521914496NANAHR Alarm [High]NANA1.072842e+00
2776128223758322500293174217111591NA10680Code StatusNAGeneral3.461268e+01
21113110312003082110752NANACVPNANA6.001540e+05
28911623224168513562507114164992048NA15614Parameters CheckedNAAlarms1.012715e+00
268450622033942753648056313269946NA8281PEEP SetNARespiratory1.124029e+00
139547184485473730218502NANAPositionNANA2.849123e+04
294944422469738302346704613193940NA8244Mean Airway PressureNARespiratory1.219368e+00
2982642225185483446255371186841771NA12580Skin CareNATreatments1.893112e+00
12513465852135910735486240743917NANAIV #1 [Location]NANA1.011962e+00
294753522469523615545419212769923NA8215Peak Insp. PressureNARespiratory1.923279e+00
44329813420667372923087NANAFiO2NANA1.122882e+05
76933455814175416561711386681NANACVP Alarm [Low]NANA3.740784e+01
125785118548173646504481336154NANACVP Alarm [High]NANA3.745957e+01
298372225187360966257927175551774NA12607Back CareNATreatments1.399489e+00
12461698849230778629959882288193NANAGI Tube #1 [Site]NANA1.027330e+00
114819075705736062142481NANAFiO2 SetNANA2.868030e+05
2814155522387624557992942NA8244Apnea IntervalNARespiratory2.278995e+05
29464482246873645020411940NA8259Minute VolumeNARespiratory1.250567e+04
294568322468423072320950612736843NA7744Tidal Volume (Set)NARespiratory1.101272e+00
5780714554366225159813NANAFingerstick GlucoseChemistryNA8.732440e+04
2959776224828270221140198151672401NA9200Arterial Base ExcessABGLabs1.927424e+00
10461047679240022190135111NANARange of MotionNANA4.446900e+03
6411047515940022137135111NANARange of MotionNANA1.081678e+04
9643416643912815132293NANALow Exhaled Min VolNANA7.825620e+04
2979183225184248003125581114841556NA11485Eye CareNATreatments1.974845e+00
295220002247381438702223541201890NA7934Inspiratory TimeNARespiratory1.545520e+00
1256659853513248319893961294491NANAStool [Color]NANA1.501619e+00
2683814220228188568137117222513874NA15379HemoglobinHematologyLabs1.375234e+00
12523466852215627015473011431128NANAIV #2 [Location]NANA1.009953e+00
26921525220615153297153010173633899NA15414CreatinineChemistryLabs1.001876e+00
30201162225624152539152475173583899NA15413BUNNALabs1.000420e+00
26941532220635155337147786170033793NA14995MagnesiumChemistryLabs1.051094e+00
1119578733229530212126351NANAPressure SupportNANA2.460850e+04
93057863392953021126351NANAPressure SupportNANA2.953020e+05
1755019752852631593611NANAApnea Time IntervalNANA1.901753e+04
26851542220546138619133356173043877NA15377WBCHematologyLabs1.039466e+00
30361534225677134714135440170153679NA14382PhosphorousChemistryLabs1.005389e+00
29485432246961754827537612314748NA7035Plateau PressureNARespiratory2.328088e+00
27881704223773225758236903384117NA2050PA line cm MarkNAHemodynamics9.529675e+00
1092183716624800318114841NANAEye CareNANA1.377794e+04
241183217324800312114841NANAEye CareNANA2.066692e+04
302881622566714863780126133282000NA7894Ionized CalciumChemistryLabs1.855041e+00
3198153322746611535499769159053480NA13487PTTCoagsLabs1.156211e+00
4762613732029991658991NANASVRNANA1.268744e+04
3199153022746710680893040157743494NA13566INRCoagsLabs1.147979e+00
1253346785239025289496626616NANAIV #3 [Location]NANA1.008447e+00
785116258761525391173581NANABUNNANA1.525390e+05
-

and below are the 50 pairs of ITEMIDs in which the number of occurrences of each pair is closest to equal and the total is >=100.

-
kable(head(iip.skewed, n=50))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
itemid1itemid2count1count2cv.pat.n1cv.pat.n2mv.pat.n1mv.pat.n2labelcat1cat2skew
30201162225624152539152475173583899NA15413BUNNALabs1.000420
26921525220615153297153010173633899NA15414CreatinineChemistryLabs1.001876
30361534225677134714135440170153679NA14382PhosphorousChemistryLabs1.005389
1254346885243526734975249242NANAIV #4 [Location]NANA1.008349
1253346785239025289496626616NANAIV #3 [Location]NANA1.008447
12523466852215627015473011431128NANAIV #2 [Location]NANA1.009953
2693817220632233742312580321719NA5925LDHEnzymesLabs1.010768
125534698525847083787573NANAIV #5 [Location]NANA1.010981
12513465852135910735486240743917NANAIV #1 [Location]NANA1.011962
38817463098493499177NANAhi MVNANA1.012170
28911623224168513562507114164992048NA15614Parameters CheckedNAAlarms1.012715
1247169984935398953277773756NANAGI Tube #2 [Site]NANA1.013364
95316036408737422NANAmulti podis bootsNANA1.013699
1541311187928328767NANAXIGRISNANA1.014134
151106618721251234246NANAshaveNANA1.016260
1053100168517898031112NANArotationNANA1.017744
1617411071057775NANAAvDO2NANA1.019048
44016633347138135617NANACalcium gluconateNANA1.022222
30381540225693299330622384470NA1897TriglycerideChemistryLabs1.023054
445373437388251805352625138NANABANDSHeme/CoagHeme/Coag1.024587
448376837708364816153195192NANALYMPHSHeme/CoagHeme/Coag1.024874
449377137798364816153195192NANAMONOsHeme/CoagCSF1.024874
331102131316215879NANAhigh exhaled min volNANA1.025316
320215372274691121159722NA77ThrombinCoagsLabs1.026786
12461698849230778629959882288193NANAGI Tube #1 [Site]NANA1.027330
26981539220650807785701155NA515Total ProteinChemistryLabs1.028026
37826743052696722NANAWAFFLE BOOTNANA1.029851
4724326434065676567NANAHep B VaccineNANA1.030769
10802095703312613044NANAOctreotideNANA1.031746
29337632246394797946452165531081NA9071Daily WeightNAGeneral1.032873
2342056213111712164NANANitric OxideNANA1.034188
77821455841575924NANAtpaNANA1.035088
26851542220546138619133356173043877NA15377WBCHematologyLabs1.039466
63546965114495122NANACalcium GlubionateNANA1.040816
100449726536495133NANAerythromycinNANA1.040816
4624225426710081051161134NANAHeparin flushNANA1.042659
3141898277820020996NANApantoprazoleNANA1.045000
453372541941141096965NANAABG POTASSIUMABG’SABG’S1.045872
2361419213221622722NANAFLOLANNANA1.050926
26941532220635155337147786170033793NA14995MagnesiumChemistryLabs1.051094
5811041468787442NANAHI/Minute/vol/alarmNANA1.054054
123265568279616571NANAPrednisolone dropsNANA1.065574
31861521227456244542294580521927NA6537AlbuminChemistryLabs1.065766
3024796225639218692042589721914NA6860Differential-BasosHematologyLabs1.070698
3027799225642218712042589721914NA6860Differential-MonosHematologyLabs1.070796
3025797225640218702042489721914NA6860Differential-EosHematologyLabs1.070799
3026798225641218722042589721914NA6860Differential-LymphsHematologyLabs1.070845
465421742794604934952NANAHep FlushNANA1.071739
12588518854916876551810587521914496NANAHR Alarm [High]NANA1.072842
7703450581516879261812919521914498NANAHR Alarm [Low]NANA1.074051
-

We can examine the distributions of commonly-occurring items to see how well they correlate. For example, here is a comparison of the cumulative distributions of BUN values (ITEMIDs 1162 and 225624). We expect them to be very similar, almost identical density plots or histograms. Note that we do need to eliminate non-sense outlier values such as negative creatinines or BUN.

-
library(ggplot2)
-
-compare.dist = function(itemid1, itemid2, top=1000, bot=0, h=TRUE) {
-  items = dbGetQuery(con, paste("select itemid, valuenum from chartevents where itemid in (", itemid1, ",", itemid2, ") and valuenum is not null and valuenum <= ", top, " and valuenum >= ", bot, sep=""))
-  items$itemid = as.character(items$itemid)
-  # xitems <<- items
-  title = chart.items[chart.items$itemid==itemid1,"label"]
-  bw = (max(items$valuenum) - min(items$valuenum)) / 30
-  if (h) {
-    ggplot(items, aes(valuenum, fill = itemid)) + ggtitle(title) + geom_histogram(alpha = 0.5, aes(y = ..density..), position = 'identity', binwidth = bw)
-  }
-  else ggplot(items, aes(valuenum, fill = itemid)) + geom_density(alpha = 0.2) + ggtitle(title)
-  #
-}
-compare.dist(1162, 225624, 250)
-

-
compare.dist(1525, 220615, 10)
-

-
compare.dist(1534, 225677, 35)
-

-
compare.dist(817, 220632)
-

-
compare.dist(211, 220045, 250)
-

-
compare.dist(618, 220210, 60)
-

-
compare.dist(506, 220339, 30)
-

-
compare.dist(444, 224697, 50)
-

-
compare.dist(535, 224695, 60)
-

-
compare.dist(683, 224684)
-

-
compare.dist(776, 224828, 25)
-

-
compare.dist(2000, 224738, 2)
-

-
compare.dist(814, 220228, 20, 5)
-

-
compare.dist(1532, 220635, 4)
-

-
compare.dist(1542, 220546, 60)
-

-
compare.dist(1704, 223773, 100)
-

-
compare.dist(816, 225667, 2)
-

-

And indeed, that is what we see for BUN, Creatinine, Phosphorus, LDH, etc. Therefore, it may be reasonable to conclude that these ITEMIDs are equivalent. However, the distribution of Heart Rates is oddly different, with a large density of fast heart rates over 150 in 211, but not in 220045. All the 211 values come from Careview patients, and the vast majority of 220045 values (15645/17714) come from Metavision. (The remainder may also, but for patients who got SUBJECT_IDs earlier in Careview.)

-
-
-

D_LABITEMS

-

We now turn to exploring D_LABITEMS and the LABEVENTS they index.

-
labitems = dbGetQuery(con, "select * from d_labitems")
-labitems.summary = dbGetQuery(con, "select category, fluid, count(*) c, group_concat(label separator ', ') from d_labitems group by category, fluid order by category, fluid")
-
-kable(labitems.summary)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
categoryfluidcgroup_concat(label separator ‘,’)
BLOOD GASBLOOD30SPECIMEN TYPE, ALVEOLAR-ARTERIAL GRADIENT, BASE EXCESS, CALCULATED BICARBONATE, WHOLE BLOOD, CALCULATED TOTAL CO2, CARBOXYHEMOGLOBIN, CHLORIDE, WHOLE BLOOD, COMMENTS, FREE CALCIUM, GLUCOSE, HEMATOCRIT, CALCULATED, HEMOGLOBIN, INTUBATED, LACTATE, METHEMOGLOBIN, O2 FLOW, OXYGEN, OXYGEN SATURATION, PCO2, PEEP, PH, PO2, POTASSIUM, WHOLE BLOOD, REQUIRED O2, SODIUM, WHOLE BLOOD, TEMPERATURE, TIDAL VOLUME, VENTILATION RATE, VENTILATOR, VOIDED SPECIMEN
BLOOD GASOTHER BODY FLUID7FLUID TYPE, PCO2, BODY FLUID, PH, PO2, BODY FLUID, POTASSIUM, SODIUM, BODY FLUID, VOIDED SPECIMEN
CHEMISTRYASCITES18ALBUMIN, ASCITES, AMYLASE, ASCITES, BICARBONATE, ASCITES, BILIRUBIN, TOTAL, ASCITES, CHLORIDE, ASCITES, CHOLESTEROL, ASCITES, CREATININE, ASCITES, GLUCOSE, ASCITES, LACTATE DEHYDROGENASE, ASCITES, LIPASE, ASCITES, MISCELLANEOUS, ASCITES, OSMOLALITY, ASCITES, POTASSIUM, ASCITES, SODIUM, ASCITES, TOTAL PROTEIN, ASCITES, TRIGLYCERIDES, ASCITES, UREA NITROGEN, ASCITES, VOIDED SPECIMEN
CHEMISTRYBLOOD169% HEMOGLOBIN A1C, 25-OH VITAMIN D, ABSOLUTE A1C, ABSOLUTE HEMOGLOBIN, ACETAMINOPHEN, ACETONE, ACID PHOSPHATASE, ACID PHOSPHATASE, NON-PROSTATIC, AFP, MATERNAL SCREEN, ALANINE AMINOTRANSFERASE (ALT), ALBUMIN, ALKALINE PHOSPHATASE, ALPHA-FETOPROTEIN, AMIKACIN, AMMONIA, AMYLASE, ANION GAP, ANTI-DGP (IGA/IGG), ANTI-GLIADIN ANTIBODY, IGA, ANTI-MITOCHONDRIAL ANTIBODY, ANTI-NEUTROPHIL CYTOPLASMIC ANTIBODY, ANTI-NUCLEAR ANTIBODY, ANTI-NUCLEAR ANTIBODY, TITER, ANTI-PARIETAL CELL ANTIBODY, ANTI-SMOOTH MUSCLE ANTIBODY, ANTI-THYROGLOBULIN ANTIBODIES, ASPARATE AMINOTRANSFERASE (AST), BARBITURATE SCREEN, BENZODIAZEPINE SCREEN, BETA-2 MICROGLOBULIN, BICARBONATE, BILIRUBIN, DIRECT, BILIRUBIN, INDIRECT, BILIRUBIN, TOTAL, BLOOD CULTURE HOLD, BLUE TOP HOLD, BLUE TOP HOLD FROZEN, C-REACTIVE PROTEIN, C3, C4, CA-125, CALCIUM, TOTAL, CALCULATED FREE TESTOSTERONE, CALCULATED TBG, CALCULATED THYROXINE (T4) INDEX, CALL, CANCER ANTIGEN 27.29, CARBAMAZEPINE, CARCINOEMBYRONIC ANTIGEN (CEA), CENTROMERE, CHLORIDE, CHOLESTEROL RATIO (TOTAL/
CHEMISTRYCEREBROSPINAL FLUID (CSF)8, BILIRUBIN, TOTAL, CSF, CHLORIDE, CSF, GLUCOSE, CSF, LACTATE DEHYDROGENASE, CSF, MISCELLANEOUS, CSF, PEP, CSF, TOTAL PROTEIN, CSF
CHEMISTRYCSF1VOIDED SPECIMEN
CHEMISTRYJOINT FLUID6ALBUMIN, JOINT FLUID, AMYLASE, JOINT FLUID, CREATININE, JOINT FLUID, GLUCOSE, JOINT FLUID, LD, JOINT FLUID, TOTAL PROTEIN, JOINT FLUID
CHEMISTRYOTHER BODY FLUID25PG, AF-AFP, ALBUMIN, BODY FLUID, AMYLASE, BODY FLUID, BICARBONATE, OTHER FLUID, BILIRUBIN, TOTAL, BODY FLUID, CALCIUM, BODY FLUID, CHLORIDE, BODY FLUID, CHOLESTEROL, BODY FLUID, CREATININE, BODY FLUID, FETALFN, GLUCOSE, BODY FLUID, LD, BODY FLUID, VOIDED SPECIMEN, LIPASE, BODY FLUID, MAGNESIUM, BODY FLUID, MISCELLANEOUS, BODY FLUID, OSMOLALITY, BODY FLUID, PHOSPHATE, BODY FLUID, POTASSIUM, BODY FLUID, SODIUM, BODY FLUID, TOTAL PROTEIN, BODY FLUID, SURFACTANT ALBUMIN RATIO, TRIGLYCER, UREA NITROGEN, BODY FLUID
CHEMISTRYPLEURAL15MISCELLANEOUS, PLEURAL, POTASSIUM, PLEURAL, SODIUM, PLEURAL, TOTAL PROTEIN, PLEURAL, TRIGLYCERIDES, PLEURAL, ALBUMIN, PLEURAL, AMYLASE, PLEURAL, BICARBONATE, PLEURAL, BILIRUBIN, TOTAL, PLEURAL, CHLORIDE, PLEURAL, CHOLESTEROL, PLEURAL, CREATININE, PLEURAL, GLUCOSE, PLEURAL, LACTATE DEHYDROGENASE, PLEURAL, LIPASE, PLEURAL
CHEMISTRYSTOOL6BICARBONATE, STOOL, CHLORIDE, STOOL, OSMOLALITY, STOOL, POTASSIUM, STOOL, SODIUM, STOOL, VOIDED SPECIMEN
CHEMISTRYURINE4724 HR CALCIUM, 24 HR CREATININE, 24 HR PROTEIN, ALBUMIN, URINE, ALBUMIN/CREATININE, URINE, AMPHETAMINE SCREEN, URINE, AMYLASE, URINE, AMYLASE/CREATININE RATIO, URINE, BARBITURATE SCREEN, URINE, BENZODIAZEPINE SCREEN, URINE, BICARBONATE, URINE, CALCIUM, URINE, CHLORIDE, URINE, COCAINE, URINE, CREATININE CLEARANCE, CREATININE, SERUM, CREATININE, URINE, ETHANOL, URINE, GLUCOSE, URINE, HCG, URINE, QUALITATIVE, IMMUNOFIXATION, URINE, LENGTH OF URINE COLLECTION, MAGNESIUM, URINE, MARIJUANA, METHADONE, URINE, MYOGLOBIN, URINE, OPIATE SCREEN, URINE, OSMOLALITY, URINE, PH, PHOSPHATE, URINE, PORPHOBILINOGEN SCREEN, POTASSIUM, URINE, PROT. ELECTROPHORESIS, URINE, PROTEIN/CREATININE RATIO, SODIUM, URINE, TOTAL COLLECTION TIME, TOTAL PROTEIN, URINE, UHOLD, UREA NITROGEN, URINE, URIC ACID, URINE, URINE CREATININE, URINE TUBE, HELD, URINE VOLUME, URINE VOLUME, TOTAL, GR HOLD, STDYURINE, VOIDED SPECIMEN
HEMATOLOGYASCITES20ATYPICAL LYMPHOCYTES, BANDS, BASOPHILS, BLASTS, EOSINOPHILS, HEMATOCRIT, ASCITES, LYMPHOCYTES, MACROPHAGE, MESOTHELIAL CELL, METAMYELOCYTES, MONOCYTES, MYELOCYTES, NUCLEATED RBC, OTHER, PLASMA, POLYS, PROMYELOCYTES, RBC, ASCITES, WBC, ASCITES, YOUNG
HEMATOLOGYBLOOD177ABSOLUTE CD3 COUNT, ABSOLUTE CD4 COUNT, ABSOLUTE CD8 COUNT, ABSOLUTE LYMPHOCYTE COUNT, ACANTHOCYTES, ADP, ALPHA ANTIPLASMIN, ANISOCYTOSIS, ANTICARDIOLIPIN ANTIBODY IGG, ANTICARDIOLIPIN ANTIBODY IGM, ANTITHROMBIN, APT TEST, ARACHADONIC ACID, ATYPICAL LYMPHOCYTES, BANDS, BASOPHILIC STIPPLING, BASOPHILS, BITE CELLS, BLASTS, BLEEDING TIME, BLOOD PARASITE SMEAR, BURR CELLS, CD10, CD103, CD117, CD11C, CD13, CD138, CD14, CD15, CD15/56, CD16, CD16/56 ABSOLUTE COUNT, CD16/56%, CD19, CD19 %, CD19 ABSOLUTE COUNT, CD2, CD20, CD20 %, CD20 ABSOLUTE COUNT, CD22, CD23, CD25, CD3 %, CD3 ABSOLUTE COUNT, CD3 CELLS, PERCENT, CD33, CD34, CD38, CD4 CELLS, PERCENT, CD4/CD8 RATIO, CD41, CD45, CD5, CD5 %, CD5 ABSOLUTE COUNT, CD55, CD56, CD57, CD59, CD64, CD7, CD71, CD8 CELLS, PERCENT, COLLAGEN, D-DIMER, ELLIPTOCYTES, ENVELOPE CELLS, EOSINOPHIL COUNT, EOSINOPHILS, EPINEPHERINE, FACTOR II, FACTOR IX, FACTOR V, FACTOR VII, FACTOR VIII, FACTOR VIII INHIBITOR, FACTOR X, FACTOR XI, FACTOR XII, FACTOR XIII, FETAL HEMOGLOBIN, FIBRIN DEGRADAT
HEMATOLOGYBONE MARROW40CD16/56, CD19, CD2, CD20, CD22, CD23, CD25, CD3, CD33, CD34, CD38, CD4, CD41, CD45, CD5, CD55, CD56, CD57, CD59, CD64, CD7, CD71, CD8, FMC-7, GLYCO A, HLA-DR, IMMUNOPHENOTYPING, IRON STAIN, KAPPA, LAMBDA, WRIGHT GIEMSA, CD10, CD103, CD117, CD11C, CD13, CD138, CD14, CD15, CD16
HEMATOLOGYCEREBROSPINAL FLUID (CSF)22ATYPICAL LYMPHOCYTES, BANDS, BASOPHILS, BLASTS, EOSINOPHILS, HEMATOCRIT, CSF, HYPERSEGMENTED NEUTROPHILS, IMMUNOPHENOTYPING, LYMPHS, MACROPHAGE, MESOTHELIAL CELLS, METAMYELOCYTES, MONOCYTES, MYELOCYTES, NRBC, OTHER, PLASMA, POLYS, PROMYELOCYTES, RBC, CSF, WBC, CSF, YOUNG
HEMATOLOGYJOINT FLUID22ATYPICAL LYMPHOCYTES, BANDS, BASOPHILS, EOSINOPHILS, HEMATOCRIT, JOINT FLUID, JOINT CRYSTALS, BIREFRINGENCE, JOINT CRYSTALS, COMMENT, JOINT CRYSTALS, LOCATION, JOINT CRYSTALS, NUMBER, JOINT CRYSTALS, SHAPE, LYMPHOCYTES, MACROPHAGE, MESOTHELIAL CELLS, METAMYELOCYTES, MONOCYTES, NRBC, OTHER, POLYS, RBC, JOINT FLUID, WBC, JOINT FLUID, MYELOS, VOIDED SPECIMEN
HEMATOLOGYOTHER BODY FLUID59ATYPICAL LYMPHOCYTES, BANDS, BASOPHILS, CD10, CD103, CD117, CD11C, CD13, CD138, CD14, CD15, CD16, CD16/56, CD19, CD2, CD20, CD22, CD23, CD25, CD3, CD33, CD34, CD38, CD4, CD4/CD8 RATIO, CD41, CD45, CD5, CD56, CD57, CD64, CD7, CD71, CD8, EOSINOPHILS, FMC-7, GLYCO A, HEMATOCRIT, OTHER FLUID, HLA-DR, IMMUNOPHENOTYPING, KAPPA, LAMBDA, LYMPHOCYTES, MACROPHAGE, MESOTHELIAL CELLS, METAMYELOCYTES, MONOS, MYELOCYTES, NRBC, OTHER CELL, PLASMA, POLYS, PROMYELOCYTES, RBC, OTHER FLUID, WBC, OTHER FLUID, CD55, CD59, TDT, VOIDED SPECIMEN
HEMATOLOGYPLEURAL20ATYPICAL LYMPHOCYTES, BANDS, BASOPHILS, BLASTS, EOSINOPHILS, HEMATOCRIT, PLEURAL, LYMPHOCYTES, MACROPHAGES, MESOTHELIAL CELLS, METAMYELOCYTES, MONOS, MYELOCYTES, NRBC, OTHER, PLASMA CELLS, POLYS, PROMYELOCYTES, RBC, PLEURAL, WBC, PLEURAL, YOUNG CELLS
HEMATOLOGYSTOOL1BLOOD, OCCULT
HEMATOLOGYURINE62AMMONIUM BIURATE, AMORPHOUS CRYSTALS, BACTERIA, BILIRUBIN, BILIRUBIN CRYSTALS, BLOOD, BROAD CASTS, CALCIUM CARBONATE CRYSTALS, CALCIUM OXALATE CRYSTALS, CALCIUM PHOSPHATE CRYSTALS, CELLULAR CAST, CHOLESTEROL CRYSTALS, CYSTEINE CRYSTALS, EOSINOPHILS, EPITHELIAL CASTS, EPITHELIAL CELLS, FREE FAT, GLUCOSE, GRANULAR CASTS, HEMATOCRIT, HEMOSIDERIN, HYALINE CASTS, HYPHENATED YEAST, KETONE, LEUCINE CRYSTALS, LEUKOCYTES, NITRITE, NON-SQUAMOUS EPITHELIAL CELLS, NONSQUAMOUS EPITHELIAL CELL, OVAL FAT BODY, PH, PROTEIN, RBC, RBC CASTS, RBC CLUMPS, REDUCING SUBSTANCES, URINE, RENAL EPITHELIAL CELLS, SPECIFIC GRAVITY, SPERM, SULFONAMIDES, TRANSITIONAL EPITHELIAL CELLS, TRICHOMONAS, TRIPLE PHOSPHATE CRYSTALS, TYROSINE CRYSTALS, URIC ACID CRYSTALS, URINE APPEARANCE, URINE CASTS, OTHER, URINE COLOR, URINE COMMENTS, URINE CRYSTALS, OTHER, URINE FAT BODIES, URINE MUCOUS, URINE SPECIMEN TYPE, UROBILINOGEN, WAXY CASTS, WBC, WBC CASTS, WBC CLUMPS, YEAST, FATTY, PROBLEM SPECIMEN, VOIDED SPECIMEN
-

There are 755 D_LABITEMS, but only 590 distinct labels. Therefore, we investigate, as we did for chart items, situations in which multiple IDs exist for identical labels.

-
labitems.same.label <- dbGetQuery(con, "select x.itemid as itemid1, y.itemid as itemid2, x.label as label, x.category as cat1, x.fluid as fluid1, y.category as cat2, y.fluid as fluid2, x.loinc_code as loinc1, y.loinc_code as loinc2 from d_labitems x join d_labitems y on x.label=y.label where x.itemid<y.itemid order by x.itemid")
-
## Warning in .local(conn, statement, ...): Unsigned INTEGER in col 0 imported
-## as numeric
-
## Warning in .local(conn, statement, ...): Unsigned INTEGER in col 1 imported
-## as numeric
-
labitems.identical <- dbGetQuery(con, "select x.itemid as itemid1, y.itemid as itemid2, x.label as label, x.category as category, x.fluid as fluid, x.loinc_code as loinc1, y.loinc_code as loinc2 from d_labitems x join d_labitems y on x.label=y.label and x.category=y.category and x.fluid=y.fluid where x.itemid<y.itemid order by x.itemid")
-
## Warning in .local(conn, statement, ...): Unsigned INTEGER in col 0 imported
-## as numeric
-
## Warning in .local(conn, statement, ...): Unsigned INTEGER in col 1 imported
-## as numeric
-

Although there are numerous lab items with the same label, the combination of {label, fluid, category} is unique in this table. Therefore, we don’t seem to have the same problem as with D_ITEMS, where multiple item numbers represent the same data.

-
if (exists("labitems.per.pat")) {
-  
-} else if (file.exists("lab-items.csv")) {
-  labitems.per.pat = read.csv("lab-items.csv", row.names = 1)
-  labitems.per.pat$LABEL = as.character(labitems.per.pat$LABEL)
-  labitems.per.pat$FLUID = as.character(labitems.per.pat$FLUID)
-  labitems.per.pat$CATEGORY = as.character(labitems.per.pat$CATEGORY)
-  labitems.per.pat$LOINC_CODE = as.character(labitems.per.pat$LOINC_CODE)
-  print("lab-items.csv read.")
-} else {
-  labitems.per.pat = dbGetQuery(con, "select itemid, count(*) count from labevents group by itemid")
-  labitems.per.mv = dbGetQuery(con, "select itemid, count(*) mvcount, count(distinct subject_id) mvpat from labevents where subject_id>=40000 group by itemid")
-  labitems.per.cv = dbGetQuery(con, "select itemid, count(*) cvcount, count(distinct subject_id) cvpat from labevents where subject_id<40000 group by itemid")
-  labitems.per.pat = merge(labitems, labitems.per.pat, by.x="ITEMID", by.y="itemid")
-  labitems.per.pat = merge(labitems.per.pat, labitems.per.cv, by.x="ITEMID", by.y="itemid", all=TRUE)
-  labitems.per.pat = merge(labitems.per.pat, labitems.per.mv, by.x="ITEMID", by.y="itemid", all=TRUE)
-}
-
## [1] "lab-items.csv read."
-
if (!file.exists("lab-items.csv")) {
-  write.csv(labitems.per.pat, "lab-items.csv")
-  print("lab-items.csv written.")
-}
-kable(labitems.per.pat)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
ITEMIDROW_IDLABELFLUIDCATEGORYLOINC_CODEcountcvcountcvpatmvcountmvpat
508001SPECIMEN TYPEBLOODBLOOD GASNA40478535482420023499613796
508012ALVEOLAR-ARTERIAL GRADIENTBLOODBLOOD GAS19991-92201617495707945212320
508023BASE EXCESSBLOODBLOOD GAS11555-04906953609852044012971010987
508034CALCULATED BICARBONATE, WHOLE BLOODBLOODBLOOD GAS1959-692465875430533712839
508045CALCULATED TOTAL CO2BLOODBLOOD GAS1959-64906853609822044112970310986
508056CARBOXYHEMOGLOBINBLOODBLOOD GAS20563-3205613271159729663
508067CHLORIDE, WHOLE BLOODBLOODBLOOD GAS2069-3481872867810580195097928
508089FREE CALCIUMBLOODBLOOD GAS1994-324911117809215810710199118
5080910GLUCOSEBLOODBLOOD GAS2339-019673414271515155540198903
5081011HEMATOCRIT, CALCULATEDBLOODBLOOD GAS20570-8897156430614283254097415
5081112HEMOGLOBINBLOODBLOOD GAS718-7897126430314283254097415
5081213INTUBATEDBLOODBLOOD GASNA16368014773717738159433027
5081314LACTATEBLOODBLOOD GAS32693-4187124113306160927381812293
5081415METHEMOGLOBINBLOODBLOOD GAS2614-6204713311100716603
5081516O2 FLOWBLOODBLOOD GAS3151-8123079876539924311655
5081617OXYGENBLOODBLOOD GAS19994-312797810453713139234414989
5081718OXYGEN SATURATIONBLOODBLOOD GAS20564-117341913190113490415186995
5081819PCO2BLOODBLOOD GAS11557-64906383609462043912969210985
5081920PEEPBLOODBLOOD GAS20077-486921676238812192983818
5082021PHBLOODBLOOD GAS11558-45307963910552100413974111473
5082122PO2BLOODBLOOD GAS11556-84906723609552043912971710985
5082223POTASSIUM, WHOLE BLOODBLOODBLOOD GAS6298-419294614105816412518889568
5082324REQUIRED O2BLOODBLOOD GASNA2205717533708345242319
5082425SODIUM, WHOLE BLOODBLOODBLOOD GAS2947-0715034798813494235158170
5082526TEMPERATUREBLOODBLOOD GASNA1294549891011498305445412
5082627TIDAL VOLUMEBLOODBLOOD GAS20112-983808670199828167893910
5082728VENTILATION RATEBLOODBLOOD GASNA77611703051102473061495
5082829VENTILATORBLOODBLOOD GASNA60544547811228757631933
5082930FLUID TYPEOTHER BODY FLUIDBLOOD GAS14725-6342342263NANA
5083031PCO2, BODY FLUIDOTHER BODY FLUIDBLOOD GAS2023-0434321NANA
5083132PHOTHER BODY FLUIDBLOOD GAS2748-21442979764463366
5083233PO2, BODY FLUIDOTHER BODY FLUIDBLOOD GAS2706-0131310NANA
5083334POTASSIUMOTHER BODY FLUIDBLOOD GAS2821-7222NANA
5083435SODIUM, BODY FLUIDOTHER BODY FLUIDBLOOD GAS2950-4111NANA
5083536ALBUMIN, ASCITESASCITESCHEMISTRY1749-115921103554489255
5083637AMYLASE, ASCITESASCITESCHEMISTRY1797-01059663446396259
5083738BICARBONATE, ASCITESASCITESCHEMISTRY54360-34328211510
5083839BILIRUBIN, TOTAL, ASCITESASCITESCHEMISTRY14422-0784489302295207
5083940CHLORIDE, ASCITESASCITESCHEMISTRY33366-64527221812
5084041CHOLESTEROL, ASCITESASCITESCHEMISTRY14441-072255
5084142CREATININE, ASCITESASCITESCHEMISTRY12191-3520312220208136
5084243GLUCOSE, ASCITESASCITESCHEMISTRY2347-31186741450445268
5084344LACTATE DEHYDROGENASE, ASCITESASCITESCHEMISTRY2531-21243771467472288
5084445LIPASE, ASCITESASCITESCHEMISTRY32722-11410644
5084546MISCELLANEOUS, ASCITESASCITESCHEMISTRYNA87611
5084647OSMOLALITY, ASCITESASCITESCHEMISTRY2691-4222NANA
5084748POTASSIUM, ASCITESASCITESCHEMISTRY49789-14830251813
5084849SODIUM, ASCITESASCITESCHEMISTRY49790-95838322013
5084950TOTAL PROTEIN, ASCITESASCITESCHEMISTRY2883-718051143571662351
5085051TRIGLYCERIDES, ASCITESASCITESCHEMISTRY14447-710655395145
5085152UREA NITROGEN, ASCITESASCITESCHEMISTRY12265-516141122
5085253% HEMOGLOBIN A1CBLOODCHEMISTRY4548-41661812128600644903732
508535425-OH VITAMIN DBLOODCHEMISTRYNA74776762
5085455ABSOLUTE A1CBLOODCHEMISTRY4548-4379137912336NANA
5085556ABSOLUTE HEMOGLOBINBLOODCHEMISTRY718-7379137912336NANA
5085657ACETAMINOPHENBLOODCHEMISTRY3297-995607579503719811430
5085758ACETONEBLOODCHEMISTRY5567-3140913298678074
5085859ACID PHOSPHATASEBLOODCHEMISTRY1715-2777NANA
5085960ACID PHOSPHATASE, NON-PROSTATICBLOODCHEMISTRYNA121212NANA
5086061AFP, MATERNAL SCREENBLOODCHEMISTRYNA887NANA
5086162ALANINE AMINOTRANSFERASE (ALT)BLOODCHEMISTRY1742-6219475148516188527095911359
5086263ALBUMINBLOODCHEMISTRY1751-7146697105045175904165210469
5086364ALKALINE PHOSPHATASEBLOODCHEMISTRY6768-6207860139438188676842211195
5086465ALPHA-FETOPROTEINBLOODCHEMISTRY1834-127792137936642422
5086566AMIKACINBLOODCHEMISTRYNA1485459418
5086667AMMONIABLOODCHEMISTRY16362-6390829141512994652
5086768AMYLASEBLOODCHEMISTRY1798-8636645284314045108214318
5086869ANION GAPBLOODCHEMISTRY1863-07698955311842556023871115609
5086970ANTI-DGP (IGA/IGG)BLOODCHEMISTRYNA113388
5087071ANTI-GLIADIN ANTIBODY, IGABLOODCHEMISTRY7893-1151515NANA
5087172ANTI-MITOCHONDRIAL ANTIBODYBLOODCHEMISTRY14236-4394291263103100
5087273ANTI-NEUTROPHIL CYTOPLASMIC ANTIBODYBLOODCHEMISTRY35279-9794668561126113
5087374ANTI-NUCLEAR ANTIBODYBLOODCHEMISTRY5047-6201016761376334308
5087475ANTI-NUCLEAR ANTIBODY, TITERBLOODCHEMISTRY8061-45654884007772
5087576ANTI-PARIETAL CELL ANTIBODYBLOODCHEMISTRY26969-616141422
5087677ANTI-SMOOTH MUSCLE ANTIBODYBLOODCHEMISTRY14252-1399289262110103
5087778ANTI-THYROGLOBULIN ANTIBODIESBLOODCHEMISTRY5380-12662201584638
5087879ASPARATE AMINOTRANSFERASE (AST)BLOODCHEMISTRY1920-8219465148653187657081211359
5087980BARBITURATE SCREENBLOODCHEMISTRY3376-1117617776514439853395
5088081BENZODIAZEPINE SCREENBLOODCHEMISTRY42662-7118237812515640113399
5088182BETA-2 MICROGLOBULINBLOODCHEMISTRY32731-235217880174103
5088283BICARBONATEBLOODCHEMISTRY1963-87807335402432557624049015609
5088384BILIRUBIN, DIRECTBLOODCHEMISTRY1968-745205394231093657822235
5088485BILIRUBIN, INDIRECTBLOODCHEMISTRY1971-144385388961077454892142
5088586BILIRUBIN, TOTALBLOODCHEMISTRY1975-2238277167744244667053311211
5088687BLOOD CULTURE HOLDBLOODCHEMISTRYNA188188167NANA
5088788BLUE TOP HOLDBLOODCHEMISTRYNA2188018605921832751980
5088889BLUE TOP HOLD FROZENBLOODCHEMISTRYNA97422
5088990C-REACTIVE PROTEINBLOODCHEMISTRY1988-566044448234521561265
5089091C3BLOODCHEMISTRY4485-91290932625358284
5089192C4BLOODCHEMISTRY4498-21352971648381305
5089293CA-125BLOODCHEMISTRY10334-188972823516190
5089394CALCIUM, TOTALBLOODCHEMISTRY2000-85919993945952293319740415060
5089495CALCULATED FREE TESTOSTERONEBLOODCHEMISTRY2991-84233672645646
5089596CALCULATED TBGBLOODCHEMISTRY3027-0592394252198119
5089697CALCULATED THYROXINE (T4) INDEXBLOODCHEMISTRY32215-6584390251194117
5089899CANCER ANTIGEN 27.29BLOODCHEMISTRY17842-65514189413351
50899100CARBAMAZEPINEBLOODCHEMISTRY3432-288272618315664
50900101CARCINOEMBYRONIC ANTIGEN (CEA)BLOODCHEMISTRY2039-630312361955670426
50901102CENTROMEREBLOODCHEMISTRY16570-439332766
50902103CHLORIDEBLOODCHEMISTRY2075-07955685429312559225263715611
50903104CHOLESTEROL RATIO (TOTAL/HDL)BLOODCHEMISTRY9322-91812814938733531902600
50904105CHOLESTEROL, HDLBLOODCHEMISTRY2085-91826015029738232312624
50905106CHOLESTEROL, LDL, CALCULATEDBLOODCHEMISTRY2090-91648613497696529892480
50906107CHOLESTEROL, LDL, MEASUREDBLOODCHEMISTRY18262-6200116441036357304
50907108CHOLESTEROL, TOTALBLOODCHEMISTRY2093-31944415991770334532777
50908109CK-MB INDEXBLOODCHEMISTRY20569-02291116738490561732123
50909110CORTISOLBLOODCHEMISTRY2143-61355410730394728241492
50910111CREATINE KINASE (CK)BLOODCHEMISTRY2157-61324049807815814343268912
50911112CREATINE KINASE, MB ISOENZYMEBLOODCHEMISTRY6773-61158078627214870295358110
50912113CREATININEBLOODCHEMISTRY2160-07974765495422368524793415666
50913114CRYOGLOBULINBLOODCHEMISTRY5117-74584082645041
50914115CYCLOSPORINBLOODCHEMISTRY3521-287777434268134373
50915116D-DIMERBLOODCHEMISTRYNA1192237172955677
50916117DHEA-SULFATEBLOODCHEMISTRY2191-5584336158
50917118DIGOXINBLOODCHEMISTRY10535-38358657718321781676
50918119DOUBLE STRANDED DNABLOODCHEMISTRY5130-03012401526142
50919120EDTA HOLDBLOODCHEMISTRYNA9155783351411322910
50920121ESTIMATED GFR (MDRD EQUATION)BLOODCHEMISTRY33914-363253432168453200375234
50921122ESTRADIOLBLOODCHEMISTRY2243-4766253146
50922123ETHANOLBLOODCHEMISTRY5642-498647712501921521621
50924125FERRITINBLOODCHEMISTRY2276-41794813033706549153392
50925126FOLATEBLOODCHEMISTRY2284-887526828495219241697
50926127FOLLICLE STIMULATING HORMONEBLOODCHEMISTRY15067-23062461906051
50927128GAMMA GLUTAMYLTRANSFERASEBLOODCHEMISTRY2324-2253019911268539399
50928129GASTRINBLOODCHEMISTRY2333-3999978NANA
50929130GENTAMICINBLOODCHEMISTRY35668-3651158081537703153
50930131GLOBULINBLOODCHEMISTRY2336-679206349349115711062
50931132GLUCOSEBLOODCHEMISTRY2345-77489815141482319423483315607
50932133GRAY TOP HOLD (PLASMA)BLOODCHEMISTRYNA717515437202186
50933134GREEN TOP HOLD (PLASMA)BLOODCHEMISTRYNA26855234221050834332246
50934135HBLOODCHEMISTRYNA65511
50935136HAPTOGLOBINBLOODCHEMISTRY4542-7119708406427735642035
50936137HCG, MATERNAL SCREENBLOODCHEMISTRYNA665NANA
50937138HEPATITIS A VIRUS ANTIBODYBLOODCHEMISTRY5183-9149412111103283260
50938139HEPATITIS A VIRUS IGM ANTIBODYBLOODCHEMISTRY22314-9722595559127121
50939140HEPATITIS B CORE ANTIBODY, IGMBLOODCHEMISTRY31204-1677554524123118
50940141HEPATITIS B SURFACE ANTIBODYBLOODCHEMISTRY5193-8417534812318694510
50941142HEPATITIS B SURFACE ANTIGENBLOODCHEMISTRY5196-1402033182395702517
50942143HEPATITIS B VIRUS CORE ANTIBODYBLOODCHEMISTRY5187-0280423041923500439
50943144HEPATITIS C VIRUS ANTIBODYBLOODCHEMISTRY16128-1339928032365596533
50944145HIV ANTIBODYBLOODCHEMISTRY5220-9159712711129326296
50945146HOMOCYSTEINEBLOODCHEMISTRY13965-98127336047973
50946147HUMAN CHORIONIC GONADOTROPINBLOODCHEMISTRY2119-6872701515171131
50947148IBLOODCHEMISTRYNA777NANA
50948149IMMUNOFIXATIONBLOODCHEMISTRY49275-1815673592142126
50949150IMMUNOGLOBULIN ABLOODCHEMISTRY2458-8274418911095853576
50950151IMMUNOGLOBULIN GBLOODCHEMISTRY2465-33320230612101014668
50951152IMMUNOGLOBULIN MBLOODCHEMISTRY2472-9260818591062749518
50952153IRONBLOODCHEMISTRY2498-41820713295707349123470
50953154IRON BINDING CAPACITY, TOTALBLOODCHEMISTRY2500-71723112602687846293349
50954155LACTATE DEHYDROGENASE (LD)BLOODCHEMISTRY2532-01071226821212992389108460
50955156LIGHT GREEN TOP HOLDBLOODCHEMISTRYNA55972285113433122195
50956157LIPASEBLOODCHEMISTRY3040-3653754882011572165557068
50957158LITHIUMBLOODCHEMISTRY3719-289065318323778
50958159LUTEINIZING HORMONEBLOODCHEMISTRY1599-02291791415045
50959160MACRO PROLACTINBLOODCHEMISTRYNA666NANA
50960161MAGNESIUMBLOODCHEMISTRY2601-36641914471872288221700415504
50961162METHOTREXATEBLOODCHEMISTRYNA406135727128
50962163N-ACETYLPROCAINAMIDE (NAPA)BLOODCHEMISTRY3834-91951925631
50963164NTPROBNPBLOODCHEMISTRY33762-669434061220328822062
50964165OSMOLALITY, MEASUREDBLOODCHEMISTRY2692-21881711431322973861952
50965166PARATHYROID HORMONEBLOODCHEMISTRY2731-8435536011595754531
50966167PHENOBARBITALBLOODCHEMISTRY3947-91871155723231445
50967168PHENYTOINBLOODCHEMISTRY3967-72245316268260061851295
50968169PHENYTOIN, FREEBLOODCHEMISTRY3969-396080530015588
50969170PHENYTOIN, PERCENT FREEBLOODCHEMISTRY10548-689574329015287
50970171PHOSPHATEBLOODCHEMISTRY2777-15905683939632289319660515020
50971172POTASSIUMBLOODCHEMISTRY2823-38458255865222559925930315611
50972173PROCAINAMIDEBLOODCHEMISTRY3982-61951925631
50973174PROLACTINBLOODCHEMISTRY2842-3406265210141108
50974175PROSTATE SPECIFIC ANTIGENBLOODCHEMISTRY2857-1363530961499539384
50975176PROTEIN ELECTROPHORESISBLOODCHEMISTRY24351-9303525501785485316
50976177PROTEIN, TOTALBLOODCHEMISTRY2885-2119359414504625211664
50977178QUINIDINEBLOODCHEMISTRY6694-410106NANA
50978179RAPAMYCINBLOODCHEMISTRY29247-43360286816049247
50979180RED TOP HOLDBLOODCHEMISTRYNA23831214431016623881483
50980181RHEUMATOID FACTORBLOODCHEMISTRY6928-6834619540215207
50981182SALICYLATEBLOODCHEMISTRY4023-890877306496517811299
50982183SEX HORMONE BINDING GLOBULINBLOODCHEMISTRY2942-11211119
50983184SODIUMBLOODCHEMISTRY2951-28084895533442559825514515611
50986187TACROFKBLOODCHEMISTRY11253-21091466843584230223
50988188TESTOSTERONEBLOODCHEMISTRY2986-85394573038261
50989189TESTOSTERONE, FREEBLOODCHEMISTRY2991-84223672645545
50990190THEOPHYLLINEBLOODCHEMISTRY4049-32532321162116
50991191THYROGLOBULINBLOODCHEMISTRY3013-0158127693126
50992192THYROID PEROXIDASE ANTIBODIESBLOODCHEMISTRY8099-41601101045048
50993193THYROID STIMULATING HORMONEBLOODCHEMISTRY3016-32822122110982661114342
50994194THYROXINE (T4)BLOODCHEMISTRY3026-2320122561552945696
50995195THYROXINE (T4), FREEBLOODCHEMISTRY3024-781046583355815211106
50996196TISSUE TRANSGLUTAMINASE AB, IGABLOODCHEMISTRY31017-7453294267159149
50997197TOBRAMYCINBLOODCHEMISTRY35670-91282823129459149
50998198TRANSFERRINBLOODCHEMISTRY2500-71728512656689746293349
50999199TRICYCLIC ANTIDEPRESSANT SCREENBLOODCHEMISTRY4073-3117237742512839813385
51000200TRIGLYCERIDESBLOODCHEMISTRY1644-42498719652927653353632
51001201TRIIODOTHYRONINE (T3)BLOODCHEMISTRY3053-6227016171049653463
51002202TROPONIN IBLOODCHEMISTRY10839-91146811465302532
51003203TROPONIN TBLOODCHEMISTRY6598-7872446221311622250316458
51004204UE3, MATERNAL SCREENBLOODCHEMISTRYNA665NANA
51005205UPTAKE RATIOBLOODCHEMISTRY3050-2592394252198119
51006206UREA NITROGENBLOODCHEMISTRY3094-07919255455272369524639815666
51007207URIC ACIDBLOODCHEMISTRY3084-1183551306633465289915
51008208VALPROIC ACIDBLOODCHEMISTRY4086-526892000429689191
51009209VANCOMYCINBLOODCHEMISTRY20578-154744386766275160683888
51010210VITAMIN B12BLOODCHEMISTRY2170-9118689004598228642482
51011211CEREBROSPINAL FLUID (CSF)CHEMISTRY1746-737333044
51012212BILIRUBIN, TOTAL, CSFCEREBROSPINAL FLUID (CSF)CHEMISTRY1973-732211
51014214GLUCOSE, CSFCEREBROSPINAL FLUID (CSF)CHEMISTRY2342-44077292219841155762
51015215LACTATE DEHYDROGENASE, CSFCEREBROSPINAL FLUID (CSF)CHEMISTRY2528-8642434312208158
51016216MISCELLANEOUS, CSFCEREBROSPINAL FLUID (CSF)CHEMISTRYNA6447241715
51017217PEP, CSFCEREBROSPINAL FLUID (CSF)CHEMISTRY24352-72101671404329
51018218TOTAL PROTEIN, CSFCEREBROSPINAL FLUID (CSF)CHEMISTRY2880-34181296620051215779
51019219ALBUMIN, JOINT FLUIDJOINT FLUIDCHEMISTRYNA1NANA11
51020220AMYLASE, JOINT FLUIDJOINT FLUIDCHEMISTRY14388-316131233
51021221CREATININE, JOINT FLUIDJOINT FLUIDCHEMISTRY14401-4121210NANA
51022222GLUCOSE, JOINT FLUIDJOINT FLUIDCHEMISTRY2348-15343371010
51023223LD, JOINT FLUIDJOINT FLUIDCHEMISTRY2533-8107733
51024224TOTAL PROTEIN, JOINT FLUIDJOINT FLUIDCHEMISTRY2886-028202088
51025225ALBUMIN, BODY FLUIDOTHER BODY FLUIDCHEMISTRY1747-5422271254151141
51026226AMYLASE, BODY FLUIDOTHER BODY FLUIDCHEMISTRY1795-4757513377244205
51027227BICARBONATE, OTHER FLUIDOTHER BODY FLUIDCHEMISTRY11211-055483177
51028228BILIRUBIN, TOTAL, BODY FLUIDOTHER BODY FLUIDCHEMISTRY1974-53552791507666
51029229CALCIUM, BODY FLUIDOTHER BODY FLUIDCHEMISTRY15155-596333
51030230CHLORIDE, BODY FLUIDOTHER BODY FLUIDCHEMISTRY2072-76553391210
51031231CHOLESTEROL, BODY FLUIDOTHER BODY FLUIDCHEMISTRY12183-0999NANA
51032232CREATININE, BODY FLUIDOTHER BODY FLUIDCHEMISTRY12190-53202741654639
51033233FETALFNOTHER BODY FLUIDCHEMISTRYNA554NANA
51034234GLUCOSE, BODY FLUIDOTHER BODY FLUIDCHEMISTRY2344-0468299272169157
51035235LD, BODY FLUIDOTHER BODY FLUIDCHEMISTRY2529-6424270256154143
51036236LIPASE, BODY FLUIDOTHER BODY FLUIDCHEMISTRY15212-4128743
51037237MAGNESIUM, BODY FLUIDOTHER BODY FLUIDCHEMISTRY29365-41211711
51038238MISCELLANEOUS, BODY FLUIDOTHER BODY FLUIDCHEMISTRYNA5943321610
51039239OSMOLALITY, BODY FLUIDOTHER BODY FLUIDCHEMISTRY15200-91110811
51040240PHOSPHATE, BODY FLUIDOTHER BODY FLUIDCHEMISTRY12242-4129933
51041241POTASSIUM, BODY FLUIDOTHER BODY FLUIDCHEMISTRY2821-77662481413
51042242SODIUM, BODY FLUIDOTHER BODY FLUIDCHEMISTRY2950-48268541412
51043243TOTAL PROTEIN, BODY FLUIDOTHER BODY FLUIDCHEMISTRY2881-1488319297169159
51044244TRIGLYCEROTHER BODY FLUIDCHEMISTRY12228-36343352016
51045245UREA NITROGEN, BODY FLUIDOTHER BODY FLUIDCHEMISTRY3093-271634588
51046246ALBUMIN, PLEURALPLEURALCHEMISTRY1748-31175862689313263
51047247AMYLASE, PLEURALPLEURALCHEMISTRY1796-2726490417236193
51048248BICARBONATE, PLEURALPLEURALCHEMISTRY54361-164222
51049249BILIRUBIN, TOTAL, PLEURALPLEURALCHEMISTRY14421-28448403632
51050250CHLORIDE, PLEURALPLEURALCHEMISTRY53627-61110611
51051251CHOLESTEROL, PLEURALPLEURALCHEMISTRY9618-0330131116199161
51052252CREATININE, PLEURALPLEURALCHEMISTRY14399-029318817310595
51053253GLUCOSE, PLEURALPLEURALCHEMISTRY2346-5222115181121703514
51054254LACTATE DEHYDROGENASE, PLEURALPLEURALCHEMISTRY2530-4250217271213775548
51055255LIPASE, PLEURALPLEURALCHEMISTRYNA111NANA
51056256MISCELLANEOUS, PLEURALPLEURALCHEMISTRYNA75321
51057257POTASSIUM, PLEURALPLEURALCHEMISTRYNA1311722
51058258SODIUM, PLEURALPLEURALCHEMISTRYNA1511743
51059259TOTAL PROTEIN, PLEURALPLEURALCHEMISTRY2882-9252517451233780552
51060260TRIGLYCERIDES, PLEURALPLEURALCHEMISTRY9619-82511198913295
51061261BICARBONATE, STOOLSTOOLCHEMISTRY14040-04534271111
51062262CHLORIDE, STOOLSTOOLCHEMISTRY15158-96145371613
51063263OSMOLALITY, STOOLSTOOLCHEMISTRY2693-08046383432
51064264POTASSIUM, STOOLSTOOLCHEMISTRY15202-511081672924
51065265SODIUM, STOOLSTOOLCHEMISTRY15207-411082662825
5106626624 HR CALCIUMURINECHEMISTRY6874-2918160109
5106726724 HR CREATININEURINECHEMISTRY2162-6653451319202159
5106826824 HR PROTEINURINECHEMISTRY2889-42251661175948
51069269ALBUMIN, URINEURINECHEMISTRY1754-1238120221124359286
51070270ALBUMIN/CREATININE, URINEURINECHEMISTRY14958-3216518211030344275
51071271AMPHETAMINE SCREEN, URINEURINECHEMISTRY3349-893106200415431102640
51072272AMYLASE, URINEURINECHEMISTRY1799-655484277
51073273AMYLASE/CREATININE RATIO, URINEURINECHEMISTRY34235-255484277
51074274BARBITURATE SCREEN, URINEURINECHEMISTRY3377-992876181415031062638
51075275BENZODIAZEPINE SCREEN, URINEURINECHEMISTRY3390-293286200415031282648
51076276BICARBONATE, URINEURINECHEMISTRY1964-61000878697122109
51077277CALCIUM, URINEURINECHEMISTRY2004-0887566473321277
51078278CHLORIDE, URINEURINECHEMISTRY2078-496073942267656653134
51079279COCAINE, URINEURINECHEMISTRY3397-793876251416331362654
51080280CREATININE CLEARANCEURINECHEMISTRY33558-81501261032422
51081281CREATININE, SERUMURINECHEMISTRYNA1501251032523
51082282CREATININE, URINEURINECHEMISTRY2161-833069230388192100314724
51083283ETHANOL, URINEURINECHEMISTRY5644-0595918NANA
51084284GLUCOSE, URINEURINECHEMISTRY2350-76246411614
51085285HCG, URINE, QUALITATIVEURINECHEMISTRY2106-3659468376191157
51086286IMMUNOFIXATION, URINEURINECHEMISTRY49276-979167656511589
51087287LENGTH OF URINE COLLECTIONURINECHEMISTRY13362-946834384071299684273282
51088288MAGNESIUM, URINEURINECHEMISTRY2598-1431246228185171
51089289MARIJUANAURINECHEMISTRY3427-23773141526341
51090290METHADONE, URINEURINECHEMISTRY3773-992636167413830962631
51091291MYOGLOBIN, URINEURINECHEMISTRY2640-12832422214132
51092292OPIATE SCREEN, URINEURINECHEMISTRY3879-493086201414831072637
51093293OSMOLALITY, URINEURINECHEMISTRY2695-51884212009602468333565
51094294PHURINECHEMISTRY2756-51157914649243189
51095295PHOSPHATE, URINEURINECHEMISTRY2778-91019677581342288
51096296PORPHOBILINOGEN SCREENURINECHEMISTRY2809-234282465
51097297POTASSIUM, URINEURINECHEMISTRY2828-2109384845309360933243
51098298PROT. ELECTROPHORESIS, URINEURINECHEMISTRY13438-7178015071141273185
51099299PROTEIN/CREATININE RATIOURINECHEMISTRY2890-27709613121881578866
51100300SODIUM, URINEURINECHEMISTRY2955-32599717322762686754381
51101301TOTAL COLLECTION TIMEURINECHEMISTRY13362-91501251032523
51102302TOTAL PROTEIN, URINEURINECHEMISTRY2887-89080724827081832996
51103303UHOLDURINECHEMISTRYNA374931182428631551
51104304UREA NITROGEN, URINEURINECHEMISTRY3095-7132997996452153033140
51105305URIC ACID, URINEURINECHEMISTRY3086-6494267241227207
51106306URINE CREATININEURINECHEMISTRYNA1511261032523
51107307URINE TUBE, HELDURINECHEMISTRYNA374931182428631551
51108308URINE VOLUMEURINECHEMISTRY28009-91174927654247192
51109309URINE VOLUME, TOTALURINECHEMISTRY28009-91511261032523
51110310ATYPICAL LYMPHOCYTESASCITESHEMATOLOGY33369-0160102835849
51111311BANDSASCITESHEMATOLOGYNA12077694340
51112312BASOPHILSASCITESHEMATOLOGY35069-48263501919
51113313BLASTSASCITESHEMATOLOGYNA21111
51114314EOSINOPHILSASCITESHEMATOLOGY30380-0566354242212134
51115315HEMATOCRIT, ASCITESASCITESHEMATOLOGYNA161101806051
51116316LYMPHOCYTESASCITESHEMATOLOGY26482-0334621127781234469
51117317MACROPHAGEASCITESHEMATOLOGY40517-522801360549920379
51118318MESOTHELIAL CELLASCITESHEMATOLOGY30432-920611284531777351
51119319METAMYELOCYTESASCITESHEMATOLOGYNA62244
51120320MONOCYTESASCITESHEMATOLOGY26488-7334521117781234469
51121321MYELOCYTESASCITESHEMATOLOGYNA31122
51122322NUCLEATED RBCASCITESHEMATOLOGYNA4837341111
51123323OTHERASCITESHEMATOLOGYNA461283202178124
51124324PLASMAASCITESHEMATOLOGY40518-37339293429
51125325POLYSASCITESHEMATOLOGY26520-7334421107781234469
51126326PROMYELOCYTESASCITESHEMATOLOGYNA1NANA11
51127327RBC, ASCITESASCITESHEMATOLOGY26457-2328020777631203461
51128328WBC, ASCITESASCITESHEMATOLOGY26468-9334721127781235470
51129329YOUNGASCITESHEMATOLOGYNA222NANA
51130330ABSOLUTE CD3 COUNTBLOODHEMATOLOGY8124-022631781468482239
51131331ABSOLUTE CD4 COUNTBLOODHEMATOLOGY8128-124211954521467228
51132332ABSOLUTE CD8 COUNTBLOODHEMATOLOGY8138-021471680437467228
51133333ABSOLUTE LYMPHOCYTE COUNTBLOODHEMATOLOGY26474-723771935480442212
51134334ACANTHOCYTESBLOODHEMATOLOGY7789-1233119681600363203
51135335ADPBLOODHEMATOLOGYNA15131322
51136336ALPHA ANTIPLASMINBLOODHEMATOLOGYNA444NANA
51137337ANISOCYTOSISBLOODHEMATOLOGY702-161381548401795865411585
51138338ANTICARDIOLIPIN ANTIBODY IGGBLOODHEMATOLOGY8065-5514283224231205
51139339ANTICARDIOLIPIN ANTIBODY IGMBLOODHEMATOLOGY3182-3514283224231205
51140340ANTITHROMBINBLOODHEMATOLOGY27811-93262352209177
51141341APT TESTBLOODHEMATOLOGYNA222NANA
51142342ARACHADONIC ACIDBLOODHEMATOLOGYNA16141422
51143343ATYPICAL LYMPHOCYTESBLOODHEMATOLOGY733-6593354247515327168604050
51144344BANDSBLOODHEMATOLOGY763-3755885727318664183154456
51145345BASOPHILIC STIPPLINGBLOODHEMATOLOGY703-9384931011890748369
51146346BASOPHILSBLOODHEMATOLOGY704-7172142121968252895017412471
51147347BITE CELLSBLOODHEMATOLOGY10371-312871070942217137
51148348BLASTSBLOODHEMATOLOGY708-8345621082761348124
51149349BLEEDING TIMEBLOODHEMATOLOGY11067-653494544
51150350BLOOD PARASITE SMEARBLOODHEMATOLOGY24429-3473281176192104
51151351BURR CELLSBLOODHEMATOLOGY7790-98432740552991027538
51152352CD10BLOODHEMATOLOGY8107-52111119610090
51153353CD103BLOODHEMATOLOGYNA84344
51154354CD117BLOODHEMATOLOGY17107-46026233432
51155355CD11CBLOODHEMATOLOGY8109-16832293635
51156356CD13BLOODHEMATOLOGY8110-97632284438
51157357CD138BLOODHEMATOLOGY42869-8444NANA
51158358CD14BLOODHEMATOLOGY8111-77032293836
51159359CD15BLOODHEMATOLOGY17117-36127243432
51160360CD15/56BLOODHEMATOLOGY18267-55022212827
51161361CD16BLOODHEMATOLOGY8115-820161444
51162362CD16/56 ABSOLUTE COUNTBLOODHEMATOLOGYNA92175
51163363CD16/56%BLOODHEMATOLOGY18267-54423212118
51164364CD19BLOODHEMATOLOGY8117-428317411110998
51165365CD19 %BLOODHEMATOLOGYNA4NANA41
51166366CD19 ABSOLUTE COUNTBLOODHEMATOLOGYNA4NANA41
51167367CD2BLOODHEMATOLOGY8118-215576687975
51168368CD20BLOODHEMATOLOGY8119-026116110210090
51169369CD20 %BLOODHEMATOLOGYNA4NANA41
51170370CD20 ABSOLUTE COUNTBLOODHEMATOLOGYNA4NANA41
51171371CD22BLOODHEMATOLOGY14017-8106544
51172372CD23BLOODHEMATOLOGY14018-615087746357
51173373CD25BLOODHEMATOLOGYNA114377
51174374CD3 %BLOODHEMATOLOGYNA17211513
51175375CD3 ABSOLUTE COUNTBLOODHEMATOLOGY8122-419001587384313116
51176376CD3 CELLS, PERCENTBLOODHEMATOLOGY8122-423331789472544291
51177377CD33BLOODHEMATOLOGY8102-67133303836
51178378CD34BLOODHEMATOLOGY8125-77834304438
51179379CD38BLOODHEMATOLOGY8126-51610965
51180380CD4 CELLS, PERCENTBLOODHEMATOLOGY8127-324211924503497257
51181381CD4/CD8 RATIOBLOODHEMATOLOGY8129-920621620395442212
51182382CD41BLOODHEMATOLOGY17148-85928263130
51183383CD45BLOODHEMATOLOGY8130-7256143110113101
51184384CD5BLOODHEMATOLOGY8132-324114110110091
51185385CD5 %BLOODHEMATOLOGYNA8NANA88
51186386CD5 ABSOLUTE COUNTBLOODHEMATOLOGYNA8NANA88
51187387CD55BLOODHEMATOLOGY17175-14018172219
51188388CD56BLOODHEMATOLOGY8133-18443394136
51189389CD57BLOODHEMATOLOGYNA169777
51190390CD59BLOODHEMATOLOGY17177-74018172219
51191391CD64BLOODHEMATOLOGY17183-55826243230
51192392CD7BLOODHEMATOLOGY8135-615878708076
51193393CD71BLOODHEMATOLOGY8136-45726253130
51194394CD8 CELLS, PERCENTBLOODHEMATOLOGY8137-221461650419496256
51195395COLLAGENBLOODHEMATOLOGYNA15131322
51196396D-DIMERBLOODHEMATOLOGY48065-7447641612620315203
51197397ELLIPTOCYTESBLOODHEMATOLOGY6681-116061283841323180
51198398ENVELOPE CELLSBLOODHEMATOLOGY681-745334630410773
51199399EOSINOPHIL COUNTBLOODHEMATOLOGY26449-92171841643328
51200400EOSINOPHILSBLOODHEMATOLOGY711-2172144121971252895017312471
51201401EPINEPHERINEBLOODHEMATOLOGYNA15131322
51202402FACTOR IIBLOODHEMATOLOGY3289-63018171212
51203403FACTOR IXBLOODHEMATOLOGY3188-0523331197
51204404FACTOR VBLOODHEMATOLOGY3193-05536351918
51205405FACTOR VIIBLOODHEMATOLOGY3198-96445391919
51206406FACTOR VIIIBLOODHEMATOLOGY3209-448237518410759
51207407FACTOR VIII INHIBITORBLOODHEMATOLOGY3204-548286203
51208408FACTOR XBLOODHEMATOLOGY3218-55434322020
51209409FACTOR XIBLOODHEMATOLOGY3226-8321615168
51210410FACTOR XIIBLOODHEMATOLOGY3232-614121222
51211411FACTOR XIIIBLOODHEMATOLOGY3240-915131322
51212412FETAL HEMOGLOBINBLOODHEMATOLOGY4576-5115565
51213413FIBRIN DEGRADATION PRODUCTSBLOODHEMATOLOGY30226-55118357319901545686
51214414FIBRINOGEN, FUNCTIONALBLOODHEMATOLOGY3255-745935290229602169136086
51215415FMC-7BLOODHEMATOLOGY17220-515388756559
51216416FRAGMENTED CELLSBLOODHEMATOLOGY10373-9155213521138200123
51217417GLYCO ABLOODHEMATOLOGY17221-35625243130
51218418GRANULOCYTE COUNTBLOODHEMATOLOGY30394-1243751848216905893571
51219419H/O SMEARBLOODHEMATOLOGY38924-71880174031414088
51220420HEINZ BODY PREPBLOODHEMATOLOGY48068-141343176
51221421HEMATOCRITBLOODHEMATOLOGY4544-38818466191622971926268415664
51222422HEMOGLOBINBLOODHEMATOLOGY718-77525235239472963522857615663
51223423HEMOGLOBIN A2BLOODHEMATOLOGY4552-68053532726
51224424HEMOGLOBIN CBLOODHEMATOLOGY4561-72852242136151
51225425HEMOGLOBIN FBLOODHEMATOLOGY9749-39668522818
51226426HEMOGLOBLIN ABLOODHEMATOLOGY10346-52852242136151
51227427HEMOGLOBLIN SBLOODHEMATOLOGY4622-72852242136151
51228428HEPARINBLOODHEMATOLOGY13055-9191123426827
51229429HEPARIN, LMWBLOODHEMATOLOGY32684-364352424811987
51230430HLA-DRBLOODHEMATOLOGY32621-5205106889991
51231431HOWELL-JOLLY BODIESBLOODHEMATOLOGY7793-382963634619363
51232432HYPERSEGMENTED NEUTROPHILSBLOODHEMATOLOGY766-62682151645349
51233433HYPOCHROMIABLOODHEMATOLOGY728-661634550921836365421585
51234434IMMUNOPHENOTYPINGBLOODHEMATOLOGYNA274146113128109
51235435INHIBITOR SCREENBLOODHEMATOLOGYNA1621111005149
51236436INPATIENT HEMATOLOGY/ONCOLOGY SMEARBLOODHEMATOLOGYNA68510863577428
51237437INR(PT)BLOODHEMATOLOGY5895-74711833289222261414226115185
51238438KAPPABLOODHEMATOLOGYNA2151129610394
51239439LAMBDABLOODHEMATOLOGYNA2141119510394
51240440LARGE PLATELETSBLOODHEMATOLOGY34167-76580546213931118272
51241441LEUKOCYTE ALKALINE PHOSPHATASEBLOODHEMATOLOGY4659-961555165
51242442LUCBLOODHEMATOLOGYNA444NANA
51243443LUPUS ANTICOAGULANTBLOODHEMATOLOGYNA554387337167156
51244444LYMPHOCYTESBLOODHEMATOLOGY731-0172149121975252905017412471
51245445LYMPHOCYTES, PERCENTBLOODHEMATOLOGY26478-823781936481442212
51246446MACROCYTESBLOODHEMATOLOGY738-560837542961805665411585
51247447MACROOVALOCYTESBLOODHEMATOLOGY10376-28062591814
51248448MCHBLOODHEMATOLOGY785-67480735209632962922711015663
51249449MCHCBLOODHEMATOLOGY786-47482265210832962922714315663
51250450MCVBLOODHEMATOLOGY787-27480565209552962922710115663
51251451METAMYELOCYTESBLOODHEMATOLOGY28541-1592074234315289168644050
51252452MICROCYTESBLOODHEMATOLOGY741-958059515181790465411585
51253453MONOCYTE COUNTBLOODHEMATOLOGY26484-6664NANA
51254454MONOCYTESBLOODHEMATOLOGY742-7172148121974252905017412471
51255455MYELOCYTESBLOODHEMATOLOGY26498-6591594230115282168584050
51256456NEUTROPHILSBLOODHEMATOLOGY761-7172149121975252905017412471
51257457NUCLEATED RED CELLSBLOODHEMATOLOGY772-41656813112767734561028
51258458OSMOTIC FRAGILITYBLOODHEMATOLOGY34964-7333NANA
51259459OTHER CELLSBLOODHEMATOLOGY729-41762925459837223
51260460OVALOCYTESBLOODHEMATOLOGY774-0161801329962492881816
51261461PAPPENHEIMER BODIESBLOODHEMATOLOGY7795-894380764613686
51262462PENCIL CELLSBLOODHEMATOLOGY10377-02932572093633
51263463PLASMA CELLSBLOODHEMATOLOGY13047-638725620013197
51264464PLATELET CLUMPSBLOODHEMATOLOGY40741-15184594005954
51265465PLATELET COUNTBLOODHEMATOLOGY777-37784445450912964523335315660
51266466PLATELET SMEARBLOODHEMATOLOGY778-144151375361397166151568
51267467POIKILOCYTOSISBLOODHEMATOLOGY779-955886493451767765411585
51268468POLYCHROMASIABLOODHEMATOLOGY10378-847280407391605765411585
51269469PROMYELOCYTESBLOODHEMATOLOGY781-524891622870867358
51270470PROTEIN C, ANTIGENBLOODHEMATOLOGY27820-05033301717
51271471PROTEIN C, FUNCTIONALBLOODHEMATOLOGY27818-43342392259592
51272472PROTEIN S, ANTIGENBLOODHEMATOLOGY27823-47246422625
51273473PROTEIN S, FUNCTIONALBLOODHEMATOLOGY31102-718192858984
51274474PTBLOODHEMATOLOGY5902-24690903268562260914223415184
51275475PTTBLOODHEMATOLOGY3173-24749373286982256714623915136
51276476QUANTITATIVE G6PDBLOODHEMATOLOGY32546-42771871709084
51277477RDWBLOODHEMATOLOGY788-07468965198162962322708015663
51278478RED BLOOD CELL FRAGMENTSBLOODHEMATOLOGYNA387638761731NANA
51279479RED BLOOD CELLSBLOODHEMATOLOGY789-87480785209642962922711415663
51280480REPTILASE TIMEBLOODHEMATOLOGY6683-722201921
51281481REPTILASE TIME CONTROLBLOODHEMATOLOGYNA22201921
51282482RETICULOCYTE COUNT, ABSOLUTEBLOODHEMATOLOGY14196-0444NANA
51283483RETICULOCYTE COUNT, AUTOMATEDBLOODHEMATOLOGY17849-1100757509465525661821
51284484RETICULOCYTE COUNT, MANUALBLOODHEMATOLOGY31112-6217318861178287212
51285485RETICULOCYTE, CELLULAR HEMOGLOBINBLOODHEMATOLOGYNA666NANA
51286486RISTOCETINBLOODHEMATOLOGYNA17141433
51287487SCHISTOCYTESBLOODHEMATOLOGY800-310339842148141918603
51288488SEDIMENTATION RATEBLOODHEMATOLOGY4537-783776208314021691328
51289489SERUM VISCOSITYBLOODHEMATOLOGY3128-69860413828
51290490SICKLE CELL PREPARATIONBLOODHEMATOLOGY6864-328252233
51291491SICKLE CELLSBLOODHEMATOLOGY801-191862055
51292492SPHEROCYTESBLOODHEMATOLOGY802-9278321111507672266
51293493SUGAR WATER TESTBLOODHEMATOLOGY12260-6333NANA
51294494TARGET CELLSBLOODHEMATOLOGY10381-2580650353033771373
51295495TDTBLOODHEMATOLOGYNA9NANA98
51296496TEARDROP CELLSBLOODHEMATOLOGY7791-78995713538071860550
51297497THROMBINBLOODHEMATOLOGY3243-3580395317185151
51298498VON WILLEBRAND FACTOR ACTIVITYBLOODHEMATOLOGY6014-513593764226
51299499VON WILLEBRAND FACTOR ANTIGENBLOODHEMATOLOGY6012-912784734325
51300500WBC COUNTBLOODHEMATOLOGY26464-823711929480442212
51301501WHITE BLOOD CELLSBLOODHEMATOLOGY804-57533015255362967222776515663
51302502YOUNG CELLSBLOODHEMATOLOGY51633-61491321081714
51303503CD10BONE MARROWHEMATOLOGY51216-02171049411399
51304504CD103BONE MARROWHEMATOLOGY51221-0444NANA
51305505CD117BONE MARROWHEMATOLOGY42866-410941296849
51306506CD11CBONE MARROWHEMATOLOGY33202-37932244741
51307507CD13BONE MARROWHEMATOLOGY51237-613961387857
51308508CD138BONE MARROWHEMATOLOGYNA54411
51309509CD14BONE MARROWHEMATOLOGY32507-69232266044
51310510CD15BONE MARROWHEMATOLOGY51251-79934266546
51311511CD16BONE MARROWHEMATOLOGYNA52233
51312512CD16/56BONE MARROWHEMATOLOGY51255-814331110
51313513CD19BONE MARROWHEMATOLOGY32525-8248123108125105
51314514CD2BONE MARROWHEMATOLOGY32527-416880678877
51315515CD20BONE MARROWHEMATOLOGYNA2161029211499
51316516CD22BONE MARROWHEMATOLOGYNA222NANA
51317517CD23BONE MARROWHEMATOLOGY51268-114172686963
51318518CD25BONE MARROWHEMATOLOGYNA63333
51319519CD3BONE MARROWHEMATOLOGY32529-02191039111699
51320520CD33BONE MARROWHEMATOLOGY51293-911549326648
51321521CD34BONE MARROWHEMATOLOGY57400-415568428761
51322522CD38BONE MARROWHEMATOLOGYNA94455
51323523CD4BONE MARROWHEMATOLOGY51301-09539335651
51324524CD41BONE MARROWHEMATOLOGY51319-27327224640
51325525CD45BONE MARROWHEMATOLOGY51340-8307151116156115
51326526CD5BONE MARROWHEMATOLOGY35640-22081019110796
51327527CD55BONE MARROWHEMATOLOGY51344-021111
51328528CD56BONE MARROWHEMATOLOGYNA7732284540
51329529CD57BONE MARROWHEMATOLOGYNA31122
51330530CD59BONE MARROWHEMATOLOGY51358-021111
51331531CD64BONE MARROWHEMATOLOGY51365-58036254439
51332532CD7BONE MARROWHEMATOLOGY35641-017585689078
51333533CD71BONE MARROWHEMATOLOGYNA6728253937
51334534CD8BONE MARROWHEMATOLOGY51372-18734295349
51335535FMC-7BONE MARROWHEMATOLOGYNA14071676963
51336536GLYCO ABONE MARROWHEMATOLOGY33208-06424214038
51337537HLA-DRBONE MARROWHEMATOLOGY51380-42181039211599
51338538IMMUNOPHENOTYPINGBONE MARROWHEMATOLOGYNA315163119152115
51339539IRON STAINBONE MARROWHEMATOLOGY13513-73402772226351
51340540KAPPABONE MARROWHEMATOLOGYNA2181079811197
51341541LAMBDABONE MARROWHEMATOLOGYNA2181079811197
51342542WRIGHT GIEMSABONE MARROWHEMATOLOGY10355-663748130415693
51343543ATYPICAL LYMPHOCYTESCEREBROSPINAL FLUID (CSF)HEMATOLOGY30416-23102181919277
51344544BANDSCEREBROSPINAL FLUID (CSF)HEMATOLOGY26509-02602001856056
51345545BASOPHILSCEREBROSPINAL FLUID (CSF)HEMATOLOGY30374-311992752724
51346546BLASTSCEREBROSPINAL FLUID (CSF)HEMATOLOGY26447-3168585
51347547EOSINOPHILSCEREBROSPINAL FLUID (CSF)HEMATOLOGY26451-5655471371184129
51348548HEMATOCRIT, CSFCEREBROSPINAL FLUID (CSF)HEMATOLOGY30398-25927223225
51349549HYPERSEGMENTED NEUTROPHILSCEREBROSPINAL FLUID (CSF)HEMATOLOGY26506-6111NANA
51350550IMMUNOPHENOTYPINGCEREBROSPINAL FLUID (CSF)HEMATOLOGYNA15221313
51351551LYMPHSCEREBROSPINAL FLUID (CSF)HEMATOLOGY26479-64732340320471329793
51352552MACROPHAGECEREBROSPINAL FLUID (CSF)HEMATOLOGY30426-11138819636319233
51353553MESOTHELIAL CELLSCEREBROSPINAL FLUID (CSF)HEMATOLOGY30429-541322999
51354554METAMYELOCYTESCEREBROSPINAL FLUID (CSF)HEMATOLOGY30366-92810101818
51355555MONOCYTESCEREBROSPINAL FLUID (CSF)HEMATOLOGY26486-14701338020471321793
51356556MYELOCYTESCEREBROSPINAL FLUID (CSF)HEMATOLOGY30447-7115566
51357557NRBCCEREBROSPINAL FLUID (CSF)HEMATOLOGY48778-51381161022222
51358558OTHERCEREBROSPINAL FLUID (CSF)HEMATOLOGYNA33222115611177
51359559PLASMACEREBROSPINAL FLUID (CSF)HEMATOLOGY47413-0251212138
51360560POLYSCEREBROSPINAL FLUID (CSF)HEMATOLOGY26517-34689337420471315793
51361561PROMYELOCYTESCEREBROSPINAL FLUID (CSF)HEMATOLOGYNA51143
51362562RBC, CSFCEREBROSPINAL FLUID (CSF)HEMATOLOGY26454-94700340320431297783
51363563WBC, CSFCEREBROSPINAL FLUID (CSF)HEMATOLOGY26465-54718339020481328792
51364564YOUNGCEREBROSPINAL FLUID (CSF)HEMATOLOGYNA101010NANA
51365565ATYPICAL LYMPHOCYTESJOINT FLUIDHEMATOLOGY33371-617141333
51366566BANDSJOINT FLUIDHEMATOLOGY33361-74333331010
51367567BASOPHILSJOINT FLUIDHEMATOLOGY17833-5108822
51368568EOSINOPHILSJOINT FLUIDHEMATOLOGY17834-38347423631
51369569HEMATOCRIT, JOINT FLUIDJOINT FLUIDHEMATOLOGYNA8742354540
51370570JOINT CRYSTALS, BIREFRINGENCEJOINT FLUIDHEMATOLOGYNA2341731306148
51372572JOINT CRYSTALS, LOCATIONJOINT FLUIDHEMATOLOGYNA2341731306148
51373573JOINT CRYSTALS, NUMBERJOINT FLUIDHEMATOLOGYNA591425303166134
51374574JOINT CRYSTALS, SHAPEJOINT FLUIDHEMATOLOGYNA2341731306148
51375575LYMPHOCYTESJOINT FLUIDHEMATOLOGY26483-8683476316207152
51376576MACROPHAGEJOINT FLUIDHEMATOLOGY33376-52361471198973
51377577MESOTHELIAL CELLSJOINT FLUIDHEMATOLOGY33365-825171688
51378578METAMYELOCYTESJOINT FLUIDHEMATOLOGYNA111NANA
51379579MONOCYTESJOINT FLUIDHEMATOLOGY17835-0683476316207152
51380580NRBCJOINT FLUIDHEMATOLOGY48040-0139844
51381581OTHERJOINT FLUIDHEMATOLOGYNA4837361111
51382582POLYSJOINT FLUIDHEMATOLOGY26522-3683476316207152
51383583RBC, JOINT FLUIDJOINT FLUIDHEMATOLOGY26458-0611446300165130
51384584WBC, JOINT FLUIDJOINT FLUIDHEMATOLOGY26469-7683476316207152
51385585ATYPICAL LYMPHOCYTESOTHER BODY FLUIDHEMATOLOGY30417-06843432525
51386586BANDSOTHER BODY FLUIDHEMATOLOGY26510-811168684339
51387587BASOPHILSOTHER BODY FLUIDHEMATOLOGY28543-77549482624
51388588CD10OTHER BODY FLUIDHEMATOLOGY51217-8384245199139122
51389589CD103OTHER BODY FLUIDHEMATOLOGYNA31122
51390590CD117OTHER BODY FLUIDHEMATOLOGY42867-228211876
51391591CD11COTHER BODY FLUIDHEMATOLOGY51232-725191766
51392592CD13OTHER BODY FLUIDHEMATOLOGY51238-45129242213
51393593CD138OTHER BODY FLUIDHEMATOLOGY42871-421991211
51394594CD14OTHER BODY FLUIDHEMATOLOGY51248-319141354
51395595CD15OTHER BODY FLUIDHEMATOLOGY51252-5251513107
51396596CD16OTHER BODY FLUIDHEMATOLOGYNA53322
51397597CD16/56OTHER BODY FLUIDHEMATOLOGY18268-331222299
51398598CD19OTHER BODY FLUIDHEMATOLOGY17829-3682414300268198
51399599CD2OTHER BODY FLUIDHEMATOLOGY17827-725815713810189
51400600CD20OTHER BODY FLUIDHEMATOLOGY57418-6387243198144124
51401601CD22OTHER BODY FLUIDHEMATOLOGY42875-596533
51402602CD23OTHER BODY FLUIDHEMATOLOGY51269-9317202170115104
51403603CD25OTHER BODY FLUIDHEMATOLOGYNA61154
51404604CD3OTHER BODY FLUIDHEMATOLOGY17826-9468307229161132
51405605CD33OTHER BODY FLUIDHEMATOLOGY51294-7321917136
51406606CD34OTHER BODY FLUIDHEMATOLOGYNA238148739045
51407607CD38OTHER BODY FLUIDHEMATOLOGY51299-6138755
51408608CD4OTHER BODY FLUIDHEMATOLOGY17822-87147422419
51409609CD4/CD8 RATIOOTHER BODY FLUIDHEMATOLOGY18266-785433
51410610CD41OTHER BODY FLUIDHEMATOLOGY51320-01210922
51411611CD45OTHER BODY FLUIDHEMATOLOGY17823-6715434307281204
51412612CD5OTHER BODY FLUIDHEMATOLOGY57423-6389249202140118
51413613CD56OTHER BODY FLUIDHEMATOLOGY57424-43517171814
51414614CD57OTHER BODY FLUIDHEMATOLOGYNA21111
51415615CD64OTHER BODY FLUIDHEMATOLOGY51366-322141385
51416616CD7OTHER BODY FLUIDHEMATOLOGY57425-126015914010189
51417617CD71OTHER BODY FLUIDHEMATOLOGY57426-9119922
51418618CD8OTHER BODY FLUIDHEMATOLOGY17824-46644412218
51419619EOSINOPHILSOTHER BODY FLUIDHEMATOLOGY26452-3601375314226202
51420620FMC-7OTHER BODY FLUIDHEMATOLOGY57428-5314199169115104
51421621GLYCO AOTHER BODY FLUIDHEMATOLOGY57430-1108822
51422622HEMATOCRIT, OTHER FLUIDOTHER BODY FLUIDHEMATOLOGY11153-4288174151114100
51423623HLA-DROTHER BODY FLUIDHEMATOLOGY51381-2332213179119106
51424624IMMUNOPHENOTYPINGOTHER BODY FLUIDHEMATOLOGYNA574299230275208
51425625KAPPAOTHER BODY FLUIDHEMATOLOGYNA555331257224176
51426626LAMBDAOTHER BODY FLUIDHEMATOLOGYNA555331257224176
51427627LYMPHOCYTESOTHER BODY FLUIDHEMATOLOGY11031-219691234930735600
51428628MACROPHAGEOTHER BODY FLUIDHEMATOLOGY12230-91078622520456392
51429629MESOTHELIAL CELLSOTHER BODY FLUIDHEMATOLOGY28544-5479312255167153
51430630METAMYELOCYTESOTHER BODY FLUIDHEMATOLOGY17801-2136677
51431631MONOSOTHER BODY FLUIDHEMATOLOGY10330-919661231930735600
51432632MYELOCYTESOTHER BODY FLUIDHEMATOLOGY17800-464422
51433633NRBCOTHER BODY FLUIDHEMATOLOGYNA4626252019
51434634OTHER CELLOTHER BODY FLUIDHEMATOLOGYNA546310286236204
51435635PLASMAOTHER BODY FLUIDHEMATOLOGY17803-8118733
51436636POLYSOTHER BODY FLUIDHEMATOLOGY26518-119691234930735600
51437637PROMYELOCYTESOTHER BODY FLUIDHEMATOLOGY17799-832211
51438638RBC, OTHER FLUIDOTHER BODY FLUIDHEMATOLOGY26455-613681071807297263
51439639WBC, OTHER FLUIDOTHER BODY FLUIDHEMATOLOGY26466-315191141867378334
51440640ATYPICAL LYMPHOCYTESPLEURALHEMATOLOGY33370-81591161084342
51441641BANDSPLEURALHEMATOLOGYNA12183773838
51442642BASOPHILSPLEURALHEMATOLOGY35070-2137104973332
51443643BLASTSPLEURALHEMATOLOGYNA52232
51444644EOSINOPHILSPLEURALHEMATOLOGY30379-2778523412255215
51445645HEMATOCRIT, PLEURALPLEURALHEMATOLOGYNA2851891609685
51446646LYMPHOCYTESPLEURALHEMATOLOGY26481-2261318021255811571
51447647MACROPHAGESPLEURALHEMATOLOGY40520-915281004787524401
51448648MESOTHELIAL CELLSPLEURALHEMATOLOGY30431-115111039812472371
51449649METAMYELOCYTESPLEURALHEMATOLOGYNA106644
51450650MONOSPLEURALHEMATOLOGY33362-5261318021255811571
51451651MYELOCYTESPLEURALHEMATOLOGYNA97722
51452652NRBCPLEURALHEMATOLOGYNA7050482020
51453653OTHERPLEURALHEMATOLOGYNA633378336255217
51454654PLASMA CELLSPLEURALHEMATOLOGY40522-58346463735
51455655POLYSPLEURALHEMATOLOGY26519-9261218011255811571
51456656PROMYELOCYTESPLEURALHEMATOLOGYNA53322
51457657RBC, PLEURALPLEURALHEMATOLOGY26456-4244617061214740524
51458658WBC, PLEURALPLEURALHEMATOLOGY26467-1261418031255811571
51459659YOUNG CELLSPLEURALHEMATOLOGYNA111NANA
51460660BLOOD, OCCULTSTOOLHEMATOLOGY2335-82241851563934
51461661AMMONIUM BIURATEURINEHEMATOLOGY12454-517121255
51462662AMORPHOUS CRYSTALSURINEHEMATOLOGY8246-168395412396014271136
51463663BACTERIAURINEHEMATOLOGY5769-5734125804516909153676413
51464664BILIRUBINURINEHEMATOLOGY5770-3996797851319830211667954
51465665BILIRUBIN CRYSTALSURINEHEMATOLOGY5771-18357542626
51466666BLOODURINEHEMATOLOGY5794-3997257854319830211827967
51467667BROAD CASTSURINEHEMATOLOGY18487-925991616
51468668CALCIUM CARBONATE CRYSTALSURINEHEMATOLOGY5773-773344
51469669CALCIUM OXALATE CRYSTALSURINEHEMATOLOGY5774-514201114887306268
51470670CALCIUM PHOSPHATE CRYSTALSURINEHEMATOLOGY5775-220111199
51471671CELLULAR CASTURINEHEMATOLOGYNA14459588584
51472672CHOLESTEROL CRYSTALSURINEHEMATOLOGY5777-853322
51473673CYSTEINE CRYSTALSURINEHEMATOLOGY5782-8554NANA
51474674EOSINOPHILSURINEHEMATOLOGY25156-1336526651979700529
51475675EPITHELIAL CASTSURINEHEMATOLOGYNA43311
51476676EPITHELIAL CELLSURINEHEMATOLOGY5787-7832065964817002235589797
51477677FREE FATURINEHEMATOLOGY53359-6222NANA
51478678GLUCOSEURINEHEMATOLOGY5792-71012797887019840224098624
51479679GRANULAR CASTSURINEHEMATOLOGY5793-565463940286726061859
51480680HEMATOCRITURINEHEMATOLOGY17809-554411
51481681HEMOSIDERINURINEHEMATOLOGY4644-11571401221716
51482682HYALINE CASTSURINEHEMATOLOGY5796-8151227525471075974721
51483683HYPHENATED YEASTURINEHEMATOLOGY41865-7111NANA
51484684KETONEURINEHEMATOLOGY5797-61016237876619843228578966
51485685LEUCINE CRYSTALSURINEHEMATOLOGY5798-421111
51486686LEUKOCYTESURINEHEMATOLOGY5799-2976247645519273211697956
51487687NITRITEURINEHEMATOLOGY5802-4996747851119830211637955
51488688NON-SQUAMOUS EPITHELIAL CELLSURINEHEMATOLOGY41284-113331010
51489689NONSQUAMOUS EPITHELIAL CELLURINEHEMATOLOGY41284-12632502391313
51490690OVAL FAT BODYURINEHEMATOLOGYNA113288
51491691PHURINEHEMATOLOGY5803-211608981325199063476412527
51492692PROTEINURINEHEMATOLOGY5804-010641879679198622673910075
51493693RBCURINEHEMATOLOGY5808-18428560003170142428210002
51494694RBC CASTSURINEHEMATOLOGY5807-35031311919
51495695RBC CLUMPSURINEHEMATOLOGYNA34292955
51496696REDUCING SUBSTANCES, URINEURINEHEMATOLOGYNA26252211
51497697RENAL EPITHELIAL CELLSURINEHEMATOLOGY26052-1205815661373492465
51498698SPECIFIC GRAVITYURINEHEMATOLOGY5811-511586681314199063455212443
51499699SPERMURINEHEMATOLOGY8248-73983022579683
51500700SULFONAMIDESURINEHEMATOLOGY5812-314111133
51501701TRANSITIONAL EPITHELIAL CELLSURINEHEMATOLOGY30089-752843195244520891607
51502702TRICHOMONASURINEHEMATOLOGY32724-736322644
51503703TRIPLE PHOSPHATE CRYSTALSURINEHEMATOLOGY5814-93402852345549
51504704TYROSINE CRYSTALSURINEHEMATOLOGY5815-6333NANA
51505705URIC ACID CRYSTALSURINEHEMATOLOGY5817-21057777668280229
51506706URINE APPEARANCEURINEHEMATOLOGY5767-9998387860919834212297995
51507707URINE CASTS, OTHERURINEHEMATOLOGY9842-61981571514140
51508708URINE COLORURINEHEMATOLOGY5778-61003767867719839216998273
51510710URINE CRYSTALS, OTHERURINEHEMATOLOGY5783-61581241203434
51511711URINE FAT BODIESURINEHEMATOLOGY2272-33623231313
51512712URINE MUCOUSURINEHEMATOLOGY8247-9142049601594846032643
51513713URINE SPECIMEN TYPEURINEHEMATOLOGY19159-3105910118924847
51514714UROBILINOGENURINEHEMATOLOGY5818-01018167879619840230208925
51515715WAXY CASTSURINEHEMATOLOGY5819-810665634141
51516716WBCURINEHEMATOLOGY5821-48468860055170212463310023
51517717WBC CASTSURINEHEMATOLOGY5820-6270155150115113
51518718WBC CLUMPSURINEHEMATOLOGYNA211017611282349288
51519719YEASTURINEHEMATOLOGY5822-28546559578170002588710390
51520720ANTI-MCBLOODCHEMISTRY32042-4444NANA
51521721ACID PHOSBLOODCHEMISTRY1711-1121212NANA
51523723GR HOLDURINECHEMISTRYNA118499308598625411804
51525725BilledBLOODCHEMISTRYNA222NANA
51526726FRUCAMN+BLOODCHEMISTRYNA11116NANA
51528728STDY HOLDBLOODCHEMISTRYNA4NANA42
51529729eAGBLOODCHEMISTRYNA320186176134125
51530730AF-AFPOTHER BODY FLUIDCHEMISTRYNA221NANA
51531731STDYURINEURINECHEMISTRYNA111NANA
51532732PLASMGNBLOODHEMATOLOGYNA222NANA
51533733WBCPBLOODHEMATOLOGYNA62244
51534734MYELOSJOINT FLUIDHEMATOLOGYNA222NANA
51535735CD55OTHER BODY FLUIDHEMATOLOGYNA111NANA
51536736CD59OTHER BODY FLUIDHEMATOLOGYNA111NANA
51537737TDTOTHER BODY FLUIDHEMATOLOGYNA1NANA11
51538738[A1c]BLOODCHEMISTRY4548-4379137912336NANA
51539739TACROFK_2BLOODCHEMISTRYNA1690413725410317997
51555755SURFACTANT ALBUMIN RATIOOTHER BODY FLUIDCHEMISTRYNA111NANA
-

Now we can compare distributions of some of the lab values from the Careview vs. Metavision eras.

-
compare.labs = function(itemid, top=1000, bot=0, h=TRUE) {
-  print(itemid)
-  items = dbGetQuery(con, paste("select if(subject_id>=40000, 'mv', 'cv') as source, valuenum from labevents where itemid = ", itemid, " and valuenum is not null and valuenum <= ", top, " and valuenum >= ", bot, sep=""))
-  print(dim(items))
-  xitems <<- items
-  title = labitems[labitems$ITEMID==itemid,"LABEL"]
-  bw = (max(items$valuenum) - min(items$valuenum)) / 30
-  if (h) ggplot(items, aes(valuenum, fill = source)) + ggtitle(title) + geom_histogram(alpha = 0.5, aes(y = ..density..), position = 'identity', binwidth = bw)
-  else ggplot(items, aes(valuenum, fill = source)) + geom_density(alpha = 0.2) + ggtitle(title)
-}
-
-compare.labs(50801)
-
## [1] 50801
-## [1] 22013     2
-

-
compare.labs(50802, 25, -25)
-
## [1] 50802
-## [1] 490095      2
-

-
compare.labs(50803)
-
## [1] 50803
-## [1] 9242    2
-

-
compare.labs(50804, 70)
-
## [1] 50804
-## [1] 490563      2
-

-
compare.labs(50805, 20)
-
## [1] 50805
-## [1] 2033    2
-

-
compare.labs(50806, 140, 70)
-
## [1] 50806
-## [1] 48138     2
-

-
compare.labs(50808, 2)
-
## [1] 50808
-## [1] 248843      2
-

-
compare.labs(50809, 500)
-
## [1] 50809
-## [1] 196037      2
-

-
compare.labs(50810, 60)
-
## [1] 50810
-## [1] 89699     2
-

-
compare.labs(51014)
-
## [1] 51014
-## [1] 4074    2
-

-
compare.labs(51018, 700)
-
## [1] 51018
-## [1] 4112    2
-

-
compare.labs(51082, 500)
-
## [1] 51082
-## [1] 33038     2
-

-

These comparisons, which are only a very small sample of the total number of labs, seem to indicate that the distributions in the older data are roughly the same as in the newer. By eyeball, it does look like the distributions of many labs are slightly higher in the Careview than in the Metavision data, but I don’t know if this is significant.

-

We now do a more systematic exploration of the distributions of all the various labs.

-
labs = dbGetQuery(con, "select itemid, if(subject_id>=40000, 'mv', 'cv') as source, count(*) as count, count(distinct subject_id) as n_pat, avg(valuenum) as avg, std(valuenum) as std from labevents where valuenum is not null group by itemid, source")
-labs.summary = merge(subset(labs, source=="cv"), subset(labs, source=="mv"), by="itemid")
-labs.summary$source.x = NULL
-labs.summary$source.y = NULL
-labs.summary = data.frame(itemid=labs.summary$itemid, cvcount=labs.summary$count.x, mvcount=labs.summary$count.y, cvpat=labs.summary$n_pat.x, mvpat=labs.summary$n_pat.y,cvavg=labs.summary$avg.x, mvavg=labs.summary$avg.y, cvstd=labs.summary$std.x, mvstd=labs.summary$std.y, std=rowMeans(labs.summary[,c("std.x", "std.y")]), delta=(labs.summary$avg.y - labs.summary$avg.x))
-labs.summary = merge(labitems[,c("ITEMID", "LABEL")], labs.summary, by.x="ITEMID", by.y="itemid")
-names(labs.summary)[1:2] = c("itemid", "label")
-nlabs = nrow(labs.summary)
-labs.summary = subset(labs.summary, cvcount+mvcount >= 100 & cvcount/mvcount < 10 & mvcount/cvcount < 10)
-labs.summary$diff = (labs.summary$mvavg - labs.summary$cvavg) / labs.summary$std
-labs.summary = labs.summary[order(abs(labs.summary$diff), decreasing=TRUE),]
-kable(head(labs.summary, n=20))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
itemidlabelcvcountmvcountcvpatmvpatcvavgmvavgcvstdmvstdstddeltadiff
5850884BILIRUBIN, INDIRECT3888154891076921425.01850261.8000914.4315012.5570193.494260-3.218412-0.9210567
28451224HEMOGLOBIN C22461213510.21919648.3688522.55025216.1528719.3515628.1496560.8714754
8250914CYCLOSPORIN7230133522671374.3457815209.847191309.705033158.308306234.006670-164.498591-0.7029654
31451276QUANTITATIVE G6PD187901708410.264438512.8833333.8637474.7610984.3124232.6188950.6072909
20451076BICARBONATE, URINE8730662821.498850611.49666724.2268949.10199516.664445-10.002184-0.6002110
28651226HEMOGLOBLIN A224612135192.449464275.47377019.14237438.81183228.977103-16.975694-0.5858313
29051232HYPERSEGMENTED NEUTROPHILS21553164493.84651161.6792456.0593711.4244973.741934-2.167266-0.5791835
10750958LUTEINIZING HORMONE142391153516.14014098.46666717.5769429.27138813.424165-7.673474-0.5716165
5750883BILIRUBIN, DIRECT3938157821092522351.03041313.4011592.9175345.3896424.1535882.3707460.5707705
13050988TESTOSTERONE4427829659346.3823529213.576923261.574766208.635526235.105146-132.805430-0.5648767
13150989TESTOSTERONE, FREE33839244368.73905335.6256417.3465744.1549925.750783-3.113412-0.5413893
4850865AMIKACIN549451812.78333328.48521310.1919496.0925298.142239-4.298120-0.5278794
9050926FOLLICLE STIMULATING HORMONE225571754925.860888912.65964933.02738119.58638926.306885-13.201240-0.5018169
24451130ABSOLUTE CD3 COUNT1675442396212184.3699284425.823303366.094975606.074154486.084565241.4533750.4967312
11450966PHENOBARBITAL14593081504031.064016441.33636416.39285527.19310821.79298110.2723470.4713603
34151357NRBC11622102226.58620693.27272711.2027313.0328787.117805-3.313480-0.4655199
6050889C-REACTIVE PROTEIN429121022268124340.755924072.06643760.63682674.50844767.57263731.3105130.4633608
21951101TOTAL COLLECTION TIME125251032323.728000021.7600003.4441285.3162394.380184-1.968000-0.4492962
8350915D-DIMER2178761636342545.66359454523.0582194242.2193454747.8733254495.0463351977.3946250.4399053
2450826TIDAL VOLUME670191678998283910547.9084140484.455358177.261328123.100339150.180834-63.453056-0.4225110
-

There are 431 labs for which there is both (imputed) Careview and Metavision data, but only 306 of these have at least a total of 100 data values and no more than 10 times as many of one era than the other. The above table shows the 20 labs in which the differences between the averages of the two groups, when standardized by their average standard deviation, are greatest. No pair of averages differ by as much as a standard deviation. We plot these distributions below, for tests 5.088410^{4}, 5.122410^{4}, 5.091410^{4}, 5.127610^{4}, 5.107610^{4}, 5.122610^{4}, 5.123210^{4}, 5.095810^{4}, 5.088310^{4}, 5.098810^{4}, 5.098910^{4}, 5.086510^{4}, 5.092610^{4}, 5.11310^{4}, 5.096610^{4}, 5.135710^{4}, 5.088910^{4}, 5.110110^{4}, 5.091510^{4}, 5.082610^{4}.

-
# for (i in 1:20) {
-  # j = labs.summary[i, "itemid"]
-  # print(j)
-  # compare.labs(j)
-# }
-compare.labs(50884)
-
## [1] 50884
-## [1] 44361     2
-

-
compare.labs(51224)
-
## [1] 51224
-## [1] 285   2
-

-
compare.labs(50914)
-
## [1] 50914
-## [1] 8082    2
-

-
compare.labs(51276)
-
## [1] 51276
-## [1] 277   2
-

-
compare.labs(51076)
-
## [1] 51076
-## [1] 117   2
-

-
compare.labs(51226)
-
## [1] 51226
-## [1] 285   2
-

-
compare.labs(51232)
-
## [1] 51232
-## [1] 268   2
-

-
compare.labs(50958)
-
## [1] 50958
-## [1] 181   2
-

-
compare.labs(50883)
-
## [1] 50883
-## [1] 45163     2
-

-
compare.labs(50988, 2000)
-
## [1] 50988
-## [1] 520   2
-

-
compare.labs(50989)
-
## [1] 50989
-## [1] 377   2
-

-
compare.labs(50865)
-
## [1] 50865
-## [1] 148   2
-

-
compare.labs(50926)
-
## [1] 50926
-## [1] 282   2
-

-
compare.labs(51130)
-
## [1] 51130
-## [1] 1969    2
-

-
compare.labs(50966)
-
## [1] 50966
-## [1] 1767    2
-

-
compare.labs(51357)
-
## [1] 51357
-## [1] 138   2
-

-
compare.labs(50889)
-
## [1] 50889
-## [1] 6393    2
-

-
compare.labs(51101)
-
## [1] 51101
-## [1] 150   2
-

-
compare.labs(50915, 10000)
-
## [1] 50915
-## [1] 970   2
-

-
compare.labs(50826)
-
## [1] 50826
-## [1] 83735     2
-

-
- - - - -
- - - - - - - - diff --git a/notebooks/first_labs.ipynb b/notebooks/first_labs.ipynb index 0c1a9dd..215e898 100644 --- a/notebooks/first_labs.ipynb +++ b/notebooks/first_labs.ipynb @@ -585,13 +585,208 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": { - "collapsed": true + "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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subject_idhadm_idicustay_idmort_icumort_hospaniongap_1stalbumin_1stbicarbonate_1stbilirubin_1stcreatinine_1st...magnesium_1stphosphate_1stplatelet_1stpotassium_1stptt_1stinr_1stpt_1stsodium_1stbun_1stwbc_1st
0216335324365300NaNNaNNaNNaNNaN...NaNNaN5NaNNaNNaNNaNNaNNaN0.1
1314583421155200171.8250.83.2...2.44.82825.430.71.313.51365312.7
2418577729463800152.8211.90.5...1.93.12013.333.21.112.8141109.7
3517898021475700NaNNaNNaNNaNNaN...NaNNaN309NaNNaNNaNNaNNaNNaN13.9
4610706422823200233.0150.211.7...2.08.53154.7139.01.414.61346210.6
\n", + "

5 rows × 25 columns

\n", + "
" + ], + "text/plain": [ + " subject_id hadm_id icustay_id mort_icu mort_hosp aniongap_1st \\\n", + "0 2 163353 243653 0 0 NaN \n", + "1 3 145834 211552 0 0 17 \n", + "2 4 185777 294638 0 0 15 \n", + "3 5 178980 214757 0 0 NaN \n", + "4 6 107064 228232 0 0 23 \n", + "\n", + " albumin_1st bicarbonate_1st bilirubin_1st creatinine_1st ... \\\n", + "0 NaN NaN NaN NaN ... \n", + "1 1.8 25 0.8 3.2 ... \n", + "2 2.8 21 1.9 0.5 ... \n", + "3 NaN NaN NaN NaN ... \n", + "4 3.0 15 0.2 11.7 ... \n", + "\n", + " magnesium_1st phosphate_1st platelet_1st potassium_1st ptt_1st \\\n", + "0 NaN NaN 5 NaN NaN \n", + "1 2.4 4.8 282 5.4 30.7 \n", + "2 1.9 3.1 201 3.3 33.2 \n", + "3 NaN NaN 309 NaN NaN \n", + "4 2.0 8.5 315 4.7 139.0 \n", + "\n", + " inr_1st pt_1st sodium_1st bun_1st wbc_1st \n", + "0 NaN NaN NaN NaN 0.1 \n", + "1 1.3 13.5 136 53 12.7 \n", + "2 1.1 12.8 141 10 9.7 \n", + "3 NaN NaN NaN NaN 13.9 \n", + "4 1.4 14.6 134 62 10.6 \n", + "\n", + "[5 rows x 25 columns]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# Risk of patients with labs outside the normal range\n" + "# Risk of patients with labs outside the normal range\n", + "data.loc[data['mort_icu']==0].head()" ] } ], @@ -611,7 +806,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.11" + "version": "2.7.12" } }, "nbformat": 4, diff --git a/styleguide.md b/styleguide.md index a7d0fd1..14eca41 100644 --- a/styleguide.md +++ b/styleguide.md @@ -9,14 +9,14 @@ This guide provides some general guidelines on formatting code for the MIMIC Cod Please include the following header information at the top of your code: ``` --- ------------------------------------------------------------------ --- Title: Short descriptive title. --- Description: More detailed description explaining the purpose. --- MIMIC version: Version of MIMIC (e.g. MIMIC-III v1.1). --- References: References to relevant academic papers etc (optional). --- ------------------------------------------------------------------ +-- ------------------------------------------------------------------ +-- Title: Short descriptive title. +-- Description: More detailed description explaining the purpose. +-- ------------------------------------------------------------------ ``` +We would also recommend adding in any relevant references or usage notes in the top of the query. + ## SQL - Always use uppercase for the reserved keywords like SELECT and WHERE. @@ -26,8 +26,4 @@ For more detail, following the guidelines at: http://www.sqlstyle.guide/ ## Python -Guidelines to follow... - - - - +Following PEP8 guidelines is recommended. Read more here: https://www.python.org/dev/peps/pep-0008/ diff --git a/tests/test_mysql_build.py b/tests/test_mysql_build.py index a1434c4..b13ca99 100644 --- a/tests/test_mysql_build.py +++ b/tests/test_mysql_build.py @@ -8,7 +8,7 @@ sqluser = 'root' testdbname = 'mimic_test_db' hostname = 'localhost' -datadir = 'testdata/v1_3/' +datadir = 'testdata/v1_4/' schema = 'mimiciii' # Set paths for scripts to be tested @@ -22,23 +22,23 @@ "ADMISSIONS": 58976, "CALLOUT": 34499, "CAREGIVERS": 7567, -"CHARTEVENTS": 263201375, +"CHARTEVENTS": 330712483, "CPTEVENTS": 573146, "D_CPT": 134, "D_ICD_DIAGNOSES": 14567, "D_ICD_PROCEDURES": 3882, "D_ITEMS": 12478, -"D_LABITEMS": 755, -"DATETIMEEVENTS": 4486049, +"D_LABITEMS": 753, +"DATETIMEEVENTS": 4485937, "DIAGNOSES_ICD": 651047, "DRGCODES": 125557, "ICUSTAYS": 61532, -"INPUTEVENTS_CV": 17528894, +"INPUTEVENTS_CV": 17527935, "INPUTEVENTS_MV": 3618991, -"LABEVENTS": 27872575, -"MICROBIOLOGYEVENTS": 328446, -"NOTEEVENTS": 2078705, -"OUTPUTEVENTS": 4349339, +"LABEVENTS": 27854055, +"MICROBIOLOGYEVENTS": 631726, +"NOTEEVENTS": 2083180, +"OUTPUTEVENTS": 4349218, "PATIENTS": 46520, "PRESCRIPTIONS": 4156848, "PROCEDUREEVENTS_MV": 258066, @@ -50,10 +50,10 @@ def run_mysql_build_scripts(cur): # Create tables and loads data fn = curpath + '../buildmimic/mysql/1-define.sql' cur.execute(open(fn, "r").read()) - if os.environ.has_key('USER') and os.environ['USER'] == 'jenkins': + if os.environ.has_key('USER') and os.environ['USER'] == 'jenkins': # use full dataset mimic_data_dir = '/home/mimicadmin/data/mimiciii_1_3/' - else: + else: mimic_data_dir = curpath+datadir call(['mysql','-f',fn,'-d',testdbname,'-U',sqluser,'-v','mimic_data_dir='+mimic_data_dir]) # # Add constraints @@ -74,7 +74,7 @@ def setUpClass(cls): cls.con = MySQLdb.connect(host=hostname, user=sqluser) cls.cur = cls.con.cursor() # Create test database - try: + try: cls.cur.execute('DROP DATABASE ' + testdbname) except MySQLdb.OperationalError: pass diff --git a/tests/test_oracle_build.py b/tests/test_oracle_build.py new file mode 100644 index 0000000..59dabc4 --- /dev/null +++ b/tests/test_oracle_build.py @@ -0,0 +1,150 @@ +import unittest +import pandas as pd +import os +from subprocess import call +# import cx_Oracle + +# Config +sqluser = 'root' +testdbname = 'mimic_test_db' +hostname = 'localhost' +datadir = 'testdata/v1_4/' +schema = 'mimiciii' + +# Set paths for scripts to be tested +curpath = os.path.join(os.path.dirname(__file__)) + '/' + +# Display environment variables +print(os.environ) + +# Create dictionary with table details for use in testing +row_dict = { +"ADMISSIONS": 58976, +"CALLOUT": 34499, +"CAREGIVERS": 7567, +"CHARTEVENTS": 330712483, +"CPTEVENTS": 573146, +"D_CPT": 134, +"D_ICD_DIAGNOSES": 14567, +"D_ICD_PROCEDURES": 3882, +"D_ITEMS": 12478, +"D_LABITEMS": 753, +"DATETIMEEVENTS": 4485937, +"DIAGNOSES_ICD": 651047, +"DRGCODES": 125557, +"ICUSTAYS": 61532, +"INPUTEVENTS_CV": 17527935, +"INPUTEVENTS_MV": 3618991, +"LABEVENTS": 27854055, +"MICROBIOLOGYEVENTS": 631726, +"NOTEEVENTS": 2083180, +"OUTPUTEVENTS": 4349218, +"PATIENTS": 46520, +"PRESCRIPTIONS": 4156848, +"PROCEDUREEVENTS_MV": 258066, +"PROCEDURES_ICD": 240095, +"SERVICES": 73343, +"TRANSFERS": 261897 } + +# def run_oracle_build_scripts(cur): +# # Create tables and loads data +# fn = curpath + '../buildmimic/mysql/1-define.sql' +# cur.execute(open(fn, "r").read()) +# if os.environ.has_key('USER') and os.environ['USER'] == 'jenkins': +# # use full dataset +# mimic_data_dir = '/home/mimicadmin/data/mimiciii_1_3/' +# else: +# mimic_data_dir = curpath+datadir +# call(['mysql','-f',fn,'-d',testdbname,'-U',sqluser,'-v','mimic_data_dir='+mimic_data_dir]) +# # # Add constraints +# # fn = curpath + '../buildmimic/mysql/3-constraints.sql' +# # cur.execute(open(fn, "r").read()) +# # # Add indexes +# # fn = curpath + '../buildmimic/mysql/2-indexes.sql' +# # cur.execute(open(fn, "r").read()) +# pass + + +# # Class to run unit tests +# class test_oracle(unittest.TestCase): +# # setUpClass runs once for the class +# @classmethod +# def setUpClass(cls): +# # Connect to default mysql database +# cls.con = MySQLdb.connect(host=hostname, user=sqluser) +# cls.cur = cls.con.cursor() +# # Create test database +# try: +# cls.cur.execute('DROP DATABASE ' + testdbname) +# except MySQLdb.OperationalError: +# pass +# cls.cur.execute('CREATE DATABASE ' + testdbname) +# cls.cur.close() +# cls.con.close() +# # Connect to the test database +# cls.con = MySQLdb.connect(db=testdbname, user=sqluser) +# cls.cur = cls.con.cursor() +# # Build the test database +# # run_mysql_build_scripts(cls.cur) +# cls.cur.close() +# cls.con.close() + +# # tearDownClass runs once for the class +# @classmethod +# def tearDownClass(cls): +# # Connect to default mysql database +# cls.con = MySQLdb.connect(host=hostname, user=sqluser) +# cls.cur = cls.con.cursor() +# # Drop test database +# cls.cur.execute('DROP DATABASE ' + testdbname) +# cls.cur.close() +# cls.con.close() + +# # setUp runs once for each test method +# def setUp(self): +# # Connect to the test database +# self.con = MySQLdb.connect(db=testdbname, user=sqluser) +# self.cur = self.con.cursor() + +# # tearDown runs once for each test method +# def tearDown(self): +# self.cur.close() +# self.con.close() + +# # The MIMIC test db has been created by this point +# # Add unit tests below +# def test_run_sample_query(self): +# test_query = """ +# SELECT 'hello world'; +# """ +# hello_world = pd.read_sql_query(test_query,self.con) +# self.assertEqual(hello_world.values[0][0],'hello world') + +# # def test_testddl(self): +# # # Creates and drops an example schema and table +# # fn = curpath + 'testddl.sql' +# # self.cur.execute(open(fn, "r").read()) +# # # self.assertEqual(1,1) + +# # -------------------------------------------------- +# # Run a series of checks to ensure ITEMIDs are valid +# # All checks should return 0. +# # -------------------------------------------------- + + +# # ---------------------------------------------------- +# # RUN THE FOLLOWING TESTS ON THE FULL DATASET ONLY --- +# # ---------------------------------------------------- + +# # if os.environ.has_key('USER') and os.environ['USER'] == 'jenkins': +# # def test_row_counts_are_as_expected(self): +# # for tablename,expectedrows in row_dict.iteritems(): +# # query = "SELECT COUNT(*) FROM " + schema + "." + tablename + ";" +# # queryresult = pd.read_sql_query(query,self.con) +# # self.assertEqual(queryresult.values[0][0],expectedrows) + +# def main(): +# unittest.main() + +# if __name__ == '__main__': +# main() diff --git a/tests/test_postgres_build.py b/tests/test_postgres_build.py index 0a1c1fd..626c856 100644 --- a/tests/test_postgres_build.py +++ b/tests/test_postgres_build.py @@ -14,7 +14,7 @@ psqluser = 'postgres' testdbname = 'mimic_test_db' hostname = 'localhost' -datadir = 'testdata/v1_3/' +datadir = 'testdata/v1_4/' schema = 'mimiciii' # Set paths for scripts to be tested @@ -28,23 +28,23 @@ "ADMISSIONS": 58976, "CALLOUT": 34499, "CAREGIVERS": 7567, -"CHARTEVENTS": 263201375, +"CHARTEVENTS": 330712483, "CPTEVENTS": 573146, "D_CPT": 134, "D_ICD_DIAGNOSES": 14567, "D_ICD_PROCEDURES": 3882, "D_ITEMS": 12478, -"D_LABITEMS": 755, -"DATETIMEEVENTS": 4486049, +"D_LABITEMS": 753, +"DATETIMEEVENTS": 4485937, "DIAGNOSES_ICD": 651047, "DRGCODES": 125557, "ICUSTAYS": 61532, -"INPUTEVENTS_CV": 17528894, +"INPUTEVENTS_CV": 17527935, "INPUTEVENTS_MV": 3618991, -"LABEVENTS": 27872575, -"MICROBIOLOGYEVENTS": 328446, -"NOTEEVENTS": 2078705, -"OUTPUTEVENTS": 4349339, +"LABEVENTS": 27854055, +"MICROBIOLOGYEVENTS": 631726, +"NOTEEVENTS": 2083180, +"OUTPUTEVENTS": 4349218, "PATIENTS": 46520, "PRESCRIPTIONS": 4156848, "PROCEDUREEVENTS_MV": 258066, @@ -78,10 +78,10 @@ def run_postgres_build_scripts(cur): cur.execute(open(fn, "r").read()) # Loads data fn = curpath + '../buildmimic/postgres/postgres_load_data.sql' - if os.environ.has_key('USER') and os.environ['USER'] == 'jenkins': + if os.environ.has_key('USER') and os.environ['USER'] == 'jenkins': # use full dataset - mimic_data_dir = '/home/mimicadmin/data/mimiciii_1_3/' - else: + mimic_data_dir = '/home/mimicadmin/data/mimiciii_1_4/' + else: mimic_data_dir = curpath+datadir call(['psql','-f',fn,'-d',testdbname,'-U',psqluser,'-v','mimic_data_dir='+mimic_data_dir]) # Add constraints @@ -98,10 +98,10 @@ def run_postgres_build_scripts(cur): # cur.execute(open(fn, "r").read()) # # Loads data # fn = curpath + '../buildmimic/mysql/mysql_load_data.sql' -# if os.environ.has_key('USER') and os.environ['USER'] == 'jenkins': +# if os.environ.has_key('USER') and os.environ['USER'] == 'jenkins': # # use full dataset # mimic_data_dir = '/home/mimicadmin/data/mimiciii_1_3/' -# else: +# else: # mimic_data_dir = curpath+datadir # call(['psql','-f',fn,'-d',testdbname,'-U',psqluser,'-v','mimic_data_dir='+mimic_data_dir]) # # Add constraints @@ -121,7 +121,7 @@ def setUpClass(cls): cls.con.set_isolation_level(ISOLATION_LEVEL_AUTOCOMMIT) cls.cur = cls.con.cursor() # Create test database - try: + try: cls.cur.execute('DROP DATABASE ' + testdbname) except psycopg2.ProgrammingError: pass @@ -180,7 +180,7 @@ def test_testddl(self): # Run a series of checks to ensure ITEMIDs are valid # All checks should return 0. # -------------------------------------------------- - + def test_itemids_in_inputevents_cv_are_shifted(self): query = """ -- prompt Number of ITEMIDs which were erroneously left as original value @@ -189,7 +189,7 @@ def test_itemids_in_inputevents_cv_are_shifted(self): """ queryresult = pd.read_sql_query(query,self.con) self.assertEqual(queryresult.values[0][0],0) - + def test_itemids_in_inputevents_mv_are_shifted(self): query = """ -- prompt Number of ITEMIDs which were erroneously left as original value @@ -198,7 +198,7 @@ def test_itemids_in_inputevents_mv_are_shifted(self): """ queryresult = pd.read_sql_query(query,self.con) self.assertEqual(queryresult.values[0][0],0) - + def test_itemids_in_outputevents_are_shifted(self): query = """ -- prompt Number of ITEMIDs which were erroneously left as original value @@ -207,7 +207,7 @@ def test_itemids_in_outputevents_are_shifted(self): """ queryresult = pd.read_sql_query(query,self.con) self.assertEqual(queryresult.values[0][0],0) - + def test_itemids_in_inputevents_cv_are_in_range(self): query = """ -- prompt Number of ITEMIDs which are above the allowable range @@ -216,7 +216,7 @@ def test_itemids_in_inputevents_cv_are_in_range(self): """ queryresult = pd.read_sql_query(query,self.con) self.assertEqual(queryresult.values[0][0],0) - + def test_itemids_in_outputevents_are_in_range(self): query = """ -- prompt Number of ITEMIDs which are not in the allowable range @@ -225,7 +225,7 @@ def test_itemids_in_outputevents_are_in_range(self): """ queryresult = pd.read_sql_query(query,self.con) self.assertEqual(queryresult.values[0][0],0) - + def test_itemids_in_chartevents_are_in_range(self): query = """ -- prompt Number of ITEMIDs which are not in the allowable range @@ -234,7 +234,7 @@ def test_itemids_in_chartevents_are_in_range(self): """ queryresult = pd.read_sql_query(query,self.con) self.assertEqual(queryresult.values[0][0],0) - + def test_itemids_in_procedureevents_mv_are_in_range(self): query = """ -- prompt Number of ITEMIDs which are not in the allowable range @@ -243,7 +243,7 @@ def test_itemids_in_procedureevents_mv_are_in_range(self): """ queryresult = pd.read_sql_query(query,self.con) self.assertEqual(queryresult.values[0][0],0) - + def test_itemids_in_labevents_are_in_range(self): query = """ -- prompt Number of ITEMIDs which are not in the allowable range @@ -252,7 +252,7 @@ def test_itemids_in_labevents_are_in_range(self): """ queryresult = pd.read_sql_query(query,self.con) self.assertEqual(queryresult.values[0][0],0) - + def test_itemids_in_microbiologyevents_are_in_range(self): query = """ -- prompt Number of ITEMIDs which are not in the allowable range @@ -267,7 +267,7 @@ def test_itemids_in_microbiologyevents_are_in_range(self): # ---------------------------------------------------- # RUN THE FOLLOWING TESTS ON THE FULL DATASET ONLY --- # ---------------------------------------------------- - + if os.environ.has_key('USER') and os.environ['USER'] == 'jenkins': def test_row_counts_are_as_expected(self): for tablename,expectedrows in row_dict.iteritems(): @@ -279,20 +279,20 @@ def test_age_and_los_is_expected(self): query = \ """ WITH icuadmissions as ( - SELECT a.subject_id, a.hadm_id, i.icustay_id, - a.admittime as hosp_admittime, a.dischtime as hosp_dischtime, - i.first_careunit, + SELECT a.subject_id, a.hadm_id, i.icustay_id, + a.admittime as hosp_admittime, a.dischtime as hosp_dischtime, + i.first_careunit, DENSE_RANK() over(PARTITION BY a.hadm_id ORDER BY i.intime ASC) as icu_seq, - p.dob, p.dod, i.intime as icu_intime, i.outtime as icu_outtime, + p.dob, p.dod, i.intime as icu_intime, i.outtime as icu_outtime, i.los as icu_los, - round((EXTRACT(EPOCH FROM (a.dischtime-a.admittime))/60/60/24) :: NUMERIC, 4) as hosp_los, - p.gender, + round((EXTRACT(EPOCH FROM (a.dischtime-a.admittime))/60/60/24) :: NUMERIC, 4) as hosp_los, + p.gender, round((EXTRACT(EPOCH FROM (a.admittime-p.dob))/60/60/24/365.242) :: NUMERIC, 4) as age_hosp_in, round((EXTRACT(EPOCH FROM (i.intime-p.dob))/60/60/24/365.242) :: NUMERIC, 4) as age_icu_in, hospital_expire_flag, - CASE WHEN p.dod IS NOT NULL + CASE WHEN p.dod IS NOT NULL AND p.dod >= i.intime - interval '6 hour' - AND p.dod <= i.outtime + interval '6 hour' THEN 1 + AND p.dod <= i.outtime + interval '6 hour' THEN 1 ELSE 0 END AS icu_expire_flag FROM admissions a INNER JOIN icustays i @@ -300,8 +300,8 @@ def test_age_and_los_is_expected(self): INNER JOIN patients p ON a.subject_id = p.subject_id ORDER BY a.subject_id, i.intime) - SELECT round(avg(age_icu_in)) as avg_age_icu, - round(avg(hosp_los)) as avg_los_hosp, + SELECT round(avg(age_icu_in)) as avg_age_icu, + round(avg(hosp_los)) as avg_los_hosp, round(avg(icu_los)) as avg_los_icu FROM icuadmissions; """ diff --git a/tests/testdata/v1_4/ADMISSIONS.csv.gz b/tests/testdata/v1_4/ADMISSIONS.csv.gz new file mode 100644 index 0000000..b156907 Binary files /dev/null and b/tests/testdata/v1_4/ADMISSIONS.csv.gz differ diff --git a/tests/testdata/v1_4/CALLOUT.csv.gz b/tests/testdata/v1_4/CALLOUT.csv.gz new file mode 100644 index 0000000..b707668 Binary files /dev/null and b/tests/testdata/v1_4/CALLOUT.csv.gz differ diff --git a/tests/testdata/v1_4/CAREGIVERS.csv.gz b/tests/testdata/v1_4/CAREGIVERS.csv.gz new file mode 100644 index 0000000..b0218db Binary files /dev/null and b/tests/testdata/v1_4/CAREGIVERS.csv.gz differ diff --git a/tests/testdata/v1_4/CHARTEVENTS.csv.gz b/tests/testdata/v1_4/CHARTEVENTS.csv.gz new file mode 100644 index 0000000..92a9815 Binary files /dev/null and b/tests/testdata/v1_4/CHARTEVENTS.csv.gz differ diff --git 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Tutorials are provided either as plain-text (`.md`), Jupyter Notebooks (`.ipynb`), or R Markdown files (`.Rmd`). If downloaded locally, the latter two tutorials are interactive, otherwise all tutorials can be viewed online. + +The tutorials currently available include: + +* cohort-selection.ipynb - Overviews selecting a cohort of patients from MIMIC-III. Topics include calculating patient ages, service type, and creating exclusion criteria. +* explore-items.Rmd - Exploring the data associated with various `itemid` in MIMIC-III, with specific focus on contrasting how there are two sources of data: CareVue and MetaVision. +* sql-intro.md - A gentle introduction to using Structured Query Language (SQL) with the MIMIC-III database, covering key tables and important SQL keywords. +* using_r_with_jupyter.ipynb - Demonstration of using a Jupyter Notebook with the R kernel to connect to a PostgreSQL database with MIMIC-III diff --git a/tutorials/cohort-selection.ipynb b/tutorials/cohort-selection.ipynb new file mode 100644 index 0000000..bd0bf43 --- /dev/null +++ b/tutorials/cohort-selection.ipynb @@ -0,0 +1,3053 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Cohort selection\n", + "\n", + "The aim of this tutorial is to describe how patients are tracked in the MIMIC-III database. By the end of this notebook you should:\n", + "\n", + "* Understand what `subject_id`, `hadm_id`, and `icustay_id` represent\n", + "* Know how to set up a cohort table for subselecting a patient population\n", + "* Understand the difference between service and physical location\n", + "\n", + "Requirements:\n", + "\n", + "* MIMIC-III in a PostgreSQL database\n", + "* Python packages installable with: \n", + " * `pip install numpy pandas matplotlib psycopg2 jupyter`\n", + " \n", + "First, as always, we open a connection to a local copy of the database. If you don't have a local copy of the database in PostgreSQL, follow the tutorial online (http://mimic.physionet.org) to install one.\n", + "\n", + "Let's begin!" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Import libraries\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import psycopg2\n", + "\n", + "# below imports are used to print out pretty pandas dataframes\n", + "from IPython.display import display, HTML\n", + "\n", + "%matplotlib inline\n", + "plt.style.use('ggplot')\n", + "\n", + "# information used to create a database connection\n", + "sqluser = 'postgres'\n", + "dbname = 'mimic'\n", + "schema_name = 'mimiciii'\n", + "\n", + "# Connect to postgres with a copy of the MIMIC-III database\n", + "con = psycopg2.connect(dbname=dbname, user=sqluser)\n", + "\n", + "# the below statement is prepended to queries to ensure they select from the right schema\n", + "query_schema = 'set search_path to ' + schema_name + ';'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Cohort selection will begin with three tables: *patients*, *admissions*, and *icustays*:\n", + "\n", + "* *patients*: information about a patient that does not change - e.g. date of birth, genotypical sex\n", + "* *admissions*: information recorded on hospital admission - admission type (elective, emergency), time of admission\n", + "* *icustays*: information recorded on intensive care unit admission - primarily admission and discharge time\n", + "\n", + "As MIMIC-III is primarily an intensive care unit (ICU) database, the focus will be on patients admitted to and discharged from the ICU. That is, rather than selecting our cohort based off the individual patient (identified by `subject_id` in the database), we will usually want to select our cohort based off the ICU stay (identified by `icustay_id`). Thus, it is sensible to begin with the *icustays* table:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " subject_id hadm_id icustay_id icu_length_of_stay\n", + "0 268 110404 280836 3.248993\n", + "1 269 106296 206613 3.278796\n", + "2 270 188028 220345 2.893854\n", + "3 271 173727 249196 2.059977\n", + "4 272 164716 210407 1.620243" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "query = query_schema + \"\"\"\n", + "SELECT subject_id, hadm_id, icustay_id\n", + ", EXTRACT(EPOCH FROM outtime - intime)/60.0/60.0/24.0 as icu_length_of_stay\n", + "FROM icustays\n", + "LIMIT 10\n", + "\"\"\"\n", + "df = pd.read_sql_query(query, con)\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the above, seconds are converted to days easily by dividing by: 60 (seconds in a minute), 60 (minutes in an hour), and 24 (hours in a day). We also omit the `icu_length_of_stay_interval` as it's now redundant for our purposes.\n", + "\n", + "If we are only interested in ICU stays lasting a certain length (say 24 hours), we need to do the following two steps:\n", + "\n", + "* use an in-line view to \"hold\" the data\n", + "* use the `WHERE` clause to filter this data using our generated column\n", + "\n", + "Here's an example of us filtering to only stays lasting at least 2 days:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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subject_idhadm_idicustay_idicu_length_of_stay
02681104042808363.248993
12691062962066133.278796
22701880282203452.893854
32711737272491962.059977
42741305462548518.814259
52751298862196497.131412
\n", + "
" + ], + "text/plain": [ + " subject_id hadm_id icustay_id icu_length_of_stay\n", + "0 268 110404 280836 3.248993\n", + "1 269 106296 206613 3.278796\n", + "2 270 188028 220345 2.893854\n", + "3 271 173727 249196 2.059977\n", + "4 274 130546 254851 8.814259\n", + "5 275 129886 219649 7.131412" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "query = query_schema + \"\"\"\n", + "WITH co AS\n", + "(\n", + "SELECT subject_id, hadm_id, icustay_id\n", + ", EXTRACT(EPOCH FROM outtime - intime)/60.0/60.0/24.0 as icu_length_of_stay\n", + "FROM icustays\n", + "LIMIT 10\n", + ")\n", + "SELECT\n", + " co.subject_id, co.hadm_id, co.icustay_id, co.icu_length_of_stay\n", + "FROM co\n", + "WHERE icu_length_of_stay >= 2\n", + "\"\"\"\n", + "df = pd.read_sql_query(query, con)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looks good - none of the above stays are shorter than a day.\n", + "\n", + "Many studies using the MIMIC-III database are focused on specific subgroups of patients. For example, MIMIC-III contains both adults and neonates, but it is rare that a study would like to evaluate some phenomenom in both groups simulatenously. As a result, the first step of many studies is selecting a subpopulation from the *icustays* table. Concretely, we will want to select a set of `icustay_id` which represent our patient population. You've just seen an example of doing this: in the above code, we limited our population to only those who were in the ICU for at least 1 day.\n", + "\n", + "When subselecting the patient population, it is generally good practice to build a \"cohort\" table - that is a table with all `icustay_id` available in the database, each associated with binary flags indicating whether or not they are excluded from your population. Let's take a look at how this would work with the above query which limited the dataset to patients who stayed longer than 2 days." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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subject_idhadm_idicustay_idicu_length_of_stayexclusion_los
02681104042808363.2489930
12691062962066133.2787960
22701880282203452.8938540
32711737272491962.0599770
42721647162104071.6202431
52731586892415071.4861811
62741305462548518.8142590
72751298862196497.1314120
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" + ], + "text/plain": [ + " subject_id hadm_id icustay_id icu_length_of_stay exclusion_los\n", + "0 268 110404 280836 3.248993 0\n", + "1 269 106296 206613 3.278796 0\n", + "2 270 188028 220345 2.893854 0\n", + "3 271 173727 249196 2.059977 0\n", + "4 272 164716 210407 1.620243 1\n", + "5 273 158689 241507 1.486181 1\n", + "6 274 130546 254851 8.814259 0\n", + "7 275 129886 219649 7.131412 0\n", + "8 276 135156 206327 1.337836 1\n", + "9 277 171601 272866 0.731273 1" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "query = query_schema + \"\"\"\n", + "WITH co AS\n", + "(\n", + "SELECT subject_id, hadm_id, icustay_id\n", + ", EXTRACT(EPOCH FROM outtime - intime)/60.0/60.0/24.0 as icu_length_of_stay\n", + "FROM icustays\n", + "LIMIT 10\n", + ")\n", + "SELECT\n", + " co.subject_id, co.hadm_id, co.icustay_id, co.icu_length_of_stay\n", + " , CASE\n", + " WHEN co.icu_length_of_stay < 2 then 1\n", + " ELSE 0 END\n", + " as exclusion_los\n", + "FROM co\n", + "\"\"\"\n", + "df = pd.read_sql_query(query, con)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the earlier query we had a total of 6 rows returned because we filtered 4 of them out. In the above query, we keep all 10 rows, but we have indicated that 4 of them should be excluded in the last column. This is a good practice to have as it will make it very easy to summarize your exclusions at the end of your study and modify them if your later work deems it necessary.\n", + "\n", + "Let's go back to the common exclusion criteria mentioned earlier: flagging non-adults for removal. First, we'll need to calculate the patient's age on ICU admission, which will require the patient's date of birth and the ICU admission time. We already have the ICU admission time (`intime` in the *icustays* table), so all we need to do is get the date of birth from the *patients* table." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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subject_idhadm_idicustay_idicu_length_of_stayageexclusion_los
021633532436530.0918290 days 21:20:071
131458342115526.06456027950 days 19:10:110
241857772946381.67847217475 days 00:29:311
351789802147570.0844440 days 06:04:241
461070642282323.67291724084 days 21:30:540
571180372784440.2677200 days 15:35:291
671180372367540.7390972 days 03:26:011
781595142622991.0755210 days 12:36:101
891507502205975.32305615263 days 13:07:020
9101841672884098.0921060 days 11:39:050
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" + ], + "text/plain": [ + " subject_id hadm_id icustay_id icu_length_of_stay age \\\n", + "0 2 163353 243653 0.091829 0 days 21:20:07 \n", + "1 3 145834 211552 6.064560 27950 days 19:10:11 \n", + "2 4 185777 294638 1.678472 17475 days 00:29:31 \n", + "3 5 178980 214757 0.084444 0 days 06:04:24 \n", + "4 6 107064 228232 3.672917 24084 days 21:30:54 \n", + "5 7 118037 278444 0.267720 0 days 15:35:29 \n", + "6 7 118037 236754 0.739097 2 days 03:26:01 \n", + "7 8 159514 262299 1.075521 0 days 12:36:10 \n", + "8 9 150750 220597 5.323056 15263 days 13:07:02 \n", + "9 10 184167 288409 8.092106 0 days 11:39:05 \n", + "\n", + " exclusion_los \n", + "0 1 \n", + "1 0 \n", + "2 1 \n", + "3 1 \n", + "4 0 \n", + "5 1 \n", + "6 1 \n", + "7 1 \n", + "8 0 \n", + "9 0 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "query = query_schema + \"\"\"\n", + "WITH co AS\n", + "(\n", + "SELECT icu.subject_id, icu.hadm_id, icu.icustay_id\n", + ", EXTRACT(EPOCH FROM outtime - intime)/60.0/60.0/24.0 as icu_length_of_stay\n", + ", icu.intime - pat.dob AS age\n", + "FROM icustays icu\n", + "INNER JOIN patients pat\n", + " ON icu.subject_id = pat.subject_id\n", + "LIMIT 10\n", + ")\n", + "SELECT\n", + " co.subject_id, co.hadm_id, co.icustay_id, co.icu_length_of_stay\n", + " , co.age\n", + " , CASE\n", + " WHEN co.icu_length_of_stay < 2 then 1\n", + " ELSE 0 END\n", + " as exclusion_los\n", + "FROM co\n", + "\"\"\"\n", + "df = pd.read_sql_query(query, con)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notes from the above query: we have to specify the table in `icu.subject_id` because there is a `subject_id` column in both the *icustays* table and the *patients* table, and the program doesn't know which we want unless we specify it.\n", + "\n", + "Now, looking at the results, it appears `age` is returned as the number of days between the `dob` and the `intime` - perhaps not what we desire! As mentioned before, this is an interval data type - it's useful when doing date operations but for our purposes it is not practical. We have three options:\n", + "\n", + "* we can use the function `EXTRACT()` to extract the seconds and convert that into an age by dividing by the number of seconds in a year (as we did before)\n", + "* we can use the PostgreSQL function `AGE()` to return a symbolic representation of the age in years followed by the function `DATE_PART()` to extract the years\n", + "* the same as the above, but calling `DATE_PART()` to get the months and days as well for more precision\n", + "\n", + "Let's look at all three." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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subject_idhadm_idicustay_idicu_length_of_stayageage_extract_yearage_extract_preciseage_extract_epochexclusion_los
021633532436530.0918290 days 21:20:070.00.0024020.0024341
131458342115526.06456027950 days 19:10:110.076.52677976.5267920
241857772946381.67847217475 days 00:29:310.047.84499047.8450471
351789802147570.0844440 days 06:04:240.00.0006860.0006931
461070642282323.67291724084 days 21:30:540.065.94224565.9422970
571180372784440.2677200 days 15:35:290.00.0017160.0017791
671180372367540.7390972 days 03:26:010.00.0058190.0058681
781595142622991.0755210 days 12:36:100.00.0013730.0014381
891507502205975.32305615263 days 13:07:020.041.79021941.7902280
9101841672884098.0921060 days 11:39:050.00.0012580.0013290
\n", + "
" + ], + "text/plain": [ + " subject_id hadm_id icustay_id icu_length_of_stay age \\\n", + "0 2 163353 243653 0.091829 0 days 21:20:07 \n", + "1 3 145834 211552 6.064560 27950 days 19:10:11 \n", + "2 4 185777 294638 1.678472 17475 days 00:29:31 \n", + "3 5 178980 214757 0.084444 0 days 06:04:24 \n", + "4 6 107064 228232 3.672917 24084 days 21:30:54 \n", + "5 7 118037 278444 0.267720 0 days 15:35:29 \n", + "6 7 118037 236754 0.739097 2 days 03:26:01 \n", + "7 8 159514 262299 1.075521 0 days 12:36:10 \n", + "8 9 150750 220597 5.323056 15263 days 13:07:02 \n", + "9 10 184167 288409 8.092106 0 days 11:39:05 \n", + "\n", + " age_extract_year age_extract_precise age_extract_epoch exclusion_los \n", + "0 0.0 0.002402 0.002434 1 \n", + "1 0.0 76.526779 76.526792 0 \n", + "2 0.0 47.844990 47.845047 1 \n", + "3 0.0 0.000686 0.000693 1 \n", + "4 0.0 65.942245 65.942297 0 \n", + "5 0.0 0.001716 0.001779 1 \n", + "6 0.0 0.005819 0.005868 1 \n", + "7 0.0 0.001373 0.001438 1 \n", + "8 0.0 41.790219 41.790228 0 \n", + "9 0.0 0.001258 0.001329 0 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "query = query_schema + \"\"\"\n", + "WITH co AS\n", + "(\n", + "SELECT icu.subject_id, icu.hadm_id, icu.icustay_id\n", + ", EXTRACT(EPOCH FROM outtime - intime)/60.0/60.0/24.0 as icu_length_of_stay\n", + ", icu.intime - pat.dob AS age\n", + "FROM icustays icu\n", + "INNER JOIN patients pat\n", + " ON icu.subject_id = pat.subject_id\n", + "LIMIT 10\n", + ")\n", + "SELECT\n", + " co.subject_id, co.hadm_id, co.icustay_id, co.icu_length_of_stay\n", + " , co.age\n", + " , EXTRACT('year' FROM co.age) as age_extract_year \n", + " , EXTRACT('year' FROM co.age) \n", + " + EXTRACT('months' FROM co.age) / 12.0\n", + " + EXTRACT('days' FROM co.age) / 365.242\n", + " + EXTRACT('hours' FROM co.age) / 24.0 / 364.242\n", + " as age_extract_precise\n", + " , EXTRACT('epoch' from co.age) / 60.0 / 60.0 / 24.0 / 365.242 as age_extract_epoch\n", + " , CASE\n", + " WHEN co.icu_length_of_stay < 2 then 1\n", + " ELSE 0 END\n", + " as exclusion_los\n", + "FROM co\n", + "\"\"\"\n", + "df = pd.read_sql_query(query, con)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, there is very little difference between the second and last approach - so it is up to preference (and desire for true precision). We will use the `EXTRACT('epoch' ... )` approach as it's the simplest.\n", + "\n", + "Now, we will filter out neonates by requiring age to be greater than 16 (note while this also removes children, there are no children in the MIMIC-III database)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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subject_idhadm_idicustay_idicu_length_of_stayageexclusion_losexclusion_age
021633532436530.0918290.00243411
131458342115526.06456076.52679200
241857772946381.67847247.84504710
351789802147570.0844440.00069311
461070642282323.67291765.94229700
571180372784440.2677200.00177911
671180372367540.7390970.00586811
781595142622991.0755210.00143811
891507502205975.32305641.79022800
9101841672884098.0921060.00132901
\n", + "
" + ], + "text/plain": [ + " subject_id hadm_id icustay_id icu_length_of_stay age \\\n", + "0 2 163353 243653 0.091829 0.002434 \n", + "1 3 145834 211552 6.064560 76.526792 \n", + "2 4 185777 294638 1.678472 47.845047 \n", + "3 5 178980 214757 0.084444 0.000693 \n", + "4 6 107064 228232 3.672917 65.942297 \n", + "5 7 118037 278444 0.267720 0.001779 \n", + "6 7 118037 236754 0.739097 0.005868 \n", + "7 8 159514 262299 1.075521 0.001438 \n", + "8 9 150750 220597 5.323056 41.790228 \n", + "9 10 184167 288409 8.092106 0.001329 \n", + "\n", + " exclusion_los exclusion_age \n", + "0 1 1 \n", + "1 0 0 \n", + "2 1 0 \n", + "3 1 1 \n", + "4 0 0 \n", + "5 1 1 \n", + "6 1 1 \n", + "7 1 1 \n", + "8 0 0 \n", + "9 0 1 " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "query = query_schema + \"\"\"\n", + "WITH co AS\n", + "(\n", + "SELECT icu.subject_id, icu.hadm_id, icu.icustay_id\n", + ", EXTRACT(EPOCH FROM outtime - intime)/60.0/60.0/24.0 as icu_length_of_stay\n", + ", EXTRACT('epoch' from icu.intime - pat.dob) / 60.0 / 60.0 / 24.0 / 365.242 as age\n", + "FROM icustays icu\n", + "INNER JOIN patients pat\n", + " ON icu.subject_id = pat.subject_id\n", + "LIMIT 10\n", + ")\n", + "SELECT\n", + " co.subject_id, co.hadm_id, co.icustay_id, co.icu_length_of_stay\n", + " , co.age\n", + " , CASE\n", + " WHEN co.icu_length_of_stay < 2 then 1\n", + " ELSE 0 END\n", + " as exclusion_los\n", + " , CASE\n", + " WHEN co.age < 16 then 1\n", + " ELSE 0 END\n", + " as exclusion_age\n", + "FROM co\n", + "\"\"\"\n", + "df = pd.read_sql_query(query, con)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Above we can see we have \"flagged\" 6 `icustay_id` for exclusion due to their age, and that many of these exclusions overlap with the requirement that their ICU length of stay be longer than 2 days.\n", + "\n", + "Let's try another common exclusion criteria: secondary admissions to the ICU - either in-hospital or out of hospital. The primary reason for this is it simplifies many statistical analyses which assume independent observations. If we kept multiple ICU stays for the same patient, then we would have to account for the fact that these ICU stays are highly correlated (e.g. the same patient may repeatedly be admitted for the same condition), and this can add an undesirable layer of complexity. To identify readmissions, we first rank ICU stays from earliest to latest using the `RANK()` function." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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subject_idhadm_idicustay_idicu_length_of_stayageicustay_id_orderexclusion_losexclusion_age
021633532436530.0918290.002434111
131458342115526.06456076.526792100
241857772946381.67847247.845047110
351789802147570.0844440.000693111
461070642282323.67291765.942297100
571180372784440.2677200.001779111
671180372367540.7390970.005868211
781595142622991.0755210.001438111
891507502205975.32305641.790228100
9101841672884098.0921060.001329101
\n", + "
" + ], + "text/plain": [ + " subject_id hadm_id icustay_id icu_length_of_stay age \\\n", + "0 2 163353 243653 0.091829 0.002434 \n", + "1 3 145834 211552 6.064560 76.526792 \n", + "2 4 185777 294638 1.678472 47.845047 \n", + "3 5 178980 214757 0.084444 0.000693 \n", + "4 6 107064 228232 3.672917 65.942297 \n", + "5 7 118037 278444 0.267720 0.001779 \n", + "6 7 118037 236754 0.739097 0.005868 \n", + "7 8 159514 262299 1.075521 0.001438 \n", + "8 9 150750 220597 5.323056 41.790228 \n", + "9 10 184167 288409 8.092106 0.001329 \n", + "\n", + " icustay_id_order exclusion_los exclusion_age \n", + "0 1 1 1 \n", + "1 1 0 0 \n", + "2 1 1 0 \n", + "3 1 1 1 \n", + "4 1 0 0 \n", + "5 1 1 1 \n", + "6 2 1 1 \n", + "7 1 1 1 \n", + "8 1 0 0 \n", + "9 1 0 1 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "query = query_schema + \"\"\"\n", + "WITH co AS\n", + "(\n", + "SELECT icu.subject_id, icu.hadm_id, icu.icustay_id\n", + ", EXTRACT(EPOCH FROM outtime - intime)/60.0/60.0/24.0 as icu_length_of_stay\n", + ", EXTRACT('epoch' from icu.intime - pat.dob) / 60.0 / 60.0 / 24.0 / 365.242 as age\n", + "\n", + ", RANK() OVER (PARTITION BY icu.subject_id ORDER BY icu.intime) AS icustay_id_order\n", + "\n", + "FROM icustays icu\n", + "INNER JOIN patients pat\n", + " ON icu.subject_id = pat.subject_id\n", + "LIMIT 10\n", + ")\n", + "SELECT\n", + " co.subject_id, co.hadm_id, co.icustay_id, co.icu_length_of_stay\n", + " , co.age\n", + " , co.icustay_id_order\n", + " \n", + " , CASE\n", + " WHEN co.icu_length_of_stay < 2 then 1\n", + " ELSE 0 END\n", + " as exclusion_los\n", + " , CASE\n", + " WHEN co.age < 16 then 1\n", + " ELSE 0 END\n", + " as exclusion_age\n", + "FROM co\n", + "\"\"\"\n", + "df = pd.read_sql_query(query, con)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that `subject_id` = 7 has been admitted twice - we would like to exclude this second admission, so we code in a `CASE` statement to do exactly this (note: while the subject would be excluded anyway, due to the other exclusion criteria, it's still a good example case!)." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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subject_idhadm_idicustay_idicu_length_of_stayageicustay_id_orderexclusion_losexclusion_ageexclusion_first_stay
021633532436530.0918290.0024341110
131458342115526.06456076.5267921000
241857772946381.67847247.8450471100
351789802147570.0844440.0006931110
461070642282323.67291765.9422971000
571180372784440.2677200.0017791110
671180372367540.7390970.0058682111
781595142622991.0755210.0014381110
891507502205975.32305641.7902281000
9101841672884098.0921060.0013291010
\n", + "
" + ], + "text/plain": [ + " subject_id hadm_id icustay_id icu_length_of_stay age \\\n", + "0 2 163353 243653 0.091829 0.002434 \n", + "1 3 145834 211552 6.064560 76.526792 \n", + "2 4 185777 294638 1.678472 47.845047 \n", + "3 5 178980 214757 0.084444 0.000693 \n", + "4 6 107064 228232 3.672917 65.942297 \n", + "5 7 118037 278444 0.267720 0.001779 \n", + "6 7 118037 236754 0.739097 0.005868 \n", + "7 8 159514 262299 1.075521 0.001438 \n", + "8 9 150750 220597 5.323056 41.790228 \n", + "9 10 184167 288409 8.092106 0.001329 \n", + "\n", + " icustay_id_order exclusion_los exclusion_age exclusion_first_stay \n", + "0 1 1 1 0 \n", + "1 1 0 0 0 \n", + "2 1 1 0 0 \n", + "3 1 1 1 0 \n", + "4 1 0 0 0 \n", + "5 1 1 1 0 \n", + "6 2 1 1 1 \n", + "7 1 1 1 0 \n", + "8 1 0 0 0 \n", + "9 1 0 1 0 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "query = query_schema + \"\"\"\n", + "WITH co AS\n", + "(\n", + "SELECT icu.subject_id, icu.hadm_id, icu.icustay_id\n", + ", EXTRACT(EPOCH FROM outtime - intime)/60.0/60.0/24.0 as icu_length_of_stay\n", + ", EXTRACT('epoch' from icu.intime - pat.dob) / 60.0 / 60.0 / 24.0 / 365.242 as age\n", + "\n", + ", RANK() OVER (PARTITION BY icu.subject_id ORDER BY icu.intime) AS icustay_id_order\n", + "\n", + "FROM icustays icu\n", + "INNER JOIN patients pat\n", + " ON icu.subject_id = pat.subject_id\n", + "LIMIT 10\n", + ")\n", + "SELECT\n", + " co.subject_id, co.hadm_id, co.icustay_id, co.icu_length_of_stay\n", + " , co.age\n", + " , co.icustay_id_order\n", + " \n", + " , CASE\n", + " WHEN co.icu_length_of_stay < 2 then 1\n", + " ELSE 0 END\n", + " AS exclusion_los\n", + " , CASE\n", + " WHEN co.age < 16 then 1\n", + " ELSE 0 END\n", + " AS exclusion_age\n", + " , CASE \n", + " WHEN co.icustay_id_order != 1 THEN 1\n", + " ELSE 0 END \n", + " AS exclusion_first_stay\n", + "FROM co\n", + "\"\"\"\n", + "df = pd.read_sql_query(query, con)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, we are now excluding this later admission.\n", + "\n", + "Finally, we may want to exclude patients who were admitted for certain services. This is commonly done as the patient demographics can vary widely based upon service type, and we may want a more homoegenous group of patients. The *services* table provides the hospital service that a patient was admitted under, and is the best place to identify the type of care the patient is receiving. Let's take a look at the *services* table now:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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subject_idhadm_idtransfertimeprev_servicecurr_service
04711358792122-07-22 14:07:27TSURGMED
14711358792122-07-26 18:31:49MEDTSURG
24721730642172-09-28 19:22:15NoneCMED
34731291942201-01-09 20:16:45NoneNB
44741942462181-03-23 08:24:41NoneNB
54741467462181-04-04 17:38:46NoneNBB
64751393512131-09-16 18:44:04NoneNB
74761610422100-07-05 10:26:45NoneNB
84771910252156-07-20 11:53:03NoneMED
94781373702194-07-15 13:55:21NoneNB
\n", + "
" + ], + "text/plain": [ + " subject_id hadm_id transfertime prev_service curr_service\n", + "0 471 135879 2122-07-22 14:07:27 TSURG MED\n", + "1 471 135879 2122-07-26 18:31:49 MED TSURG\n", + "2 472 173064 2172-09-28 19:22:15 None CMED\n", + "3 473 129194 2201-01-09 20:16:45 None NB\n", + "4 474 194246 2181-03-23 08:24:41 None NB\n", + "5 474 146746 2181-04-04 17:38:46 None NBB\n", + "6 475 139351 2131-09-16 18:44:04 None NB\n", + "7 476 161042 2100-07-05 10:26:45 None NB\n", + "8 477 191025 2156-07-20 11:53:03 None MED\n", + "9 478 137370 2194-07-15 13:55:21 None NB" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "query = query_schema + \"\"\"\n", + "SELECT subject_id, hadm_id, transfertime, prev_service, curr_service\n", + "FROM services\n", + "LIMIT 10\n", + "\"\"\"\n", + "df = pd.read_sql_query(query, con)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Above we can see that the `curr_service` column gives an abbreviation for the current service. The `prev_service` column is null, *unless* the patient had a transfer of service, in which case it identifies the previous service. For example, we can see `subject_id` has had at least two service changes: once from TSURG to MED and once from MED back to TSURG (note: there may be more as we have limited this query using `LIMIT 10`, and you could examine this patient in detail using `SELECT * FROM services WHERE subject_id = 471` if you like).\n", + "\n", + "A list of the unique services and their descriptions can be found at:\n", + "http://mimic.physionet.org/mimictables/services/\n", + "\n", + "In particular, if we want to exclude surgery, we should exclude patients who were admitted under:\n", + "\n", + "* CSURG\n", + "* NSURG\n", + "* ORTHO\n", + "* PSURG\n", + "* SURG\n", + "* TSURG\n", + "* VSURG\n", + "\n", + "We can simplify this to patients who were under service `'%SURG'` or `'ORTHO'` - where `'%'` is a wildcard matching any letter(s)." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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hadm_idcurr_servicesurgical
0135879MED0
1135879TSURG1
2173064CMED0
3129194NB0
4194246NB0
5146746NBB0
6139351NB0
7161042NB0
8191025MED0
9137370NB0
\n", + "
" + ], + "text/plain": [ + " hadm_id curr_service surgical\n", + "0 135879 MED 0\n", + "1 135879 TSURG 1\n", + "2 173064 CMED 0\n", + "3 129194 NB 0\n", + "4 194246 NB 0\n", + "5 146746 NBB 0\n", + "6 139351 NB 0\n", + "7 161042 NB 0\n", + "8 191025 MED 0\n", + "9 137370 NB 0" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "query = query_schema + \"\"\"\n", + "SELECT hadm_id, curr_service\n", + ", CASE\n", + " WHEN curr_service like '%SURG' then 1\n", + " WHEN curr_service = 'ORTHO' then 1\n", + " ELSE 0 END\n", + " AS surgical\n", + "FROM services se\n", + "LIMIT 10\n", + "\"\"\"\n", + "df = pd.read_sql_query(query, con)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This seems to be working nicely - except we only have `hadm_id`, and we are basing our cohort off of `icustay_id`. No problem, we can join from the *icustays* table to get the `icustay_id` for each `hadm_id`." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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hadm_idicustay_idcurr_servicesurgical
0100001275225MED0
1100003209281MED0
2100006291788MED0
3100006291788OMED0
4100007217937SURG1
5100009253656CSURG1
6100010271147GU0
7100011214619TRAUM0
8100012239289SURG1
9100016217590MED0
\n", + "
" + ], + "text/plain": [ + " hadm_id icustay_id curr_service surgical\n", + "0 100001 275225 MED 0\n", + "1 100003 209281 MED 0\n", + "2 100006 291788 MED 0\n", + "3 100006 291788 OMED 0\n", + "4 100007 217937 SURG 1\n", + "5 100009 253656 CSURG 1\n", + "6 100010 271147 GU 0\n", + "7 100011 214619 TRAUM 0\n", + "8 100012 239289 SURG 1\n", + "9 100016 217590 MED 0" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "query = query_schema + \"\"\"\n", + "SELECT icu.hadm_id, icu.icustay_id, curr_service\n", + ", CASE\n", + " WHEN curr_service like '%SURG' then 1\n", + " WHEN curr_service = 'ORTHO' then 1\n", + " ELSE 0 END\n", + " AS surgical\n", + "FROM icustays icu\n", + "LEFT JOIN services se\n", + " ON icu.hadm_id = se.hadm_id\n", + "LIMIT 10\n", + "\"\"\"\n", + "df = pd.read_sql_query(query, con)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note however that now we have a new issue: which service do we pick for each `icustay_id`? This is a cohort selection question, not a syntax question. We choose to exclude patients whose **last** service **before** ICU admission was surgical. We can update our join clause to reflect this choice:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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hadm_idicustay_idcurr_servicesurgical
0100001275225MED0
1100003209281MED0
2100006291788MED0
3100007217937SURG1
4100009253656CSURG1
5100010271147GU0
6100011214619TRAUM0
7100012239289SURG1
8100016217590MED0
9100017258320MED0
\n", + "
" + ], + "text/plain": [ + " hadm_id icustay_id curr_service surgical\n", + "0 100001 275225 MED 0\n", + "1 100003 209281 MED 0\n", + "2 100006 291788 MED 0\n", + "3 100007 217937 SURG 1\n", + "4 100009 253656 CSURG 1\n", + "5 100010 271147 GU 0\n", + "6 100011 214619 TRAUM 0\n", + "7 100012 239289 SURG 1\n", + "8 100016 217590 MED 0\n", + "9 100017 258320 MED 0" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "query = query_schema + \"\"\"\n", + "SELECT icu.hadm_id, icu.icustay_id, se.curr_service\n", + ", CASE\n", + " WHEN curr_service like '%SURG' then 1\n", + " WHEN curr_service = 'ORTHO' then 1\n", + " ELSE 0 END\n", + " AS surgical\n", + "FROM icustays icu\n", + "LEFT JOIN services se\n", + " ON icu.hadm_id = se.hadm_id\n", + "AND se.transfertime < icu.intime + interval '12' hour\n", + "LIMIT 10\n", + "\"\"\"\n", + "df = pd.read_sql_query(query, con)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note how `icustay_id` = 100006 no longer has an entry for OMED above: this is because this service was given after their ICU admission, so we do not want to consider it. Also note that our join clause has `+ interval '12' hour` - this adds a bit of \"fuzziness\" to our criteria. As these times are entered asynchronously by different people in varying locations in the hospital, there can be some minor inconsistencies in the order. For example, a patient may be transferred to the surgical service for ICU admission, but the `transfertime` in *services* occurs after the `intime` in *icustays* by an hour or so. This is administrative \"noise\" - and a fuzzy interval can be useful in these cases. Again, this is a cohort selection decision - you may not want to use an interval as large as 12 hours - perhaps only 2 or 4 - though in this case there is likely to be very minor differences as 80% of patients never have a change in hospital service.\n", + "\n", + "Finally, we want to collapse this down so we only have one service for a given ICU admission. As done earlier, we will use RANK() to do this." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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hadm_idicustay_idcurr_servicesurgical
0100001275225MED0
1100003209281MED0
2100006291788MED0
3100007217937SURG1
4100009253656CSURG1
5100010271147GU0
6100011214619TRAUM0
7100012239289SURG1
8100016217590MED0
9100017258320MED0
\n", + "
" + ], + "text/plain": [ + " hadm_id icustay_id curr_service surgical\n", + "0 100001 275225 MED 0\n", + "1 100003 209281 MED 0\n", + "2 100006 291788 MED 0\n", + "3 100007 217937 SURG 1\n", + "4 100009 253656 CSURG 1\n", + "5 100010 271147 GU 0\n", + "6 100011 214619 TRAUM 0\n", + "7 100012 239289 SURG 1\n", + "8 100016 217590 MED 0\n", + "9 100017 258320 MED 0" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "query = query_schema + \"\"\"\n", + "WITH serv AS\n", + "(\n", + "SELECT icu.hadm_id, icu.icustay_id, se.curr_service\n", + ", CASE\n", + " WHEN curr_service like '%SURG' then 1\n", + " WHEN curr_service = 'ORTHO' then 1\n", + " ELSE 0 END\n", + " AS surgical\n", + ", RANK() OVER (PARTITION BY icu.hadm_id ORDER BY se.transfertime DESC) as rank\n", + "FROM icustays icu\n", + "LEFT JOIN services se\n", + " ON icu.hadm_id = se.hadm_id\n", + "AND se.transfertime < icu.intime + interval '12' hour\n", + "LIMIT 10\n", + ")\n", + "SELECT hadm_id, icustay_id, curr_service, surgical\n", + "FROM serv\n", + "WHERE rank = 1\n", + "\"\"\"\n", + "df = pd.read_sql_query(query, con)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can join this table to our original cohort from above." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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subject_idhadm_idicustay_idicu_length_of_stayageicustay_id_orderexclusion_losexclusion_ageexclusion_first_stayexclusion_surgical
061070642282323.67291765.94229710001
171180372784440.2677200.00177911100
271180372367540.7390970.00586821110
331458342115526.06456076.52679210001
491507502205975.32305641.79022810000
581595142622991.0755210.00143811100
621633532436530.0918290.00243411100
751789802147570.0844440.00069311100
8101841672884098.0921060.00132910100
941857772946381.67847247.84504711000
\n", + "
" + ], + "text/plain": [ + " subject_id hadm_id icustay_id icu_length_of_stay age \\\n", + "0 6 107064 228232 3.672917 65.942297 \n", + "1 7 118037 278444 0.267720 0.001779 \n", + "2 7 118037 236754 0.739097 0.005868 \n", + "3 3 145834 211552 6.064560 76.526792 \n", + "4 9 150750 220597 5.323056 41.790228 \n", + "5 8 159514 262299 1.075521 0.001438 \n", + "6 2 163353 243653 0.091829 0.002434 \n", + "7 5 178980 214757 0.084444 0.000693 \n", + "8 10 184167 288409 8.092106 0.001329 \n", + "9 4 185777 294638 1.678472 47.845047 \n", + "\n", + " icustay_id_order exclusion_los exclusion_age exclusion_first_stay \\\n", + "0 1 0 0 0 \n", + "1 1 1 1 0 \n", + "2 2 1 1 1 \n", + "3 1 0 0 0 \n", + "4 1 0 0 0 \n", + "5 1 1 1 0 \n", + "6 1 1 1 0 \n", + "7 1 1 1 0 \n", + "8 1 0 1 0 \n", + "9 1 1 0 0 \n", + "\n", + " exclusion_surgical \n", + "0 1 \n", + "1 0 \n", + "2 0 \n", + "3 1 \n", + "4 0 \n", + "5 0 \n", + "6 0 \n", + "7 0 \n", + "8 0 \n", + "9 0 " + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "query = query_schema + \"\"\"\n", + "WITH co AS\n", + "(\n", + "SELECT icu.subject_id, icu.hadm_id, icu.icustay_id\n", + ", EXTRACT(EPOCH FROM outtime - intime)/60.0/60.0/24.0 as icu_length_of_stay\n", + ", EXTRACT('epoch' from icu.intime - pat.dob) / 60.0 / 60.0 / 24.0 / 365.242 as age\n", + ", RANK() OVER (PARTITION BY icu.subject_id ORDER BY icu.intime) AS icustay_id_order\n", + "FROM icustays icu\n", + "INNER JOIN patients pat\n", + " ON icu.subject_id = pat.subject_id\n", + "LIMIT 10\n", + ")\n", + ", serv AS\n", + "(\n", + "SELECT icu.hadm_id, icu.icustay_id, se.curr_service\n", + ", CASE\n", + " WHEN curr_service like '%SURG' then 1\n", + " WHEN curr_service = 'ORTHO' then 1\n", + " ELSE 0 END\n", + " as surgical\n", + ", RANK() OVER (PARTITION BY icu.hadm_id ORDER BY se.transfertime DESC) as rank\n", + "FROM icustays icu\n", + "LEFT JOIN services se\n", + " ON icu.hadm_id = se.hadm_id\n", + "AND se.transfertime < icu.intime + interval '12' hour\n", + ")\n", + "SELECT\n", + " co.subject_id, co.hadm_id, co.icustay_id, co.icu_length_of_stay\n", + " , co.age\n", + " , co.icustay_id_order\n", + " \n", + " , CASE\n", + " WHEN co.icu_length_of_stay < 2 then 1\n", + " ELSE 0 END\n", + " AS exclusion_los\n", + " , CASE\n", + " WHEN co.age < 16 then 1\n", + " ELSE 0 END\n", + " AS exclusion_age\n", + " , CASE \n", + " WHEN co.icustay_id_order != 1 THEN 1\n", + " ELSE 0 END \n", + " AS exclusion_first_stay\n", + " , CASE\n", + " WHEN serv.surgical = 1 THEN 1\n", + " ELSE 0 END\n", + " as exclusion_surgical\n", + "FROM co\n", + "LEFT JOIN serv\n", + " ON co.icustay_id = serv.icustay_id\n", + " AND serv.rank = 1\n", + "\"\"\"\n", + "df = pd.read_sql_query(query, con)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great! You now have a cohort for which you can start extracting data.\n", + "\n", + "A common question asked is: why did we use the *services* table for identifying surgical patients, rather than the `first_careunit` column from the *icustays*? This is a very important concept in the MIMIC-III database: while patients may be cared for by the surgical service, they are **not necessarily in the surgical ICU**. These patients are called \"boarders\", and the reason why they are not in the usual ICU for their service is multifactorial. Let's take a look at some care units:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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subject_idhadm_idicustay_idicu_length_of_stayageicustay_id_ordercurr_servicefirst_careunitexclusion_losexclusion_ageexclusion_first_stayexclusion_surgical
061070642282323.67291765.9422971SURGSICU0001
171180372784440.2677200.0017791NBNICU1100
271180372367540.7390970.0058682NBNICU1110
331458342115526.06456076.5267921VSURGMICU0001
491507502205975.32305641.7902281NMEDMICU0000
581595142622991.0755210.0014381NBNICU1100
621633532436530.0918290.0024341NBNICU1100
751789802147570.0844440.0006931NBNICU1100
8101841672884098.0921060.0013291NBNICU0100
941857772946381.67847247.8450471MEDMICU1000
\n", + "
" + ], + "text/plain": [ + " subject_id hadm_id icustay_id icu_length_of_stay age \\\n", + "0 6 107064 228232 3.672917 65.942297 \n", + "1 7 118037 278444 0.267720 0.001779 \n", + "2 7 118037 236754 0.739097 0.005868 \n", + "3 3 145834 211552 6.064560 76.526792 \n", + "4 9 150750 220597 5.323056 41.790228 \n", + "5 8 159514 262299 1.075521 0.001438 \n", + "6 2 163353 243653 0.091829 0.002434 \n", + "7 5 178980 214757 0.084444 0.000693 \n", + "8 10 184167 288409 8.092106 0.001329 \n", + "9 4 185777 294638 1.678472 47.845047 \n", + "\n", + " icustay_id_order curr_service first_careunit exclusion_los exclusion_age \\\n", + "0 1 SURG SICU 0 0 \n", + "1 1 NB NICU 1 1 \n", + "2 2 NB NICU 1 1 \n", + "3 1 VSURG MICU 0 0 \n", + "4 1 NMED MICU 0 0 \n", + "5 1 NB NICU 1 1 \n", + "6 1 NB NICU 1 1 \n", + "7 1 NB NICU 1 1 \n", + "8 1 NB NICU 0 1 \n", + "9 1 MED MICU 1 0 \n", + "\n", + " exclusion_first_stay exclusion_surgical \n", + "0 0 1 \n", + "1 0 0 \n", + "2 1 0 \n", + "3 0 1 \n", + "4 0 0 \n", + "5 0 0 \n", + "6 0 0 \n", + "7 0 0 \n", + "8 0 0 \n", + "9 0 0 " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "query = query_schema + \"\"\"\n", + "WITH co AS\n", + "(\n", + "SELECT icu.subject_id, icu.hadm_id, icu.icustay_id, first_careunit\n", + ", EXTRACT(EPOCH FROM outtime - intime)/60.0/60.0/24.0 as icu_length_of_stay\n", + ", EXTRACT('epoch' from icu.intime - pat.dob) / 60.0 / 60.0 / 24.0 / 365.242 as age\n", + ", RANK() OVER (PARTITION BY icu.subject_id ORDER BY icu.intime) AS icustay_id_order\n", + "FROM icustays icu\n", + "INNER JOIN patients pat\n", + " ON icu.subject_id = pat.subject_id\n", + "LIMIT 10\n", + ")\n", + ", serv AS\n", + "(\n", + "SELECT icu.hadm_id, icu.icustay_id, se.curr_service\n", + ", CASE\n", + " WHEN curr_service like '%SURG' then 1\n", + " WHEN curr_service = 'ORTHO' then 1\n", + " ELSE 0 END\n", + " as surgical\n", + ", RANK() OVER (PARTITION BY icu.hadm_id ORDER BY se.transfertime DESC) as rank\n", + "FROM icustays icu\n", + "LEFT JOIN services se\n", + " ON icu.hadm_id = se.hadm_id\n", + "AND se.transfertime < icu.intime + interval '12' hour\n", + ")\n", + "SELECT\n", + " co.subject_id, co.hadm_id, co.icustay_id, co.icu_length_of_stay\n", + " , co.age\n", + " , co.icustay_id_order\n", + " , serv.curr_service\n", + " , co.first_careunit\n", + " , CASE\n", + " WHEN co.icu_length_of_stay < 2 then 1\n", + " ELSE 0 END\n", + " AS exclusion_los\n", + " , CASE\n", + " WHEN co.age < 16 then 1\n", + " ELSE 0 END\n", + " AS exclusion_age\n", + " , CASE \n", + " WHEN co.icustay_id_order != 1 THEN 1\n", + " ELSE 0 END \n", + " AS exclusion_first_stay\n", + " , CASE\n", + " WHEN serv.surgical = 1 THEN 1\n", + " ELSE 0 END\n", + " as exclusion_surgical\n", + "FROM co\n", + "LEFT JOIN serv\n", + " ON co.icustay_id = serv.icustay_id\n", + " AND serv.rank = 1\n", + "\"\"\"\n", + "df = pd.read_sql_query(query, con)\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Without specifically looking for it, we have found an example in `icustay_id` 211552: they were admitted under the VSURG service, but admitted to a medical ICU (MICU). If we used the `first_careunit`, then we would undesirably include this \"boarder\" in our study.\n", + "\n", + "Let's summarize our exclusions by looking at some simple summary measures of the dataframe df." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Observations 10\n", + "exclusion_los 6 (60.00%)\n", + "exclusion_age 6 (60.00%)\n", + "exclusion_first_stay 1 (10.00%)\n", + "exclusion_surgical 2 (20.00%)\n", + "\n", + "Total excluded 9 (90.00%)\n" + ] + } + ], + "source": [ + "print('{:20s} {:5d}'.format('Observations', df.shape[0]))\n", + "idxExcl = np.zeros(df.shape[0],dtype=bool)\n", + "for col in df.columns:\n", + " if \"exclusion_\" in col:\n", + " print('{:20s} {:5d} ({:2.2f}%)'.format(col, df[col].sum(), df[col].sum()*100.0/df.shape[0]))\n", + " idxExcl = (idxExcl) | (df[col]==1)\n", + "\n", + "# print a summary of how many were excluded in total\n", + "print('')\n", + "print('{:20s} {:5d} ({:2.2f}%)'.format('Total excluded', np.sum(idxExcl), np.sum(idxExcl)*100.0/df.shape[0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, summarizing the exclusions is very simple because we have created this cohort table. With that, we conclude this tutorial on cohort selection. To recap, you have learned that:\n", + "\n", + "* best practice is to create a \"cohort\" table with a single row for every unique `icustay_id`, which is usually the identifier of interest\n", + "* exclusions flags can be created based off rules, allowing easy prototyping, modification, and summarization later\n", + "* when identifying the type of care provided, use the *services* table\n", + "* read the docs, and don't make assumptions!\n", + "\n", + "Also, remember that when prototyping the `LIMIT` clause is very useful for speed gains, but don't forget to remove it once you want to test your code on all 60,000+ admissions :)\n", + "\n", + "Good luck in your analysis!" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# close out the database connection\n", + "con.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/notebooks/explore-items.Rmd b/tutorials/explore-items.Rmd similarity index 85% rename from notebooks/explore-items.Rmd rename to tutorials/explore-items.Rmd index 032ee09..59c233e 100644 --- a/notebooks/explore-items.Rmd +++ b/tutorials/explore-items.Rmd @@ -7,7 +7,7 @@ output: html_document Exploring data in MIMIC-III. -We use a slightly incorrect heuristic in comparing Careview and Metavision data, namely that patients registered in those systems may be recognized by whether the `SUBJECT_ID < 40000`. This is wrong for patients with data in Metavision if the patient had been previously registered under Careview. +We use a slightly incorrect heuristic in comparing CareVue and Metavision data, namely that patients registered in those systems may be recognized by whether the `SUBJECT_ID < 40000`. This is wrong for patients with data in Metavision if the patient had been previously registered under CareVue. # D_ITEMS @@ -15,7 +15,7 @@ We use a slightly incorrect heuristic in comparing Careview and Metavision data, # To run this non-interactively (e.g., via Knit), enter the password for the database here: pwd = "" library(RMySQL) -con <- dbConnect(MySQL(), user="mimic3", password=ifelse(pwd=="", readline("MIMIC3 Password: "), pwd), +con <- dbConnect(MySQL(), user="mimic3", password=ifelse(pwd=="", readline("MIMIC3 Password: "), pwd), dbname="mimiciiiv13", host="safar.csail.mit.edu") library(knitr) @@ -28,7 +28,7 @@ item.summary <- dbGetQuery(con, "select category, count(*) c, group_concat(label kable(item.summary) ``` -We next investigate how many chartevents exist for each of the `D_ITEM`s, how many distinct patients have such values, and whether these patients' data came from Careview (I believe `SUBJECT_ID < 40000`) or Metavision (`SUBJECT_ID >= 40000`). +We next investigate how many chartevents exist for each of the `D_ITEM`s, how many distinct patients have such values, and whether these patients' data came from CareVue (I believe `SUBJECT_ID < 40000`) or Metavision (`SUBJECT_ID >= 40000`). ```{r, warning=FALSE} if (exists("chart.items")) { @@ -59,10 +59,10 @@ if (!file.exists("chart-items.csv")) { chart.items.both = subset(chart.items, !is.na(cv_pat) & !is.na(mv_pat)) ``` -Of the `r nrow(chart.items)` distinct `D_ITEM`s, there are only `r nrow(subset(chart.items, !is.na(n_pat)))` that are recorded for any of the patients in `CHARTEVENTS`, of which only `r nrow(chart.items.both)` occur in both Careview and Metavision patients. Careview seems to use many more of the items (`r nrow(subset(chart.items, !is.na(cv_pat)))`) than Metavision (`r nrow(subset(chart.items, !is.na(mv_pat)))`). +Of the `r nrow(chart.items)` distinct `D_ITEM`s, there are only `r nrow(subset(chart.items, !is.na(n_pat)))` that are recorded for any of the patients in `CHARTEVENTS`, of which only `r nrow(chart.items.both)` occur in both CareVue and Metavision patients. CareVue seems to use many more of the items (`r nrow(subset(chart.items, !is.na(cv_pat)))`) than Metavision (`r nrow(subset(chart.items, !is.na(mv_pat)))`). -From previous examination of the data, we know that in the move from Careview to Metavision, some similar items have been coded with different ITEMIDs. We see whether these matching IDs can be recovered by textual identity of their labels. +From previous examination of the data, we know that in the move from CareVue to Metavision, some similar items have been coded with different ITEMIDs. We see whether these matching IDs can be recovered by textual identity of their labels. ```{r, warning=FALSE} item.identical <- dbGetQuery(con, "select x.itemid as itemid1, y.itemid as itemid2, x.label, x.category as cat1, y.category as cat2 from d_items x join d_items y on x.label=y.label where x.itemid60,000 hospital stays. Spanning 2001-2012, it includes demographics, vital signs, laboratory tests, medications, and more. A paper describing MIMIC-III is available from: http://www.nature.com/articles/sdata201635 + +The dataset is provided as a collection of comma-separated value (CSV) files, which can be loaded into a database system such as PostgreSQL. A list of tables is provided on the MIMIC website: http://mimic.physionet.org/mimictables/admissions/ + +We have highlighted some of the key tables below: + +- *patients*: a list of patients covered in MIMIC-III, each identified by a unique `subject_id`. +- *admissions*: a list of hospital admissions, each identified by a unique `hadm_id`. +- *icustays*: a list of ICU stays, each identified by a unique `icustay_id`. + +## Exercise 1 + +1. Where would I find a patient's date of birth? +2. Where would I find a patient's hospital admission time? + +## Solution 1 + +1. The patient's date of birth can be found in the *patients* table, as detailed in the documentation: http://mimic.physionet.org/mimictables/patients/ +2. The patient's hospital admission time can be found in the *admissions* table, which tracks hospital admission information, as detailed here: http://mimic.physionet.org/mimictables/admissions/ + +# Comma separated value files + +Comma separated value (CSV) files are a plain text format used for storing data in a tabular, spreadsheet-style structure. While there is no hard and fast rule for structuring tabular data, it is usually considered good practice to include a header row, to list each variable in a separate column, and to list observations in rows. + +As there is no official standard for the CSV format, the term is used somewhat loosely, which can often cause issues when seeking to load the data into a data analysis package. A general recommendation is to follow the definition for CSVs set out by the Internet Engineering Task Force in the RFC 4180 specification document. + +![CSV file](./csvformat.png) + +Summarized briefly, RFC 4180 specifies that: + +- files may optionally begin with a header row, with each field separated by a comma; +- records should be listed in subsequent rows. Fields should be separated by commas, and each row should be terminated with a line break; +- fields that contain numbers may be optionally enclosed within double quotes; +- fields that contain text ("strings") should be enclosed within double quotes; +- if a double quote appears inside a string of text then it must be escaped with a preceding double quote. + +The CSV format is popular largely because of its simplicity and versatility. CSV files can be edited with a text editor, loaded as a spreadsheet in packages such as Microsoft Excel, and imported and processed by most data analysis packages. Often CSV files are an intermediate data format used to hold data that has been extracted from a relational database in preparation for analysis. + +# Relational databases + +Relational databases can be thought of as a collection of tables which are linked together by shared keys. Organizing data across tables can help to maintain data integrity and enable faster analysis and more efficient storage. + +# Motivation: why would we want a relational database? + +Imagine trying to store data about a person: their name, age, and height. We can easily save this data in a CSV: + + "Name", "Age", "Height" + "Penny", 30, 182 + +Now what if we measure Penny's heart rate every hour for four hours at 8:00am, 9:00am, 10:00am, and 11:00 am. +How should we store this data? The naive approach would be to simply concatenate the information we have all in one file: + + "Name", "Age", "Height", "Time", "Heart rate" + "Penny", 30, 182, "8:00", 65 + "Penny", 30, 182, "9:00", 71 + "Penny", 30, 182, "10:00", 72 + "Penny", 30, 182, "11:00", 68 + +This works, but it feels very inefficient. We have repeated her name ("Penny"), her age (30), and her height (182) every time we get a heart rate measurement. The immediate solution is to not store both of these in the same file: we make one file for Penny's demographics (age, height), and we make another file for heart rate measurements. Then, we make sure that her name is the same in both, so that we know that both sets of data relate to Penny. We've created a relational database. Since her name is what links the two tables together, we would call the name column a "key". + +# Database terminology + +- "Database schema": The model that defines the structure and relationships of the tables. +- "Database query": Data is extracted from relational databases using structured "queries". +- "Primary key": A primary key is a field that uniquely identifies each row in a table. +- "Foreign key": A foreign key is a field that refers to a primary key in another table. +- "Normalisation": The concept of structuring a database in a way that reduces data repetition and improves data integrity, usually by requiring one or more tables to be joined. +- "Denormalisation": The concept of structuring a database to improve readability, sometimes at the expense of data repetition and data integrity. +- "Data type": A term used to describe the behaviour of data and the possible values that it can hold (for example, integer, text, and date are all data types in PostgreSQL). + +Giving a simple example of a hospital database with four tables, it might comprise of: Table 1, a list of all patients; Table 2, a log of hospital admissions; Table 3, a list of vital sign measurements; Table 4, a dictionary of vital sign codes and associated labels. + +The patients table lists unique patients. The admissions table lists unique hospital admissions. The chartevents table lists charted events such as heart rate measurements. The `d_items` table is a dictionary that lists `itemid`s and associated labels, as shown in the example query. pk is primary key. fk is foreign key. + +![Relational databases consist of multiple data tables linked by keys.](./relationaldb.png) + +# What is Structured Query Language (SQL)? + +Structured Query Language (SQL) is a programming language used to manage relational databases. +An SQL query has the following format: + +``` +SELECT [columns] +FROM [table_name]; +``` + +The result of a query is generally a list of rows selected from your table/s of interest. For example, the following query lists the unique patient identifiers (`subject_id`s) of all female patients: + +``` +SELECT subject_id +FROM patients; +``` + +The `*` character is a wildcard that can be used to select all columns. + +``` +SELECT * +FROM patients; +``` + +## Exercise 2 + +1. Open your database querying tool (e.g. PgAdmin3) and connect to the MIMIC-III database. +2. Select all the data from the patients table +3. Select only the `subject_id`, `dob`, and `gender` columns from the patients table + +## Solution 2 + +1. If you have not installed the MIMIC-III database into a PostgreSQL server either locally or otherwise, you can follow the tutorial on installing MIMIC-III: + * OS X or Ubuntu: http://mimic.physionet.org/tutorials/install-mimic-locally-ubuntu/ + * Windows: http://mimic.physionet.org/tutorials/install-mimic-locally-windows/ +2. `SELECT * FROM patients` +3. `SELECT subject_id, dob, gender FROM patients` + + +# `WHERE` keyword + +Often you will want to select a subset of the data which satisfy some set of conditions. +For example, you may want to select only female subjects from the database. +This is easily accomplished with the `WHERE` keyword. The framework of our query becomes: + +``` +SELECT [columns] +FROM [table_name] +WHERE [conditions]; +``` + +We can easily select all the `subject_id` corresponding to female subjects as follows: + +``` +SELECT subject_id +FROM patients +WHERE gender = 'F'; +``` + +`WHERE` clauses are used to make a query return rows meeting only our specified criteria (our previous query, for example, returning only female patients). The simplest criteria is equality, `WHERE gender = 'F'`. Note that in this situation we specify a string, but this syntax will work for numbers as well. For example, we could select all the data for a single subject: + +``` +SELECT * +FROM patients +WHERE subject_id = 109; +``` + +`WHERE` clauses can be combined with standard logical operators `AND`/`OR`: + +``` +SELECT * +FROM patients +WHERE subject_id = 109 +OR subject_id = 117 +OR subject_id = 127; +``` + +A useful shorthand for `OR` statements on the same column is the `IN` condition: + +``` +SELECT * +FROM patients +WHERE subject_id IN (109, 117, 127); +``` + +We can also use the "less than" (`<`), "less than or equal to" `<=`, "greater than" (`>`), or "greater than or equal to" `>=` operators: + +``` +SELECT * +FROM patients +WHERE subject_id >= 109 +AND subject_id <= 127; +``` + +SQL also offers shorthand for `>=` and `<=` combinations with the `BETWEEN` condition: + +``` +SELECT * +FROM patients +WHERE subject_id BETWEEN 109 AND 127; +``` + +Note the `BETWEEN` operator is inclusive. Verify for yourself that the above two queries give the same result. + +When working with text data, we'll often want to search for partial string matches rather than exact matches. This can be accomplished with the `LIKE` keyword: + +``` +-- use `LIKE` to match text +-- The `%` is a wildcard that will match all characters +SELECT * +FROM icustays +WHERE first_careunit LIKE '%ICU%'; +``` + +Note the use of the wildcard character `%`. + +## Exercise 3 + +1. Investigate the importance of the wildcard character `%`. To do this, execute the following two queries. What is the difference in output between these two queries? Why do the outputs differ? + +``` +SELECT * +FROM icustays +WHERE first_careunit LIKE 'ICU%'; +``` + +... and: + +``` +SELECT * +FROM icustays +WHERE first_careunit LIKE '%ICU'; +``` + +2. How many rows are returned when you select all columns from the *patients* table where the gender is 'M'? How many rows are returned when the gender is 'm'? Why is the number different? +3. Each row in the *icustays* table represents a patient stay in the ICU. For hospital stay ID (`hadm_id`) 100242, how many times did the patient visit the ICU? How many days was his/her longest ICU stay on this hospital visit? +4. Which table would you check to find the gender of this patient? Is the patient male or female? + +## Solutions + +1. `LIKE 'ICU%'` requires the string to begin with the word 'ICU': i.e. it will match 'ICU' but it *will not match* 'SICU' since the string does not begin with 'ICU'. Conversely, `LIKE '%ICU'` requires the string to end in 'ICU', and will match 'SICU' but would not match 'ICU-B'. In the MIMIC-III database, careunits always end in the string 'ICU', so the first query returns no results, while the second query returns many rows. +2. String comparisons are case sensitive - `SELECT * FROM patients WHERE gender = 'M'` returns many rows but `SELECT * FROM patients WHERE gender = 'm'` returns no rows. You can avoid this issue by using `lower()` to convert all case to lower case, e.g. `SELECT * FROM patients WHERE lower(gender) = 'm'`. +3. `SELECT * FROM icustays WHERE hadm_id = 100242`. We can see the hospital admission has three unique `icustay_id` associated with it, therefore we can conclude the patient was admitted to the ICU three times. The longest ICU stay was approximately 3 days, as given by the `los` column. +4. The *patients* table has gender, and so we need to find the `subject_id` associated with the given `hadm_id` and select from the *patients* table. `SELECT gender FROM patients WHERE subject_id = 18996` tells us the patient was male. + + +# ORDER BY keyword + +The `ORDER BY` keyword is relatively straightforward: it will sort the data in the order you specify. + +``` +SELECT [columns] +FROM [table_name] +WHERE [conditions] +ORDER BY [columns]; +``` + +The below query orders the results by the patient `dob` + +``` +SELECT subject_id, dob +FROM patients +ORDER BY dob; +``` + +Note that the `WHERE` clause is optional, and in the above query we have omitted it. However, we must respect the order of the keywords - and if we use the `WHERE` keyword it must appear after the `FROM` keyword and before the `ORDER BY` keyword. + +## Exercise 4 + +1. Write a query that selects all of the data in the admissions table, sorted by ascending hospital admission ID (`hadm_id`). What is the lowest `hadm_id`? + +## Solution 4 + +1. `SELECT hadm_id FROM admissions ORDER BY hadm_id` gives us the lowest `hadm_id`: 100001. + +# Using SQL JOIN to query multiple tables + +Often we need information coming from multiple tables. This can be achieved using the SQL `JOIN` keyword. There are several types of join, including `INNER JOIN`, `LEFT JOIN`, and `RIGHT JOIN`. It is important to understand the difference between these joins because their usage can significantly impact query results. Detailed guidance on joins is widely available on the web. + +![SQL joins. Adapted from an image by Arbeck on Wikipedia: https://commons.wikimedia.org/wiki/File:SQL_Joins.svg](./sql-joins.png) + +Using the `INNER JOIN` keyword, let’s select a list of patients from the *patients* table along with dates of birth, and join to the *admissions* table to get the admission time for each hospital admission. We use the `INNER JOIN` to indicate that two or more tables should be combined based on a common attribute, which in our case is `subject_id`: + +``` +-- INNER JOIN will only return rows where subject_id +-- appears in both the patients table and the admissions table +SELECT p.subject_id, p.dob, a.hadm_id, a.admittime +FROM patients p +INNER JOIN admissions a +ON p.subject_id = a.subject_id +ORDER BY subject_id, hadm_id; +``` + +## Exercise 5 + +1. Join the *admissions* table to the *icustays* table +2. Join the *patients* table to both the *admissions* table and the *icustays* table + +## Solution 5 + +1. Two equivalent answers: + * `SELECT * FROM admissions INNER JOIN icustays ON admissions.hadm_id = icustays.hadm_id` + * `SELECT * FROM admissions adm INNER JOIN icustays icu ON adm.hadm_id = icu.hadm_id` +2. +```sql +SELECT * FROM icustays icu +INNER JOIN admissions adm + ON icu.hadm_id = adm.hadm_id +INNER JOIN patients pat + on icu.subject_id = pat.subject_id +``` + +# Performing operations on columns + +Sometimes we will want to perform operations on columns. For example, if we are only interested in length of stay (`los`) to the nearest day, we can use the `round` function: + +``` +SELECT icustay_id, round(los) +FROM icustays; +``` + +Note that the column name ends up being `round(los)`. We can specify the column name using the `AS` keyword: + +``` +SELECT icustay_id, round(los) AS los_integer_days +FROM icustays; +``` + +There are a large number of operations available in PostgreSQL (e.g. a list of mathematical operators are listed [here](https://www.postgresql.org/docs/9.5/static/functions-math.html)). + +Operations can involve multiple columns at once. For example, we may be interested in calculating, for patients who died, how long they spent in the hospital: + +``` +-- When combining columns in an operation, it is sometimes necessary +-- to convert ('cast') them to the same data type +SELECT subject_id, admittime, deathtime + , deathtime - admittime AS length_of_stay +FROM admissions +WHERE deathtime IS NOT NULL; +``` + +Here we have introduced another concept: the `IS NOT NULL` clause which checks that the value is not null (a "null" is an empty value, and represents missing data). + +# How can we use temporary tables to help manage queries? + +It is sometimes helpful to create temporary views or tables to break a large query into smaller, more manageable chunks. There are several approaches that can be used to create temporary tables. One method uses the `WITH` keyword. For example, we'll create a temporary view called `patient_dates` using the previous query, and then select all of its columns: + +``` +WITH patient_dates AS ( +SELECT p.subject_id, p.dob, a.hadm_id, a.admittime, + cast(a.admittime as date) - cast(p.dob as date) / 365.2 ) as age +FROM patients p +INNER JOIN admissions a +ON p.subject_id = a.subject_id +ORDER BY subject_id, hadm_id +) +SELECT * +FROM patient_dates; +``` + +Another method is "materialised views", which create a new table on your database schema. We can then treat this view as any other database table. + +``` +-- we begin by dropping any existing views with the same name +DROP MATERIALIZED VIEW IF EXISTS patient_dates_view; +CREATE MATERIALIZED VIEW patient_dates_view AS +SELECT p.subject_id, p.dob, a.hadm_id, a.admittime, + cast(a.admittime as date) - cast(p.dob as date) / 365.2 ) as age +FROM patients p +INNER JOIN admissions a +ON p.subject_id = a.subject_id +ORDER BY subject_id, hadm_id; +``` + +# CASE statement for if/else logic + +The `CASE` statement is used to handle if/else logic. For example, using the *icustays* table you may want to group length of ICU stay (`los`) into short, medium, and long: + +```sql +-- Use if/else logic to categorise length of stay +-- into 'short', 'medium', and 'long' +SELECT subject_id, hadm_id, icustay_id, los, + CASE WHEN los < 2 THEN 'short' + WHEN los >=2 AND los < 7 THEN 'medium' + WHEN los >=7 THEN 'long' + ELSE NULL END AS los_group +FROM icustays; +``` + +## Exercise 6 + +Write a query that selects `subject_id` and `gender` from the *patients* table and adds a column that codes the gender as 0/1 (female/male) + +## Solution 6 + +```sql +SELECT subject_id, gender +, CASE WHEN gender = 'M' then 1 + WHEN gender = 'F' then 0 + ELSE NULL END + as gender_binary +FROM patients +``` + +# Aggregate functions + +We are often interested in finding an aggregate value across multiple rows, such as the number of patients meeting a condition, an average heart rate, or a maximum blood pressure. We can do this using aggregate functions, such as `COUNT()`, `MAX()`, `SUM()`, and `AVG()`. + +Count the number of rows in the *icustays* table with the `COUNT()` function: + +```sql +-- count the number of rows in a table +SELECT count(*) +FROM icustays; +``` + +Find the maximum length of stay in the *icustays* table with the `MAX()` function: + +```sql +-- find the maximum length of stay in the ICU +SELECT MAX(los) +FROM icustays; +``` + +Aggregate are often combined with a `GROUP BY` clause, so that the aggregate function is applied to specific groups. For example, we can find the maximum length of stay, grouped for each patient: + +```sql +-- find the maximum length of stay in the ICU +-- for each patient +SELECT subject_id, MAX(los) +FROM icustays +GROUP BY subject_id; +``` + +We may want to add a condition based on our new aggregate column. The `WHERE` clause won’t filter on an aggregate column, so instead we use the `HAVING` keyword. For example, we can find the maximum length of stay, grouped for each patient, returning only patients whose maximum stay is less than 10 days: + +```sql +-- find the maximum length of stay in the ICU +-- for each patient +-- where the maximum length of stay is < 10 days +SELECT subject_id, MAX(los) +FROM icustays +GROUP BY subject_id +HAVING MAX(los) <= 10; +``` + +## Exercise 7 + +The `chartevents` table contains charted data such as vital sign measurements. The `itemid`s `211` and `220045` correspond to heart rate (you can double check this in the `d_items` table). + +1. Write a query to select all of the heart rate measurements in the `chartevents` table. Use the `GROUP BY` keyword to find the maximum heart rate for each patient. Note this query may take a some time. +2. Modify the query to exclude patients with a maximum heart rate of > 140 bpm. + +## Solution 7 + +1. +```sql +SELECT icustay_id, max(valuenum) as HeartRate_Max +FROM chartevents +WHERE itemid = 211 +GROUP BY icustay_id +``` +2. +```sql +SELECT icustay_id, max(valuenum) as HeartRate_Max +FROM chartevents +WHERE itemid = 211 +GROUP BY icustay_id +HAVING max(valuenum) > 140 +``` + +# Window functions + +Sometimes an aggregate function isn't quite what we need. For example, we might want to create a column that lists the order of admissions to the ICU for each patient. In this case we do not want to group all of the rows with the same `subject_id` into a single row, so a simple a aggregate function is insufficient. Instead, we want to return multiple rows for each `subject_id`, with the order of admission computed over a `subject_id` window. For example, let's find the order of admission to the ICU for each patient using the `RANK()` window function: + +```sql +-- find the order of admissions to the ICU for a patient +SELECT subject_id, icustay_id, intime, + RANK() OVER (PARTITION BY subject_id ORDER BY intime) +FROM icustays; +``` + +We can then combine this query with a temporary table to select only the first ICU stay for each patient: + +```sql +WITH icustayorder AS ( +SELECT subject_id, icustay_id, intime, + RANK() OVER (PARTITION BY subject_id ORDER BY intime) +FROM icustays +) +SELECT * +FROM icustayorder +WHERE rank = 1; +``` + +## Exercise 8 + +1. Extract the length of stay of the each patient's first ICU stay. +2. Filter to only patients who stayed for at least one day. + +## Solution 8 + +1. +```sql +WITH icustayorder AS ( +SELECT subject_id, icustay_id, intime, + RANK() OVER (PARTITION BY subject_id ORDER BY intime), + los +FROM icustays +) +SELECT subject_id, icustay_id, intime, los +FROM icustays +WHERE rank = 1 +``` +2. +```sql +WITH icustayorder AS ( +SELECT subject_id, icustay_id, intime, + RANK() OVER (PARTITION BY subject_id ORDER BY intime), + los +FROM icustays +) +SELECT subject_id, icustay_id, intime, los +FROM icustays +WHERE rank = 1 +AND los >= 1 +``` + +# Multiple temporary views + +Using the `WITH` statement, you can have more than one inline view. The `services` table contains information about what type of care a patient is receiving in the hospital. + +```sql +-- find the care service provided to each hospital admission +SELECT subject_id, hadm_id, transfertime, prev_service, curr_service +FROM services; +``` + +Note that the `services` table doesn't have `icustay_id`, but we can join to it using `hadm_id`. + +```sql +WITH serv as ( + SELECT subject_id, hadm_id, transfertime, prev_service, curr_service + FROM services +) +, icu as +( + SELECT subject_id, hadm_id, icustay_id, intime, outtime + FROM icustays +) +SELECT icu.subject_id, icu.hadm_id, icu.icustay_id, icu.intime, icu.outtime +, serv.transfertime, serv.prev_service, serv.curr_service +FROM icu +INNER JOIN serv +ON icu.hadm_id = serv.hadm_id +``` + +However, something subtle has happened in this join. Let's see how many rows of data are returned by the above query. We can do this using the aggregate operator `COUNT()`: + + +```sql +WITH serv as ( + SELECT subject_id, hadm_id, transfertime, prev_service, curr_service + FROM services +) +, icu as +( + SELECT subject_id, hadm_id, icustay_id, intime, outtime + FROM icustays +) +SELECT COUNT(*) +FROM icu +INNER JOIN serv +ON icu.hadm_id = serv.hadm_id +``` + +Notice we have replaced all the column names with `COUNT(*)` - which means "count all the rows". What result do you get? Let's compare it to the original *icustays* table: + +```sql +SELECT count(*) +FROM icustays +``` + +## Exercise 9 + +1. Was there a difference between the two counts above? Can you explain why or why there wouldn't be? +2. Suggest an alternative query, making use of the aggregate functions, which would ensure no change in row count (Hint: perhaps we only want the *first* service). + +## Solution 9 + +1. Yes. Intuitively, each hospital admission (`hadm_id`) can have multiple service types (i.e. the patient transferred from surgical to medical), and can have multiple ICU stays (i.e. the patient was readmitted to the ICU). As a result, the first query duplicates rows by matching every service the patient was under to every ICU stay the patient had, regardless of whether they match. This is because we are only joining on `hadm_id`, so the only constraint is that the two events occurred in the same hospitalization. Of course, we do not want this to happen, since a patient is only ever on one service at a time. More technically, the first query joined two tables on non-unique keys: there may be multiple `hadm_id` with the same value in the *services* table, and there may be multiple `hadm_id` with the same value in the *admissions* table. For example, if the *services* table has `hadm_id = 100001` repeated N times, and the *admissions* table has `hadm_id = 100001` repeated M times, then joining these two on `hadm_id` will result in a table with NxM rows: one for every pair. With MIMIC, it is generally very bad practice to join two tables on non-unique columns: at least one of the tables should have unique values for the column, otherwise you end up with duplicate rows and the query results can be confusing. +2. Note the addition of `AND serv.rank = 1` causes the first table to have unique `hadm_id`. +```sql +WITH serv as ( + SELECT subject_id, hadm_id, transfertime, prev_service, curr_service, + RANK() OVER (PARTITION BY hadm_id ORDER BY transfertime) as rank + FROM services +) +, icu as +( + SELECT subject_id, hadm_id, icustay_id, intime, outtime + FROM icustays +) +SELECT COUNT(*) +FROM icu +INNER JOIN serv +ON icu.hadm_id = serv.hadm_id +AND serv.rank = 1 +``` + +# Using other concepts available in the MIMIC Code Repository + +The MIMIC Code Repository is a repository of code shared by the research community - it's where this tutorial is hosted! It is intended to be a central hub for sharing, refining, and reusing code used for analysis of the MIMIC critical care database. For example, you may be interested in identifying which patients have severe sepsis according to the [Angus Criteria](https://github.com/MIT-LCP/mimic-code/blob/master/concepts/sepsis/angus2001.pdf). Rather than re-implementing the criteria, you can make use of existing code in the MIMIC Code Repository. + +A materialised view for the Angus Criteria is available at: https://github.com/MIT-LCP/mimic-code/blob/master/concepts/sepsis/angus.sql. Running this query will generate a table with columns for `subject_id`,`hadm_id`, and `angus` status. + +## Exercise 10 + +Build a materialized view of the Angus criteria on your local database using the code at: https://github.com/MIT-LCP/mimic-code/blob/master/concepts/sepsis/angus.sql + +## Solution 10 + +If you are running psql, you can call files as follows: + +```sql +\i angus.sql +``` + +Which should return: + +``` +DROP MATERIALIZED VIEW +SELECT 58976 +``` + +Or, you may instead get a warning that the view did not exist - that's fine too. If you are running the query in a GUI such as PgAdmin3 or PgAdmin4, then you may simply see `Query returned with no results`. That's expected - the results have been saved into a materialized view. Try to select from the view: `SELECT * FROM angus`. If it returns rows, you've built the view successfully. + +# Conclusion + +Now you should be able to query the MIMIC-III database and take advantage of already created concepts - good luck! If you have issues while working with MIMIC or any of the concepts, feel free to raise them at: https://github.com/MIT-LCP/mimic-code diff --git a/notebooks/r_iculos.ipynb b/tutorials/using_r_with_jupyter.ipynb similarity index 100% rename from notebooks/r_iculos.ipynb rename to tutorials/using_r_with_jupyter.ipynb