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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height_first896.0169.89368317.06940115.240000NaNNaNNaN444.500000
bmi896.00.0034340.0145130.000815NaNNaNNaN0.434431
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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..283cbfc --- /dev/null +++ b/notebooks/aline/aline_propensity_score.Rmd @@ -0,0 +1,137 @@ +--- +title: "aline-propensity-score" +author: "Alistair Johnson" +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$service_unit == 'SURG', levels=c(FALSE,TRUE)) +``` + +```{r impute, echo = FALSE} +# we could impute data if we like - e.g. the below imputes the mean +# we currently do complete case analysis however +imputeFlag = 0 +if (imputeFlag != 0){ + print("Imputing missing data for some features...") +for (col in c("weight_first","temp_first","spo2_first", + "bun_first","creatinine_first", "chloride_first", "hgb_first", + "platelet_first", "potassium_first", "sodium_first", "tco2_first", "wbc_first")) +{ + print(paste("Imputing data for: ", col)) + dataset[is.na(dataset[,col]),col] = mean(dataset[,col], na.rm=TRUE) +} +} +``` + + +## Propensity score model + +Now, we build a logistic regression to predict the need for an arterial line catheter from physiology and administrative data. + +```{r propensity} +library(Matching) + +set.seed(43770) + +# fit GLM +glm_fitted = glm(aline_flag ~ age + gender + weight_first + + # bmi + + sofa_first + service_surg + day_icu_intime + hour_icu_intime + + #icu_hour_flag + + # sedative usage + # sedative_flag + fentanyl_flag + midazolam_flag + propofol_flag + + # comorbidities + chf_flag + afib_flag + renal_flag + + liver_flag + copd_flag + cad_flag + + stroke_flag + malignancy_flag + respfail_flag + + # ards_flag + pneumonia_flag + + # vitals + map_first + hr_first + temp_first + spo2_first + + # labs + bun_first + chloride_first + creatinine_first + + hgb_first + platelet_first + potassium_first + + sodium_first + tco2_first + wbc_first, data=dataset, na.action = na.exclude) + +X <- fitted(glm_fitted) +y <- dataset$day_28_flag +Tr <- dataset$aline_flag + +# remove the rows with missing data +idxMiss = is.na(X) +print(paste('Missing data for ', sum(idxMiss))) +X <- X[!idxMiss] +y <- y[!idxMiss] +Tr <- Tr[!idxMiss] +dat <- dataset[!idxMiss,] +``` + +The above shows how many rows we have excluded due to missing data (this analysis uses complete case analysis). + +We have built the propensity score using logistic regression in the previous block. +We now use the `Matching` package to match patients with a caliper size of 0.01. +After matching, we'll apply McNemar's test for paired samples to determine if patients with and without an a-line have a difference in mortality. + +```{r ps} +ps <- Match(Y=NULL, Tr=Tr, X=X, M=1, estimand='ATC', caliper=0.01, exact=FALSE, replace=FALSE); + +# jesse's propensity score workshop -> get pairs with treatment/outcome as cols + +outcome <- data.frame(aline_pt=dat[ps$index.treated,"hosp_exp_flag"], match_pt=dat[ps$index.control,"hosp_exp_flag"]) +head(outcome) + +# mcnemar's test to see if iac related to mort (test should use matched pairs) +tab.match1 <- table(outcome$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..8d85599 --- /dev/null +++ b/notebooks/aline/aline_propensity_score.html @@ -0,0 +1,243 @@ + + + + + + + + + + + + + + + +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=",")
+
+
+

Propensity score model

+

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

+
library(Matching)
+
## Loading required package: MASS
+
## ## 
+## ##  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)
+
+# fit GLM
+glm_fitted = glm(aline_flag ~  age + gender + weight_first + 
+                      # bmi +  
+                     sofa_first + service_surg + day_icu_intime + hour_icu_intime + 
+                     #icu_hour_flag +
+                     # sedative usage
+                     # sedative_flag + fentanyl_flag + midazolam_flag + propofol_flag +
+                     # comorbidities
+                     chf_flag + afib_flag + renal_flag + 
+                     liver_flag + copd_flag + cad_flag +
+                     stroke_flag + malignancy_flag + respfail_flag + 
+                     # ards_flag + pneumonia_flag + 
+                     # vitals
+                     map_first + hr_first + temp_first + spo2_first + 
+                     # labs
+                     bun_first + chloride_first + creatinine_first + 
+                     hgb_first + platelet_first + potassium_first +
+                     sodium_first + tco2_first + wbc_first, data=dataset, na.action = na.exclude)
+
+X <- fitted(glm_fitted)
+y <- dataset$day_28_flag
+Tr <- dataset$aline_flag
+
+# remove the rows with missing data
+idxMiss = is.na(X)
+print(paste('Missing data for ', sum(idxMiss)))
+
## [1] "Missing data for  160"
+
X <- X[!idxMiss]
+y <- y[!idxMiss]
+Tr <- Tr[!idxMiss]
+dat <- dataset[!idxMiss,]
+

The above shows how many rows we have excluded due to missing data (this analysis uses complete case analysis).

+

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 0.01. 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.

+
ps <- Match(Y=NULL, Tr=Tr, X=X, M=1, estimand='ATC', caliper=0.01, exact=FALSE, replace=FALSE);
+
+# jesse's propensity score workshop -> get pairs with treatment/outcome as cols
+
+outcome <- data.frame(aline_pt=dat[ps$index.treated,"hosp_exp_flag"], match_pt=dat[ps$index.control,"hosp_exp_flag"])
+head(outcome)
+
##   aline_pt match_pt
+## 1        0        1
+## 2        0        0
+## 3        0        0
+## 4        0        0
+## 5        0        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
+##     0 573 100
+##     1  90  11
+
tab.match1[1,2]/tab.match1[2,1]
+
## [1] 1.111111
+
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 1.2"
+
mcnemar.test(tab.match1) # for 1-1 pairs
+
## 
+##  McNemar's Chi-squared test with continuity correction
+## 
+## data:  tab.match1
+## McNemar's chi-squared = 0.42632, df = 1, p-value = 0.5138
+

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;