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Make_prognostic_dataset.Rmd
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Make_prognostic_dataset.Rmd
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---
title: "Pigment_prognosticators_SM"
author: "James Watson"
date: "4/20/2021"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(dplyr)
```
**** THIS SCRIPT ONLY RUNS WITH ACCESS TO RAW DATA ******
## Load all datasets
Conventions for merged dataset:
* Outcome: died is encoded as 1
* Age: in years
* Hypo: < 2.2 mmol/L
### Vietnamese adults
AQ Vietnam
```{r}
## Load the AQ Vietnam data
aqViet_dat = read.csv('~/Dropbox/Datasets/AQ study/Pigment AQ from Vietnam.csv')
aqViet_dat$STUDY_NO = gsub(pattern = ' ', replacement = '', x = aqViet_dat$STUDY_NO)
aqViet_clinical = readstata13::read.dta13('~/Dropbox/Datasets/AQ study/macpet97.dta')
# check drug allocation
print(aggregate(outcome ~ drug, aqViet_clinical, mean))
aqViet_clinical$studyno = paste('AQ', aqViet_clinical$studyno, sep='')
aqViet_clinical = dplyr::arrange(aqViet_clinical, studyno)
aqViet_dat = dplyr::arrange(aqViet_dat, STUDY_NO)
aqViet_clinical$pig_neut = NA
aqViet_clinical$pig_mono = NA
aqViet_clinical$pig_neut[aqViet_clinical$studyno%in%aqViet_dat$STUDY_NO]=
aqViet_dat$PIG..NEUT
aqViet_clinical$pig_mono[aqViet_clinical$studyno%in%aqViet_dat$STUDY_NO]=
aqViet_dat$PIG.MON
AQViet_data_set = data.frame(study = 'AQ',
site = 'HCMC',
country = 'Vietnam',
id = aqViet_clinical$studyno,
art = as.numeric(aqViet_clinical$drug==0),
age = aqViet_clinical$age,
hb = aqViet_clinical$admhct/3,
outcome = aqViet_clinical$outcome,
resp_rate = aqViet_clinical$admrr,
lactate = aqViet_clinical$admlac,
hypoglycaemia = aqViet_clinical$hypoad,
acidosis = NA,
bun = aqViet_clinical$admbun,
acute_renal_failure = aqViet_clinical$arfad,
base_excess = aqViet_clinical$sbe,
coma = aqViet_clinical$cerebad,
parasitaemia = aqViet_clinical$admpct,
pig_neut = aqViet_clinical$pig_neut,
pig_neut_denom=100,
pig_mono = aqViet_clinical$pig_mono,
pig_mono_denom=30)
# Additional later data file for patients with missing data
aqViet_dat2 = readxl::read_excel('~/Dropbox/Datasets/AQ study/Pigment from MacPeto8.xlsx')
aqViet_dat2$ID = paste0('AQ', aqViet_dat2$studyno)
aqViet_dat2 = aqViet_dat2[!is.na(aqViet_dat2$pigneut), ]
ind = is.na(AQViet_data_set$pig_neut)
table(ind)
for(id in AQViet_data_set$id[ind]){
if(id %in% aqViet_dat2$ID){
i = AQViet_data_set$id==id
j = aqViet_dat2$ID==id
AQViet_data_set$pig_neut[i]=aqViet_dat2$pigneut[j]
AQViet_data_set$pig_mono[i]=aqViet_dat2$pigmon[j]
}
}
ind = is.na(AQViet_data_set$pig_neut)
table(ind)
ind = is.na(AQViet_data_set$pig_mono)
table(ind)
```
### SEAQUAMAT
```{r}
SQ_clinical = haven::read_sav('~/Dropbox/Datasets/SEAQUAMAT/seaquamat stage 3.sav')
SQ_Pig = read.csv('~/Dropbox/Datasets/SEAQUAMAT/Merged_pigmentdata_SQ.csv')
SQ_Pig[!SQ_Pig$ID %in% SQ_clinical$studyno,]
SQ_clinical = merge(SQ_clinical, SQ_Pig, by.x = 'studyno', by.y = 'ID', all.x=T)
SQ_clinical$site = apply(SQ_clinical[,c('country','hospital')],1,function(x) paste(x[1], x[2], sep=': '))
ind = is.na(SQ_clinical$hbad) & !is.na(SQ_clinical$hctad)
SQ_clinical$hbad[ind] = SQ_clinical$hctad[ind]/3
SQ_data_set = data.frame(study = 'SEAQUAMAT',
site = SQ_clinical$site,
country = SQ_clinical$country,
id = SQ_clinical$studyno,
age = SQ_clinical$age,
art = as.numeric(SQ_clinical$drug=='Artesunate'),
outcome = as.numeric(SQ_clinical$outcome==1),
resp_rate = SQ_clinical$resratead,
lactate = NA,
acute_renal_failure = NA,
bun = SQ_clinical$bunad,
hb = SQ_clinical$hbad,
hypoglycaemia = SQ_clinical$hypogly,
acidosis = SQ_clinical$acidad,
base_excess = SQ_clinical$bead,
coma = SQ_clinical$comaad,
parasitaemia = SQ_clinical$admpct,
pig_neut = as.numeric(SQ_clinical$PosPigmentPer100Neu),
pig_neut_denom = as.numeric(SQ_clinical$Neutdenominator),
pig_mono = as.numeric(SQ_clinical$PosPigmentPer30Mono),
pig_mono_denom = as.numeric(SQ_clinical$Monodenominator))
```
### AQUAMAT
```{r}
library(haven)
AQUAMAT <- read.csv('~/Dropbox/Datasets/AQUAMAT/Nick_DayVersion_AQUAMAT_data.csv')
AQUAMAT = dplyr::arrange(AQUAMAT, studynumber)
AQ_paraUl = read.csv("~/Dropbox/Datasets/AQUAMAT/AQ_paraUl.csv")
AQ_paraUl = dplyr::filter(AQ_paraUl, StudyNumber%in%AQUAMAT$studynumber)
AQ_paraUl = dplyr::arrange(AQ_paraUl, StudyNumber)
all(AQ_paraUl$StudyNumber==AQUAMAT$studynumber)
sum(is.na(AQUAMAT$parasitaemia) & !is.na(AQ_paraUl$Aparasitemia))
ind = is.na(AQUAMAT$parasitaemia) & !is.na(AQ_paraUl$Aparasitemia)
AQUAMAT$parasitaemia[ind] = AQ_paraUl$Aparasitemia[ind]
Pig_AQ = read.csv('~/Dropbox/Datasets/AQUAMAT/Merged_pigmentdata_AQUAMAT.csv')
Pig_AQ = filter(Pig_AQ, !is.na(Neutcount) | !is.na(Monocount),
ID %in% AQUAMAT$studynumber)
Pig_AQ = dplyr::arrange(Pig_AQ, ID)
AQUAMAT$hb[(is.na(AQUAMAT$hb) & !is.na(AQUAMAT$hct))] =
AQUAMAT$hct[(is.na(AQUAMAT$hb) & !is.na(AQUAMAT$hct))]/3
AQUAMAT$country[AQUAMAT$country=='Congo']='DRC'
AQUAMAT$country[AQUAMAT$country=='Gambia']='The Gambia'
AQ_data_set = data.frame(study = 'AQUAMAT',
site = AQUAMAT$city,
country = AQUAMAT$country,
id = AQUAMAT$studynumber,
age = AQUAMAT$patage,
art = as.numeric(AQUAMAT$odrug2=='Artesunate'),
outcome = as.numeric(AQUAMAT$odead==1),
resp_rate = AQUAMAT$aresp,
base_excess = AQUAMAT$be,
hypoglycaemia = AQUAMAT$ahypoglyc,
lactate = NA,
acute_renal_failure = NA,
bun = AQUAMAT$bun,
hb = AQUAMAT$hb,
acidosis = AQUAMAT$aacidosis,
coma = AQUAMAT$acoma,
parasitaemia = AQUAMAT$parasitaemia,
pig_neut = NA,
pig_neut_denom = 100,
pig_mono = NA,
pig_mono_denom = 30)
AQ_data_set$pig_neut[AQ_data_set$id %in% Pig_AQ$ID]=
Pig_AQ$Neutcount
AQ_data_set$pig_mono[AQ_data_set$id %in% Pig_AQ$ID]=
Pig_AQ$Monocount
AQ_data_set$pig_neut_denom[AQ_data_set$id %in% Pig_AQ$ID]=
Pig_AQ$Neutdenominator
AQ_data_set$pig_mono_denom[AQ_data_set$id %in% Pig_AQ$ID]=
Pig_AQ$Monodenominator
```
### SMAC
```{r}
load('RData/SMAC_data.RData')
myMergedData$coma_score = myMergedData$BES+myMergedData$BMS+myMergedData$BVS
myMergedData$coma = as.numeric(myMergedData$coma_score <= 2)
myMergedData$country =
plyr::mapvalues(myMergedData$country,
from = c("gam","gha","ken","lam","lib","mal"),
to = c('The Gambia','Ghana','Kenya','Gabon (Lambarene)',
'Gabon (Libreville)','Malawi'))
SMAC_data_set = data.frame(study = 'SMAC',
site = myMergedData$country,
country = myMergedData$country,
id = NA,
age = myMergedData$AGE/12,
art=0,
outcome = as.numeric(myMergedData$OUTCOME==1),
resp_rate = myMergedData$RESPRATE,
hb = myMergedData$HB,
base_excess = myMergedData$BE,
hypoglycaemia = as.numeric(myMergedData$GLUCOSE<2.2),
acute_renal_failure=NA,
bun=NA,
lactate = myMergedData$LACTATE,
acidosis = as.numeric(myMergedData$DEEPBR==1),
coma = myMergedData$coma,
parasitaemia = myMergedData$PARASIT,
pig_neut = myMergedData$POLYL200,
pig_neut_denom = 200,
pig_mono = myMergedData$MONOL200,
pig_mono_denom = 200)
writeLines(sprintf('The number of patients in SMAC with missing coma score data is %s', sum(is.na(SMAC_data_set$coma))))
```
### Lyke et al
```{r}
lyke_pig = read.csv('~/Dropbox/Datasets/Lyke_Pigment/pigment_database.csv')
lyke_pig = filter(lyke_pig, StudyGp=='PG')
LYKE_data_set = data.frame(study = 'Lyke et al',
site = 'Mali',
country = 'Mali',
id = NA,
age = lyke_pig$Age_in_mo/12,
art=0,
outcome = as.numeric(lyke_pig$Survived=='Died'),
resp_rate = NA,
hb = lyke_pig$Hb/10,
base_excess = NA,
hypoglycaemia = NA,
acute_renal_failure=NA,
bun=NA,
lactate = NA,
acidosis = NA,
coma = as.numeric(lyke_pig$Coma=='yes'),
parasitaemia = lyke_pig$Para.mm3,
pig_neut = lyke_pig$Polypig,
pig_neut_denom = 100,
pig_mono = lyke_pig$Monopig,
pig_mono_denom = 30)
```
### Compare datasets
```{r}
par(mfrow=c(1,2), las=1, family='serif')
##******** Parasite counts ***********
qqplot(log10(AQ_data_set$parasitaemia+1),
log10(SQ_data_set$parasitaemia+1), pch=20,
xlab='Parasitaemia AQUAMAT',
ylab = 'Parasitaemia SEAQUAMAT',
panel.first=grid())
lines(0:7, 0:7)
qqplot(log10(AQ_data_set$parasitaemia+1),
log10(SMAC_data_set$parasitaemia+1), pch=20,
xlab='Parasitaemia AQUAMAT',
ylab = 'Parasitaemia SMAC',
panel.first=grid())
lines(0:7, 0:7)
par(mfrow=c(1,2), las=1, family='serif')
##******** Base excess ***********
qqplot(AQ_data_set$base_excess,
SQ_data_set$base_excess, pch=20,
xlab='Base excess AQUAMAT',
ylab = 'Base excess SEAQUAMAT',
panel.first=grid())
lines(-50:50, -50:50)
qqplot(AQ_data_set$base_excess,
SMAC_data_set$base_excess, pch=20,
xlab='Base excess AQUAMAT',
ylab = 'Base excess SMAC',
panel.first=grid())
lines(-50:50, -50:50)
##******** Respiratory rate ***********
par(mfrow=c(1,2), las=1, family='serif')
qqplot(AQ_data_set$resp_rate,
SQ_data_set$resp_rate, pch=20,
xlab='Respiratory rate AQUAMAT',
ylab = 'Respiratory rate SEAQUAMAT',
panel.first=grid())
lines(-50:500, -50:500)
qqplot(AQ_data_set$resp_rate,
SMAC_data_set$resp_rate, pch=20,
xlab='Respiratory rate AQUAMAT',
ylab = 'Respiratory rate SMAC',
panel.first=grid())
lines(-50:500, -50:500)
##******** Age ***********
par(mfrow=c(1,2), las=1, family='serif')
qqplot(AQ_data_set$age,
SQ_data_set$age, pch=20,
xlab='Age AQUAMAT',
ylab = 'Age SEAQUAMAT',
panel.first=grid())
lines(-50:50, -50:50)
qqplot(AQ_data_set$age,
SMAC_data_set$age, pch=20,
xlab='Age AQUAMAT',
ylab = 'Age SMAC',
panel.first=grid())
lines(-50:50, -50:50)
##******** Pigmented Neutrophils ***********
par(mfrow=c(1,2), las=1, family='serif')
qqplot(AQ_data_set$pig_neut/AQ_data_set$pig_neut_denom,
SQ_data_set$pig_neut/SQ_data_set$pig_neut_denom, pch=20,
xlab='Pigmented neutrophils AQUAMAT',
ylab = 'Pigmented neutrophils SEAQUAMAT',
panel.first=grid())
lines(-50:50, -50:50)
qqplot(AQ_data_set$pig_neut/AQ_data_set$pig_neut_denom,
SMAC_data_set$pig_neut/SMAC_data_set$pig_neut_denom, pch=20,
xlab='Pigmented neutrophils AQUAMAT',
ylab = 'Pigmented neutrophils SMAC',
panel.first=grid())
lines(-50:50, -50:50)
#
# ind_lam = SMAC_data_set$site=='lam'
# ind_gam = SMAC_data_set$site=='gam'
#
# qqplot(AQ_data_set$pig_neut,
# SMAC_data_set$pig_neut[ind_lam], pch=20,
# xlab='Pigmented neutrophils AQUAMAT',
# ylab = 'Pigmented neutrophils SMAC (Lambarene)',
# panel.first=grid())
# title('Lambarene')
# lines(-50:50, -50:50)
#
# qqplot(AQ_data_set$pig_neut,
# SMAC_data_set$pig_neut[!ind_lam], pch=20,
# xlab='Pigmented neutrophils AQUAMAT',
# ylab = 'Pigmented neutrophils SMAC (not Lambarene)',
# panel.first=grid())
# lines(-50:50, -50:50)
# title('NOT Lambarene')
#
# qqplot(AQ_data_set$pig_neut,
# SMAC_data_set$pig_neut[ind_gam], pch=20,
# xlab='Pigmented neutrophils AQUAMAT',
# ylab = 'Pigmented neutrophils SMAC (Gambia)',
# panel.first=grid())
# title('Banjul')
# lines(-50:50, -50:50)
##******** Pigmented monocytes ***********
par(mfrow=c(1,2), las=1, family='serif')
qqplot(AQ_data_set$pig_mono/AQ_data_set$pig_mono_denom,
SQ_data_set$pig_mono/SQ_data_set$pig_mono_denom, pch=20,
xlab='Pigmented monocytes AQUAMAT',
ylab = 'Pigmented monocytes SEAQUAMAT',
panel.first=grid())
lines(-50:50, -50:50)
qqplot(AQ_data_set$pig_mono/AQ_data_set$pig_mono_denom,
SMAC_data_set$pig_mono/SMAC_data_set$pig_mono_denom, pch=20,
xlab='Pigmented monocytes AQUAMAT',
ylab = 'Pigmented monocytes SMAC',
panel.first=grid())
lines(-50:50, -50:50)
```
Proportion with coma across sites:
```{r}
writeLines('Cerebral malaria across sites in SEAQUAMAT:')
aggregate(coma ~ site, data = SQ_data_set,FUN = mean)
writeLines('Cerebral malaria across sites in AQUAMAT:')
aggregate(coma ~ site, data = AQ_data_set,FUN = mean)
writeLines('Cerebral malaria across sites in SMAC:')
aggregate(coma ~ site, data = SMAC_data_set,FUN = mean)
```
Proportion with acidosis across sites:
```{r}
writeLines('Acidosis across sites in SEAQUAMAT:')
aggregate(acidosis ~ site, data = SQ_data_set,FUN = mean)
writeLines('Acidosis across sites in AQUAMAT:')
aggregate(acidosis ~ site, data = AQ_data_set,FUN = mean)
writeLines('Acidosis across sites in SMAC:')
aggregate(acidosis ~ site, data = SMAC_data_set,FUN = mean)
```
Mortality across sites:
```{r}
writeLines('Mortality across sites in SEAQUAMAT:')
aggregate(outcome ~ site, data = SQ_data_set,FUN = mean)
writeLines('Mortality across sites in AQUAMAT:')
aggregate(outcome ~ site, data = AQ_data_set,FUN = mean)
writeLines('Mortality across sites in SMAC:')
aggregate(outcome ~ site, data = SMAC_data_set,FUN = mean)
```
Make composite dataset
```{r}
my_cols = colnames(SQ_data_set)
pigmt_data = rbind(AQViet_data_set[, my_cols],
SQ_data_set[,my_cols],
AQ_data_set[,my_cols],
SMAC_data_set[,my_cols],
LYKE_data_set[,my_cols])
save(pigmt_data, file = 'RData/Merged_pigment_data.RData')
```