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Table_demo.R
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Table_demo.R
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library(foreach)
library(data.table)
ncores <- parallel::detectCores(logical = T) - 1
data.table::setDTthreads(ncores)
source(here::here("analysis","Utils.R"))
###########################################################
dt.tv <- data.table::setDT(feather::read_feather(here::here("output","cohort_long.feather")))
procedures <- c('Abdominal','Cardiac','Obstetrics','Orthopaedic','Thoracic', 'Vascular', 'Colectomy','Cholecystectomy',
'HipReplacement','KneeReplacement')
## Count variables for demographic tables
dt.tv[,postVTE90.perepisode := event.VTE == 1 & (postcovid.VTE.cohort)]
data.table::setkey(dt.tv,patient_id,tstart,tstop)
max.grp.col_(dt = 'dt.tv',max.var.name = 'postVTE90.perepisode',aggregate.cols = 'postVTE90.perepisode',id.vars = c("patient_id", "end.fu"))
dt.tv[!is.finite(postVTE90.perepisode),postVTE90.perepisode := 0]
dt.tv[,postCOVID30.perepisode := event == 1 & (postop.covid.cohort) & end <= 30]
data.table::setkey(dt.tv,patient_id,tstart,tstop)
max.grp.col_(dt = 'dt.tv',max.var.name = 'postCOVID30.perepisode',aggregate.cols = 'postCOVID30.perepisode',id.vars = c("patient_id", "end.fu"))
dt.tv[!is.finite(postCOVID30.perepisode),postCOVID30.perepisode := 0]
n.ops <- rnd(dt.tv[start ==0 & is.finite(admit.date) & any.op == T, lapply(.SD,function(x) sum(x == T)), .SDcols = c(procedures)])
n.pats <- rnd(length(unique(dt.tv[start ==0 & is.finite(admit.date) & any.op == T,patient_id])))
start.date <- dt.tv[start ==0 & is.finite(admit.date) & any.op == T, min(as.Date(as.integer(admit.date), origin = as.Date('1970-01-01')))]
last.date <- dt.tv[start ==0 & is.finite(admit.date) & any.op == T, max(as.Date(as.integer(admit.date), origin = as.Date('1970-01-01')))]
demo.tab <-
data.table::transpose(cbind(data.table::data.table("procedures" = procedures),
foreach::foreach(i = 1:length(procedures), .combine = 'rbind', .inorder = T) %do% dt.tv[start ==0 & final.date >= tstop & end <= 90 & any.op == T & get(paste0(procedures[i])) == T,tail(.SD,1),keyby = .(patient_id,end.fu)][,
.("Procedures" = rnd(.N),
"Patients" = rnd(length(unique(patient_id))),
"Female" = n.perc(sex=='F',dig = 3),
"Age (IQR)" = median.iqr(age,dig = 0),
"BMI < 19" = n.perc(as.numeric(bmi.cat) == 1, dig = 3),
"BMI 19-24" = n.perc(as.numeric(bmi.cat) == 2, dig = 3),
"BMI 15-29" = n.perc(as.numeric(bmi.cat) == 3, dig = 3),
"BMI > 30" = n.perc(as.numeric(bmi.cat) == 4, dig = 3),
"IMD quintile 1" = n.perc(as.numeric(imd5) == 1, dig = 3),
"IMD quintile 2" = n.perc(as.numeric(imd5) == 2, dig = 3),
"IMD quintile 3" = n.perc(as.numeric(imd5) == 3, dig = 3),
"IMD quintile 4" = n.perc(as.numeric(imd5) == 4, dig = 3),
"IMD quintile 5" = n.perc(as.numeric(imd5) == 5, dig = 3),
"Wave 1" = n.perc(wave == 'Wave_1', dig = 3),
"Wave 2" = n.perc(wave == 'Wave_2', dig = 3),
"Wave 3" = n.perc(wave == 'Wave_3', dig = 3),
"Wave 4" = n.perc(wave == 'Wave_4', dig = 3),
"1st Vaccination" = n.perc(vaccination.status.factor==1,dig = 3),
"2nd Vaccination" = n.perc(vaccination.status.factor==2,dig = 3),
"3rd Vaccination" = n.perc(vaccination.status.factor==3,dig = 3),
"Current Cancer" = n.perc(substr(primary_diagnosis,1,1) =='C',dig = 3),
"Emergency" = n.perc(substr(admission_method,1,1) == "2",dig = 3),
"Charlson index" = median.iqr(Charlson, dig = 1),
"Length of stay (IQR)" = median.iqr(discharge.date - admit.date,dig = 0),
"90 day mortality (%)" =n.perc(date.90.day(x = date_death_ons, ref.dat = admit.date),dig = 4),
"30 day post operative COVID-19 (%)" = n.perc(postCOVID30.perepisode, dig = 3),
"Recent 7 to 42 days COVID-19 (%)" = paste0(rnd(sum(recentCOVID, na.rm = T))," (", round(100*mean(recentCOVID != "", na.rm = T), digits = 1),"%)"),
"Previous > 42 COVID-19 (%)" = paste0(rnd(sum(previousCOVID, na.rm = T))," (", round(100*mean(previousCOVID != "", na.rm = T), digits = 1),"%)"),
"90 day VTE (%)" = n.perc(postVTE90.perepisode, dig = 4))],
data.table::transpose(
cbind(
data.table::transpose(
dt.tv[(postop.covid.cohort) & start ==0,
lapply(.SD,function(x) rnd(sum(x))),
keyby = region, .SDcols = c(procedures)],
keep.names = "procedure",make.names = "region")[,
lapply(.SD,
function(x) paste0(x,
' (',
round(100*x/sum(x,na.rm = T),
digits = 1),
'%)')
),
.SDcols = 2:(length(procedures)+ 1)],
sort(unique(dt.tv$region))
),
make.names = "V2"),
data.table::transpose(
cbind(1:5,
dt.tv[(postop.covid.cohort) & start ==0,
lapply(.SD,function(x) rnd(sum(x))),
keyby = c('primary_diagnosis'), .SDcols = c(procedures)][,
lapply(.SD,
function(x) paste0(primary_diagnosis,
": ",
x,
' (',
round(100*x/sum(x,na.rm = T),
digits = 1),
'%)')[order(-x)]),
.SDcols = 2:(length(procedures) + 1)][1:5,]),
make.names = 'V1')
),
make.names = 'procedures',
keep.names = 'Characteristics')
save(demo.tab,file = here::here("output","table_demo.RData"))