run_Build_PK_IV: Build-PK-IV Module

In EHR: Electronic Health Record (EHR) Data Processing and Analysis Tool

Build-PK-IV Module

Description

This module builds PK data for intravenously (IV) administered medications.

Usage

run_Build_PK_IV(
  conc,
  conc.columns = list(),
  dose,
  dose.columns = list(),
  censor = NULL,
  censor.columns = list(),
  demo.list = NULL,
  demo.columns = list(),
  lab.list = NULL,
  lab.columns = list(),
  dosePriorWindow = 7,
  labPriorWindow = 7,
  postWindow = NA,
  pk.vars = NULL,
  drugname = NULL,
  check.path = NULL,
  missdemo_fn = "-missing-demo",
  faildupbol_fn = "DuplicateBolus-",
  date.format = "%m/%d/%y %H:%M:%S",
  date.tz = "America/Chicago",
  isStrict = FALSE
)

Arguments

conc	concentration data, the output of run_DrugLevel, a filename (CSV, RData, RDS), or a correctly formatted data.frame
conc.columns	a named list that should specify columns in concentration data; ‘id’, ‘datetime’, ‘druglevel’ are required. ‘idvisit’ may also be specified; ‘idvisit’ can be used when there are multiple visits (i.e., several occasions) for the same subject. ‘datetime’ is date and time for concentration measurement, which can refer to a single date-time variable (datetime = ‘date_time’) or two variables holding date and time separately (e.g., datetime = c(‘Date’, ‘Time’)).
dose	dose data, the output of run_MedStrI, a filename (CSV, RData, RDS), or a correctly formatted data.frame
dose.columns	a named list that should specify columns in dose data; ‘id’ is required. ‘infuseDatetime’ and ‘infuseDose’ should be set if infusion dose data is present. ‘infuseTimeExact’ may also be specified for infusion data – this variable represents an precise time, if for example the ‘infuseDatetime’ variable is rounded. ‘bolusDatetime’ and ‘bolusDose’ should be set if bolus dose data is present. A generic ‘date’ variable may be provided, agnostic to either infusion or bolus dosing. ‘gap’ and ‘weight’ column names may also be set. Any of the date-time variables can be specified as a single date-time variable (infuseDatetime = ‘date_time’) or two variables holding date and time separately (e.g., infuseDatetime = c(‘Date’, ‘Time’)).
censor	censoring information, if available; this will censor concentration and dose data for dates occuring after the censor datetime variable.
censor.columns	a named list that should specify columns in censoring data; ‘id’, and ‘datetime’ are required. ‘datetime’ is the date and time when data should be censored. This can refer to a single date-time variable (datetime = ‘date_time’) or two variables holding date and time separately (e.g., datetime = c(‘Date’, ‘Time’)).
demo.list	demographic information, if available; the output from run_Demo or a correctly formatted data.frame
demo.columns	a named list that should specify columns in demographic data; ‘id’ is required. ‘weight’ and ‘idvisit’ may also be used to specify columns for weight or the unique idvisit. Any other columns present in the demographic data are treated as covariates.
lab.list	lab data, if available; the output from run_Labs or a correctly formatted list
lab.columns	a named list that should specify columns in lab data; ‘id’, and ‘datetime’ are required. ‘datetime’ is the date and time when the lab data was obtained, which can refer to a single date-time variable (datetime = ‘date_time’) or two variables holding date and time separately (e.g., datetime = c(‘Date’, ‘Time’)). Any other columns present in lab data are treated as lab values.
dosePriorWindow	Dose data is merged with drug level data. This value sets the time frame window with the number of days prior to the first drug level data; defaults to 7.
labPriorWindow	Lab data is merged with drug level data. This value sets the time frame window with the number of days prior to the first drug level data; defaults to 7.
postWindow	Data is merged with drug level data. This postWindow can set the end time for the drug level data, being the number of days after the first drug level data. The default (NA) will use the date of the last drug level data.
pk.vars	variables to include in the returned PK data. The variable ‘date’ is a special case; when included, it maps the ‘time’ offset to its original date-time. Other named variables will be merged from the concentration data set. For example, rather than being separate data sets, labs or demographics may already be present in the concentration data. These columns should be named here.
drugname	drug of interest, included in filename of check files. The default (NULL) will produce filenames without drugname included.
check.path	path to ‘check’ directory, where check files are created. The default (NULL) will not produce any check files.
missdemo_fn	filename for checking NA frequency among demographic data
faildupbol_fn	filename for duplicate bolus data
date.format	output format for ‘date’ variable
date.tz	output time zone for ‘date’ variable
isStrict	logical; when TRUE dose amount totals are strictly summed rather than repeated hourly until stopped

Details

See EHR Vignette for Structured Data.

Regarding the ‘gap’ variable in the dose dataset, if ‘gap’ is specified in ‘dose.columns’, it allows a continuous infusion given when there are missing records between infusion dosing records. For example, suppose that ‘gap’ = 60 is defined (which is typical gap size when infusion dosing is supposed to be recorded hourly for inpatients) and time between two records (i.e., gap) are greater than 1 hour (i.e., missing records). If the gap between the two records is less or equal to twice of the gap (i.e., 2*60 = 120 min), a continuous infusion is assumed until the 2nd dose record; otherwise, the first infusion is assumed to be stopped (i.e., add zero doses) after 60 min (i.e., equal to the gap size) and a new infusion (the 2nd record) starts at its recorded time.

Value

PK data set

Examples

# make fake data
set.seed(6543)

build_date <- function(x) format(seq(x, length.out=5, by="1 hour"), "%Y-%m-%d %H:%M")
dates <- unlist(lapply(rep(Sys.time(),3), build_date))

plconc <- data.frame(mod_id = rep(1:3,each=5),
                   mod_id_visit = rep(1:3,each=5)+0.1,
                   event = rep(1:5,times=3),
                   conc.level = 15*exp(-1*rep(1:5,times=3))+rnorm(15,0,0.1),
                   date.time = as.POSIXct(dates))

ivdose <- data.frame(mod_id = 1:3,
                     date.dose = substr(dates[seq(1,15,by=5)],1,10),
                     infuse.time.real = NA, infuse.time = NA, infuse.dose = NA,
                     bolus.time = as.POSIXct(dates[seq(1,15,by=5)])-300,
                     bolus.dose = 90,
                     maxint = 0L,
                     weight = 45)


run_Build_PK_IV(conc = plconc,
                conc.columns = list(id = 'mod_id', datetime = 'date.time',
                  druglevel = 'conc.level', idvisit = 'mod_id_visit'),
                dose = ivdose,
                dose.columns = list(id = 'mod_id', date = 'date.dose',
                  bolusDatetime = 'bolus.time', bolusDose = 'bolus.dose',
                  gap = 'maxint', weight = 'weight'),
                pk.vars = 'date')

EHR documentation built on Dec. 28, 2022, 1:31 a.m.