mirror of https://github.com/msberends/AMR.git
157 lines
7.1 KiB
R
Executable File
157 lines
7.1 KiB
R
Executable File
% Generated by roxygen2: do not edit by hand
|
|
% Please edit documentation in R/first_isolate.R
|
|
\name{first_isolate}
|
|
\alias{first_isolate}
|
|
\title{Determine first (weighted) isolates}
|
|
\source{
|
|
Methodology of this function is based on: \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
|
|
}
|
|
\usage{
|
|
first_isolate(tbl, col_date = NULL, col_patient_id = NULL,
|
|
col_mo = NULL, col_testcode = NULL, col_specimen = NULL,
|
|
col_icu = NULL, col_keyantibiotics = NULL, episode_days = 365,
|
|
testcodes_exclude = NULL, icu_exclude = FALSE,
|
|
filter_specimen = NULL, output_logical = TRUE,
|
|
type = "keyantibiotics", ignore_I = TRUE, points_threshold = 2,
|
|
info = TRUE, col_bactid = NULL, col_genus = NULL,
|
|
col_species = NULL)
|
|
}
|
|
\arguments{
|
|
\item{tbl}{a \code{data.frame} containing isolates.}
|
|
|
|
\item{col_date}{column name of the result date (or date that is was received on the lab), defaults to the first column of class \code{Date}}
|
|
|
|
\item{col_patient_id}{column name of the unique IDs of the patients, defaults to the first column that starts with 'patient' or 'patid' (case insensitive)}
|
|
|
|
\item{col_mo}{column name of the unique IDs of the microorganisms (see \code{\link{mo}}), defaults to the first column of class \code{mo}. Values will be coerced using \code{\link{as.mo}}.}
|
|
|
|
\item{col_testcode}{column name of the test codes. Use \code{col_testcode = NULL} to \strong{not} exclude certain test codes (like test codes for screening). In that case \code{testcodes_exclude} will be ignored.}
|
|
|
|
\item{col_specimen}{column name of the specimen type or group}
|
|
|
|
\item{col_icu}{column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU)}
|
|
|
|
\item{col_keyantibiotics}{column name of the key antibiotics to determine first \emph{weighted} isolates, see \code{\link{key_antibiotics}}. Defaults to the first column that starts with 'key' followed by 'ab' or 'antibiotics' (case insensitive). Use \code{col_keyantibiotics = FALSE} to prevent this.}
|
|
|
|
\item{episode_days}{episode in days after which a genus/species combination will be determined as 'first isolate' again}
|
|
|
|
\item{testcodes_exclude}{character vector with test codes that should be excluded (case-insensitive)}
|
|
|
|
\item{icu_exclude}{logical whether ICU isolates should be excluded}
|
|
|
|
\item{filter_specimen}{specimen group or type that should be excluded}
|
|
|
|
\item{output_logical}{return output as \code{logical} (will else be the values \code{0} or \code{1})}
|
|
|
|
\item{type}{type to determine weighed isolates; can be \code{"keyantibiotics"} or \code{"points"}, see Details}
|
|
|
|
\item{ignore_I}{logical to determine whether antibiotic interpretations with \code{"I"} will be ignored when \code{type = "keyantibiotics"}, see Details}
|
|
|
|
\item{points_threshold}{points until the comparison of key antibiotics will lead to inclusion of an isolate when \code{type = "points"}, see Details}
|
|
|
|
\item{info}{print progress}
|
|
|
|
\item{col_bactid}{(deprecated, use \code{col_mo} instead)}
|
|
|
|
\item{col_genus}{(deprecated, use \code{col_mo} instead) column name of the genus of the microorganisms}
|
|
|
|
\item{col_species}{(deprecated, use \code{col_mo} instead) column name of the species of the microorganisms}
|
|
}
|
|
\value{
|
|
A vector to add to table, see Examples.
|
|
}
|
|
\description{
|
|
Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type.
|
|
}
|
|
\details{
|
|
\strong{WHY THIS IS SO IMPORTANT} \cr
|
|
To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode \href{https://www.ncbi.nlm.nih.gov/pubmed/17304462}{[1]}. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all \emph{S. aureus} isolates would be overestimated, because you included this MRSA more than once. It would be \href{https://en.wikipedia.org/wiki/Selection_bias}{selection bias}.
|
|
}
|
|
\section{Key antibiotics}{
|
|
|
|
There are two ways to determine whether isolates can be included as first \emph{weighted} isolates which will give generally the same results: \cr
|
|
|
|
\strong{1. Using} \code{type = "keyantibiotics"} \strong{and parameter} \code{ignore_I} \cr
|
|
Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With \code{ignore_I = FALSE}, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. \cr
|
|
|
|
\strong{2. Using} \code{type = "points"} \strong{and parameter} \code{points_threshold} \cr
|
|
A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds \code{points_threshold}, an isolate will be (re)selected as a first weighted isolate.
|
|
}
|
|
|
|
\examples{
|
|
# septic_patients is a dataset available in the AMR package. It is true, genuine data.
|
|
?septic_patients
|
|
|
|
library(dplyr)
|
|
my_patients <- septic_patients \%>\%
|
|
mutate(first_isolate = first_isolate(.,
|
|
col_date = "date",
|
|
col_patient_id = "patient_id",
|
|
col_mo = "mo"))
|
|
|
|
# Now let's see if first isolates matter:
|
|
A <- my_patients \%>\%
|
|
group_by(hospital_id) \%>\%
|
|
summarise(count = n_rsi(gent), # gentamicin availability
|
|
resistance = portion_IR(gent)) # gentamicin resistance
|
|
|
|
B <- my_patients \%>\%
|
|
filter(first_isolate == TRUE) \%>\% # the 1st isolate filter
|
|
group_by(hospital_id) \%>\%
|
|
summarise(count = n_rsi(gent), # gentamicin availability
|
|
resistance = portion_IR(gent)) # gentamicin resistance
|
|
|
|
# Have a look at A and B.
|
|
# B is more reliable because every isolate is only counted once.
|
|
# Gentamicin resitance in hospital D appears to be 5.4\% higher than
|
|
# when you (erroneously) would have used all isolates!
|
|
|
|
## OTHER EXAMPLES:
|
|
|
|
\dontrun{
|
|
|
|
# set key antibiotics to a new variable
|
|
tbl$keyab <- key_antibiotics(tbl)
|
|
|
|
tbl$first_isolate <-
|
|
first_isolate(tbl)
|
|
|
|
tbl$first_isolate_weighed <-
|
|
first_isolate(tbl,
|
|
col_keyantibiotics = 'keyab')
|
|
|
|
tbl$first_blood_isolate <-
|
|
first_isolate(tbl,
|
|
filter_specimen = 'Blood')
|
|
|
|
tbl$first_blood_isolate_weighed <-
|
|
first_isolate(tbl,
|
|
filter_specimen = 'Blood',
|
|
col_keyantibiotics = 'keyab')
|
|
|
|
tbl$first_urine_isolate <-
|
|
first_isolate(tbl,
|
|
filter_specimen = 'Urine')
|
|
|
|
tbl$first_urine_isolate_weighed <-
|
|
first_isolate(tbl,
|
|
filter_specimen = 'Urine',
|
|
col_keyantibiotics = 'keyab')
|
|
|
|
tbl$first_resp_isolate <-
|
|
first_isolate(tbl,
|
|
filter_specimen = 'Respiratory')
|
|
|
|
tbl$first_resp_isolate_weighed <-
|
|
first_isolate(tbl,
|
|
filter_specimen = 'Respiratory',
|
|
col_keyantibiotics = 'keyab')
|
|
}
|
|
}
|
|
\seealso{
|
|
\code{\link{key_antibiotics}}
|
|
}
|
|
\keyword{first}
|
|
\keyword{isolate}
|
|
\keyword{isolates}
|