mirror of https://github.com/msberends/AMR.git
194 lines
9.0 KiB
R
Executable File
194 lines
9.0 KiB
R
Executable File
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/first_isolate.R
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\name{first_isolate}
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\alias{first_isolate}
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\alias{filter_first_isolate}
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\alias{filter_first_weighted_isolate}
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\title{Determine first (weighted) isolates}
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\source{
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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/}.
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}
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\usage{
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first_isolate(x, col_date = NULL, col_patient_id = NULL,
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col_mo = NULL, col_testcode = NULL, col_specimen = NULL,
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col_icu = NULL, col_keyantibiotics = NULL, episode_days = 365,
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testcodes_exclude = NULL, icu_exclude = FALSE,
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specimen_group = NULL, type = "keyantibiotics", ignore_I = TRUE,
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points_threshold = 2, info = TRUE, include_unknown = FALSE, ...)
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filter_first_isolate(x, col_date = NULL, col_patient_id = NULL,
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col_mo = NULL, ...)
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filter_first_weighted_isolate(x, col_date = NULL,
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col_patient_id = NULL, col_mo = NULL, col_keyantibiotics = NULL,
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...)
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}
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\arguments{
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\item{x}{a \code{data.frame} containing isolates.}
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\item{col_date}{column name of the result date (or date that is was received on the lab), defaults to the first column of with a date class}
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\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)}
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\item{col_mo}{column name of the IDs of the microorganisms (see \code{\link{as.mo}}), defaults to the first column of class \code{mo}. Values will be coerced using \code{\link{as.mo}}.}
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\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.}
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\item{col_specimen}{column name of the specimen type or group}
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\item{col_icu}{column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU)}
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\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.}
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\item{episode_days}{episode in days after which a genus/species combination will be determined as 'first isolate' again. The default of 365 days is based on the guideline by CLSI, see Source.}
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\item{testcodes_exclude}{character vector with test codes that should be excluded (case-insensitive)}
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\item{icu_exclude}{logical whether ICU isolates should be excluded (rows with value \code{TRUE} in column \code{col_icu})}
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\item{specimen_group}{value in column \code{col_specimen} to filter on}
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\item{type}{type to determine weighed isolates; can be \code{"keyantibiotics"} or \code{"points"}, see Details}
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\item{ignore_I}{logical to determine whether antibiotic interpretations with \code{"I"} will be ignored when \code{type = "keyantibiotics"}, see Details}
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\item{points_threshold}{points until the comparison of key antibiotics will lead to inclusion of an isolate when \code{type = "points"}, see Details}
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\item{info}{print progress}
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\item{include_unknown}{logical to determine whether 'unknown' microorganisms should be included too, i.e. microbial code \code{"UNKNOWN"}, which defaults to \code{FALSE}. For WHONET users, this means that all records with organism code \code{"con"} (\emph{contamination}) will be excluded at default. Isolates with a microbial ID of \code{NA} will always be excluded as first isolate.}
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\item{...}{parameters passed on to the \code{first_isolate} function}
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}
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\value{
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Logical vector
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}
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\description{
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Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type.
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}
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\details{
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\strong{WHY THIS IS SO IMPORTANT} \cr
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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}.
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All isolates with a microbial ID of \code{NA} will be excluded as first isolate.
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The functions \code{filter_first_isolate} and \code{filter_first_weighted_isolate} are helper functions to quickly filter on first isolates. The function \code{filter_first_isolate} is essentially equal to:
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\preformatted{
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x \%>\%
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mutate(only_firsts = first_isolate(x, ...)) \%>\%
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filter(only_firsts == TRUE) \%>\%
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select(-only_firsts)
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}
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The function \code{filter_first_weighted_isolate} is essentially equal to:
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\preformatted{
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x \%>\%
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mutate(keyab = key_antibiotics(.)) \%>\%
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mutate(only_weighted_firsts = first_isolate(x,
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col_keyantibiotics = "keyab", ...)) \%>\%
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filter(only_weighted_firsts == TRUE) \%>\%
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select(-only_weighted_firsts)
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}
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}
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\section{Key antibiotics}{
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There are two ways to determine whether isolates can be included as first \emph{weighted} isolates which will give generally the same results: \cr
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\strong{1. Using} \code{type = "keyantibiotics"} \strong{and parameter} \code{ignore_I} \cr
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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. Read more about this in the \code{\link{key_antibiotics}} function. \cr
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\strong{2. Using} \code{type = "points"} \strong{and parameter} \code{points_threshold} \cr
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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}, which default to \code{2}, an isolate will be (re)selected as a first weighted isolate.
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}
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\section{Read more on our website!}{
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On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{a tutorial} about how to conduct AMR analysis, the \href{https://msberends.gitlab.io/AMR/reference}{complete documentation of all functions} (which reads a lot easier than here in R) and \href{https://msberends.gitlab.io/AMR/articles/WHONET.html}{an example analysis using WHONET data}.
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}
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\examples{
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# `example_isolates` is a dataset available in the AMR package.
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# See ?example_isolates.
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library(dplyr)
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# Filter on first isolates:
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example_isolates \%>\%
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mutate(first_isolate = first_isolate(.,
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col_date = "date",
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col_patient_id = "patient_id",
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col_mo = "mo")) \%>\%
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filter(first_isolate == TRUE)
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# Which can be shortened to:
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example_isolates \%>\%
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filter_first_isolate()
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# or for first weighted isolates:
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example_isolates \%>\%
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filter_first_weighted_isolate()
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# Now let's see if first isolates matter:
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A <- example_isolates \%>\%
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group_by(hospital_id) \%>\%
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summarise(count = n_rsi(GEN), # gentamicin availability
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resistance = portion_IR(GEN)) # gentamicin resistance
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B <- example_isolates \%>\%
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filter_first_weighted_isolate() \%>\% # the 1st isolate filter
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group_by(hospital_id) \%>\%
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summarise(count = n_rsi(GEN), # gentamicin availability
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resistance = portion_IR(GEN)) # gentamicin resistance
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# Have a look at A and B.
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# B is more reliable because every isolate is only counted once.
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# Gentamicin resitance in hospital D appears to be 3.1\% higher than
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# when you (erroneously) would have used all isolates for analysis.
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## OTHER EXAMPLES:
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\dontrun{
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# set key antibiotics to a new variable
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x$keyab <- key_antibiotics(x)
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x$first_isolate <-
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first_isolate(x)
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x$first_isolate_weighed <-
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first_isolate(x,
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col_keyantibiotics = 'keyab')
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x$first_blood_isolate <-
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first_isolate(x,
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specimen_group = 'Blood')
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x$first_blood_isolate_weighed <-
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first_isolate(x,
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specimen_group = 'Blood',
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col_keyantibiotics = 'keyab')
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x$first_urine_isolate <-
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first_isolate(x,
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specimen_group = 'Urine')
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x$first_urine_isolate_weighed <-
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first_isolate(x,
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specimen_group = 'Urine',
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col_keyantibiotics = 'keyab')
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x$first_resp_isolate <-
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first_isolate(x,
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specimen_group = 'Respiratory')
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x$first_resp_isolate_weighed <-
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first_isolate(x,
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specimen_group = 'Respiratory',
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col_keyantibiotics = 'keyab')
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}
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}
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\seealso{
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\code{\link{key_antibiotics}}
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}
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\keyword{first}
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\keyword{isolate}
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\keyword{isolates}
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