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(v1.4.0.9024) is_new_episode()
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@ -4,6 +4,7 @@
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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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\alias{is_new_episode}
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\title{Determine first (weighted) isolates}
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\source{
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Methodology of this function is strictly based on:
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@ -48,11 +49,18 @@ filter_first_weighted_isolate(
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col_keyantibiotics = NULL,
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...
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)
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is_new_episode(
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.data,
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episode_days = 365,
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col_date = NULL,
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col_patient_id = NULL
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)
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}
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\arguments{
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\item{x}{a \link{data.frame} containing isolates.}
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\item{x, .data}{a \link{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_date}{column name of the result date (or date that is was received on the lab), defaults to the first column 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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@ -90,15 +98,22 @@ filter_first_weighted_isolate(
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A \code{\link{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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Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type. To determine patient episodes not necessarily based on microorganisms, use \code{\link[=is_new_episode]{is_new_episode()}} that also supports grouping with the \code{dplyr} package, see \emph{Examples}.
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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:/pubmed.ncbi.nlm.nih.gov/17304462/}{(ref)}. 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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The \code{\link[=is_new_episode]{is_new_episode()}} function is a wrapper around the \code{\link[=first_isolate]{first_isolate()}} function and can be used for data sets without isolates to just determine patient episodes based on any combination of grouping variables (using \code{dplyr}), please see \emph{Examples}. Since it runs \code{\link[=first_isolate]{first_isolate()}} for every group, it is quite slow.
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All isolates with a microbial ID of \code{NA} will be excluded as first isolate.
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\subsection{Why this is so important}{
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The functions \code{\link[=filter_first_isolate]{filter_first_isolate()}} and \code{\link[=filter_first_weighted_isolate]{filter_first_weighted_isolate()}} are helper functions to quickly filter on first isolates. The function \code{\link[=filter_first_isolate]{filter_first_isolate()}} is essentially equal to either:\preformatted{ x[first_isolate(x, ...), ]
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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:/pubmed.ncbi.nlm.nih.gov/17304462/}{(ref)}. 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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}
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\subsection{\verb{filter_*()} shortcuts}{
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The functions \code{\link[=filter_first_isolate]{filter_first_isolate()}} and \code{\link[=filter_first_weighted_isolate]{filter_first_weighted_isolate()}} are helper functions to quickly filter on first isolates.
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The function \code{\link[=filter_first_isolate]{filter_first_isolate()}} is essentially equal to either:\preformatted{ x[first_isolate(x, ...), ]
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x \%>\% filter(first_isolate(x, ...))
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}
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@ -110,6 +125,7 @@ The function \code{\link[=filter_first_weighted_isolate]{filter_first_weighted_i
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select(-only_weighted_firsts, -keyab)
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}
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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:
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@ -143,21 +159,22 @@ On our website \url{https://msberends.github.io/AMR/} you can find \href{https:/
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# basic filtering on first isolates
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example_isolates[first_isolate(example_isolates), ]
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# filtering based on isolates ----------------------------------------------
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\donttest{
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if (require("dplyr")) {
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# Filter on first isolates:
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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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filter(first_isolate == TRUE)
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# Short-hand versions:
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# short-hand versions:
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example_isolates \%>\%
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filter_first_isolate()
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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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# 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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@ -174,6 +191,42 @@ if (require("dplyr")) {
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# Gentamicin resistance in hospital D appears to be 3.7\% higher than
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# when you (erroneously) would have used all isolates for analysis.
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}
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# filtering based on any other condition -----------------------------------
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if (require("dplyr")) {
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# is_new_episode() can be used in dplyr verbs to determine patient
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# episodes based on any (combination of) grouping variables:
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example_isolates \%>\%
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mutate(condition = sample(x = c("A", "B", "C"),
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size = 2000,
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replace = TRUE)) \%>\%
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group_by(condition) \%>\%
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mutate(new_episode = is_new_episode())
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example_isolates \%>\%
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group_by(hospital_id) \%>\%
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summarise(patients = n_distinct(patient_id),
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n_episodes_365 = sum(is_new_episode(episode_days = 365)),
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n_episodes_60 = sum(is_new_episode(episode_days = 60)),
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n_episodes_30 = sum(is_new_episode(episode_days = 30)))
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# grouping on microorganisms leads to the same results as first_isolate():
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x <- example_isolates \%>\%
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filter_first_isolate(include_unknown = TRUE)
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y <- example_isolates \%>\%
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group_by(mo) \%>\%
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filter(is_new_episode())
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identical(x$patient_id, y$patient_id)
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# but now you can group on isolates and many more:
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example_isolates \%>\%
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group_by(mo, hospital_id, ward_icu) \%>\%
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mutate(flag_episode = is_new_episode())
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}
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}
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}
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\seealso{
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