mirror of https://github.com/msberends/AMR.git
142 lines
5.6 KiB
R
142 lines
5.6 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/rsi_IR.R
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\name{rsi_IR}
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\alias{rsi_IR}
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\alias{rsi_R}
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\alias{rsi_I}
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\alias{rsi_SI}
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\alias{rsi_S}
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\alias{resistance}
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\alias{intermediate}
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\alias{susceptibility}
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\alias{rsi_n}
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\alias{n_rsi}
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\title{Calculate resistance of isolates}
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\source{
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\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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rsi_R(ab1, minimum = 30, as_percent = FALSE)
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rsi_IR(ab1, minimum = 30, as_percent = FALSE)
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rsi_I(ab1, minimum = 30, as_percent = FALSE)
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rsi_SI(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
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rsi_S(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
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resistance(ab1, include_I = TRUE, minimum = 30, as_percent = FALSE)
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intermediate(ab1, minimum = 30, as_percent = FALSE)
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susceptibility(ab1, ab2 = NULL, include_I = FALSE, minimum = 30,
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as_percent = FALSE)
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rsi_n(ab1, ab2 = NULL)
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n_rsi(ab1, ab2 = NULL)
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}
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\arguments{
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\item{ab1}{vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}}}
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\item{minimum}{minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA}. The default number of \code{30} isolates is advised by the CLSI as best practice, see Source.}
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\item{as_percent}{logical to indicate whether the output must be returned as percent (text), will else be a double}
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\item{ab2}{like \code{ab}, a vector of antibiotic interpretations. Use this to calculate (the lack of) co-resistance: the probability where one of two drugs have a susceptible result. See Examples.}
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\item{include_I}{logical to indicate whether antimicrobial interpretations of "I" should be included}
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}
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\value{
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Double or, when \code{as_percent = TRUE}, a character.
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}
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\description{
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These functions can be used to calculate the (co-)resistance of microbial isolates (i.e. percentage S, SI, I, IR or R). All functions can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}. \cr\cr
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\code{rsi_R} and \code{rsi_IR} can be used to calculate resistance, \code{rsi_S} and \code{rsi_SI} can be used to calculate susceptibility.\cr
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\code{rsi_n} counts all cases where antimicrobial interpretations are available.
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}
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\details{
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\strong{Remember that you should filter your table to let it contain only first isolates!} Use \code{\link{first_isolate}} to determine them in your data set.
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The functions \code{resistance} and \code{susceptibility} are wrappers around \code{rsi_IR} and \code{rsi_S}, respectively. All functions use hybrid evaluation (i.e. using C++), which makes these functions 20-30 times faster than the old \code{\link{rsi}} function. This latter function is still available for backwards compatibility but is deprecated.
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\if{html}{
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\cr\cr
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To calculate the probability (\emph{p}) of susceptibility of one antibiotic, we use this formula:
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\out{<div style="text-align: center">}\figure{mono_therapy.png}\out{</div>}
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To calculate the probability (\emph{p}) of susceptibility of more antibiotics (i.e. combination therapy), we need to check whether one of them has a susceptible result (as numerator) and count all cases where all antibiotics were tested (as denominator). \cr
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\cr
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For two antibiotics:
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\out{<div style="text-align: center">}\figure{combi_therapy_2.png}\out{</div>}
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\cr
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Theoretically for three antibiotics:
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\out{<div style="text-align: center">}\figure{combi_therapy_3.png}\out{</div>}
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}
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}
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\examples{
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# Calculate resistance
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rsi_R(septic_patients$amox)
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rsi_IR(septic_patients$amox)
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# Or susceptibility
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rsi_S(septic_patients$amox)
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rsi_SI(septic_patients$amox)
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# Since n_rsi counts available isolates (and is used as denominator),
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# you can calculate back to e.g. count resistant isolates:
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rsi_IR(septic_patients$amox) * n_rsi(septic_patients$amox)
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library(dplyr)
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septic_patients \%>\%
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group_by(hospital_id) \%>\%
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summarise(p = rsi_S(cipr),
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n = rsi_n(cipr)) # n_rsi works like n_distinct in dplyr
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septic_patients \%>\%
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group_by(hospital_id) \%>\%
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summarise(R = rsi_R(cipr, as_percent = TRUE),
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I = rsi_I(cipr, as_percent = TRUE),
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S = rsi_S(cipr, as_percent = TRUE),
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n = rsi_n(cipr), # also: n_rsi, works like n_distinct in dplyr
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total = n()) # this is the length, NOT the amount of tested isolates
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# Calculate co-resistance between amoxicillin/clav acid and gentamicin,
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# so we can see that combination therapy does a lot more than mono therapy:
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rsi_S(septic_patients$amcl) # S = 67.3\%
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rsi_n(septic_patients$amcl) # n = 1570
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rsi_S(septic_patients$gent) # S = 74.0\%
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rsi_n(septic_patients$gent) # n = 1842
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with(septic_patients,
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rsi_S(amcl, gent)) # S = 92.1\%
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with(septic_patients, # n = 1504
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rsi_n(amcl, gent))
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septic_patients \%>\%
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group_by(hospital_id) \%>\%
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summarise(cipro_p = rsi_S(cipr, as_percent = TRUE),
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cipro_n = rsi_n(cipr),
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genta_p = rsi_S(gent, as_percent = TRUE),
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genta_n = rsi_n(gent),
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combination_p = rsi_S(cipr, gent, as_percent = TRUE),
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combination_n = rsi_n(cipr, gent))
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\dontrun{
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# calculate current empiric combination therapy of Helicobacter gastritis:
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my_table \%>\%
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filter(first_isolate == TRUE,
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genus == "Helicobacter") \%>\%
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summarise(p = rsi_S(amox, metr), # amoxicillin with metronidazole
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n = rsi_n(amox, metr))
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}
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}
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\keyword{antibiotics}
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\keyword{isolate}
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\keyword{isolates}
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\keyword{resistance}
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\keyword{rsi}
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\keyword{rsi_df}
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\keyword{susceptibility}
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