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97 lines
4.0 KiB
R
Executable File
97 lines
4.0 KiB
R
Executable File
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/rsi_analysis.R
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\name{rsi}
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\alias{rsi}
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\alias{rsi_df}
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\alias{n_rsi}
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\title{Resistance of isolates}
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\usage{
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rsi(ab1, ab2 = NA, interpretation = "IR", minimum = 30,
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as_percent = FALSE, info = FALSE, warning = TRUE)
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rsi_df(tbl, ab, interpretation = "IR", minimum = 30, as_percent = FALSE,
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info = TRUE, warning = TRUE)
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n_rsi(ab1, ab2 = NA)
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}
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\arguments{
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\item{ab1, ab2}{vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}}}
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\item{interpretation}{antimicrobial interpretation of which the portion must be calculated. Valid values are \code{"S"}, \code{"SI"}, \code{"I"}, \code{"IR"} or \code{"R"}.}
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\item{minimum}{minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA} with a warning (when \code{warning = TRUE}).}
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\item{as_percent}{return output as percent (text), will else (at default) be a double}
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\item{info}{calculate the amount of available isolates and print it, like \code{n = 423}}
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\item{warning}{show a warning when the available amount of isolates is below \code{minimum}}
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\item{tbl}{\code{data.frame} containing columns with antibiotic interpretations.}
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\item{ab}{character vector with 1, 2 or 3 antibiotics that occur as column names in \code{tbl}, like \code{ab = c("amox", "amcl")}}
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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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This functions can be used to calculate the (co-)resistance of isolates (i.e. percentage S, SI, I, IR or R [of a vector] of isolates). The functions \code{rsi} and \code{n_rsi} can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
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}
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\details{
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Remember that you should filter your table to let it contain \strong{only first isolates}!
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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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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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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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library(dplyr)
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septic_patients \%>\%
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group_by(hospital_id) \%>\%
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summarise(cipro_susceptibility = rsi(cipr, interpretation = "S"),
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n = n_rsi(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(cipro_S = rsi(cipr, interpretation = "S",
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as_percent = TRUE, warning = FALSE),
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cipro_n = n_rsi(cipr),
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genta_S = rsi(gent, interpretation = "S",
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as_percent = TRUE, warning = FALSE),
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genta_n = n_rsi(gent),
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combination_S = rsi(cipr, gent, interpretation = "S",
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as_percent = TRUE, warning = FALSE),
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combination_n = n_rsi(cipr, gent))
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# calculate resistance
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rsi(septic_patients$amox)
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# or susceptibility
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rsi(septic_patients$amox, interpretation = "S")
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# calculate co-resistance between amoxicillin/clav acid and gentamicin,
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# so we can review that combination therapy does a lot more than mono therapy:
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septic_patients \%>\% rsi_df(ab = "amcl", interpretation = "S") # = 67.8\%
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septic_patients \%>\% rsi_df(ab = "gent", interpretation = "S") # = 69.1\%
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septic_patients \%>\% rsi_df(ab = c("amcl", "gent"), interpretation = "S") # = 90.6\%
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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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rsi_df(ab = c("amox", "metr")) # amoxicillin with metronidazole
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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{rsi}
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