mirror of https://github.com/msberends/AMR.git
308 lines
12 KiB
R
Executable File
308 lines
12 KiB
R
Executable File
# ==================================================================== #
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# TITLE #
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# Antimicrobial Resistance (AMR) Analysis #
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# #
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# AUTHORS #
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# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
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# #
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# LICENCE #
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# This program is free software; you can redistribute it and/or modify #
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# it under the terms of the GNU General Public License version 2.0, #
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# as published by the Free Software Foundation. #
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# #
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# This program is distributed in the hope that it will be useful, #
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# but WITHOUT ANY WARRANTY; without even the implied warranty of #
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
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# GNU General Public License for more details. #
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# ==================================================================== #
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#' Calculate resistance of isolates
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#'
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#' @description 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}.
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#'
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#' \code{portion_R} and \code{portion_IR} can be used to calculate resistance, \code{portion_S} and \code{portion_SI} can be used to calculate susceptibility.\cr
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#' @param ab1 vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}} if needed
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#' @param 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 resistant or susceptible result. See Examples.
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#' @param 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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#' @param as_percent logical to indicate whether the output must be returned as percent (text), will else be a double
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#' @param data a code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})
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#' @param translate_ab a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations to, using \code{\link{abname}}. This can be set with \code{\link{getOption}("get_antibiotic_names")}.
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#' @details \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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#'
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#' \code{portion_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and calculates the portions R, I and S. The resulting \emph{tidy data} (see Source) \code{data.frame} will have three rows (S/I/R) and a column for each variable with class \code{"rsi"}.
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#'
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#' The old \code{\link{rsi}} 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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#' @source \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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#' Wickham H. \strong{Tidy Data.} The Journal of Statistical Software, vol. 59, 2014. \url{http://vita.had.co.nz/papers/tidy-data.html}
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#' @seealso \code{\link{n_rsi}} to count cases with antimicrobial results.
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#' @keywords resistance susceptibility rsi_df rsi antibiotics isolate isolates
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#' @return Double or, when \code{as_percent = TRUE}, a character.
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#' @rdname portion
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#' @name portion
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#' @export
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#' @examples
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#' # septic_patients is a data set available in the AMR package. It is true, genuine data.
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#' ?septic_patients
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#'
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#' # Calculate resistance
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#' portion_R(septic_patients$amox)
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#' portion_IR(septic_patients$amox)
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#'
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#' # Or susceptibility
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#' portion_S(septic_patients$amox)
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#' portion_SI(septic_patients$amox)
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#'
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#' # Since n_rsi counts available isolates (and is used as denominator),
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#' # you can calculate back to count e.g. non-susceptible isolates:
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#' portion_IR(septic_patients$amox) * n_rsi(septic_patients$amox)
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#'
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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 = portion_S(cipr),
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#' n = n_rsi(cipr)) # n_rsi works like n_distinct in dplyr
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#'
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#' septic_patients %>%
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#' group_by(hospital_id) %>%
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#' summarise(R = portion_R(cipr, as_percent = TRUE),
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#' I = portion_I(cipr, as_percent = TRUE),
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#' S = portion_S(cipr, as_percent = TRUE),
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#' n = n_rsi(cipr), # works like n_distinct in dplyr
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#' total = n()) # NOT the amount of tested isolates!
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#'
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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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#' portion_S(septic_patients$amcl) # S = 67.3%
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#' n_rsi(septic_patients$amcl) # n = 1570
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#'
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#' portion_S(septic_patients$gent) # S = 74.0%
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#' n_rsi(septic_patients$gent) # n = 1842
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#'
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#' with(septic_patients,
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#' portion_S(amcl, gent)) # S = 92.1%
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#' with(septic_patients, # n = 1504
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#' n_rsi(amcl, gent))
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#'
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#' septic_patients %>%
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#' group_by(hospital_id) %>%
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#' summarise(cipro_p = portion_S(cipr, as_percent = TRUE),
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#' cipro_n = n_rsi(cipr),
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#' genta_p = portion_S(gent, as_percent = TRUE),
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#' genta_n = n_rsi(gent),
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#' combination_p = portion_S(cipr, gent, as_percent = TRUE),
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#' combination_n = n_rsi(cipr, gent))
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#'
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#' # Get portions S/I/R immediately of all rsi columns
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#' septic_patients %>%
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#' select(amox, cipr) %>%
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#' portion_df(translate = FALSE)
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#'
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#' # It also supports grouping variables
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#' septic_patients %>%
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#' select(hospital_id, amox, cipr) %>%
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#' group_by(hospital_id) %>%
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#' portion_df(translate = FALSE)
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#'
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#'
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#' \dontrun{
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#'
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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 = portion_S(amox, metr), # amoxicillin with metronidazole
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#' n = n_rsi(amox, metr))
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#' }
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portion_R <- function(ab1,
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ab2 = NULL,
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minimum = 30,
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as_percent = FALSE) {
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rsi_calc(type = "R",
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ab1 = ab1,
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ab2 = ab2,
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include_I = FALSE,
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minimum = minimum,
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as_percent = as_percent)
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}
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#' @rdname portion
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#' @export
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portion_IR <- function(ab1,
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ab2 = NULL,
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minimum = 30,
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as_percent = FALSE) {
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rsi_calc(type = "R",
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ab1 = ab1,
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ab2 = ab2,
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include_I = TRUE,
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minimum = minimum,
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as_percent = as_percent)
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}
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#' @rdname portion
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#' @export
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portion_I <- function(ab1,
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minimum = 30,
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as_percent = FALSE) {
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rsi_calc(type = "I",
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ab1 = ab1,
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ab2 = NULL,
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include_I = FALSE,
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minimum = minimum,
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as_percent = as_percent)
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}
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#' @rdname portion
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#' @export
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portion_SI <- function(ab1,
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ab2 = NULL,
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minimum = 30,
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as_percent = FALSE) {
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rsi_calc(type = "S",
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ab1 = ab1,
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ab2 = ab2,
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include_I = TRUE,
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minimum = minimum,
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as_percent = as_percent)
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}
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#' @rdname portion
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#' @export
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portion_S <- function(ab1,
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ab2 = NULL,
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minimum = 30,
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as_percent = FALSE) {
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rsi_calc(type = "S",
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ab1 = ab1,
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ab2 = ab2,
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include_I = FALSE,
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minimum = minimum,
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as_percent = as_percent)
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}
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#' @rdname portion
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#' @importFrom dplyr bind_rows summarise_if mutate group_vars select everything
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#' @export
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portion_df <- function(data, translate_ab = getOption("get_antibiotic_names", "official")) {
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if (as.character(translate_ab) == "TRUE") {
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translate_ab <- "official"
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}
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options(get_antibiotic_names = translate_ab)
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resS <- summarise_if(.tbl = data,
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.predicate = is.rsi,
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.funs = portion_S) %>%
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mutate(Interpretation = "S") %>%
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select(Interpretation, everything())
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resI <- summarise_if(.tbl = data,
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.predicate = is.rsi,
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.funs = portion_I) %>%
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mutate(Interpretation = "I") %>%
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select(Interpretation, everything())
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resR <- summarise_if(.tbl = data,
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.predicate = is.rsi,
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.funs = portion_R) %>%
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mutate(Interpretation = "R") %>%
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select(Interpretation, everything())
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data.groups <- group_vars(data)
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res <- bind_rows(resS, resI, resR) %>%
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mutate(Interpretation = factor(Interpretation, levels = c("R", "I", "S"), ordered = TRUE)) %>%
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tidyr::gather(Antibiotic, Percentage, -Interpretation, -data.groups)
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if (!translate_ab == FALSE) {
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if (!tolower(translate_ab) %in% tolower(colnames(AMR::antibiotics))) {
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stop("Parameter `translate_ab` does not occur in the `antibiotics` data set.", call. = FALSE)
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}
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res <- res %>% mutate(Antibiotic = abname(Antibiotic, from = "guess", to = translate_ab))
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}
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res
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}
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rsi_calc <- function(type,
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ab1,
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ab2,
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include_I,
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minimum,
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as_percent) {
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if (NCOL(ab1) > 1) {
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stop('`ab1` must be a vector of antimicrobial interpretations', call. = FALSE)
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}
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if (!is.logical(include_I)) {
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stop('`include_I` must be logical', call. = FALSE)
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}
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if (!is.numeric(minimum)) {
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stop('`minimum` must be numeric', call. = FALSE)
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}
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if (!is.logical(as_percent)) {
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stop('`as_percent` must be logical', call. = FALSE)
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}
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print_warning <- FALSE
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if (!is.rsi(ab1)) {
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ab1 <- as.rsi(ab1)
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print_warning <- TRUE
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}
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if (!is.null(ab2)) {
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# ab_name <- paste(deparse(substitute(ab1)), "and", deparse(substitute(ab2)))
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if (NCOL(ab2) > 1) {
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stop('`ab2` must be a vector of antimicrobial interpretations', call. = FALSE)
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}
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if (!is.rsi(ab2)) {
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ab2 <- as.rsi(ab2)
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print_warning <- TRUE
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}
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x <- apply(X = data.frame(ab1 = as.integer(ab1),
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ab2 = as.integer(ab2)),
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MARGIN = 1,
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FUN = min)
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} else {
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x <- ab1
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# ab_name <- deparse(substitute(ab1))
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}
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if (print_warning == TRUE) {
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warning("Increase speed by transforming to class `rsi` on beforehand: df %>% mutate_at(vars(col10:col20), as.rsi)")
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}
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total <- length(x) - sum(is.na(x))
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if (total < minimum) {
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return(NA)
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}
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if (type == "S") {
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found <- sum(as.integer(x) <= 1 + include_I, na.rm = TRUE)
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} else if (type == "I") {
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found <- sum(as.integer(x) == 2, na.rm = TRUE)
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} else if (type == "R") {
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found <- sum(as.integer(x) >= 3 - include_I, na.rm = TRUE)
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} else {
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stop("invalid type")
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}
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if (as_percent == TRUE) {
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percent(found / total, force_zero = TRUE)
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} else {
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found / total
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}
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}
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