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334 lines
14 KiB
R
Executable File
334 lines
14 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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# SOURCE #
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# https://github.com/msberends/AMR #
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# #
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# LICENCE #
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# (c) 2018-2020 Berends MS, Luz CF et al. #
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# #
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# This R package is free software; you can freely use and distribute #
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# it for both personal and commercial purposes under the terms of the #
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# GNU General Public License version 2.0 (GNU GPL-2), as published by #
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# the Free Software Foundation. #
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# #
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# We created this package for both routine data analysis and academic #
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# research and it was publicly released in the hope that it will be #
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# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
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# Visit our website for more info: https://msberends.github.io/AMR. #
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# ==================================================================== #
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#' Key antibiotics for first *weighted* isolates
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#'
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#' These function can be used to determine first isolates (see [first_isolate()]). Using key antibiotics to determine first isolates is more reliable than without key antibiotics. These selected isolates will then be called first *weighted* isolates.
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#' @inheritSection lifecycle Stable lifecycle
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#' @param x table with antibiotics coloms, like `AMX` or `amox`
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#' @param y,z characters to compare
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#' @inheritParams first_isolate
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#' @param universal_1,universal_2,universal_3,universal_4,universal_5,universal_6 column names of **broad-spectrum** antibiotics, case-insensitive. At default, the columns containing these antibiotics will be guessed with [guess_ab_col()].
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#' @param GramPos_1,GramPos_2,GramPos_3,GramPos_4,GramPos_5,GramPos_6 column names of antibiotics for **Gram-positives**, case-insensitive. At default, the columns containing these antibiotics will be guessed with [guess_ab_col()].
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#' @param GramNeg_1,GramNeg_2,GramNeg_3,GramNeg_4,GramNeg_5,GramNeg_6 column names of antibiotics for **Gram-negatives**, case-insensitive. At default, the columns containing these antibiotics will be guessed with [guess_ab_col()].
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#' @param warnings give warning about missing antibiotic columns, they will anyway be ignored
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#' @param ... other parameters passed on to function
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#' @details The function [key_antibiotics()] returns a character vector with 12 antibiotic results for every isolate. These isolates can then be compared using [key_antibiotics_equal()], to check if two isolates have generally the same antibiogram. Missing and invalid values are replaced with a dot (`"."`) by [key_antibiotics()] and ignored by [key_antibiotics_equal()].
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#'
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#' The [first_isolate()] function only uses this function on the same microbial species from the same patient. Using this, e.g. an MRSA will be included after a susceptible *S. aureus* (MSSA) is found within the same patient episode. Without key antibiotic comparison it would not. See [first_isolate()] for more info.
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#'
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#' At default, the antibiotics that are used for **Gram-positive bacteria** are:
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#' - Amoxicillin
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#' - Amoxicillin/clavulanic acid
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#' - Cefuroxime
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#' - Piperacillin/tazobactam
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#' - Ciprofloxacin
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#' - Trimethoprim/sulfamethoxazole
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#' - Vancomycin
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#' - Teicoplanin
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#' - Tetracycline
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#' - Erythromycin
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#' - Oxacillin
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#' - Rifampin
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#'
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#' At default the antibiotics that are used for **Gram-negative bacteria** are:
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#' - Amoxicillin
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#' - Amoxicillin/clavulanic acid
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#' - Cefuroxime
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#' - Piperacillin/tazobactam
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#' - Ciprofloxacin
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#' - Trimethoprim/sulfamethoxazole
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#' - Gentamicin
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#' - Tobramycin
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#' - Colistin
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#' - Cefotaxime
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#' - Ceftazidime
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#' - Meropenem
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#'
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#' The function [key_antibiotics_equal()] checks the characters returned by [key_antibiotics()] for equality, and returns a [`logical`] vector.
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#' @inheritSection first_isolate Key antibiotics
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#' @rdname key_antibiotics
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#' @export
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#' @seealso [first_isolate()]
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#' @inheritSection AMR Read more on our website!
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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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#'
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#' \dontrun{
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#' library(dplyr)
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#' # set key antibiotics to a new variable
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#' my_patients <- example_isolates %>%
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#' mutate(keyab = key_antibiotics(.)) %>%
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#' mutate(
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#' # now calculate first isolates
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#' first_regular = first_isolate(., col_keyantibiotics = FALSE),
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#' # and first WEIGHTED isolates
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#' first_weighted = first_isolate(., col_keyantibiotics = "keyab")
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#' )
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#'
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#' # Check the difference, in this data set it results in 7% more isolates:
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#' sum(my_patients$first_regular, na.rm = TRUE)
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#' sum(my_patients$first_weighted, na.rm = TRUE)
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#' }
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#'
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#' # output of the `key_antibiotics` function could be like this:
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#' strainA <- "SSSRR.S.R..S"
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#' strainB <- "SSSIRSSSRSSS"
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#'
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#' key_antibiotics_equal(strainA, strainB)
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#' # TRUE, because I is ignored (as well as missing values)
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#'
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#' key_antibiotics_equal(strainA, strainB, ignore_I = FALSE)
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#' # FALSE, because I is not ignored and so the 4th value differs
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key_antibiotics <- function(x,
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col_mo = NULL,
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universal_1 = guess_ab_col(x, "amoxicillin"),
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universal_2 = guess_ab_col(x, "amoxicillin/clavulanic acid"),
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universal_3 = guess_ab_col(x, "cefuroxime"),
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universal_4 = guess_ab_col(x, "piperacillin/tazobactam"),
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universal_5 = guess_ab_col(x, "ciprofloxacin"),
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universal_6 = guess_ab_col(x, "trimethoprim/sulfamethoxazole"),
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GramPos_1 = guess_ab_col(x, "vancomycin"),
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GramPos_2 = guess_ab_col(x, "teicoplanin"),
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GramPos_3 = guess_ab_col(x, "tetracycline"),
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GramPos_4 = guess_ab_col(x, "erythromycin"),
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GramPos_5 = guess_ab_col(x, "oxacillin"),
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GramPos_6 = guess_ab_col(x, "rifampin"),
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GramNeg_1 = guess_ab_col(x, "gentamicin"),
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GramNeg_2 = guess_ab_col(x, "tobramycin"),
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GramNeg_3 = guess_ab_col(x, "colistin"),
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GramNeg_4 = guess_ab_col(x, "cefotaxime"),
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GramNeg_5 = guess_ab_col(x, "ceftazidime"),
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GramNeg_6 = guess_ab_col(x, "meropenem"),
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warnings = TRUE,
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...) {
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dots <- unlist(list(...))
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if (length(dots) != 0) {
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# backwards compatibility with old parameters
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dots.names <- dots %>% names()
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if ("info" %in% dots.names) {
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warnings <- dots[which(dots.names == "info")]
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}
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}
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# try to find columns based on type
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# -- mo
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if (is.null(col_mo)) {
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col_mo <- search_type_in_df(x = x, type = "mo")
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}
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stop_if(is.null(col_mo), "`col_mo` must be set")
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# check columns
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col.list <- c(universal_1, universal_2, universal_3, universal_4, universal_5, universal_6,
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GramPos_1, GramPos_2, GramPos_3, GramPos_4, GramPos_5, GramPos_6,
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GramNeg_1, GramNeg_2, GramNeg_3, GramNeg_4, GramNeg_5, GramNeg_6)
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check_available_columns <- function(x, col.list, warnings = TRUE) {
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# check columns
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col.list <- col.list[!is.na(col.list) & !is.null(col.list)]
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names(col.list) <- col.list
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col.list.bak <- col.list
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# are they available as upper case or lower case then?
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for (i in seq_len(length(col.list))) {
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if (is.null(col.list[i]) | isTRUE(is.na(col.list[i]))) {
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col.list[i] <- NA
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} else if (toupper(col.list[i]) %in% colnames(x)) {
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col.list[i] <- toupper(col.list[i])
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} else if (tolower(col.list[i]) %in% colnames(x)) {
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col.list[i] <- tolower(col.list[i])
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} else if (!col.list[i] %in% colnames(x)) {
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col.list[i] <- NA
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}
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}
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if (!all(col.list %in% colnames(x))) {
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if (warnings == TRUE) {
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warning("Some columns do not exist and will be ignored: ",
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col.list.bak[!(col.list %in% colnames(x))] %>% toString(),
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".\nTHIS MAY STRONGLY INFLUENCE THE OUTCOME.",
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immediate. = TRUE,
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call. = FALSE)
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}
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}
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col.list
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}
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col.list <- check_available_columns(x = x, col.list = col.list, warnings = warnings)
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universal_1 <- col.list[universal_1]
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universal_2 <- col.list[universal_2]
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universal_3 <- col.list[universal_3]
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universal_4 <- col.list[universal_4]
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universal_5 <- col.list[universal_5]
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universal_6 <- col.list[universal_6]
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GramPos_1 <- col.list[GramPos_1]
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GramPos_2 <- col.list[GramPos_2]
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GramPos_3 <- col.list[GramPos_3]
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GramPos_4 <- col.list[GramPos_4]
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GramPos_5 <- col.list[GramPos_5]
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GramPos_6 <- col.list[GramPos_6]
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GramNeg_1 <- col.list[GramNeg_1]
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GramNeg_2 <- col.list[GramNeg_2]
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GramNeg_3 <- col.list[GramNeg_3]
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GramNeg_4 <- col.list[GramNeg_4]
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GramNeg_5 <- col.list[GramNeg_5]
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GramNeg_6 <- col.list[GramNeg_6]
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universal <- c(universal_1, universal_2, universal_3,
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universal_4, universal_5, universal_6)
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gram_positive <- c(universal,
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GramPos_1, GramPos_2, GramPos_3,
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GramPos_4, GramPos_5, GramPos_6)
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gram_positive <- gram_positive[!is.null(gram_positive)]
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gram_positive <- gram_positive[!is.na(gram_positive)]
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if (length(gram_positive) < 12) {
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warning("only using ", length(gram_positive), " different antibiotics as key antibiotics for Gram-positives. See ?key_antibiotics.", call. = FALSE)
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}
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gram_negative <- c(universal,
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GramNeg_1, GramNeg_2, GramNeg_3,
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GramNeg_4, GramNeg_5, GramNeg_6)
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gram_negative <- gram_negative[!is.null(gram_negative)]
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gram_negative <- gram_negative[!is.na(gram_negative)]
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if (length(gram_negative) < 12) {
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warning("only using ", length(gram_negative), " different antibiotics as key antibiotics for Gram-negatives. See ?key_antibiotics.", call. = FALSE)
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}
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x <- as.data.frame(x, stringsAsFactors = FALSE)
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x[, col_mo] <- as.mo(x[, col_mo, drop = TRUE])
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x$gramstain <- mo_gramstain(x[, col_mo, drop = TRUE], language = NULL)
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x$key_ab <- NA_character_
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# Gram +
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x$key_ab <- if_else(x$gramstain == "Gram-positive",
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tryCatch(apply(X = x[, gram_positive],
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MARGIN = 1,
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FUN = function(x) paste(x, collapse = "")),
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error = function(e) paste0(rep(".", 12), collapse = "")),
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x$key_ab)
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# Gram -
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x$key_ab <- if_else(x$gramstain == "Gram-negative",
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tryCatch(apply(X = x[, gram_negative],
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MARGIN = 1,
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FUN = function(x) paste(x, collapse = "")),
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error = function(e) paste0(rep(".", 12), collapse = "")),
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x$key_ab)
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# format
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key_abs <- toupper(gsub("[^SIR]", ".", gsub("(NA|NULL)", ".", x$key_ab)))
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if (n_distinct(key_abs) == 1) {
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warning("No distinct key antibiotics determined.", call. = FALSE)
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}
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key_abs
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}
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#' @rdname key_antibiotics
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#' @export
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key_antibiotics_equal <- function(y,
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z,
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type = c("keyantibiotics", "points"),
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ignore_I = TRUE,
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points_threshold = 2,
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info = FALSE) {
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# y is active row, z is lag
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x <- y
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y <- z
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type <- type[1]
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stop_ifnot(length(x) == length(y), "length of `x` and `y` must be equal")
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# only show progress bar on points or when at least 5000 isolates
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info_needed <- info == TRUE & (type == "points" | length(x) > 5000)
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result <- logical(length(x))
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if (info_needed == TRUE) {
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p <- progress_estimated(length(x))
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on.exit(close(p))
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}
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for (i in seq_len(length(x))) {
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if (info_needed == TRUE) {
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p$tick()
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}
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if (is.na(x[i])) {
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x[i] <- ""
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}
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if (is.na(y[i])) {
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y[i] <- ""
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}
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if (x[i] == y[i]) {
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result[i] <- TRUE
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} else if (nchar(x[i]) != nchar(y[i])) {
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result[i] <- FALSE
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} else {
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x_split <- strsplit(x[i], "")[[1]]
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y_split <- strsplit(y[i], "")[[1]]
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if (type == "keyantibiotics") {
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if (ignore_I == TRUE) {
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x_split[x_split == "I"] <- "."
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y_split[y_split == "I"] <- "."
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}
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y_split[x_split == "."] <- "."
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x_split[y_split == "."] <- "."
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result[i] <- all(x_split == y_split)
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} else if (type == "points") {
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# count points for every single character:
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# - no change is 0 points
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# - I <-> S|R is 0.5 point
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# - S|R <-> R|S is 1 point
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# use the levels of as.rsi (S = 1, I = 2, R = 3)
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suppressWarnings(x_split <- x_split %>% as.rsi() %>% as.double())
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suppressWarnings(y_split <- y_split %>% as.rsi() %>% as.double())
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points <- (x_split - y_split) %>% abs() %>% sum(na.rm = TRUE) / 2
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result[i] <- points >= points_threshold
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} else {
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stop("`", type, '` is not a valid value for type, must be "points" or "keyantibiotics". See ?key_antibiotics')
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}
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}
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}
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if (info_needed == TRUE) {
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close(p)
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}
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result
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}
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