mirror of https://github.com/msberends/AMR.git
307 lines
13 KiB
R
Executable File
307 lines
13 KiB
R
Executable File
# ==================================================================== #
|
|
# TITLE #
|
|
# Antimicrobial Resistance (AMR) Analysis #
|
|
# #
|
|
# SOURCE #
|
|
# https://gitlab.com/msberends/AMR #
|
|
# #
|
|
# LICENCE #
|
|
# (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
|
# #
|
|
# This R package is free software; you can freely use and distribute #
|
|
# it for both personal and commercial purposes under the terms of the #
|
|
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
|
|
# the Free Software Foundation. #
|
|
# #
|
|
# This R package was created for academic research and was publicly #
|
|
# released in the hope that it will be useful, but it comes WITHOUT #
|
|
# ANY WARRANTY OR LIABILITY. #
|
|
# Visit our website for more info: https://msberends.gitlab.io/AMR. #
|
|
# ==================================================================== #
|
|
|
|
#' Key antibiotics for first \emph{weighted} isolates
|
|
#'
|
|
#' These function can be used to determine first isolates (see \code{\link{first_isolate}}). Using key antibiotics to determine first isolates is more reliable than without key antibiotics. These selected isolates will then be called first \emph{weighted} isolates.
|
|
#' @param x table with antibiotics coloms, like \code{AMX} or \code{amox}
|
|
#' @param y,z characters to compare
|
|
#' @inheritParams first_isolate
|
|
#' @param universal_1,universal_2,universal_3,universal_4,universal_5,universal_6 column names of \strong{broad-spectrum} antibiotics, case-insensitive. At default, the columns containing these antibiotics will be guessed with \code{\link{guess_ab_col}}.
|
|
#' @param GramPos_1,GramPos_2,GramPos_3,GramPos_4,GramPos_5,GramPos_6 column names of antibiotics for \strong{Gram positives}, case-insensitive. At default, the columns containing these antibiotics will be guessed with \code{\link{guess_ab_col}}.
|
|
#' @param GramNeg_1,GramNeg_2,GramNeg_3,GramNeg_4,GramNeg_5,GramNeg_6 column names of antibiotics for \strong{Gram negatives}, case-insensitive. At default, the columns containing these antibiotics will be guessed with \code{\link{guess_ab_col}}.
|
|
#' @param warnings give warning about missing antibiotic columns, they will anyway be ignored
|
|
#' @param ... other parameters passed on to function
|
|
#' @details The function \code{key_antibiotics} returns a character vector with 12 antibiotic results for every isolate. These isolates can then be compared using \code{key_antibiotics_equal}, to check if two isolates have generally the same antibiogram. Missing and invalid values are replaced with a dot (\code{"."}). The \code{\link{first_isolate}} function only uses this function on the same microbial species from the same patient. Using this, an MRSA will be included after a susceptible \emph{S. aureus} (MSSA) found within the same episode (see \code{episode} parameter of \code{\link{first_isolate}}). Without key antibiotic comparison it would not.
|
|
#'
|
|
#' At default, the antibiotics that are used for \strong{Gram positive bacteria} are (colum names): \cr
|
|
#' \code{"amox"}, \code{"amcl"}, \code{"cfur"}, \code{"pita"}, \code{"cipr"}, \code{"trsu"} (until here is universal), \code{"vanc"}, \code{"teic"}, \code{"tetr"}, \code{"eryt"}, \code{"oxac"}, \code{"rifa"}.
|
|
#'
|
|
#' At default, the antibiotics that are used for \strong{Gram negative bacteria} are (colum names): \cr
|
|
#' \code{"amox"}, \code{"amcl"}, \code{"cfur"}, \code{"pita"}, \code{"cipr"}, \code{"trsu"} (until here is universal), \code{"gent"}, \code{"tobr"}, \code{"coli"}, \code{"cfot"}, \code{"cfta"}, \code{"mero"}.
|
|
#'
|
|
#'
|
|
#' The function \code{key_antibiotics_equal} checks the characters returned by \code{key_antibiotics} for equality, and returns a logical vector.
|
|
#' @inheritSection first_isolate Key antibiotics
|
|
#' @rdname key_antibiotics
|
|
#' @export
|
|
#' @importFrom dplyr %>% mutate if_else pull
|
|
#' @importFrom crayon blue bold
|
|
#' @seealso \code{\link{first_isolate}}
|
|
#' @inheritSection AMR Read more on our website!
|
|
#' @examples
|
|
#' # septic_patients is a dataset available in the AMR package
|
|
#' ?septic_patients
|
|
|
|
#' library(dplyr)
|
|
#' # set key antibiotics to a new variable
|
|
#' my_patients <- septic_patients %>%
|
|
#' mutate(keyab = key_antibiotics(.)) %>%
|
|
#' mutate(
|
|
#' # now calculate first isolates
|
|
#' first_regular = first_isolate(., col_keyantibiotics = FALSE),
|
|
#' # and first WEIGHTED isolates
|
|
#' first_weighted = first_isolate(., col_keyantibiotics = "keyab")
|
|
#' )
|
|
#'
|
|
#' # Check the difference, in this data set it results in 7% more isolates:
|
|
#' sum(my_patients$first_regular, na.rm = TRUE)
|
|
#' sum(my_patients$first_weighted, na.rm = TRUE)
|
|
#'
|
|
#'
|
|
#' # output of the `key_antibiotics` function could be like this:
|
|
#' strainA <- "SSSRR.S.R..S"
|
|
#' strainB <- "SSSIRSSSRSSS"
|
|
#'
|
|
#' key_antibiotics_equal(strainA, strainB)
|
|
#' # TRUE, because I is ignored (as well as missing values)
|
|
#'
|
|
#' key_antibiotics_equal(strainA, strainB, ignore_I = FALSE)
|
|
#' # FALSE, because I is not ignored and so the 4th value differs
|
|
key_antibiotics <- function(x,
|
|
col_mo = NULL,
|
|
universal_1 = guess_ab_col(x, "AMX"),
|
|
universal_2 = guess_ab_col(x, "AMC"),
|
|
universal_3 = guess_ab_col(x, "CXM"),
|
|
universal_4 = guess_ab_col(x, "TZP"),
|
|
universal_5 = guess_ab_col(x, "CIP"),
|
|
universal_6 = guess_ab_col(x, "SXT"),
|
|
GramPos_1 = guess_ab_col(x, "VAN"),
|
|
GramPos_2 = guess_ab_col(x, "TEC"),
|
|
GramPos_3 = guess_ab_col(x, "TCY"),
|
|
GramPos_4 = guess_ab_col(x, "ERY"),
|
|
GramPos_5 = guess_ab_col(x, "OXA"),
|
|
GramPos_6 = guess_ab_col(x, "RIF"),
|
|
GramNeg_1 = guess_ab_col(x, "GEN"),
|
|
GramNeg_2 = guess_ab_col(x, "TOB"),
|
|
GramNeg_3 = guess_ab_col(x, "COL"),
|
|
GramNeg_4 = guess_ab_col(x, "CTX"),
|
|
GramNeg_5 = guess_ab_col(x, "CAZ"),
|
|
GramNeg_6 = guess_ab_col(x, "MEM"),
|
|
warnings = TRUE,
|
|
...) {
|
|
|
|
# try to find columns based on type
|
|
# -- mo
|
|
if (is.null(col_mo)) {
|
|
col_mo <- search_type_in_df(x = x, type = "mo")
|
|
}
|
|
if (is.null(col_mo)) {
|
|
stop("`col_mo` must be set.", call. = FALSE)
|
|
}
|
|
|
|
# check columns
|
|
col.list <- c(universal_1, universal_2, universal_3, universal_4, universal_5, universal_6,
|
|
GramPos_1, GramPos_2, GramPos_3, GramPos_4, GramPos_5, GramPos_6,
|
|
GramNeg_1, GramNeg_2, GramNeg_3, GramNeg_4, GramNeg_5, GramNeg_6)
|
|
check_available_columns <- function(x, col.list, info = TRUE) {
|
|
# check columns
|
|
col.list <- col.list[!is.na(col.list) & !is.null(col.list)]
|
|
names(col.list) <- col.list
|
|
col.list.bak <- col.list
|
|
# are they available as upper case or lower case then?
|
|
for (i in 1:length(col.list)) {
|
|
if (is.null(col.list[i]) | isTRUE(is.na(col.list[i]))) {
|
|
col.list[i] <- NA
|
|
} else if (toupper(col.list[i]) %in% colnames(x)) {
|
|
col.list[i] <- toupper(col.list[i])
|
|
} else if (tolower(col.list[i]) %in% colnames(x)) {
|
|
col.list[i] <- tolower(col.list[i])
|
|
} else if (!col.list[i] %in% colnames(x)) {
|
|
col.list[i] <- NA
|
|
}
|
|
}
|
|
if (!all(col.list %in% colnames(x))) {
|
|
if (info == TRUE) {
|
|
warning('Some columns do not exist and will be ignored: ',
|
|
col.list.bak[!(col.list %in% colnames(x))] %>% toString(),
|
|
'.\nTHIS MAY STRONGLY INFLUENCE THE OUTCOME.',
|
|
immediate. = TRUE,
|
|
call. = FALSE)
|
|
}
|
|
}
|
|
col.list
|
|
}
|
|
|
|
col.list <- check_available_columns(x = x, col.list = col.list, info = warnings)
|
|
universal_1 <- col.list[universal_1]
|
|
universal_2 <- col.list[universal_2]
|
|
universal_3 <- col.list[universal_3]
|
|
universal_4 <- col.list[universal_4]
|
|
universal_5 <- col.list[universal_5]
|
|
universal_6 <- col.list[universal_6]
|
|
GramPos_1 <- col.list[GramPos_1]
|
|
GramPos_2 <- col.list[GramPos_2]
|
|
GramPos_3 <- col.list[GramPos_3]
|
|
GramPos_4 <- col.list[GramPos_4]
|
|
GramPos_5 <- col.list[GramPos_5]
|
|
GramPos_6 <- col.list[GramPos_6]
|
|
GramNeg_1 <- col.list[GramNeg_1]
|
|
GramNeg_2 <- col.list[GramNeg_2]
|
|
GramNeg_3 <- col.list[GramNeg_3]
|
|
GramNeg_4 <- col.list[GramNeg_4]
|
|
GramNeg_5 <- col.list[GramNeg_5]
|
|
GramNeg_6 <- col.list[GramNeg_6]
|
|
|
|
universal <- c(universal_1, universal_2, universal_3,
|
|
universal_4, universal_5, universal_6)
|
|
|
|
gram_positive = c(universal,
|
|
GramPos_1, GramPos_2, GramPos_3,
|
|
GramPos_4, GramPos_5, GramPos_6)
|
|
gram_positive <- gram_positive[!is.null(gram_positive)]
|
|
gram_positive <- gram_positive[!is.na(gram_positive)]
|
|
if (length(gram_positive) < 12) {
|
|
warning("only using ", length(gram_positive), " different antibiotics as key antibiotics for Gram positives. See ?key_antibiotics.", call. = FALSE)
|
|
}
|
|
|
|
gram_negative = c(universal,
|
|
GramNeg_1, GramNeg_2, GramNeg_3,
|
|
GramNeg_4, GramNeg_5, GramNeg_6)
|
|
gram_negative <- gram_negative[!is.null(gram_negative)]
|
|
gram_negative <- gram_negative[!is.na(gram_negative)]
|
|
if (length(gram_negative) < 12) {
|
|
warning("only using ", length(gram_negative), " different antibiotics as key antibiotics for Gram negatives. See ?key_antibiotics.", call. = FALSE)
|
|
}
|
|
|
|
# join to microorganisms data set
|
|
x <- x %>%
|
|
mutate_at(vars(col_mo), as.mo) %>%
|
|
left_join_microorganisms(by = col_mo) %>%
|
|
mutate(key_ab = NA_character_,
|
|
gramstain = mo_gramstain(pull(., col_mo)))
|
|
|
|
# Gram +
|
|
x <- x %>% mutate(key_ab =
|
|
if_else(gramstain == "Gram positive",
|
|
apply(X = x[, gram_positive],
|
|
MARGIN = 1,
|
|
FUN = function(x) paste(x, collapse = "")),
|
|
key_ab))
|
|
|
|
# Gram -
|
|
x <- x %>% mutate(key_ab =
|
|
if_else(gramstain == "Gram negative",
|
|
apply(X = x[, gram_negative],
|
|
MARGIN = 1,
|
|
FUN = function(x) paste(x, collapse = "")),
|
|
key_ab))
|
|
|
|
# format
|
|
key_abs <- x %>%
|
|
pull(key_ab) %>%
|
|
gsub('(NA|NULL)', '.', .) %>%
|
|
gsub('[^SIR]', '.', ., ignore.case = TRUE)
|
|
|
|
key_abs
|
|
|
|
}
|
|
|
|
#' @importFrom dplyr progress_estimated %>%
|
|
#' @rdname key_antibiotics
|
|
#' @export
|
|
key_antibiotics_equal <- function(y,
|
|
z,
|
|
type = c("keyantibiotics", "points"),
|
|
ignore_I = TRUE,
|
|
points_threshold = 2,
|
|
info = FALSE) {
|
|
# y is active row, z is lag
|
|
x <- y
|
|
y <- z
|
|
|
|
type <- type[1]
|
|
|
|
if (length(x) != length(y)) {
|
|
stop('Length of `x` and `y` must be equal.')
|
|
}
|
|
|
|
# only show progress bar on points or when at least 5000 isolates
|
|
info_needed <- info == TRUE & (type == "points" | length(x) > 5000)
|
|
|
|
result <- logical(length(x))
|
|
|
|
if (info_needed == TRUE) {
|
|
p <- dplyr::progress_estimated(length(x))
|
|
}
|
|
|
|
for (i in 1:length(x)) {
|
|
|
|
if (info_needed == TRUE) {
|
|
p$tick()$print()
|
|
}
|
|
|
|
if (is.na(x[i])) {
|
|
x[i] <- ''
|
|
}
|
|
if (is.na(y[i])) {
|
|
y[i] <- ''
|
|
}
|
|
|
|
if (x[i] == y[i]) {
|
|
|
|
result[i] <- TRUE
|
|
|
|
} else if (nchar(x[i]) != nchar(y[i])) {
|
|
|
|
result[i] <- FALSE
|
|
|
|
} else {
|
|
|
|
x_split <- strsplit(x[i], "")[[1]]
|
|
y_split <- strsplit(y[i], "")[[1]]
|
|
|
|
if (type == 'keyantibiotics') {
|
|
|
|
if (ignore_I == TRUE) {
|
|
x_split[x_split == "I"] <- "."
|
|
y_split[y_split == "I"] <- "."
|
|
}
|
|
|
|
y_split[x_split == "."] <- "."
|
|
x_split[y_split == "."] <- "."
|
|
|
|
result[i] <- all(x_split == y_split)
|
|
|
|
} else if (type == 'points') {
|
|
# count points for every single character:
|
|
# - no change is 0 points
|
|
# - I <-> S|R is 0.5 point
|
|
# - S|R <-> R|S is 1 point
|
|
# use the levels of as.rsi (S = 1, I = 2, R = 3)
|
|
|
|
suppressWarnings(x_split <- x_split %>% as.rsi() %>% as.double())
|
|
suppressWarnings(y_split <- y_split %>% as.rsi() %>% as.double())
|
|
|
|
points <- (x_split - y_split) %>% abs() %>% sum(na.rm = TRUE) / 2
|
|
result[i] <- points >= points_threshold
|
|
|
|
} else {
|
|
stop('`', type, '` is not a valid value for type, must be "points" or "keyantibiotics". See ?first_isolate.')
|
|
}
|
|
}
|
|
}
|
|
if (info_needed == TRUE) {
|
|
cat('\n')
|
|
}
|
|
result
|
|
}
|