You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.

584 lines
24 KiB

# ==================================================================== #
# TITLE #
# Antimicrobial Resistance (AMR) Data Analysis for R #
# #
# SOURCE #
# https://github.com/msberends/AMR #
# #
# LICENCE #
# (c) 2018-2021 Berends MS, Luz CF et al. #
# Developed at the University of Groningen, the Netherlands, in #
# collaboration with non-profit organisations Certe Medical #
# Diagnostics & Advice, and University Medical Center Groningen. #
# #
# 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. #
# We created this package for both routine data analysis and academic #
# research and it was publicly released in the hope that it will be #
# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
# #
# Visit our website for the full manual and a complete tutorial about #
# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
# ==================================================================== #
#' Transform Input to an Antibiotic ID
#'
#' Use this function to determine the antibiotic code of one or more antibiotics. The data set [antibiotics] will be searched for abbreviations, official names and synonyms (brand names).
#' @inheritSection lifecycle Stable Lifecycle
#' @param x a [character] vector to determine to antibiotic ID
#' @param flag_multiple_results a [logical] to indicate whether a note should be printed to the console that probably more than one antibiotic code or name can be retrieved from a single input value.
#' @param info a [logical] to indicate whether a progress bar should be printed, defaults to `TRUE` only in interactive mode
#' @param ... arguments passed on to internal functions
#' @rdname as.ab
#' @inheritSection WHOCC WHOCC
#' @details All entries in the [antibiotics] data set have three different identifiers: a human readable EARS-Net code (column `ab`, used by ECDC and WHONET), an ATC code (column `atc`, used by WHO), and a CID code (column `cid`, Compound ID, used by PubChem). The data set contains more than 5,000 official brand names from many different countries, as found in PubChem. Not that some drugs contain multiple ATC codes.
#'
#' All these properties will be searched for the user input. The [as.ab()] can correct for different forms of misspelling:
#'
#' * Wrong spelling of drug names (such as "tobramicin" or "gentamycin"), which corrects for most audible similarities such as f/ph, x/ks, c/z/s, t/th, etc.
#' * Too few or too many vowels or consonants
#' * Switching two characters (such as "mreopenem", often the case in clinical data, when doctors typed too fast)
#' * Digitalised paper records, leaving artefacts like 0/o/O (zero and O's), B/8, n/r, etc.
#'
#' Use the [`ab_*`][ab_property()] functions to get properties based on the returned antibiotic ID, see *Examples*.
#'
#' Note: the [as.ab()] and [`ab_*`][ab_property()] functions may use very long regular expression to match brand names of antimicrobial agents. This may fail on some systems.
#' @section Source:
#' World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology: \url{https://www.whocc.no/atc_ddd_index/}
#'
#' WHONET 2019 software: \url{http://www.whonet.org/software.html}
#'
#' European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: \url{https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm}
#' @aliases ab
#' @return A [character] [vector] with additional class [`ab`]
#' @seealso
#' * [antibiotics] for the [data.frame] that is being used to determine ATCs
#' * [ab_from_text()] for a function to retrieve antimicrobial drugs from clinical text (from health care records)
#' @inheritSection AMR Reference Data Publicly Available
#' @inheritSection AMR Read more on Our Website!
#' @export
#' @examples
#' # these examples all return "ERY", the ID of erythromycin:
#' as.ab("J01FA01")
#' as.ab("J 01 FA 01")
#' as.ab("Erythromycin")
#' as.ab("eryt")
#' as.ab(" eryt 123")
#' as.ab("ERYT")
#' as.ab("ERY")
#' as.ab("eritromicine") # spelled wrong, yet works
#' as.ab("Erythrocin") # trade name
#' as.ab("Romycin") # trade name
#'
#' # spelling from different languages and dyslexia are no problem
#' ab_atc("ceftriaxon")
#' ab_atc("cephtriaxone") # small spelling error
#' ab_atc("cephthriaxone") # or a bit more severe
#' ab_atc("seephthriaaksone") # and even this works
#'
#' # use ab_* functions to get a specific properties (see ?ab_property);
#' # they use as.ab() internally:
#' ab_name("J01FA01") # "Erythromycin"
#' ab_name("eryt") # "Erythromycin"
#' \donttest{
#' if (require("dplyr")) {
#'
#' # you can quickly rename <rsi> columns using dplyr >= 1.0.0:
#' example_isolates %>%
#' rename_with(as.ab, where(is.rsi))
#'
#' }
#' }
as.ab <- function(x, flag_multiple_results = TRUE, info = interactive(), ...) {
meet_criteria(x, allow_class = c("character", "numeric", "integer", "factor"), allow_NA = TRUE)
meet_criteria(flag_multiple_results, allow_class = "logical", has_length = 1)
meet_criteria(info, allow_class = "logical", has_length = 1)
check_dataset_integrity()
if (is.ab(x)) {
return(x)
}
if (all(x %in% c(AB_lookup$ab, NA))) {
# all valid AB codes, but not yet right class
return(set_clean_class(x,
new_class = c("ab", "character")))
}
initial_search <- is.null(list(...)$initial_search)
already_regex <- isTRUE(list(...)$already_regex)
fast_mode <- isTRUE(list(...)$fast_mode)
x_bak <- x
x <- toupper(x)
x_nonNA <- x[!is.na(x)]
# remove diacritics
x <- iconv(x, from = "UTF-8", to = "ASCII//TRANSLIT")
x <- gsub('"', "", x, fixed = TRUE)
x <- gsub("(specimen|specimen date|specimen_date|spec_date|gender|^dates?$)", "", x, ignore.case = TRUE, perl = TRUE)
x_bak_clean <- x
if (already_regex == FALSE) {
x_bak_clean <- generalise_antibiotic_name(x_bak_clean)
}
x <- unique(x_bak_clean) # this means that every x is in fact generalise_antibiotic_name(x)
x_new <- rep(NA_character_, length(x))
x_unknown <- character(0)
note_if_more_than_one_found <- function(found, index, from_text) {
if (initial_search == TRUE & isTRUE(length(from_text) > 1)) {
abnames <- ab_name(from_text, tolower = TRUE, initial_search = FALSE)
if (ab_name(found[1L], language = NULL) %like% "clavulanic acid") {
abnames <- abnames[!abnames == "clavulanic acid"]
}
if (length(abnames) > 1) {
message_("More than one result was found for item ", index, ": ",
vector_and(abnames, quotes = FALSE))
}
}
found[1L]
}
# Fill in names, AB codes, CID codes and ATC codes directly (`x` is already clean and uppercase)
known_names <- x %in% AB_lookup$generalised_name
x_new[known_names] <- AB_lookup$ab[match(x[known_names], AB_lookup$generalised_name)]
known_codes_ab <- x %in% AB_lookup$ab
known_codes_atc <- vapply(FUN.VALUE = logical(1), x, function(x_) x_ %in% unlist(AB_lookup$atc), USE.NAMES = FALSE)
known_codes_cid <- x %in% AB_lookup$cid
x_new[known_codes_ab] <- AB_lookup$ab[match(x[known_codes_ab], AB_lookup$ab)]
x_new[known_codes_atc] <- AB_lookup$ab[vapply(FUN.VALUE = integer(1),
x[known_codes_atc],
function(x_) which(vapply(FUN.VALUE = logical(1),
AB_lookup$atc,
function(atc) x_ %in% atc)),
USE.NAMES = FALSE)]
x_new[known_codes_cid] <- AB_lookup$ab[match(x[known_codes_cid], AB_lookup$cid)]
already_known <- known_names | known_codes_ab | known_codes_atc | known_codes_cid
if (initial_search == TRUE & sum(already_known) < length(x)) {
progress <- progress_ticker(n = sum(!already_known), n_min = 25, print = info) # start if n >= 25
on.exit(close(progress))
}
for (i in which(!already_known)) {
if (initial_search == TRUE) {
progress$tick()
}
if (is.na(x[i]) | is.null(x[i])) {
next
}
if (identical(x[i], "") |
# prevent "bacteria" from coercing to TMP, since Bacterial is a brand name of it:
identical(tolower(x[i]), "bacteria")) {
x_unknown <- c(x_unknown, x_bak[x[i] == x_bak_clean][1])
next
}
if (fast_mode == FALSE && flag_multiple_results == TRUE && x[i] %like% "[ ]") {
from_text <- tryCatch(suppressWarnings(ab_from_text(x[i], initial_search = FALSE, translate_ab = FALSE)[[1]]),
error = function(e) character(0))
} else {
from_text <- character(0)
}
# old code for phenoxymethylpenicillin (Peni V)
if (x[i] == "PNV") {
x_new[i] <- "PHN"
next
}
# exact LOINC code
loinc_found <- unlist(lapply(AB_lookup$generalised_loinc,
function(s) x[i] %in% s))
found <- antibiotics$ab[loinc_found == TRUE]
if (length(found) > 0) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
# exact synonym
synonym_found <- unlist(lapply(AB_lookup$generalised_synonyms,
function(s) x[i] %in% s))
found <- antibiotics$ab[synonym_found == TRUE]
if (length(found) > 0) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
# exact abbreviation
abbr_found <- unlist(lapply(AB_lookup$generalised_abbreviations,
# require at least 2 characters for abbreviations
function(s) x[i] %in% s & nchar(x[i]) >= 2))
found <- antibiotics$ab[abbr_found == TRUE]
if (length(found) > 0) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
# allow characters that resemble others, but only continue when having more than 3 characters
if (nchar(x[i]) <= 3) {
x_unknown <- c(x_unknown, x_bak[x[i] == x_bak_clean][1])
next
}
x_spelling <- x[i]
if (already_regex == FALSE) {
x_spelling <- gsub("[IY]+", "[IY]+", x_spelling, perl = TRUE)
x_spelling <- gsub("(C|K|Q|QU|S|Z|X|KS)+", "(C|K|Q|QU|S|Z|X|KS)+", x_spelling, perl = TRUE)
x_spelling <- gsub("(PH|F|V)+", "(PH|F|V)+", x_spelling, perl = TRUE)
x_spelling <- gsub("(TH|T)+", "(TH|T)+", x_spelling, perl = TRUE)
x_spelling <- gsub("A+", "A+", x_spelling, perl = TRUE)
x_spelling <- gsub("E+", "E+", x_spelling, perl = TRUE)
x_spelling <- gsub("O+", "O+", x_spelling, perl = TRUE)
# allow any ending of -in/-ine and -im/-ime
x_spelling <- gsub("(\\[IY\\]\\+(N|M)|\\[IY\\]\\+(N|M)E\\+)$", "[IY]+(N|M)E*", x_spelling, perl = TRUE)
# allow any ending of -ol/-ole
x_spelling <- gsub("(O\\+L|O\\+LE\\+)$", "O+LE*", x_spelling, perl = TRUE)
# allow any ending of -on/-one
x_spelling <- gsub("(O\\+N|O\\+NE\\+)$", "O+NE*", x_spelling, perl = TRUE)
# replace multiple same characters to single one with '+', like "ll" -> "l+"
x_spelling <- gsub("(.)\\1+", "\\1+", x_spelling, perl = TRUE)
# replace spaces and slashes with a possibility on both
x_spelling <- gsub("[ /]", "( .*|.*/)", x_spelling, perl = TRUE)
# correct for digital reading text (OCR)
x_spelling <- gsub("[NRD8B]", "[NRD8B]", x_spelling, perl = TRUE)
x_spelling <- gsub("(O|0)", "(O|0)+", x_spelling, perl = TRUE)
x_spelling <- gsub("++", "+", x_spelling, fixed = TRUE)
}
# try if name starts with it
found <- antibiotics[which(AB_lookup$generalised_name %like% paste0("^", x_spelling)), ]$ab
if (length(found) > 0) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
# try if name ends with it
found <- antibiotics[which(AB_lookup$generalised_name %like% paste0(x_spelling, "$")), ]$ab
if (nchar(x[i]) >= 4 & length(found) > 0) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
# and try if any synonym starts with it
synonym_found <- unlist(lapply(AB_lookup$generalised_synonyms,
function(s) any(s %like% paste0("^", x_spelling))))
found <- antibiotics$ab[synonym_found == TRUE]
if (length(found) > 0) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
# INITIAL SEARCH - More uncertain results ----
if (initial_search == TRUE && fast_mode == FALSE) {
# only run on first try
# try by removing all spaces
if (x[i] %like% " ") {
found <- suppressWarnings(as.ab(gsub(" +", "", x[i], perl = TRUE), initial_search = FALSE))
if (length(found) > 0 & !is.na(found)) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
}
# try by removing all spaces and numbers
if (x[i] %like% " " | x[i] %like% "[0-9]") {
found <- suppressWarnings(as.ab(gsub("[ 0-9]", "", x[i], perl = TRUE), initial_search = FALSE))
if (length(found) > 0 & !is.na(found)) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
}
# transform back from other languages and try again
x_translated <- paste(lapply(strsplit(x[i], "[^A-Z0-9]"),
function(y) {
for (i in seq_len(length(y))) {
for (lang in LANGUAGES_SUPPORTED[LANGUAGES_SUPPORTED != "en"]) {
y[i] <- ifelse(tolower(y[i]) %in% tolower(TRANSLATIONS[, lang, drop = TRUE]),
TRANSLATIONS[which(tolower(TRANSLATIONS[, lang, drop = TRUE]) == tolower(y[i]) &
!isFALSE(TRANSLATIONS$fixed)), "pattern"],
y[i])
}
}
generalise_antibiotic_name(y)
})[[1]],
collapse = "/")
x_translated_guess <- suppressWarnings(as.ab(x_translated, initial_search = FALSE))
if (!is.na(x_translated_guess)) {
x_new[i] <- x_translated_guess
next
}
# now also try to coerce brandname combinations like "Amoxy/clavulanic acid"
x_translated <- paste(lapply(strsplit(x_translated, "[^A-Z0-9 ]"),
function(y) {
for (i in seq_len(length(y))) {
y_name <- suppressWarnings(ab_name(y[i], language = NULL, initial_search = FALSE))
y[i] <- ifelse(!is.na(y_name),
y_name,
y[i])
}
generalise_antibiotic_name(y)
})[[1]],
collapse = "/")
x_translated_guess <- suppressWarnings(as.ab(x_translated, initial_search = FALSE))
if (!is.na(x_translated_guess)) {
x_new[i] <- x_translated_guess
next
}
# try by removing all trailing capitals
if (x[i] %like_case% "[a-z]+[A-Z]+$") {
found <- suppressWarnings(as.ab(gsub("[A-Z]+$", "", x[i], perl = TRUE), initial_search = FALSE))
if (!is.na(found)) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
}
# keep only letters
found <- suppressWarnings(as.ab(gsub("[^A-Z]", "", x[i], perl = TRUE), initial_search = FALSE))
if (!is.na(found)) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
# try from a bigger text, like from a health care record, see ?ab_from_text
# already calculated above if flag_multiple_results = TRUE
if (flag_multiple_results == TRUE) {
found <- from_text[1L]
} else {
found <- tryCatch(suppressWarnings(ab_from_text(x[i], initial_search = FALSE, translate_ab = FALSE)[[1]][1L]),
error = function(e) NA_character_)
}
if (!is.na(found)) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
# first 5 except for cephalosporins, then first 7 (those cephalosporins all start quite the same!)
found <- suppressWarnings(as.ab(substr(x[i], 1, 5), initial_search = FALSE))
if (!is.na(found) && ab_group(found, initial_search = FALSE) %unlike% "cephalosporins") {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
found <- suppressWarnings(as.ab(substr(x[i], 1, 7), initial_search = FALSE))
if (!is.na(found)) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
# make all consonants facultative
search_str <- gsub("([BCDFGHJKLMNPQRSTVWXZ])", "\\1*", x[i], perl = TRUE)
found <- suppressWarnings(as.ab(search_str, initial_search = FALSE, already_regex = TRUE))
# keep at least 4 normal characters
if (nchar(gsub(".\\*", "", search_str, perl = TRUE)) < 4) {
found <- NA
}
if (!is.na(found)) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
# make all vowels facultative
search_str <- gsub("([AEIOUY])", "\\1*", x[i], perl = TRUE)
found <- suppressWarnings(as.ab(search_str, initial_search = FALSE, already_regex = TRUE))
# keep at least 5 normal characters
if (nchar(gsub(".\\*", "", search_str, perl = TRUE)) < 5) {
found <- NA
}
if (!is.na(found)) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
# allow misspelling of vowels
x_spelling <- gsub("A+", "[AEIOU]+", x_spelling, fixed = TRUE)
x_spelling <- gsub("E+", "[AEIOU]+", x_spelling, fixed = TRUE)
x_spelling <- gsub("I+", "[AEIOU]+", x_spelling, fixed = TRUE)
x_spelling <- gsub("O+", "[AEIOU]+", x_spelling, fixed = TRUE)
x_spelling <- gsub("U+", "[AEIOU]+", x_spelling, fixed = TRUE)
found <- suppressWarnings(as.ab(x_spelling, initial_search = FALSE, already_regex = TRUE))
if (!is.na(found)) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
# try with switched character, like "mreopenem"
for (j in seq_len(nchar(x[i]))) {
x_switched <- paste0(
# beginning part:
substr(x[i], 1, j - 1),
# here is the switching of 2 characters:
substr(x[i], j + 1, j + 1),
substr(x[i], j, j),
# ending part:
substr(x[i], j + 2, nchar(x[i])))
found <- suppressWarnings(as.ab(x_switched, initial_search = FALSE))
if (!is.na(found)) {
break
}
}
if (!is.na(found)) {
x_new[i] <- found[1L]
next
}
} # end of initial_search = TRUE
# not found
x_unknown <- c(x_unknown, x_bak[x[i] == x_bak_clean][1])
}
if (initial_search == TRUE & sum(already_known) < length(x)) {
close(progress)
}
# take failed ATC codes apart from rest
x_unknown_ATCs <- x_unknown[x_unknown %like% "[A-Z][0-9][0-9][A-Z][A-Z][0-9][0-9]"]
x_unknown <- x_unknown[!x_unknown %in% x_unknown_ATCs]
if (length(x_unknown_ATCs) > 0 & fast_mode == FALSE) {
warning_("These ATC codes are not (yet) in the antibiotics data set: ",
vector_and(x_unknown_ATCs), ".",
call = FALSE)
}
if (length(x_unknown) > 0 & fast_mode == FALSE) {
warning_("These values could not be coerced to a valid antimicrobial ID: ",
vector_and(x_unknown), ".",
call = FALSE)
}
x_result <- x_new[match(x_bak_clean, x)]
if (length(x_result) == 0) {
x_result <- NA_character_
}
set_clean_class(x_result,
new_class = c("ab", "character"))
}
#' @rdname as.ab
#' @export
is.ab <- function(x) {
inherits(x, "ab")
}
# will be exported using s3_register() in R/zzz.R
pillar_shaft.ab <- function(x, ...) {
out <- trimws(format(x))
out[is.na(x)] <- font_na(NA)
create_pillar_column(out, align = "left", min_width = 4)
}
# will be exported using s3_register() in R/zzz.R
type_sum.ab <- function(x, ...) {
"ab"
}
#' @method print ab
#' @export
#' @noRd
print.ab <- function(x, ...) {
cat("Class <ab>\n")
print(as.character(x), quote = FALSE)
}
#' @method as.data.frame ab
#' @export
#' @noRd
as.data.frame.ab <- function(x, ...) {
nm <- deparse1(substitute(x))
if (!"nm" %in% names(list(...))) {
as.data.frame.vector(as.ab(x), ..., nm = nm)
} else {
as.data.frame.vector(as.ab(x), ...)
}
}
#' @method [ ab
#' @export
#' @noRd
"[.ab" <- function(x, ...) {
y <- NextMethod()
attributes(y) <- attributes(x)
y
}
#' @method [[ ab
#' @export
#' @noRd
"[[.ab" <- function(x, ...) {
y <- NextMethod()
attributes(y) <- attributes(x)
y
}
#' @method [<- ab
#' @export
#' @noRd
"[<-.ab" <- function(i, j, ..., value) {
y <- NextMethod()
attributes(y) <- attributes(i)
return_after_integrity_check(y, "antimicrobial code", antibiotics$ab)
}
#' @method [[<- ab
#' @export
#' @noRd
"[[<-.ab" <- function(i, j, ..., value) {
y <- NextMethod()
attributes(y) <- attributes(i)
return_after_integrity_check(y, "antimicrobial code", antibiotics$ab)
}
#' @method c ab
#' @export
#' @noRd
c.ab <- function(...) {
x <- list(...)[[1L]]
y <- NextMethod()
attributes(y) <- attributes(x)
return_after_integrity_check(y, "antimicrobial code", antibiotics$ab)
}
#' @method unique ab
#' @export
#' @noRd
unique.ab <- function(x, incomparables = FALSE, ...) {
y <- NextMethod()
attributes(y) <- attributes(x)
y
}
#' @method rep ab
#' @export
#' @noRd
rep.ab <- function(x, ...) {
y <- NextMethod()
attributes(y) <- attributes(x)
y
}
generalise_antibiotic_name <- function(x) {
x <- toupper(x)
# remove suffices
x <- gsub("_(MIC|RSI|DIS[CK])$", "", x, perl = TRUE)
# remove disk concentrations, like LVX_NM -> LVX
x <- gsub("_[A-Z]{2}[0-9_.]{0,3}$", "", x, perl = TRUE)
# remove part between brackets if that's followed by another string
x <- gsub("(.*)+ [(].*[)]", "\\1", x)
# keep only max 1 space
x <- trimws2(gsub(" +", " ", x, perl = TRUE))
# non-character, space or number should be a slash
x <- gsub("[^A-Z0-9 -]", "/", x, perl = TRUE)
# spaces around non-characters must be removed: amox + clav -> amox/clav
x <- gsub("(.*[A-Z0-9]) ([^A-Z0-9].*)", "\\1\\2", x, perl = TRUE)
x <- gsub("(.*[^A-Z0-9]) ([A-Z0-9].*)", "\\1\\2", x, perl = TRUE)
# remove hyphen after a starting "co"
x <- gsub("^CO-", "CO", x, perl = TRUE)
# replace operators with a space
x <- gsub("(/| AND | WITH | W/|[+]|[-])+", " ", x, perl = TRUE)
x
}