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cfta streptococci, codecov.yml

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dr. M.S. (Matthijs) Berends 2019-04-09 14:59:17 +02:00
parent cffb7787d8
commit 461eec9bac
18 changed files with 203 additions and 114 deletions
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3
.Rbuildignore
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@ -1,6 +1,6 @@
^.*\.Rproj$ ^.*\.Rproj$
^\.gitlab-ci\.yml$
^\.gitlab-ci\.R$ ^\.gitlab-ci\.R$
^\.gitlab-ci\.yml$
^\.Renviron$ ^\.Renviron$
^\.Rprofile$ ^\.Rprofile$
^\.Rproj\.user$ ^\.Rproj\.user$
@ -9,6 +9,7 @@
^_noinclude$ ^_noinclude$
^_pkgdown\.yml$ ^_pkgdown\.yml$
^appveyor\.yml$ ^appveyor\.yml$
^codecov\.yml$
^cran-comments\.md$ ^cran-comments\.md$
^CRAN-RELEASE$ ^CRAN-RELEASE$
^doc$ ^doc$

2
R/eucast_rules.R
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@ -35,6 +35,8 @@ EUCAST_VERSION_EXPERT_RULES <- "3.1, 2016"
#' @param ... parameters that are passed on to \code{eucast_rules} #' @param ... parameters that are passed on to \code{eucast_rules}
#' @inheritParams first_isolate #' @inheritParams first_isolate
#' @details #' @details
#' \strong{NOTE:} This function does not translate MIC values to RSI values. It only applies (1) inferred susceptibility and resistance based on results of other antibiotics and (2) intrinsic resistance based on taxonomic properties of a microorganism.
#'
#' The file used for applying all EUCAST rules can be retrieved with \code{\link{eucast_rules_file}()}. It returns an easily readable data set containing all rules. The original TSV file (tab separated file) that is being read by this function can be found when running this command: \cr #' The file used for applying all EUCAST rules can be retrieved with \code{\link{eucast_rules_file}()}. It returns an easily readable data set containing all rules. The original TSV file (tab separated file) that is being read by this function can be found when running this command: \cr
#' \code{AMR::EUCAST_RULES_FILE_LOCATION} (without brackets). #' \code{AMR::EUCAST_RULES_FILE_LOCATION} (without brackets).
#' #'

10
R/first_isolate.R
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@ -42,7 +42,7 @@
#' @details \strong{WHY THIS IS SO IMPORTANT} \cr #' @details \strong{WHY THIS IS SO IMPORTANT} \cr
#' To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode \href{https://www.ncbi.nlm.nih.gov/pubmed/17304462}{[1]}. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all \emph{S. aureus} isolates would be overestimated, because you included this MRSA more than once. It would be \href{https://en.wikipedia.org/wiki/Selection_bias}{selection bias}. #' To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode \href{https://www.ncbi.nlm.nih.gov/pubmed/17304462}{[1]}. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all \emph{S. aureus} isolates would be overestimated, because you included this MRSA more than once. It would be \href{https://en.wikipedia.org/wiki/Selection_bias}{selection bias}.
#' #'
#' The function \code{filter_first_isolate} is essentially equal to: #' The functions \code{filter_first_isolate} and \code{filter_first_weighted_isolate} are helper functions to quickly filter on first isolates. The function \code{filter_first_isolate} is essentially equal to:
#' \preformatted{ #' \preformatted{
#' tbl \%>\% #' tbl \%>\%
#' mutate(only_firsts = first_isolate(tbl, ...)) \%>\% #' mutate(only_firsts = first_isolate(tbl, ...)) \%>\%
@ -62,10 +62,10 @@
#' There are two ways to determine whether isolates can be included as first \emph{weighted} isolates which will give generally the same results: \cr #' There are two ways to determine whether isolates can be included as first \emph{weighted} isolates which will give generally the same results: \cr
#' #'
#' \strong{1. Using} \code{type = "keyantibiotics"} \strong{and parameter} \code{ignore_I} \cr #' \strong{1. Using} \code{type = "keyantibiotics"} \strong{and parameter} \code{ignore_I} \cr
#' Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With \code{ignore_I = FALSE}, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. \cr #' Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With \code{ignore_I = FALSE}, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. Read more about this in the \code{\link{key_antibiotics}} function. \cr
#' #'
#' \strong{2. Using} \code{type = "points"} \strong{and parameter} \code{points_threshold} \cr #' \strong{2. Using} \code{type = "points"} \strong{and parameter} \code{points_threshold} \cr
#' A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds \code{points_threshold}, an isolate will be (re)selected as a first weighted isolate. #' A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds \code{points_threshold}, which default to \code{2}, an isolate will be (re)selected as a first weighted isolate.
#' @rdname first_isolate #' @rdname first_isolate
#' @keywords isolate isolates first #' @keywords isolate isolates first
#' @seealso \code{\link{key_antibiotics}} #' @seealso \code{\link{key_antibiotics}}
@ -109,8 +109,8 @@
#' #'
#' # Have a look at A and B. #' # Have a look at A and B.
#' # B is more reliable because every isolate is only counted once. #' # B is more reliable because every isolate is only counted once.
#' # Gentamicin resitance in hospital D appears to be 5.4% higher than #' # Gentamicin resitance in hospital D appears to be 3.1% higher than
#' # when you (erroneously) would have used all isolates! #' # when you (erroneously) would have used all isolates for analysis.
#' #'
#' #'
#' ## OTHER EXAMPLES: #' ## OTHER EXAMPLES:

35
R/guess_ab_col.R
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@ -26,6 +26,7 @@
#' @param col a character to look for #' @param col a character to look for
#' @param verbose a logical to indicate whether additional info should be printed #' @param verbose a logical to indicate whether additional info should be printed
#' @importFrom dplyr %>% select filter_all any_vars #' @importFrom dplyr %>% select filter_all any_vars
#' @importFrom crayon blue
#' @export #' @export
#' @inheritSection AMR Read more on our website! #' @inheritSection AMR Read more on our website!
#' @examples #' @examples
@ -70,6 +71,9 @@ guess_ab_col <- function(tbl = NULL, col = NULL, verbose = FALSE) {
unlist() unlist()
if (col %in% tbl_names) { if (col %in% tbl_names) {
if (verbose == TRUE) {
message(blue(paste0("NOTE: Using column `", bold(col), "` as input for `", col, "`.")))
}
return(col) return(col)
} }
ab_result <- antibiotics %>% ab_result <- antibiotics %>%
@ -77,7 +81,7 @@ guess_ab_col <- function(tbl = NULL, col = NULL, verbose = FALSE) {
filter_all(any_vars(tolower(.) == tolower(col))) %>% filter_all(any_vars(tolower(.) == tolower(col))) %>%
filter_all(any_vars(. %in% tbl_names)) filter_all(any_vars(. %in% tbl_names))
if (nrow(ab_result) == 0 & nchar(col) > 4) { if (nrow(ab_result) == 0 & nchar(col) >= 5) {
# use like when col >= 5 characters # use like when col >= 5 characters
ab_result <- antibiotics %>% ab_result <- antibiotics %>%
select(atc:trade_name) %>% select(atc:trade_name) %>%
@ -87,14 +91,28 @@ guess_ab_col <- function(tbl = NULL, col = NULL, verbose = FALSE) {
# WHONET # WHONET
if (nrow(ab_result) == 0) { if (nrow(ab_result) == 0) {
# use like when col >= 5 characters # use like for any case
ab_result <- antibiotics %>% ab_result <- antibiotics %>%
select(atc:trade_name) %>% select(atc:trade_name) %>%
filter_all(any_vars(tolower(.) == tolower(col))) %>% filter_all(any_vars(tolower(.) == tolower(col))) %>%
filter_all(any_vars(. %in% tbl_names_stripped)) filter_all(any_vars(. %in% tbl_names_stripped))
} }
if (nrow(ab_result) > 1) { found_based_on_official_name <- FALSE
if (nrow(ab_result) == 0) {
# check if first part of official name resembles the columns that's been looking for
name <- suppressWarnings(atc_name(col))
if (!is.null(name)) {
ab_result <-
antibiotics %>%
filter(official == name) %>%
pull(official)
ab_result <- tbl_names[tbl_names %like% paste0("^", substr(ab_result, 1, 5))]
found_based_on_official_name <- TRUE
}
}
if (NROW(ab_result) > 1 & found_based_on_official_name == FALSE) {
# looking more and more for reliable hit # looking more and more for reliable hit
ab_result_1 <- ab_result %>% filter(tolower(atc) == tolower(col)) ab_result_1 <- ab_result %>% filter(tolower(atc) == tolower(col))
if (nrow(ab_result_1) == 0) { if (nrow(ab_result_1) == 0) {
@ -106,6 +124,9 @@ guess_ab_col <- function(tbl = NULL, col = NULL, verbose = FALSE) {
if (nrow(ab_result_1) == 0) { if (nrow(ab_result_1) == 0) {
ab_result_1 <- ab_result %>% filter(tolower(official) == tolower(col)) ab_result_1 <- ab_result %>% filter(tolower(official) == tolower(col))
} }
if (nrow(ab_result_1) == 0) {
ab_result_1 <- ab_result %>% filter(tolower(official) == tolower(col))
}
if (nrow(ab_result_1) == 0) { if (nrow(ab_result_1) == 0) {
ab_result_1 <- ab_result[1, ] ab_result_1 <- ab_result[1, ]
} }
@ -114,7 +135,7 @@ guess_ab_col <- function(tbl = NULL, col = NULL, verbose = FALSE) {
if (length(ab_result) == 0) { if (length(ab_result) == 0) {
if (verbose == TRUE) { if (verbose == TRUE) {
message('no column found for input "', col, '"') message('No column found as input for `', col, '`.')
} }
return(NULL) return(NULL)
} else { } else {
@ -122,14 +143,14 @@ guess_ab_col <- function(tbl = NULL, col = NULL, verbose = FALSE) {
if (length(result) == 0) { if (length(result) == 0) {
result <- tbl_names[tbl_names_stripped %in% ab_result] result <- tbl_names[tbl_names_stripped %in% ab_result]
} }
if (length(result) == 0) { if (length(result) == 0 | length(result) > 1) {
if (verbose == TRUE) { if (verbose == TRUE) {
message('no column found for input "', col, '"') message('No column found as input for `', col, '`.')
} }
return(NULL) return(NULL)
} }
if (verbose == TRUE) { if (verbose == TRUE) {
message('using column `', result, '` for col "', col, '"') message(blue(paste0("NOTE: Using column `", bold(result), "` as input for `", col, "`.")))
} }
return(result) return(result)
} }

148
R/mdro.R
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@ -27,9 +27,10 @@
#' @param info print progress #' @param info print progress
#' @inheritParams eucast_rules #' @inheritParams eucast_rules
#' @param metr column name of an antibiotic, see Antibiotics #' @param metr column name of an antibiotic, see Antibiotics
#' @param verbose print additional info: missing antibiotic columns per parameter
#' @param ... parameters that are passed on to methods #' @param ... parameters that are passed on to methods
#' @inheritSection eucast_rules Antibiotics #' @inheritSection eucast_rules Antibiotics
#' @details When \code{country} will be left blank, guidelines will be taken from EUCAST Expert Rules Version 3.1 "Intrinsic Resistance and Exceptional Phenotypes Tables" (\url{http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf}). #' @details When \code{country} will be left blank, guidelines will be taken from EUCAST Expert Rules Version 3.1 "Intrinsic Resistance and Exceptional Phenotypes Tables" (\href{http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf}{link}).
#' @return Ordered factor with levels \code{Negative < Positive, unconfirmed < Positive}. #' @return Ordered factor with levels \code{Negative < Positive, unconfirmed < Positive}.
#' @rdname mdro #' @rdname mdro
#' @importFrom dplyr %>% #' @importFrom dplyr %>%
@ -105,7 +106,8 @@ mdro <- function(tbl,
tobr = guess_ab_col(), tobr = guess_ab_col(),
trim = guess_ab_col(), trim = guess_ab_col(),
trsu = guess_ab_col(), trsu = guess_ab_col(),
vanc = guess_ab_col()) { vanc = guess_ab_col(),
verbose = FALSE) {
if (!is.data.frame(tbl)) { if (!is.data.frame(tbl)) {
stop("`tbl` must be a data frame.", call. = FALSE) stop("`tbl` must be a data frame.", call. = FALSE)
@ -168,66 +170,66 @@ mdro <- function(tbl,
} }
# check columns # check columns
if (identical(amcl, as.name("guess_ab_col"))) { amcl <- guess_ab_col(tbl, "amcl", verbose = info) } if (identical(amcl, as.name("guess_ab_col"))) { amcl <- guess_ab_col(tbl, "amcl", verbose = verbose) }
if (identical(amik, as.name("guess_ab_col"))) { amik <- guess_ab_col(tbl, "amik", verbose = info) } if (identical(amik, as.name("guess_ab_col"))) { amik <- guess_ab_col(tbl, "amik", verbose = verbose) }
if (identical(amox, as.name("guess_ab_col"))) { amox <- guess_ab_col(tbl, "amox", verbose = info) } if (identical(amox, as.name("guess_ab_col"))) { amox <- guess_ab_col(tbl, "amox", verbose = verbose) }
if (identical(ampi, as.name("guess_ab_col"))) { ampi <- guess_ab_col(tbl, "ampi", verbose = info) } if (identical(ampi, as.name("guess_ab_col"))) { ampi <- guess_ab_col(tbl, "ampi", verbose = verbose) }
if (identical(azit, as.name("guess_ab_col"))) { azit <- guess_ab_col(tbl, "azit", verbose = info) } if (identical(azit, as.name("guess_ab_col"))) { azit <- guess_ab_col(tbl, "azit", verbose = verbose) }
if (identical(aztr, as.name("guess_ab_col"))) { aztr <- guess_ab_col(tbl, "aztr", verbose = info) } if (identical(aztr, as.name("guess_ab_col"))) { aztr <- guess_ab_col(tbl, "aztr", verbose = verbose) }
if (identical(cefa, as.name("guess_ab_col"))) { cefa <- guess_ab_col(tbl, "cefa", verbose = info) } if (identical(cefa, as.name("guess_ab_col"))) { cefa <- guess_ab_col(tbl, "cefa", verbose = verbose) }
if (identical(cfra, as.name("guess_ab_col"))) { cfra <- guess_ab_col(tbl, "cfra", verbose = info) } if (identical(cfra, as.name("guess_ab_col"))) { cfra <- guess_ab_col(tbl, "cfra", verbose = verbose) }
if (identical(cfep, as.name("guess_ab_col"))) { cfep <- guess_ab_col(tbl, "cfep", verbose = info) } if (identical(cfep, as.name("guess_ab_col"))) { cfep <- guess_ab_col(tbl, "cfep", verbose = verbose) }
if (identical(cfot, as.name("guess_ab_col"))) { cfot <- guess_ab_col(tbl, "cfot", verbose = info) } if (identical(cfot, as.name("guess_ab_col"))) { cfot <- guess_ab_col(tbl, "cfot", verbose = verbose) }
if (identical(cfox, as.name("guess_ab_col"))) { cfox <- guess_ab_col(tbl, "cfox", verbose = info) } if (identical(cfox, as.name("guess_ab_col"))) { cfox <- guess_ab_col(tbl, "cfox", verbose = verbose) }
if (identical(cfta, as.name("guess_ab_col"))) { cfta <- guess_ab_col(tbl, "cfta", verbose = info) } if (identical(cfta, as.name("guess_ab_col"))) { cfta <- guess_ab_col(tbl, "cfta", verbose = verbose) }
if (identical(cftr, as.name("guess_ab_col"))) { cftr <- guess_ab_col(tbl, "cftr", verbose = info) } if (identical(cftr, as.name("guess_ab_col"))) { cftr <- guess_ab_col(tbl, "cftr", verbose = verbose) }
if (identical(cfur, as.name("guess_ab_col"))) { cfur <- guess_ab_col(tbl, "cfur", verbose = info) } if (identical(cfur, as.name("guess_ab_col"))) { cfur <- guess_ab_col(tbl, "cfur", verbose = verbose) }
if (identical(chlo, as.name("guess_ab_col"))) { chlo <- guess_ab_col(tbl, "chlo", verbose = info) } if (identical(chlo, as.name("guess_ab_col"))) { chlo <- guess_ab_col(tbl, "chlo", verbose = verbose) }
if (identical(cipr, as.name("guess_ab_col"))) { cipr <- guess_ab_col(tbl, "cipr", verbose = info) } if (identical(cipr, as.name("guess_ab_col"))) { cipr <- guess_ab_col(tbl, "cipr", verbose = verbose) }
if (identical(clar, as.name("guess_ab_col"))) { clar <- guess_ab_col(tbl, "clar", verbose = info) } if (identical(clar, as.name("guess_ab_col"))) { clar <- guess_ab_col(tbl, "clar", verbose = verbose) }
if (identical(clin, as.name("guess_ab_col"))) { clin <- guess_ab_col(tbl, "clin", verbose = info) } if (identical(clin, as.name("guess_ab_col"))) { clin <- guess_ab_col(tbl, "clin", verbose = verbose) }
if (identical(clox, as.name("guess_ab_col"))) { clox <- guess_ab_col(tbl, "clox", verbose = info) } if (identical(clox, as.name("guess_ab_col"))) { clox <- guess_ab_col(tbl, "clox", verbose = verbose) }
if (identical(coli, as.name("guess_ab_col"))) { coli <- guess_ab_col(tbl, "coli", verbose = info) } if (identical(coli, as.name("guess_ab_col"))) { coli <- guess_ab_col(tbl, "coli", verbose = verbose) }
if (identical(czol, as.name("guess_ab_col"))) { czol <- guess_ab_col(tbl, "czol", verbose = info) } if (identical(czol, as.name("guess_ab_col"))) { czol <- guess_ab_col(tbl, "czol", verbose = verbose) }
if (identical(dapt, as.name("guess_ab_col"))) { dapt <- guess_ab_col(tbl, "dapt", verbose = info) } if (identical(dapt, as.name("guess_ab_col"))) { dapt <- guess_ab_col(tbl, "dapt", verbose = verbose) }
if (identical(doxy, as.name("guess_ab_col"))) { doxy <- guess_ab_col(tbl, "doxy", verbose = info) } if (identical(doxy, as.name("guess_ab_col"))) { doxy <- guess_ab_col(tbl, "doxy", verbose = verbose) }
if (identical(erta, as.name("guess_ab_col"))) { erta <- guess_ab_col(tbl, "erta", verbose = info) } if (identical(erta, as.name("guess_ab_col"))) { erta <- guess_ab_col(tbl, "erta", verbose = verbose) }
if (identical(eryt, as.name("guess_ab_col"))) { eryt <- guess_ab_col(tbl, "eryt", verbose = info) } if (identical(eryt, as.name("guess_ab_col"))) { eryt <- guess_ab_col(tbl, "eryt", verbose = verbose) }
if (identical(fosf, as.name("guess_ab_col"))) { fosf <- guess_ab_col(tbl, "fosf", verbose = info) } if (identical(fosf, as.name("guess_ab_col"))) { fosf <- guess_ab_col(tbl, "fosf", verbose = verbose) }
if (identical(fusi, as.name("guess_ab_col"))) { fusi <- guess_ab_col(tbl, "fusi", verbose = info) } if (identical(fusi, as.name("guess_ab_col"))) { fusi <- guess_ab_col(tbl, "fusi", verbose = verbose) }
if (identical(gent, as.name("guess_ab_col"))) { gent <- guess_ab_col(tbl, "gent", verbose = info) } if (identical(gent, as.name("guess_ab_col"))) { gent <- guess_ab_col(tbl, "gent", verbose = verbose) }
if (identical(imip, as.name("guess_ab_col"))) { imip <- guess_ab_col(tbl, "imip", verbose = info) } if (identical(imip, as.name("guess_ab_col"))) { imip <- guess_ab_col(tbl, "imip", verbose = verbose) }
if (identical(kana, as.name("guess_ab_col"))) { kana <- guess_ab_col(tbl, "kana", verbose = info) } if (identical(kana, as.name("guess_ab_col"))) { kana <- guess_ab_col(tbl, "kana", verbose = verbose) }
if (identical(levo, as.name("guess_ab_col"))) { levo <- guess_ab_col(tbl, "levo", verbose = info) } if (identical(levo, as.name("guess_ab_col"))) { levo <- guess_ab_col(tbl, "levo", verbose = verbose) }
if (identical(linc, as.name("guess_ab_col"))) { linc <- guess_ab_col(tbl, "linc", verbose = info) } if (identical(linc, as.name("guess_ab_col"))) { linc <- guess_ab_col(tbl, "linc", verbose = verbose) }
if (identical(line, as.name("guess_ab_col"))) { line <- guess_ab_col(tbl, "line", verbose = info) } if (identical(line, as.name("guess_ab_col"))) { line <- guess_ab_col(tbl, "line", verbose = verbose) }
if (identical(mero, as.name("guess_ab_col"))) { mero <- guess_ab_col(tbl, "mero", verbose = info) } if (identical(mero, as.name("guess_ab_col"))) { mero <- guess_ab_col(tbl, "mero", verbose = verbose) }
if (identical(metr, as.name("guess_ab_col"))) { metr <- guess_ab_col(tbl, "metr", verbose = info) } if (identical(metr, as.name("guess_ab_col"))) { metr <- guess_ab_col(tbl, "metr", verbose = verbose) }
if (identical(mino, as.name("guess_ab_col"))) { mino <- guess_ab_col(tbl, "mino", verbose = info) } if (identical(mino, as.name("guess_ab_col"))) { mino <- guess_ab_col(tbl, "mino", verbose = verbose) }
if (identical(moxi, as.name("guess_ab_col"))) { moxi <- guess_ab_col(tbl, "moxi", verbose = info) } if (identical(moxi, as.name("guess_ab_col"))) { moxi <- guess_ab_col(tbl, "moxi", verbose = verbose) }
if (identical(nali, as.name("guess_ab_col"))) { nali <- guess_ab_col(tbl, "nali", verbose = info) } if (identical(nali, as.name("guess_ab_col"))) { nali <- guess_ab_col(tbl, "nali", verbose = verbose) }
if (identical(neom, as.name("guess_ab_col"))) { neom <- guess_ab_col(tbl, "neom", verbose = info) } if (identical(neom, as.name("guess_ab_col"))) { neom <- guess_ab_col(tbl, "neom", verbose = verbose) }
if (identical(neti, as.name("guess_ab_col"))) { neti <- guess_ab_col(tbl, "neti", verbose = info) } if (identical(neti, as.name("guess_ab_col"))) { neti <- guess_ab_col(tbl, "neti", verbose = verbose) }
if (identical(nitr, as.name("guess_ab_col"))) { nitr <- guess_ab_col(tbl, "nitr", verbose = info) } if (identical(nitr, as.name("guess_ab_col"))) { nitr <- guess_ab_col(tbl, "nitr", verbose = verbose) }
if (identical(novo, as.name("guess_ab_col"))) { novo <- guess_ab_col(tbl, "novo", verbose = info) } if (identical(novo, as.name("guess_ab_col"))) { novo <- guess_ab_col(tbl, "novo", verbose = verbose) }
if (identical(norf, as.name("guess_ab_col"))) { norf <- guess_ab_col(tbl, "norf", verbose = info) } if (identical(norf, as.name("guess_ab_col"))) { norf <- guess_ab_col(tbl, "norf", verbose = verbose) }
if (identical(oflo, as.name("guess_ab_col"))) { oflo <- guess_ab_col(tbl, "oflo", verbose = info) } if (identical(oflo, as.name("guess_ab_col"))) { oflo <- guess_ab_col(tbl, "oflo", verbose = verbose) }
if (identical(peni, as.name("guess_ab_col"))) { peni <- guess_ab_col(tbl, "peni", verbose = info) } if (identical(peni, as.name("guess_ab_col"))) { peni <- guess_ab_col(tbl, "peni", verbose = verbose) }
if (identical(pipe, as.name("guess_ab_col"))) { pipe <- guess_ab_col(tbl, "pipe", verbose = info) } if (identical(pipe, as.name("guess_ab_col"))) { pipe <- guess_ab_col(tbl, "pipe", verbose = verbose) }
if (identical(pita, as.name("guess_ab_col"))) { pita <- guess_ab_col(tbl, "pita", verbose = info) } if (identical(pita, as.name("guess_ab_col"))) { pita <- guess_ab_col(tbl, "pita", verbose = verbose) }
if (identical(poly, as.name("guess_ab_col"))) { poly <- guess_ab_col(tbl, "poly", verbose = info) } if (identical(poly, as.name("guess_ab_col"))) { poly <- guess_ab_col(tbl, "poly", verbose = verbose) }
if (identical(qida, as.name("guess_ab_col"))) { qida <- guess_ab_col(tbl, "qida", verbose = info) } if (identical(qida, as.name("guess_ab_col"))) { qida <- guess_ab_col(tbl, "qida", verbose = verbose) }
if (identical(rifa, as.name("guess_ab_col"))) { rifa <- guess_ab_col(tbl, "rifa", verbose = info) } if (identical(rifa, as.name("guess_ab_col"))) { rifa <- guess_ab_col(tbl, "rifa", verbose = verbose) }
if (identical(roxi, as.name("guess_ab_col"))) { roxi <- guess_ab_col(tbl, "roxi", verbose = info) } if (identical(roxi, as.name("guess_ab_col"))) { roxi <- guess_ab_col(tbl, "roxi", verbose = verbose) }
if (identical(siso, as.name("guess_ab_col"))) { siso <- guess_ab_col(tbl, "siso", verbose = info) } if (identical(siso, as.name("guess_ab_col"))) { siso <- guess_ab_col(tbl, "siso", verbose = verbose) }
if (identical(teic, as.name("guess_ab_col"))) { teic <- guess_ab_col(tbl, "teic", verbose = info) } if (identical(teic, as.name("guess_ab_col"))) { teic <- guess_ab_col(tbl, "teic", verbose = verbose) }
if (identical(tetr, as.name("guess_ab_col"))) { tetr <- guess_ab_col(tbl, "tetr", verbose = info) } if (identical(tetr, as.name("guess_ab_col"))) { tetr <- guess_ab_col(tbl, "tetr", verbose = verbose) }
if (identical(tica, as.name("guess_ab_col"))) { tica <- guess_ab_col(tbl, "tica", verbose = info) } if (identical(tica, as.name("guess_ab_col"))) { tica <- guess_ab_col(tbl, "tica", verbose = verbose) }
if (identical(tige, as.name("guess_ab_col"))) { tige <- guess_ab_col(tbl, "tige", verbose = info) } if (identical(tige, as.name("guess_ab_col"))) { tige <- guess_ab_col(tbl, "tige", verbose = verbose) }
if (identical(tobr, as.name("guess_ab_col"))) { tobr <- guess_ab_col(tbl, "tobr", verbose = info) } if (identical(tobr, as.name("guess_ab_col"))) { tobr <- guess_ab_col(tbl, "tobr", verbose = verbose) }
if (identical(trim, as.name("guess_ab_col"))) { trim <- guess_ab_col(tbl, "trim", verbose = info) } if (identical(trim, as.name("guess_ab_col"))) { trim <- guess_ab_col(tbl, "trim", verbose = verbose) }
if (identical(trsu, as.name("guess_ab_col"))) { trsu <- guess_ab_col(tbl, "trsu", verbose = info) } if (identical(trsu, as.name("guess_ab_col"))) { trsu <- guess_ab_col(tbl, "trsu", verbose = verbose) }
if (identical(vanc, as.name("guess_ab_col"))) { vanc <- guess_ab_col(tbl, "vanc", verbose = info) } if (identical(vanc, as.name("guess_ab_col"))) { vanc <- guess_ab_col(tbl, "vanc", verbose = verbose) }
col.list <- c(amcl, amik, amox, ampi, azit, aztr, cefa, cfra, cfep, cfot, col.list <- c(amcl, amik, amox, ampi, azit, aztr, cefa, cfra, cfep, cfot,
cfox, cfta, cftr, cfur, chlo, cipr, clar, clin, clox, coli, cfox, cfta, cftr, cfur, chlo, cipr, clar, clin, clox, coli,
czol, dapt, doxy, erta, eryt, fosf, fusi, gent, imip, kana, czol, dapt, doxy, erta, eryt, fosf, fusi, gent, imip, kana,
@ -301,6 +303,10 @@ mdro <- function(tbl,
trsu <- col.list[trsu] trsu <- col.list[trsu]
vanc <- col.list[vanc] vanc <- col.list[vanc]
ab_missing <- function(ab) {
isTRUE(ab %in% c(NULL, NA)) | length(ab) == 0
}
# antibiotic classes # antibiotic classes
aminoglycosides <- c(tobr, gent) # can also be kana but that one is often intrinsic R aminoglycosides <- c(tobr, gent) # can also be kana but that one is often intrinsic R
cephalosporins <- c(cfep, cfot, cfox, cfra, cfta, cftr, cfur, czol) cephalosporins <- c(cfep, cfot, cfox, cfra, cfta, cftr, cfur, czol)
@ -310,7 +316,7 @@ mdro <- function(tbl,
# helper function for editing the table # helper function for editing the table
trans_tbl <- function(to, rows, cols, any_all) { trans_tbl <- function(to, rows, cols, any_all) {
cols <- cols[!is.na(cols)] cols <- cols[!ab_missing(cols)]
if (length(rows) > 0 & length(cols) > 0) { if (length(rows) > 0 & length(cols) > 0) {
if (any_all == "any") { if (any_all == "any") {
col_filter <- which(tbl[, cols] == 'R') col_filter <- which(tbl[, cols] == 'R')
@ -404,9 +410,9 @@ mdro <- function(tbl,
if (guideline$country$code == 'nl') { if (guideline$country$code == 'nl') {
# Netherlands ------------------------------------------------------------- # Netherlands -------------------------------------------------------------
aminoglycosides <- aminoglycosides[!is.na(aminoglycosides)] aminoglycosides <- aminoglycosides[!ab_missing(aminoglycosides)]
fluoroquinolones <- fluoroquinolones[!is.na(fluoroquinolones)] fluoroquinolones <- fluoroquinolones[!ab_missing(fluoroquinolones)]
carbapenems <- carbapenems[!is.na(carbapenems)] carbapenems <- carbapenems[!ab_missing(carbapenems)]
# Table 1 # Table 1
trans_tbl(3, trans_tbl(3,
@ -434,11 +440,11 @@ mdro <- function(tbl,
trsu, trsu,
"all") "all")
if (!is.na(mero) & !is.na(imip) if (!ab_missing(mero) & !ab_missing(imip)
& !is.na(gent) & !is.na(tobr) & !ab_missing(gent) & !ab_missing(tobr)
& !is.na(cipr) & !ab_missing(cipr)
& !is.na(cfta) & !ab_missing(cfta)
& !is.na(pita) ) { & !ab_missing(pita) ) {
tbl <- tbl %>% mutate( tbl <- tbl %>% mutate(
psae = 0, psae = 0,
psae = ifelse(mero == "R" | imip == "R", psae + 1, psae), psae = ifelse(mero == "R" | imip == "R", psae + 1, psae),

9
R/rsi.R
View File

@ -25,7 +25,10 @@
#' @rdname as.rsi #' @rdname as.rsi
#' @param x vector #' @param x vector
#' @param threshold maximum fraction of \code{x} that is allowed to fail transformation, see Examples #' @param threshold maximum fraction of \code{x} that is allowed to fail transformation, see Examples
#' @details The function \code{is.rsi.eligible} returns \code{TRUE} when a columns contains only valid antimicrobial interpretations (S and/or I and/or R), and \code{FALSE} otherwise. #' @details
#' \strong{NOTE:} This function does not translate MIC values to RSI values. If more than 50\% of the input resembles MIC values, it will warn about this.\cr You can use \code{\link{eucast_rules}} to (1) apply inferred susceptibility and resistance based on results of other antibiotics and (2) apply intrinsic resistance based on taxonomic properties of a microorganism.
#'
#' The function \code{is.rsi.eligible} returns \code{TRUE} when a columns contains only valid antimicrobial interpretations (S and/or I and/or R), and \code{FALSE} otherwise.
#' @return Ordered factor with new class \code{rsi} #' @return Ordered factor with new class \code{rsi}
#' @keywords rsi #' @keywords rsi
#' @export #' @export
@ -64,7 +67,7 @@ as.rsi <- function(x) {
} else if (identical(levels(x), c("S", "I", "R"))) { } else if (identical(levels(x), c("S", "I", "R"))) {
structure(x, class = c('rsi', 'ordered', 'factor')) structure(x, class = c('rsi', 'ordered', 'factor'))
} else { } else {
if (mic_like(x) > 0.5) { if (input_resembles_mic(x) > 0.5) {
warning("`as.rsi` is intended to clean antimicrobial interpretations - not to interpret MIC values.", call. = FALSE) warning("`as.rsi` is intended to clean antimicrobial interpretations - not to interpret MIC values.", call. = FALSE)
} }
@ -109,7 +112,7 @@ as.rsi <- function(x) {
} }
} }
mic_like <- function(x) { input_resembles_mic <- function(x) {
mic <- x %>% mic <- x %>%
gsub("[^0-9.,]+", "", .) %>% gsub("[^0-9.,]+", "", .) %>%
unique() unique()

16
codecov.yml Normal file
View File

@ -0,0 +1,16 @@
codecov:
notify:
require_ci_to_pass: no
ci:
- !appveyor # ignore CI builds by AppVeyor
comment: no
coverage:
precision: 5
round: up
range: "0...100"
status:
project: no
patch: no
changes: no

5
docs/reference/as.rsi.html
View File

@ -80,7 +80,7 @@
</button> </button>
<span class="navbar-brand"> <span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a> <a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.6.0</span> <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.6.1.9002</span>
</span> </span>
</div> </div>
@ -266,7 +266,8 @@
<h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2> <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
<p>The function <code>is.rsi.eligible</code> returns <code>TRUE</code> when a columns contains only valid antimicrobial interpretations (S and/or I and/or R), and <code>FALSE</code> otherwise.</p> <p><strong>NOTE:</strong> This function does not translate MIC values to RSI values. If more than 50% of the input resembles MIC values, it will warn about this.<br /> You can use <code><a href='eucast_rules.html'>eucast_rules</a></code> to (1) apply inferred susceptibility and resistance based on results of other antibiotics and (2) apply intrinsic resistance based on taxonomic properties of a microorganism.</p>
<p>The function <code>is.rsi.eligible</code> returns <code>TRUE</code> when a columns contains only valid antimicrobial interpretations (S and/or I and/or R), and <code>FALSE</code> otherwise.</p>
<h2 class="hasAnchor" id="read-more-on-our-website-"><a class="anchor" href="#read-more-on-our-website-"></a>Read more on our website!</h2> <h2 class="hasAnchor" id="read-more-on-our-website-"><a class="anchor" href="#read-more-on-our-website-"></a>Read more on our website!</h2>

3
docs/reference/eucast_rules.html
View File

@ -329,7 +329,8 @@
<h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2> <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
<p>The file used for applying all EUCAST rules can be retrieved with <code>eucast_rules_file()</code>. It returns an easily readable data set containing all rules. The original TSV file (tab separated file) that is being read by this function can be found when running this command: <br /> <p><strong>NOTE:</strong> This function does not translate MIC values to RSI values. It only applies (1) inferred susceptibility and resistance based on results of other antibiotics and (2) intrinsic resistance based on taxonomic properties of a microorganism.</p>
<p>The file used for applying all EUCAST rules can be retrieved with <code>eucast_rules_file()</code>. It returns an easily readable data set containing all rules. The original TSV file (tab separated file) that is being read by this function can be found when running this command: <br />
<code>AMR::EUCAST_RULES_FILE_LOCATION</code> (without brackets).</p> <code>AMR::EUCAST_RULES_FILE_LOCATION</code> (without brackets).</p>
<p>In the source code it is located under <a href='https://gitlab.com/msberends/AMR/blob/master/inst/eucast/eucast_rules.tsv'><code>./inst/eucast/eucast_rules.tsv</code></a>.</p> <p>In the source code it is located under <a href='https://gitlab.com/msberends/AMR/blob/master/inst/eucast/eucast_rules.tsv'><code>./inst/eucast/eucast_rules.tsv</code></a>.</p>
<p><strong>Note:</strong> When ampicillin (J01CA01) is not available but amoxicillin (J01CA04) is, the latter will be used for all rules where there is a dependency on ampicillin. These drugs are interchangeable when it comes to expression of antimicrobial resistance.</p> <p><strong>Note:</strong> When ampicillin (J01CA01) is not available but amoxicillin (J01CA04) is, the latter will be used for all rules where there is a dependency on ampicillin. These drugs are interchangeable when it comes to expression of antimicrobial resistance.</p>

12
docs/reference/first_isolate.html
View File

@ -80,7 +80,7 @@
</button> </button>
<span class="navbar-brand"> <span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a> <a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.6.0</span> <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.6.1.9002</span>
</span> </span>
</div> </div>
@ -340,7 +340,7 @@
<p><strong>WHY THIS IS SO IMPORTANT</strong> <br /> <p><strong>WHY THIS IS SO IMPORTANT</strong> <br />
To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode <a href='https://www.ncbi.nlm.nih.gov/pubmed/17304462'>[1]</a>. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all <em>S. aureus</em> isolates would be overestimated, because you included this MRSA more than once. It would be <a href='https://en.wikipedia.org/wiki/Selection_bias'>selection bias</a>.</p> To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode <a href='https://www.ncbi.nlm.nih.gov/pubmed/17304462'>[1]</a>. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all <em>S. aureus</em> isolates would be overestimated, because you included this MRSA more than once. It would be <a href='https://en.wikipedia.org/wiki/Selection_bias'>selection bias</a>.</p>
<p>The function <code>filter_first_isolate</code> is essentially equal to:</p><pre> <p>The functions <code>filter_first_isolate</code> and <code>filter_first_weighted_isolate</code> are helper functions to quickly filter on first isolates. The function <code>filter_first_isolate</code> is essentially equal to:</p><pre>
tbl %&gt;% tbl %&gt;%
mutate(only_firsts = first_isolate(tbl, ...)) %&gt;% mutate(only_firsts = first_isolate(tbl, ...)) %&gt;%
filter(only_firsts == TRUE) %&gt;% filter(only_firsts == TRUE) %&gt;%
@ -359,9 +359,9 @@ To conduct an analysis of antimicrobial resistance, you should only include the
<p>There are two ways to determine whether isolates can be included as first <em>weighted</em> isolates which will give generally the same results: <br /></p> <p>There are two ways to determine whether isolates can be included as first <em>weighted</em> isolates which will give generally the same results: <br /></p>
<p><strong>1. Using</strong> <code>type = "keyantibiotics"</code> <strong>and parameter</strong> <code>ignore_I</code> <br /> <p><strong>1. Using</strong> <code>type = "keyantibiotics"</code> <strong>and parameter</strong> <code>ignore_I</code> <br />
Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With <code>ignore_I = FALSE</code>, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. <br /></p> Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With <code>ignore_I = FALSE</code>, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. Read more about this in the <code><a href='key_antibiotics.html'>key_antibiotics</a></code> function. <br /></p>
<p><strong>2. Using</strong> <code>type = "points"</code> <strong>and parameter</strong> <code>points_threshold</code> <br /> <p><strong>2. Using</strong> <code>type = "points"</code> <strong>and parameter</strong> <code>points_threshold</code> <br />
A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds <code>points_threshold</code>, an isolate will be (re)selected as a first weighted isolate.</p> A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds <code>points_threshold</code>, which default to <code>2</code>, an isolate will be (re)selected as a first weighted isolate.</p>
<h2 class="hasAnchor" id="read-more-on-our-website-"><a class="anchor" href="#read-more-on-our-website-"></a>Read more on our website!</h2> <h2 class="hasAnchor" id="read-more-on-our-website-"><a class="anchor" href="#read-more-on-our-website-"></a>Read more on our website!</h2>
@ -408,8 +408,8 @@ To conduct an analysis of antimicrobial resistance, you should only include the
<span class='co'># Have a look at A and B.</span> <span class='co'># Have a look at A and B.</span>
<span class='co'># B is more reliable because every isolate is only counted once.</span> <span class='co'># B is more reliable because every isolate is only counted once.</span>
<span class='co'># Gentamicin resitance in hospital D appears to be 5.4% higher than</span> <span class='co'># Gentamicin resitance in hospital D appears to be 3.1% higher than</span>
<span class='co'># when you (erroneously) would have used all isolates!</span> <span class='co'># when you (erroneously) would have used all isolates for analysis.</span>
<span class='co'>## OTHER EXAMPLES:</span> <span class='co'>## OTHER EXAMPLES:</span>

6
docs/reference/key_antibiotics.html
View File

@ -80,7 +80,7 @@
</button> </button>
<span class="navbar-brand"> <span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a> <a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.6.0</span> <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.6.1.9002</span>
</span> </span>
</div> </div>
@ -326,9 +326,9 @@
<p>There are two ways to determine whether isolates can be included as first <em>weighted</em> isolates which will give generally the same results: <br /></p> <p>There are two ways to determine whether isolates can be included as first <em>weighted</em> isolates which will give generally the same results: <br /></p>
<p><strong>1. Using</strong> <code>type = "keyantibiotics"</code> <strong>and parameter</strong> <code>ignore_I</code> <br /> <p><strong>1. Using</strong> <code>type = "keyantibiotics"</code> <strong>and parameter</strong> <code>ignore_I</code> <br />
Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With <code>ignore_I = FALSE</code>, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. <br /></p> Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With <code>ignore_I = FALSE</code>, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. Read more about this in the <code>key_antibiotics</code> function. <br /></p>
<p><strong>2. Using</strong> <code>type = "points"</code> <strong>and parameter</strong> <code>points_threshold</code> <br /> <p><strong>2. Using</strong> <code>type = "points"</code> <strong>and parameter</strong> <code>points_threshold</code> <br />
A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds <code>points_threshold</code>, an isolate will be (re)selected as a first weighted isolate.</p> A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds <code>points_threshold</code>, which default to <code>2</code>, an isolate will be (re)selected as a first weighted isolate.</p>
<h2 class="hasAnchor" id="read-more-on-our-website-"><a class="anchor" href="#read-more-on-our-website-"></a>Read more on our website!</h2> <h2 class="hasAnchor" id="read-more-on-our-website-"><a class="anchor" href="#read-more-on-our-website-"></a>Read more on our website!</h2>

10
docs/reference/mdro.html
View File

@ -80,7 +80,7 @@
</button> </button>
<span class="navbar-brand"> <span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a> <a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.6.0</span> <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.6.1.9002</span>
</span> </span>
</div> </div>
@ -271,7 +271,7 @@
<span class='kw'>teic</span> <span class='kw'>=</span> <span class='fu'><a href='guess_ab_col.html'>guess_ab_col</a></span>(), <span class='kw'>tetr</span> <span class='kw'>=</span> <span class='fu'><a href='guess_ab_col.html'>guess_ab_col</a></span>(), <span class='kw'>teic</span> <span class='kw'>=</span> <span class='fu'><a href='guess_ab_col.html'>guess_ab_col</a></span>(), <span class='kw'>tetr</span> <span class='kw'>=</span> <span class='fu'><a href='guess_ab_col.html'>guess_ab_col</a></span>(),
<span class='kw'>tica</span> <span class='kw'>=</span> <span class='fu'><a href='guess_ab_col.html'>guess_ab_col</a></span>(), <span class='kw'>tige</span> <span class='kw'>=</span> <span class='fu'><a href='guess_ab_col.html'>guess_ab_col</a></span>(), <span class='kw'>tica</span> <span class='kw'>=</span> <span class='fu'><a href='guess_ab_col.html'>guess_ab_col</a></span>(), <span class='kw'>tige</span> <span class='kw'>=</span> <span class='fu'><a href='guess_ab_col.html'>guess_ab_col</a></span>(),
<span class='kw'>tobr</span> <span class='kw'>=</span> <span class='fu'><a href='guess_ab_col.html'>guess_ab_col</a></span>(), <span class='kw'>trim</span> <span class='kw'>=</span> <span class='fu'><a href='guess_ab_col.html'>guess_ab_col</a></span>(), <span class='kw'>tobr</span> <span class='kw'>=</span> <span class='fu'><a href='guess_ab_col.html'>guess_ab_col</a></span>(), <span class='kw'>trim</span> <span class='kw'>=</span> <span class='fu'><a href='guess_ab_col.html'>guess_ab_col</a></span>(),
<span class='kw'>trsu</span> <span class='kw'>=</span> <span class='fu'><a href='guess_ab_col.html'>guess_ab_col</a></span>(), <span class='kw'>vanc</span> <span class='kw'>=</span> <span class='fu'><a href='guess_ab_col.html'>guess_ab_col</a></span>()) <span class='kw'>trsu</span> <span class='kw'>=</span> <span class='fu'><a href='guess_ab_col.html'>guess_ab_col</a></span>(), <span class='kw'>vanc</span> <span class='kw'>=</span> <span class='fu'><a href='guess_ab_col.html'>guess_ab_col</a></span>(), <span class='kw'>verbose</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)
<span class='fu'>brmo</span>(<span class='no'>...</span>, <span class='kw'>country</span> <span class='kw'>=</span> <span class='st'>"nl"</span>) <span class='fu'>brmo</span>(<span class='no'>...</span>, <span class='kw'>country</span> <span class='kw'>=</span> <span class='st'>"nl"</span>)
@ -538,6 +538,10 @@
<th>vanc</th> <th>vanc</th>
<td><p>column name of an antibiotic, see Antibiotics</p></td> <td><p>column name of an antibiotic, see Antibiotics</p></td>
</tr> </tr>
<tr>
<th>verbose</th>
<td><p>print additional info: missing antibiotic columns per parameter</p></td>
</tr>
<tr> <tr>
<th>...</th> <th>...</th>
<td><p>parameters that are passed on to methods</p></td> <td><p>parameters that are passed on to methods</p></td>
@ -550,7 +554,7 @@
<h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2> <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
<p>When <code>country</code> will be left blank, guidelines will be taken from EUCAST Expert Rules Version 3.1 "Intrinsic Resistance and Exceptional Phenotypes Tables" (<a href='http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf'>http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf</a>).</p> <p>When <code>country</code> will be left blank, guidelines will be taken from EUCAST Expert Rules Version 3.1 "Intrinsic Resistance and Exceptional Phenotypes Tables" (<a href='http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf'>link</a>).</p>
<h2 class="hasAnchor" id="antibiotics"><a class="anchor" href="#antibiotics"></a>Antibiotics</h2> <h2 class="hasAnchor" id="antibiotics"><a class="anchor" href="#antibiotics"></a>Antibiotics</h2>

2
man/as.rsi.Rd
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@ -24,6 +24,8 @@ Ordered factor with new class \code{rsi}
This transforms a vector to a new class \code{rsi}, which is an ordered factor with levels \code{S < I < R}. Invalid antimicrobial interpretations will be translated as \code{NA} with a warning. This transforms a vector to a new class \code{rsi}, which is an ordered factor with levels \code{S < I < R}. Invalid antimicrobial interpretations will be translated as \code{NA} with a warning.
} }
\details{ \details{
\strong{NOTE:} This function does not translate MIC values to RSI values. If more than 50\% of the input resembles MIC values, it will warn about this.\cr You can use \code{\link{eucast_rules}} to (1) apply inferred susceptibility and resistance based on results of other antibiotics and (2) apply intrinsic resistance based on taxonomic properties of a microorganism.
The function \code{is.rsi.eligible} returns \code{TRUE} when a columns contains only valid antimicrobial interpretations (S and/or I and/or R), and \code{FALSE} otherwise. The function \code{is.rsi.eligible} returns \code{TRUE} when a columns contains only valid antimicrobial interpretations (S and/or I and/or R), and \code{FALSE} otherwise.
} }
\section{Read more on our website!}{ \section{Read more on our website!}{

2
man/eucast_rules.Rd
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@ -82,6 +82,8 @@ The input of \code{tbl_}, possibly with edited values of antibiotics. Or, if \co
Apply susceptibility rules as defined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST, \url{http://eucast.org}), see \emph{Source}. This includes (1) expert rules, (2) intrinsic resistance and (3) inferred resistance as defined in their breakpoint tables. Apply susceptibility rules as defined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST, \url{http://eucast.org}), see \emph{Source}. This includes (1) expert rules, (2) intrinsic resistance and (3) inferred resistance as defined in their breakpoint tables.
} }
\details{ \details{
\strong{NOTE:} This function does not translate MIC values to RSI values. It only applies (1) inferred susceptibility and resistance based on results of other antibiotics and (2) intrinsic resistance based on taxonomic properties of a microorganism.
The file used for applying all EUCAST rules can be retrieved with \code{\link{eucast_rules_file}()}. It returns an easily readable data set containing all rules. The original TSV file (tab separated file) that is being read by this function can be found when running this command: \cr The file used for applying all EUCAST rules can be retrieved with \code{\link{eucast_rules_file}()}. It returns an easily readable data set containing all rules. The original TSV file (tab separated file) that is being read by this function can be found when running this command: \cr
\code{AMR::EUCAST_RULES_FILE_LOCATION} (without brackets). \code{AMR::EUCAST_RULES_FILE_LOCATION} (without brackets).

10
man/first_isolate.Rd
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@ -68,7 +68,7 @@ Determine first (weighted) isolates of all microorganisms of every patient per e
\strong{WHY THIS IS SO IMPORTANT} \cr \strong{WHY THIS IS SO IMPORTANT} \cr
To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode \href{https://www.ncbi.nlm.nih.gov/pubmed/17304462}{[1]}. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all \emph{S. aureus} isolates would be overestimated, because you included this MRSA more than once. It would be \href{https://en.wikipedia.org/wiki/Selection_bias}{selection bias}. To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode \href{https://www.ncbi.nlm.nih.gov/pubmed/17304462}{[1]}. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all \emph{S. aureus} isolates would be overestimated, because you included this MRSA more than once. It would be \href{https://en.wikipedia.org/wiki/Selection_bias}{selection bias}.
The function \code{filter_first_isolate} is essentially equal to: The functions \code{filter_first_isolate} and \code{filter_first_weighted_isolate} are helper functions to quickly filter on first isolates. The function \code{filter_first_isolate} is essentially equal to:
\preformatted{ \preformatted{
tbl \%>\% tbl \%>\%
mutate(only_firsts = first_isolate(tbl, ...)) \%>\% mutate(only_firsts = first_isolate(tbl, ...)) \%>\%
@ -90,10 +90,10 @@ The function \code{filter_first_weighted_isolate} is essentially equal to:
There are two ways to determine whether isolates can be included as first \emph{weighted} isolates which will give generally the same results: \cr There are two ways to determine whether isolates can be included as first \emph{weighted} isolates which will give generally the same results: \cr
\strong{1. Using} \code{type = "keyantibiotics"} \strong{and parameter} \code{ignore_I} \cr \strong{1. Using} \code{type = "keyantibiotics"} \strong{and parameter} \code{ignore_I} \cr
Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With \code{ignore_I = FALSE}, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. \cr Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With \code{ignore_I = FALSE}, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. Read more about this in the \code{\link{key_antibiotics}} function. \cr
\strong{2. Using} \code{type = "points"} \strong{and parameter} \code{points_threshold} \cr \strong{2. Using} \code{type = "points"} \strong{and parameter} \code{points_threshold} \cr
A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds \code{points_threshold}, an isolate will be (re)selected as a first weighted isolate. A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds \code{points_threshold}, which default to \code{2}, an isolate will be (re)selected as a first weighted isolate.
} }
\section{Read more on our website!}{ \section{Read more on our website!}{
@ -135,8 +135,8 @@ B <- septic_patients \%>\%
# Have a look at A and B. # Have a look at A and B.
# B is more reliable because every isolate is only counted once. # B is more reliable because every isolate is only counted once.
# Gentamicin resitance in hospital D appears to be 5.4\% higher than # Gentamicin resitance in hospital D appears to be 3.1\% higher than
# when you (erroneously) would have used all isolates! # when you (erroneously) would have used all isolates for analysis.
## OTHER EXAMPLES: ## OTHER EXAMPLES:

4
man/key_antibiotics.Rd
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@ -68,10 +68,10 @@ The function \code{key_antibiotics} returns a character vector with 12 antibioti
There are two ways to determine whether isolates can be included as first \emph{weighted} isolates which will give generally the same results: \cr There are two ways to determine whether isolates can be included as first \emph{weighted} isolates which will give generally the same results: \cr
\strong{1. Using} \code{type = "keyantibiotics"} \strong{and parameter} \code{ignore_I} \cr \strong{1. Using} \code{type = "keyantibiotics"} \strong{and parameter} \code{ignore_I} \cr
Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With \code{ignore_I = FALSE}, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. \cr Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With \code{ignore_I = FALSE}, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. Read more about this in the \code{\link{key_antibiotics}} function. \cr
\strong{2. Using} \code{type = "points"} \strong{and parameter} \code{points_threshold} \cr \strong{2. Using} \code{type = "points"} \strong{and parameter} \code{points_threshold} \cr
A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds \code{points_threshold}, an isolate will be (re)selected as a first weighted isolate. A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds \code{points_threshold}, which default to \code{2}, an isolate will be (re)selected as a first weighted isolate.
} }
\section{Read more on our website!}{ \section{Read more on our website!}{

6
man/mdro.Rd
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@ -37,7 +37,7 @@ mdro(tbl, country = NULL, col_mo = NULL, info = TRUE,
teic = guess_ab_col(), tetr = guess_ab_col(), teic = guess_ab_col(), tetr = guess_ab_col(),
tica = guess_ab_col(), tige = guess_ab_col(), tica = guess_ab_col(), tige = guess_ab_col(),
tobr = guess_ab_col(), trim = guess_ab_col(), tobr = guess_ab_col(), trim = guess_ab_col(),
trsu = guess_ab_col(), vanc = guess_ab_col()) trsu = guess_ab_col(), vanc = guess_ab_col(), verbose = FALSE)
brmo(..., country = "nl") brmo(..., country = "nl")
@ -174,6 +174,8 @@ eucast_exceptional_phenotypes(tbl, country = "EUCAST", ...)
\item{vanc}{column name of an antibiotic, see Antibiotics} \item{vanc}{column name of an antibiotic, see Antibiotics}
\item{verbose}{print additional info: missing antibiotic columns per parameter}
\item{...}{parameters that are passed on to methods} \item{...}{parameters that are passed on to methods}
} }
\value{ \value{
@ -183,7 +185,7 @@ Ordered factor with levels \code{Negative < Positive, unconfirmed < Positive}.
Determine which isolates are multidrug-resistant organisms (MDRO) according to country-specific guidelines. Determine which isolates are multidrug-resistant organisms (MDRO) according to country-specific guidelines.
} }
\details{ \details{
When \code{country} will be left blank, guidelines will be taken from EUCAST Expert Rules Version 3.1 "Intrinsic Resistance and Exceptional Phenotypes Tables" (\url{http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf}). When \code{country} will be left blank, guidelines will be taken from EUCAST Expert Rules Version 3.1 "Intrinsic Resistance and Exceptional Phenotypes Tables" (\href{http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf}{link}).
} }
\section{Antibiotics}{ \section{Antibiotics}{

28
tests/testthat/test-mdro.R
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@ -53,4 +53,32 @@ test_that("mdro works", {
# still working on German guidelines # still working on German guidelines
expect_error(suppressWarnings(mrgn(septic_patients, info = TRUE))) expect_error(suppressWarnings(mrgn(septic_patients, info = TRUE)))
# test Dutch P. aeruginosa MDRO
expect_equal(suppressWarnings(
as.character(mdro(data.frame(mo = as.mo("P. aeruginosa"),
cfta = "S",
cipr = "S",
mero = "S",
imip = "S",
gent = "S",
tobr = "S",
pita = "S"),
country = "nl",
col_mo = "mo",
info = FALSE))
), "Negative")
expect_equal(suppressWarnings(
as.character(mdro(data.frame(mo = as.mo("P. aeruginosa"),
cefta = "R",
cipr = "R",
mero = "R",
imip = "R",
gent = "R",
tobr = "R",
pita = "R"),
country = "nl",
col_mo = "mo",
info = FALSE))
), "Positive")
}) })