mirror of https://github.com/msberends/AMR.git
improvement for forecasting resistance
This commit is contained in:
parent
d9e204031d
commit
498e88b5cf
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@ -1,5 +1,5 @@
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Package: AMR
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Version: 0.2.0.9016
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Version: 0.2.0.9017
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Date: 2018-07-25
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Title: Antimicrobial Resistance Analysis
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Authors@R: c(
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@ -43,7 +43,8 @@ Suggests:
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covr (>= 3.0.1),
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rmarkdown,
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rstudioapi,
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tidyr
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tidyr,
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ggplot2
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LinkingTo: Rcpp
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VignetteBuilder: knitr
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URL: https://github.com/msberends/AMR
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@ -139,15 +139,17 @@ importFrom(graphics,text)
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importFrom(knitr,kable)
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importFrom(readr,locale)
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importFrom(readr,parse_guess)
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importFrom(reshape2,dcast)
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importFrom(rvest,html_children)
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importFrom(rvest,html_node)
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importFrom(rvest,html_nodes)
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importFrom(rvest,html_table)
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importFrom(stats,complete.cases)
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importFrom(stats,fivenum)
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importFrom(stats,glm)
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importFrom(stats,lm)
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importFrom(stats,mad)
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importFrom(stats,pchisq)
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importFrom(stats,predict)
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importFrom(stats,sd)
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importFrom(tibble,tibble)
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importFrom(utils,View)
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4
NEWS.md
4
NEWS.md
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@ -24,7 +24,9 @@
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* Possibility to globally set the default for the amount of items to print, with `options(max.print.freq = n)` where *n* is your preset value
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#### Changed
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* Updates version of the `setic_patients` dataset to better reflect the reality
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* Improvements for forcasting with `resistance_predict` and added more examples
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* More antibiotics for EUCAST rules
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* Updated version of the `setic_patients` data set to better reflect the reality
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* Pretty printing for tibbles removed as it is not really the scope of this package
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* Improved speed of key antibiotics comparison for determining first isolates
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* Column names for the `key_antibiotics` function are now generic: 6 for broadspectrum ABs, 6 for Gram-positive specific and 6 for Gram-negative specific ABs
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43
R/atc.R
43
R/atc.R
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@ -267,33 +267,33 @@ abname <- function(abcode, from = c("guess", "atc", "molis", "umcg"), to = 'offi
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}
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abcode <- as.character(abcode)
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abcode.bak <- abcode
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for (i in 1:length(abcode)) {
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drug <- abcode[i]
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if (!grepl('+', drug, fixed = TRUE) & !grepl(' en ', drug, fixed = TRUE)) {
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abcode[i] <- abcode[i]
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if (!grepl('+', abcode[i], fixed = TRUE) & !grepl(' en ', abcode[i], fixed = TRUE)) {
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# only 1 drug
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if (drug %in% (antibiotics %>% pull(from))) {
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if (abcode[i] %in% (antibiotics %>% pull(from))) {
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abcode[i] <-
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antibiotics %>%
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filter(.[, from] == drug) %>%
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filter(.[, from] == abcode[i]) %>%
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select(to) %>%
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slice(1) %>%
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as.character()
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} else {
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# not found
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warning('Code "', drug, '" not found in antibiotics list.', call. = FALSE)
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abcode[i] <- NA
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}
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} else {
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# more than 1 drug
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if (grepl('+', drug, fixed = TRUE)) {
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drug.group <-
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strsplit(drug, '+', fixed = TRUE) %>%
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if (grepl('+', abcode[i], fixed = TRUE)) {
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abcode.group <-
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strsplit(abcode[i], '+', fixed = TRUE) %>%
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unlist() %>%
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trimws('both')
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} else if (grepl(' en ', drug, fixed = TRUE)) {
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drug.group <-
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strsplit(drug, ' en ', fixed = TRUE) %>%
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} else if (grepl(' en ', abcode[i], fixed = TRUE)) {
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abcode.group <-
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strsplit(abcode[i], ' en ', fixed = TRUE) %>%
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unlist() %>%
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trimws('both')
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} else {
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@ -302,18 +302,29 @@ abname <- function(abcode, from = c("guess", "atc", "molis", "umcg"), to = 'offi
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next
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}
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for (j in 1:length(drug.group)) {
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drug.group[j] <-
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for (j in 1:length(abcode.group)) {
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abcode.group[j] <-
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antibiotics %>%
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filter(.[, from] == drug.group[j]) %>%
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filter(.[, from] == abcode.group[j]) %>%
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select(to) %>%
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slice(1) %>%
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as.character()
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if (j > 1 & to %in% c('official', 'trivial_nl')) {
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drug.group[j] <- drug.group[j] %>% tolower()
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abcode.group[j] <- abcode.group[j] %>% tolower()
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}
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}
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abcode[i] <- paste(drug.group, collapse = textbetween)
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abcode[i] <- paste(abcode.group, collapse = textbetween)
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}
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# when nothing found, try first chars of official name
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if (is.na(abcode[i])) {
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abcode[i] <- antibiotics %>%
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filter(official %like% paste0('^', abcode.bak[i])) %>%
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pull(to) %>%
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.[1]
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}
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if (is.na(abcode[i])) {
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warning('Code "', abcode.bak[i], '" not found in antibiotics list.', call. = FALSE)
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}
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}
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137
R/eucast.R
137
R/eucast.R
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@ -22,8 +22,73 @@
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#' @param tbl table with antibiotic columns, like e.g. \code{amox} and \code{amcl}
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#' @param col_bactid column name of the bacteria ID in \code{tbl} - values of this column should be present in \code{microorganisms$bactid}, see \code{\link{microorganisms}}
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#' @param info print progress
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#' @param amcl,amik,amox,ampi,azit,aztr,cefa,cfra,cfep,cfot,cfox,cfta,cftr,cfur,chlo,cipr,clar,clin,clox,coli,czol,dapt,doxy,erta,eryt,fosf,fusi,gent,imip,kana,levo,linc,line,mero,mino,moxi,nali,neom,neti,nitr,novo,norf,oflo,peni,pita,poly,qida,rifa,roxi,siso,teic,tetr,tica,tige,tobr,trim,trsu,vanc column names of antibiotics. Use \code{NA} to skip a column, like \code{tica = NA}. Non-existing column will be skipped.
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#' @param amcl,amik,amox,ampi,azit,azlo,aztr,cefa,cfep,cfot,cfox,cfra,cfta,cftr,cfur,chlo,cipr,clar,clin,clox,coli,czol,dapt,doxy,erta,eryt,fosf,fusi,gent,imip,kana,levo,linc,line,mero,mezl,mino,moxi,nali,neom,neti,nitr,norf,novo,oflo,peni,pita,poly,pris,qida,rifa,roxi,siso,teic,tetr,tica,tige,tobr,trim,trsu,vanc column names of antibiotics. Use \code{NA} to skip a column, like \code{tica = NA}. Non-existing columns will anyway be skipped. See the Antibiotics section for an explanation of the abbreviations.
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#' @param ... parameters that are passed on to \code{EUCAST_rules}
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#' @section Abbrevations of antibiotics:
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#' Abbrevations of the column containing antibiotics:
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#'
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#' \strong{amcl}: amoxicillin and beta-lactamase inhibitor (\emph{J01CR02}),
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#' \strong{amik}: amikacin (\emph{J01GB06}),
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#' \strong{amox}: amoxicillin (\emph{J01CA04}),
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#' \strong{ampi}: ampicillin (\emph{J01CA01}),
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#' \strong{azit}: azithromycin (\emph{J01FA10}),
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#' \strong{azlo}: azlocillin (\emph{J01CA09}),
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#' \strong{aztr}: aztreonam (\emph{J01DF01}),
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#' \strong{cefa}: cefaloridine (\emph{J01DB02}),
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#' \strong{cfep}: cefepime (\emph{J01DE01}),
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#' \strong{cfot}: cefotaxime (\emph{J01DD01}),
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#' \strong{cfox}: cefoxitin (\emph{J01DC01}),
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#' \strong{cfra}: cefradine (\emph{J01DB09}),
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#' \strong{cfta}: ceftazidime (\emph{J01DD02}),
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#' \strong{cftr}: ceftriaxone (\emph{J01DD04}),
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#' \strong{cfur}: cefuroxime (\emph{J01DC02}),
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#' \strong{chlo}: chloramphenicol (\emph{J01BA01}),
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#' \strong{cipr}: ciprofloxacin (\emph{J01MA02}),
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#' \strong{clar}: clarithromycin (\emph{J01FA09}),
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#' \strong{clin}: clindamycin (\emph{J01FF01}),
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#' \strong{clox}: flucloxacillin (\emph{J01CF05}),
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#' \strong{coli}: colistin (\emph{J01XB01}),
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#' \strong{czol}: cefazolin (\emph{J01DB04}),
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#' \strong{dapt}: daptomycin (\emph{J01XX09}),
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#' \strong{doxy}: doxycycline (\emph{J01AA02}),
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#' \strong{erta}: ertapenem (\emph{J01DH03}),
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#' \strong{eryt}: erythromycin (\emph{J01FA01}),
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#' \strong{fosf}: fosfomycin (\emph{J01XX01}),
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#' \strong{fusi}: fusidic acid (\emph{J01XC01}),
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#' \strong{gent}: gentamicin (\emph{J01GB03}),
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#' \strong{imip}: imipenem and cilastatin (\emph{J01DH51}),
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#' \strong{kana}: kanamycin (\emph{J01GB04}),
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#' \strong{levo}: levofloxacin (\emph{J01MA12}),
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#' \strong{linc}: lincomycin (\emph{J01FF02}),
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#' \strong{line}: linezolid (\emph{J01XX08}),
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#' \strong{mero}: meropenem (\emph{J01DH02}),
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#' \strong{mezl}: mezlocillin (\emph{J01CA10}),
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#' \strong{mino}: minocycline (\emph{J01AA08}),
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#' \strong{moxi}: moxifloxacin (\emph{J01MA14}),
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#' \strong{nali}: nalidixic acid (\emph{J01MB02}),
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#' \strong{neom}: neomycin (\emph{J01GB05}),
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#' \strong{neti}: netilmicin (\emph{J01GB07}),
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#' \strong{nitr}: nitrofurantoin (\emph{J01XE01}),
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#' \strong{norf}: norfloxacin (\emph{J01MA06}),
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#' \strong{novo}: novobiocin (an ATCvet code: \emph{QJ01XX95}),
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#' \strong{oflo}: ofloxacin (\emph{J01MA01}),
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#' \strong{peni}: penicillins, combinations with other antibacterials (\emph{J01RA01}),
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#' \strong{pita}: piperacillin and beta-lactamase inhibitor (\emph{J01CR05}),
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#' \strong{poly}: polymyxin B (\emph{J01XB02}),
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#' \strong{pris}: pristinamycin (\emph{J01FG01}),
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#' \strong{qida}: quinupristin/dalfopristin (\emph{J01FG02}),
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#' \strong{rifa}: rifampicin (\emph{J04AB02}),
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#' \strong{roxi}: roxithromycin (\emph{J01FA06}),
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#' \strong{siso}: sisomicin (\emph{J01GB08}),
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#' \strong{teic}: teicoplanin (\emph{J01XA02}),
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#' \strong{tetr}: tetracycline (\emph{J01AA07}),
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#' \strong{tica}: ticarcillin (\emph{J01CA13}),
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#' \strong{tige}: tigecycline (\emph{J01AA12}),
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#' \strong{tobr}: tobramycin (\emph{J01GB01}),
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#' \strong{trim}: trimethoprim (\emph{J01EA01}),
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#' \strong{trsu}: sulfamethoxazole and trimethoprim (\emph{J01EE01}),
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#' \strong{vanc}: vancomycin (\emph{J01XA01}).
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#' @keywords interpretive eucast reading resistance
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#' @rdname EUCAST
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#' @export
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#' @importFrom dplyr %>% left_join select
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@ -60,12 +125,13 @@ EUCAST_rules <- function(tbl,
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amox = 'amox',
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ampi = 'ampi',
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azit = 'azit',
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azlo = 'azlo',
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aztr = 'aztr',
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cefa = 'cefa',
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cfra = 'cfra',
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cfep = 'cfep',
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cfot = 'cfot',
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cfox = 'cfox',
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cfra = 'cfra',
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cfta = 'cfta',
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cftr = 'cftr',
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cfur = 'cfur',
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@ -89,18 +155,20 @@ EUCAST_rules <- function(tbl,
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linc = 'linc',
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line = 'line',
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mero = 'mero',
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mezl = 'mezl',
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mino = 'mino',
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moxi = 'moxi',
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nali = 'nali',
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neom = 'neom',
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neti = 'neti',
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nitr = 'nitr',
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novo = 'novo',
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norf = 'norf',
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novo = 'novo',
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oflo = 'oflo',
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peni = 'peni',
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pita = 'pita',
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poly = 'poly',
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pris = 'pris',
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qida = 'qida',
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rifa = 'rifa',
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roxi = 'roxi',
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@ -121,11 +189,11 @@ EUCAST_rules <- function(tbl,
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}
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# check columns
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col.list <- c(amcl, amik, amox, ampi, azit, aztr, cefa, cfra, cfep, cfot,
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col.list <- c(amcl, amik, amox, ampi, azit, azlo, aztr, cefa, cfra, cfep, cfot,
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cfox, cfta, cftr, cfur, chlo, cipr, clar, clin, clox, coli,
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czol, dapt, doxy, erta, eryt, fosf, fusi, gent, imip, kana,
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levo, linc, line, mero, mino, moxi, nali, neom, neti, nitr,
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novo, norf, oflo, peni, pita, poly, qida, rifa, roxi, siso,
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levo, linc, line, mero, mezl, mino, moxi, nali, neom, neti, nitr,
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novo, norf, oflo, peni, pita, poly, pris, qida, rifa, roxi, siso,
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teic, tetr, tica, tige, tobr, trim, trsu, vanc)
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col.list <- check_available_columns(tbl = tbl, col.list = col.list, info = info)
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amcl <- col.list[amcl]
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@ -133,12 +201,13 @@ EUCAST_rules <- function(tbl,
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amox <- col.list[amox]
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ampi <- col.list[ampi]
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azit <- col.list[azit]
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azlo <- col.list[azlo]
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aztr <- col.list[aztr]
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cefa <- col.list[cefa]
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cfra <- col.list[cfra]
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cfep <- col.list[cfep]
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cfot <- col.list[cfot]
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cfox <- col.list[cfox]
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cfra <- col.list[cfra]
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cfta <- col.list[cfta]
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cftr <- col.list[cftr]
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cfur <- col.list[cfur]
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@ -162,18 +231,20 @@ EUCAST_rules <- function(tbl,
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linc <- col.list[linc]
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line <- col.list[line]
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mero <- col.list[mero]
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mezl <- col.list[mezl]
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mino <- col.list[mino]
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moxi <- col.list[moxi]
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nali <- col.list[nali]
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neom <- col.list[neom]
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neti <- col.list[neti]
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nitr <- col.list[nitr]
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novo <- col.list[novo]
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norf <- col.list[norf]
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novo <- col.list[novo]
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oflo <- col.list[oflo]
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peni <- col.list[peni]
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pita <- col.list[pita]
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poly <- col.list[poly]
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pris <- col.list[pris]
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qida <- col.list[qida]
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rifa <- col.list[rifa]
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roxi <- col.list[roxi]
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@ -191,7 +262,9 @@ EUCAST_rules <- function(tbl,
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total_rows <- integer(0)
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# helper function for editing the table
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edit_rsi <- function(to, rows, cols) {
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edit_rsi <- function(to, rows, cols, EUCAST_rule = "") {
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# later: use this as attribute for the edited observations
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EUCAST_rule <- trimws(paste("EUCAST rule", EUCAST_rule))
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cols <- cols[!is.na(cols)]
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if (length(rows) > 0 & length(cols) > 0) {
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tbl[rows, cols] <<- to
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@ -200,9 +273,9 @@ EUCAST_rules <- function(tbl,
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}
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}
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# join to microorganisms table
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# join to microorganisms data set
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if (!tbl %>% pull(col_bactid) %>% is.bactid()) {
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tbl[, col_bactid] <- tbl %>% pull(col_bactid) %>% as.bactid()
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# warning("Improve integrity of the `", col_bactid, "` column by transforming it with 'as.bactid'.")
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}
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tbl <- tbl %>% left_join_microorganisms(by = col_bactid, suffix = c("_tempmicroorganisms", ""))
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@ -212,11 +285,11 @@ EUCAST_rules <- function(tbl,
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polymyxins <- c(poly, coli)
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macrolides <- c(eryt, azit, roxi, clar) # since EUCAST v3.1 clinda is set apart
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glycopeptides <- c(vanc, teic)
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streptogramins <- qida # should officially also be pristinamycin and quinupristin/dalfopristin
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streptogramins <- c(qida, pris) # should officially also be quinupristin/dalfopristin
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cephalosporins <- c(cfep, cfot, cfox, cfra, cfta, cftr, cfur, czol)
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carbapenems <- c(erta, imip, mero)
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aminopenicillins <- c(ampi, amox)
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ureidopenicillins <- pita # should officially also be azlo and mezlo
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ureidopenicillins <- c(pita, azlo, mezl)
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fluoroquinolones <- c(oflo, cipr, norf, levo, moxi)
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if (info == TRUE) {
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@ -230,7 +303,7 @@ EUCAST_rules <- function(tbl,
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# Table 1: Intrinsic resistance in Enterobacteriaceae ----
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if (info == TRUE) {
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cat('...Table 1: Intrinsic resistance in Enterobacteriaceae\n')
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cat('- Table 1: Intrinsic resistance in Enterobacteriaceae\n')
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}
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# Intrisiek R for this group
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edit_rsi(to = 'R',
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@ -301,7 +374,7 @@ EUCAST_rules <- function(tbl,
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# Table 2: Intrinsic resistance in non-fermentative Gram-negative bacteria ----
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if (info == TRUE) {
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cat('...Table 2: Intrinsic resistance in non-fermentative Gram-negative bacteria\n')
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cat('- Table 2: Intrinsic resistance in non-fermentative Gram-negative bacteria\n')
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}
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# Intrisiek R for this group
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edit_rsi(to = 'R',
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@ -349,7 +422,7 @@ EUCAST_rules <- function(tbl,
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|
||||
# Table 3: Intrinsic resistance in other Gram-negative bacteria ----
|
||||
if (info == TRUE) {
|
||||
cat('...Table 3: Intrinsic resistance in other Gram-negative bacteria\n')
|
||||
cat('- Table 3: Intrinsic resistance in other Gram-negative bacteria\n')
|
||||
}
|
||||
# Intrisiek R for this group
|
||||
edit_rsi(to = 'R',
|
||||
|
|
@ -381,7 +454,7 @@ EUCAST_rules <- function(tbl,
|
|||
|
||||
# Table 4: Intrinsic resistance in Gram-positive bacteria ----
|
||||
if (info == TRUE) {
|
||||
cat('...Table 4: Intrinsic resistance in Gram-positive bacteria\n')
|
||||
cat('- Table 4: Intrinsic resistance in Gram-positive bacteria\n')
|
||||
}
|
||||
# Intrisiek R for this group
|
||||
edit_rsi(to = 'R',
|
||||
|
|
@ -435,7 +508,7 @@ EUCAST_rules <- function(tbl,
|
|||
|
||||
# Table 8: Interpretive rules for B-lactam agents and Gram-positive cocci ----
|
||||
if (info == TRUE) {
|
||||
cat('...Table 8: Interpretive rules for B-lactam agents and Gram-positive cocci\n')
|
||||
cat('- Table 8: Interpretive rules for B-lactam agents and Gram-positive cocci\n')
|
||||
}
|
||||
# rule 8.3
|
||||
if (!is.na(peni)) {
|
||||
|
|
@ -460,7 +533,7 @@ EUCAST_rules <- function(tbl,
|
|||
|
||||
# Table 9: Interpretive rules for B-lactam agents and Gram-negative rods ----
|
||||
if (info == TRUE) {
|
||||
cat('...Table 9: Interpretive rules for B-lactam agents and Gram-negative rods\n')
|
||||
cat('- Table 9: Interpretive rules for B-lactam agents and Gram-negative rods\n')
|
||||
}
|
||||
# rule 9.3
|
||||
if (!is.na(tica) & !is.na(pita)) {
|
||||
|
|
@ -473,7 +546,7 @@ EUCAST_rules <- function(tbl,
|
|||
|
||||
# Table 10: Interpretive rules for B-lactam agents and other Gram-negative bacteria ----
|
||||
if (info == TRUE) {
|
||||
cat('...Table 10: Interpretive rules for B-lactam agents and other Gram-negative bacteria\n')
|
||||
cat('- Table 10: Interpretive rules for B-lactam agents and other Gram-negative bacteria\n')
|
||||
}
|
||||
# rule 10.2
|
||||
if (!is.na(ampi)) {
|
||||
|
|
@ -486,7 +559,7 @@ EUCAST_rules <- function(tbl,
|
|||
|
||||
# Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins ----
|
||||
if (info == TRUE) {
|
||||
cat('...Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins\n')
|
||||
cat('- Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins\n')
|
||||
}
|
||||
# rule 11.1
|
||||
if (!is.na(eryt)) {
|
||||
|
|
@ -500,7 +573,7 @@ EUCAST_rules <- function(tbl,
|
|||
|
||||
# Table 12: Interpretive rules for aminoglycosides ----
|
||||
if (info == TRUE) {
|
||||
cat('...Table 12: Interpretive rules for aminoglycosides\n')
|
||||
cat('- Table 12: Interpretive rules for aminoglycosides\n')
|
||||
}
|
||||
# rule 12.2
|
||||
if (!is.na(tobr)) {
|
||||
|
|
@ -536,7 +609,7 @@ EUCAST_rules <- function(tbl,
|
|||
|
||||
# Table 13: Interpretive rules for quinolones ----
|
||||
if (info == TRUE) {
|
||||
cat('...Table 13: Interpretive rules for quinolones\n')
|
||||
cat('- Table 13: Interpretive rules for quinolones\n')
|
||||
}
|
||||
# rule 13.2
|
||||
if (!is.na(moxi)) {
|
||||
|
|
@ -570,7 +643,7 @@ EUCAST_rules <- function(tbl,
|
|||
|
||||
# Other ----
|
||||
if (info == TRUE) {
|
||||
cat('...Non-EUCAST: trim = R where trsu = R and ampi = R where amcl = R\n')
|
||||
cat('- Non-EUCAST: trim = R where trsu = R and ampi = R where amcl = R\n')
|
||||
}
|
||||
if (!is.na(amcl)) {
|
||||
edit_rsi(to = 'R',
|
||||
|
|
@ -582,6 +655,20 @@ EUCAST_rules <- function(tbl,
|
|||
rows = which(tbl[, trsu] == 'R'),
|
||||
cols = trim)
|
||||
}
|
||||
if (info == TRUE) {
|
||||
cat('- Non-EUCAST: trsu = S where trim = S and amcl = S where ampi = S\n')
|
||||
}
|
||||
if (!is.na(amcl)) {
|
||||
edit_rsi(to = 'S',
|
||||
rows = which(tbl[, ampi] == 'S'),
|
||||
cols = amcl)
|
||||
}
|
||||
if (!is.na(trsu)) {
|
||||
edit_rsi(to = 'S',
|
||||
rows = which(tbl[, trim] == 'S'),
|
||||
cols = trsu)
|
||||
}
|
||||
# amox = ampi
|
||||
if (!is.na(ampi) & !is.na(amox)) {
|
||||
tbl[, amox] <- tbl %>% pull(ampi)
|
||||
}
|
||||
|
|
@ -596,7 +683,7 @@ EUCAST_rules <- function(tbl,
|
|||
if (info == TRUE) {
|
||||
cat('Done.\n\nEUCAST Expert rules applied to',
|
||||
total_rows %>% unique() %>% length() %>% format(big.mark = ","),
|
||||
'different rows (isolates); edited a total of',
|
||||
'different rows; overwritten a total of',
|
||||
total %>% format(big.mark = ","), 'test results.\n\n')
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -72,13 +72,13 @@
|
|||
#' resistance = resistance(gent))
|
||||
#'
|
||||
#' B <- my_patients %>%
|
||||
#' filter(first_isolate == TRUE) %>%
|
||||
#' filter(first_isolate == TRUE) %>% # the 1st isolate filter
|
||||
#' group_by(hospital_id) %>%
|
||||
#' summarise(count = n_rsi(gent), # gentamicin
|
||||
#' summarise(count = n_rsi(gent),
|
||||
#' resistance = resistance(gent))
|
||||
#'
|
||||
#' # Have a look at A and B. B is more reliable because every isolate is
|
||||
#' # counted once. Gentamicin resitance in hospital D seems to be 5%
|
||||
#' # counted once. Gentamicin resitance in hospital D appears to be 5%
|
||||
#' # higher than originally thought.
|
||||
#'
|
||||
#' ## OTHER EXAMPLES:
|
||||
|
|
@ -171,8 +171,9 @@ first_isolate <- function(tbl,
|
|||
|
||||
if (!is.na(col_bactid)) {
|
||||
if (!tbl %>% pull(col_bactid) %>% is.bactid()) {
|
||||
warning("Improve integrity of the `", col_bactid, "` column by transforming it with 'as.bactid'.")
|
||||
# warning("Improve integrity of the `", col_bactid, "` column by transforming it with 'as.bactid'.")
|
||||
}
|
||||
# join to microorganisms data set
|
||||
tbl <- tbl %>% left_join_microorganisms(by = col_bactid)
|
||||
col_genus <- "genus"
|
||||
col_species <- "species"
|
||||
|
|
|
|||
|
|
@ -17,6 +17,7 @@
|
|||
# ==================================================================== #
|
||||
|
||||
globalVariables(c('abname',
|
||||
'antibiotic',
|
||||
'antibiotics',
|
||||
'atc',
|
||||
'bactid',
|
||||
|
|
@ -47,11 +48,15 @@ globalVariables(c('abname',
|
|||
'molis',
|
||||
'n',
|
||||
'na.omit',
|
||||
'observations',
|
||||
'official',
|
||||
'other_pat_or_mo',
|
||||
'Pasted',
|
||||
'patient_id',
|
||||
'quantile',
|
||||
'R',
|
||||
'real_first_isolate',
|
||||
'S',
|
||||
'septic_patients',
|
||||
'species',
|
||||
'umcg',
|
||||
|
|
|
|||
174
R/resistance.R
174
R/resistance.R
|
|
@ -415,33 +415,45 @@ rsi_df <- function(tbl,
|
|||
#' Predict antimicrobial resistance
|
||||
#'
|
||||
#' Create a prediction model to predict antimicrobial resistance for the next years on statistical solid ground. Standard errors (SE) will be returned as columns \code{se_min} and \code{se_max}. See Examples for a real live example.
|
||||
#' @param tbl table that contains columns \code{col_ab} and \code{col_date}
|
||||
#' @param col_ab column name of \code{tbl} with antimicrobial interpretations (\code{R}, \code{I} and \code{S}), supports tidyverse-like quotation
|
||||
#' @param col_date column name of the date, will be used to calculate years if this column doesn't consist of years already, supports tidyverse-like quotation
|
||||
#' @param year_max highest year to use in the prediction model, deafults to 15 years after today
|
||||
#' @inheritParams first_isolate
|
||||
#' @param col_ab column name of \code{tbl} with antimicrobial interpretations (\code{R}, \code{I} and \code{S})
|
||||
#' @param col_date column name of the date, will be used to calculate years if this column doesn't consist of years already
|
||||
#' @param year_min lowest year to use in the prediction model, dafaults the lowest year in \code{col_date}
|
||||
#' @param year_max highest year to use in the prediction model, defaults to 15 years after today
|
||||
#' @param year_every unit of sequence between lowest year found in the data and \code{year_max}
|
||||
#' @param minimum minimal amount of available isolates per year to include. Years containing less observations will be estimated by the model.
|
||||
#' @param model the statistical model of choice. Valid values are \code{"binomial"} (or \code{"binom"} or \code{"logit"}) or \code{"loglin"} or \code{"linear"} (or \code{"lin"}).
|
||||
#' @param I_as_R treat \code{I} as \code{R}
|
||||
#' @param preserve_measurements overwrite predictions of years that are actually available in the data, with the original data. The standard errors of those years will be \code{NA}.
|
||||
#' @param preserve_measurements logical to indicate whether predictions of years that are actually available in the data should be overwritten with the original data. The standard errors of those years will be \code{NA}.
|
||||
#' @param info print textual analysis with the name and \code{\link{summary}} of the model.
|
||||
#' @return \code{data.frame} with columns \code{year}, \code{probR}, \code{se_min} and \code{se_max}.
|
||||
#' @return \code{data.frame} with columns:
|
||||
#' \itemize{
|
||||
#' \item{\code{year}}
|
||||
#' \item{\code{resistance}, the same as \code{estimated} when \code{preserve_measurements = FALSE}, and a combination of \code{observed} and \code{estimated} otherwise}
|
||||
#' \item{\code{se_min}, the lower bound of the standard error with a minimum of \code{0}}
|
||||
#' \item{\code{se_max} the upper bound of the standard error with a maximum of \code{1}}
|
||||
#' \item{\code{observations}, the total number of observations, i.e. S + I + R}
|
||||
#' \item{\code{observed}, the original observed values}
|
||||
#' \item{\code{estimated}, the estimated values, calculated by the model}
|
||||
#' }
|
||||
#' @seealso \code{\link{resistance}} \cr \code{\link{lm}} \code{\link{glm}}
|
||||
#' @rdname resistance_predict
|
||||
#' @export
|
||||
#' @importFrom dplyr %>% pull mutate group_by_at summarise filter
|
||||
#' @importFrom reshape2 dcast
|
||||
#' @importFrom stats predict glm lm
|
||||
#' @importFrom dplyr %>% pull mutate group_by_at summarise filter n_distinct arrange
|
||||
# @importFrom tidyr spread
|
||||
#' @examples
|
||||
#' \dontrun{
|
||||
#' # use it directly:
|
||||
#' rsi_predict(tbl = tbl[which(first_isolate == TRUE & genus == "Haemophilus"),],
|
||||
#' col_ab = "amcl", col_date = "date")
|
||||
#' # use it with base R:
|
||||
#' resistance_predict(tbl = tbl[which(first_isolate == TRUE & genus == "Haemophilus"),],
|
||||
#' col_ab = "amcl", col_date = "date")
|
||||
#'
|
||||
#' # or with dplyr so you can actually read it:
|
||||
#' # or use dplyr so you can actually read it:
|
||||
#' library(dplyr)
|
||||
#' tbl %>%
|
||||
#' filter(first_isolate == TRUE,
|
||||
#' genus == "Haemophilus") %>%
|
||||
#' rsi_predict(amcl, date)
|
||||
#' resistance_predict(amcl, date)
|
||||
#' }
|
||||
#'
|
||||
#'
|
||||
|
|
@ -463,20 +475,49 @@ rsi_df <- function(tbl,
|
|||
#' species == "coli",
|
||||
#' first_isolate == TRUE) %>%
|
||||
#' # predict resistance of cefotaxime for next years
|
||||
#' rsi_predict(col_ab = "cfot",
|
||||
#' col_date = "date",
|
||||
#' year_max = 2025,
|
||||
#' preserve_measurements = FALSE)
|
||||
#' resistance_predict(col_ab = "cfot",
|
||||
#' col_date = "date",
|
||||
#' year_max = 2025,
|
||||
#' preserve_measurements = TRUE,
|
||||
#' minimum = 0)
|
||||
#'
|
||||
#' # create nice plots with ggplot
|
||||
#' if (!require(ggplot2)) {
|
||||
#'
|
||||
#' data <- septic_patients %>%
|
||||
#' filter(bactid == "ESCCOL") %>%
|
||||
#' resistance_predict(col_ab = "amox",
|
||||
#' col_date = "date",
|
||||
#' info = FALSE,
|
||||
#' minimum = 15)
|
||||
#'
|
||||
#' ggplot(data,
|
||||
#' aes(x = year)) +
|
||||
#' geom_col(aes(y = resistance),
|
||||
#' fill = "grey75") +
|
||||
#' geom_errorbar(aes(ymin = se_min,
|
||||
#' ymax = se_max),
|
||||
#' colour = "grey50") +
|
||||
#' scale_y_continuous(limits = c(0, 1),
|
||||
#' breaks = seq(0, 1, 0.1),
|
||||
#' labels = paste0(seq(0, 100, 10), "%")) +
|
||||
#' labs(title = expression(paste("Forecast of amoxicillin resistance in ",
|
||||
#' italic("E. coli"))),
|
||||
#' y = "%IR",
|
||||
#' x = "Year") +
|
||||
#' theme_minimal(base_size = 13)
|
||||
#' }
|
||||
resistance_predict <- function(tbl,
|
||||
col_ab,
|
||||
col_date,
|
||||
year_max = as.integer(format(as.Date(Sys.Date()), '%Y')) + 15,
|
||||
year_every = 1,
|
||||
model = 'binomial',
|
||||
I_as_R = TRUE,
|
||||
preserve_measurements = TRUE,
|
||||
info = TRUE) {
|
||||
col_ab,
|
||||
col_date,
|
||||
year_min = NULL,
|
||||
year_max = NULL,
|
||||
year_every = 1,
|
||||
minimum = 30,
|
||||
model = 'binomial',
|
||||
I_as_R = TRUE,
|
||||
preserve_measurements = TRUE,
|
||||
info = TRUE) {
|
||||
|
||||
if (nrow(tbl) == 0) {
|
||||
stop('This table does not contain any observations.')
|
||||
|
|
@ -498,8 +539,8 @@ resistance_predict <- function(tbl,
|
|||
tbl[, col_ab] <- gsub('I', 'R', tbl %>% pull(col_ab))
|
||||
}
|
||||
|
||||
if (!all(tbl %>% pull(col_ab) %>% as.rsi() %in% c(NA, 'S', 'I', 'R'))) {
|
||||
stop('Column ', col_ab, ' must contain antimicrobial interpretations (S, I, R).')
|
||||
if (!tbl %>% pull(col_ab) %>% is.rsi()) {
|
||||
tbl[, col_ab] <- tbl %>% pull(col_ab) %>% as.rsi()
|
||||
}
|
||||
|
||||
year <- function(x) {
|
||||
|
|
@ -510,15 +551,39 @@ resistance_predict <- function(tbl,
|
|||
}
|
||||
}
|
||||
|
||||
years_predict <- seq(from = min(year(tbl %>% pull(col_date))), to = year_max, by = year_every)
|
||||
|
||||
df <- tbl %>%
|
||||
mutate(year = year(tbl %>% pull(col_date))) %>%
|
||||
mutate(year = tbl %>% pull(col_date) %>% year()) %>%
|
||||
group_by_at(c('year', col_ab)) %>%
|
||||
summarise(n())
|
||||
colnames(df) <- c('year', 'antibiotic', 'count')
|
||||
|
||||
if (df %>% pull(col_ab) %>% n_distinct(na.rm = TRUE) < 2) {
|
||||
stop("No variety in antimicrobial interpretations - all isolates are '",
|
||||
df %>% pull(col_ab) %>% unique() %>% .[!is.na(.)], "'.",
|
||||
call. = FALSE)
|
||||
}
|
||||
|
||||
colnames(df) <- c('year', 'antibiotic', 'observations')
|
||||
df <- df %>%
|
||||
reshape2::dcast(year ~ antibiotic, value.var = 'count')
|
||||
filter(!is.na(antibiotic)) %>%
|
||||
tidyr::spread(antibiotic, observations, fill = 0) %>%
|
||||
mutate(total = R + S) %>%
|
||||
filter(total >= minimum)
|
||||
|
||||
if (NROW(df) == 0) {
|
||||
stop('There are no observations.')
|
||||
}
|
||||
|
||||
year_lowest <- min(df$year)
|
||||
if (is.null(year_min)) {
|
||||
year_min <- year_lowest
|
||||
} else {
|
||||
year_min <- max(year_min, year_lowest, na.rm = TRUE)
|
||||
}
|
||||
if (is.null(year_max)) {
|
||||
year_max <- year(Sys.Date()) + 15
|
||||
}
|
||||
|
||||
years_predict <- seq(from = year_min, to = year_max, by = year_every)
|
||||
|
||||
if (model %in% c('binomial', 'binom', 'logit')) {
|
||||
logitmodel <- with(df, glm(cbind(R, S) ~ year, family = binomial))
|
||||
|
|
@ -528,7 +593,7 @@ resistance_predict <- function(tbl,
|
|||
print(summary(logitmodel))
|
||||
}
|
||||
|
||||
predictmodel <- stats::predict(logitmodel, newdata = with(df, list(year = years_predict)), type = "response", se.fit = TRUE)
|
||||
predictmodel <- predict(logitmodel, newdata = with(df, list(year = years_predict)), type = "response", se.fit = TRUE)
|
||||
prediction <- predictmodel$fit
|
||||
se <- predictmodel$se.fit
|
||||
|
||||
|
|
@ -540,7 +605,7 @@ resistance_predict <- function(tbl,
|
|||
print(summary(loglinmodel))
|
||||
}
|
||||
|
||||
predictmodel <- stats::predict(loglinmodel, newdata = with(df, list(year = years_predict)), type = "response", se.fit = TRUE)
|
||||
predictmodel <- predict(loglinmodel, newdata = with(df, list(year = years_predict)), type = "response", se.fit = TRUE)
|
||||
prediction <- predictmodel$fit
|
||||
se <- predictmodel$se.fit
|
||||
|
||||
|
|
@ -552,7 +617,7 @@ resistance_predict <- function(tbl,
|
|||
print(summary(linmodel))
|
||||
}
|
||||
|
||||
predictmodel <- stats::predict(linmodel, newdata = with(df, list(year = years_predict)), se.fit = TRUE)
|
||||
predictmodel <- predict(linmodel, newdata = with(df, list(year = years_predict)), se.fit = TRUE)
|
||||
prediction <- predictmodel$fit
|
||||
se <- predictmodel$se.fit
|
||||
|
||||
|
|
@ -561,13 +626,13 @@ resistance_predict <- function(tbl,
|
|||
}
|
||||
|
||||
# prepare the output dataframe
|
||||
prediction <- data.frame(year = years_predict, probR = prediction, stringsAsFactors = FALSE)
|
||||
prediction <- data.frame(year = years_predict, resistance = prediction, stringsAsFactors = FALSE)
|
||||
|
||||
prediction$se_min <- prediction$probR - se
|
||||
prediction$se_max <- prediction$probR + se
|
||||
prediction$se_min <- prediction$resistance - se
|
||||
prediction$se_max <- prediction$resistance + se
|
||||
|
||||
if (model == 'loglin') {
|
||||
prediction$probR <- prediction$probR %>%
|
||||
prediction$resistance <- prediction$resistance %>%
|
||||
format(scientific = FALSE) %>%
|
||||
as.integer()
|
||||
prediction$se_min <- prediction$se_min %>% as.integer()
|
||||
|
|
@ -578,31 +643,42 @@ resistance_predict <- function(tbl,
|
|||
prediction$se_max[which(prediction$se_max > 1)] <- 1
|
||||
}
|
||||
prediction$se_min[which(prediction$se_min < 0)] <- 0
|
||||
prediction$observations = NA
|
||||
|
||||
total <- prediction
|
||||
|
||||
if (preserve_measurements == TRUE) {
|
||||
# geschatte data vervangen door gemeten data
|
||||
# replace estimated data by observed data
|
||||
if (I_as_R == TRUE) {
|
||||
if (!'I' %in% colnames(df)) {
|
||||
df$I <- 0
|
||||
}
|
||||
df$probR <- df$R / rowSums(df[, c('R', 'S', 'I')])
|
||||
df$resistance <- df$R / rowSums(df[, c('R', 'S', 'I')])
|
||||
} else {
|
||||
df$probR <- df$R / rowSums(df[, c('R', 'S')])
|
||||
df$resistance <- df$R / rowSums(df[, c('R', 'S')])
|
||||
}
|
||||
measurements <- data.frame(year = df$year,
|
||||
probR = df$probR,
|
||||
se_min = NA,
|
||||
se_max = NA,
|
||||
stringsAsFactors = FALSE)
|
||||
resistance = df$resistance,
|
||||
se_min = NA,
|
||||
se_max = NA,
|
||||
observations = df$total,
|
||||
stringsAsFactors = FALSE)
|
||||
colnames(measurements) <- colnames(prediction)
|
||||
prediction <- prediction %>% filter(!year %in% df$year)
|
||||
|
||||
total <- rbind(measurements, prediction)
|
||||
total <- rbind(measurements,
|
||||
prediction %>% filter(!year %in% df$year))
|
||||
if (model %in% c('binomial', 'binom', 'logit')) {
|
||||
total <- total %>% mutate(observed = ifelse(is.na(observations), NA, resistance),
|
||||
estimated = prediction$resistance)
|
||||
}
|
||||
}
|
||||
|
||||
total
|
||||
try(
|
||||
total$resistance[which(total$resistance > 1)] <- 1,
|
||||
total$resistance[which(total$resistance < 0)] <- 0,
|
||||
silent = TRUE
|
||||
)
|
||||
total %>% arrange(year)
|
||||
|
||||
}
|
||||
|
||||
|
|
|
|||
101
man/EUCAST.Rd
101
man/EUCAST.Rd
|
|
@ -15,20 +15,20 @@ EUCAST Expert Rules Version 2.0: \cr
|
|||
\usage{
|
||||
EUCAST_rules(tbl, col_bactid = "bactid", info = TRUE, amcl = "amcl",
|
||||
amik = "amik", amox = "amox", ampi = "ampi", azit = "azit",
|
||||
aztr = "aztr", cefa = "cefa", cfra = "cfra", cfep = "cfep",
|
||||
cfot = "cfot", cfox = "cfox", cfta = "cfta", cftr = "cftr",
|
||||
cfur = "cfur", chlo = "chlo", cipr = "cipr", clar = "clar",
|
||||
clin = "clin", clox = "clox", coli = "coli", czol = "czol",
|
||||
dapt = "dapt", doxy = "doxy", erta = "erta", eryt = "eryt",
|
||||
fosf = "fosf", fusi = "fusi", gent = "gent", imip = "imip",
|
||||
kana = "kana", levo = "levo", linc = "linc", line = "line",
|
||||
mero = "mero", mino = "mino", moxi = "moxi", nali = "nali",
|
||||
neom = "neom", neti = "neti", nitr = "nitr", novo = "novo",
|
||||
norf = "norf", oflo = "oflo", peni = "peni", pita = "pita",
|
||||
poly = "poly", qida = "qida", rifa = "rifa", roxi = "roxi",
|
||||
siso = "siso", teic = "teic", tetr = "tetr", tica = "tica",
|
||||
tige = "tige", tobr = "tobr", trim = "trim", trsu = "trsu",
|
||||
vanc = "vanc")
|
||||
azlo = "azlo", aztr = "aztr", cefa = "cefa", cfep = "cfep",
|
||||
cfot = "cfot", cfox = "cfox", cfra = "cfra", cfta = "cfta",
|
||||
cftr = "cftr", cfur = "cfur", chlo = "chlo", cipr = "cipr",
|
||||
clar = "clar", clin = "clin", clox = "clox", coli = "coli",
|
||||
czol = "czol", dapt = "dapt", doxy = "doxy", erta = "erta",
|
||||
eryt = "eryt", fosf = "fosf", fusi = "fusi", gent = "gent",
|
||||
imip = "imip", kana = "kana", levo = "levo", linc = "linc",
|
||||
line = "line", mero = "mero", mezl = "mezl", mino = "mino",
|
||||
moxi = "moxi", nali = "nali", neom = "neom", neti = "neti",
|
||||
nitr = "nitr", norf = "norf", novo = "novo", oflo = "oflo",
|
||||
peni = "peni", pita = "pita", poly = "poly", pris = "pris",
|
||||
qida = "qida", rifa = "rifa", roxi = "roxi", siso = "siso",
|
||||
teic = "teic", tetr = "tetr", tica = "tica", tige = "tige",
|
||||
tobr = "tobr", trim = "trim", trsu = "trsu", vanc = "vanc")
|
||||
|
||||
interpretive_reading(...)
|
||||
}
|
||||
|
|
@ -39,7 +39,7 @@ interpretive_reading(...)
|
|||
|
||||
\item{info}{print progress}
|
||||
|
||||
\item{amcl, amik, amox, ampi, azit, aztr, cefa, cfra, cfep, cfot, cfox, cfta, cftr, cfur, chlo, cipr, clar, clin, clox, coli, czol, dapt, doxy, erta, eryt, fosf, fusi, gent, imip, kana, levo, linc, line, mero, mino, moxi, nali, neom, neti, nitr, novo, norf, oflo, peni, pita, poly, qida, rifa, roxi, siso, teic, tetr, tica, tige, tobr, trim, trsu, vanc}{column names of antibiotics. Use \code{NA} to skip a column, like \code{tica = NA}. Non-existing column will be skipped.}
|
||||
\item{amcl, amik, amox, ampi, azit, azlo, aztr, cefa, cfep, cfot, cfox, cfra, cfta, cftr, cfur, chlo, cipr, clar, clin, clox, coli, czol, dapt, doxy, erta, eryt, fosf, fusi, gent, imip, kana, levo, linc, line, mero, mezl, mino, moxi, nali, neom, neti, nitr, norf, novo, oflo, peni, pita, poly, pris, qida, rifa, roxi, siso, teic, tetr, tica, tige, tobr, trim, trsu, vanc}{column names of antibiotics. Use \code{NA} to skip a column, like \code{tica = NA}. Non-existing columns will anyway be skipped. See the Antibiotics section for an explanation of the abbreviations.}
|
||||
|
||||
\item{...}{parameters that are passed on to \code{EUCAST_rules}}
|
||||
}
|
||||
|
|
@ -49,6 +49,73 @@ table with edited variables of antibiotics.
|
|||
\description{
|
||||
Apply expert rules (like intrinsic resistance), as defined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST, \url{http://eucast.org}), see \emph{Source}.
|
||||
}
|
||||
\section{Abbrevations of antibiotics}{
|
||||
|
||||
Abbrevations of the column containing antibiotics:
|
||||
|
||||
\strong{amcl}: amoxicillin and beta-lactamase inhibitor (\emph{J01CR02}),
|
||||
\strong{amik}: amikacin (\emph{J01GB06}),
|
||||
\strong{amox}: amoxicillin (\emph{J01CA04}),
|
||||
\strong{ampi}: ampicillin (\emph{J01CA01}),
|
||||
\strong{azit}: azithromycin (\emph{J01FA10}),
|
||||
\strong{azlo}: azlocillin (\emph{J01CA09}),
|
||||
\strong{aztr}: aztreonam (\emph{J01DF01}),
|
||||
\strong{cefa}: cefaloridine (\emph{J01DB02}),
|
||||
\strong{cfep}: cefepime (\emph{J01DE01}),
|
||||
\strong{cfot}: cefotaxime (\emph{J01DD01}),
|
||||
\strong{cfox}: cefoxitin (\emph{J01DC01}),
|
||||
\strong{cfra}: cefradine (\emph{J01DB09}),
|
||||
\strong{cfta}: ceftazidime (\emph{J01DD02}),
|
||||
\strong{cftr}: ceftriaxone (\emph{J01DD04}),
|
||||
\strong{cfur}: cefuroxime (\emph{J01DC02}),
|
||||
\strong{chlo}: chloramphenicol (\emph{J01BA01}),
|
||||
\strong{cipr}: ciprofloxacin (\emph{J01MA02}),
|
||||
\strong{clar}: clarithromycin (\emph{J01FA09}),
|
||||
\strong{clin}: clindamycin (\emph{J01FF01}),
|
||||
\strong{clox}: flucloxacillin (\emph{J01CF05}),
|
||||
\strong{coli}: colistin (\emph{J01XB01}),
|
||||
\strong{czol}: cefazolin (\emph{J01DB04}),
|
||||
\strong{dapt}: daptomycin (\emph{J01XX09}),
|
||||
\strong{doxy}: doxycycline (\emph{J01AA02}),
|
||||
\strong{erta}: ertapenem (\emph{J01DH03}),
|
||||
\strong{eryt}: erythromycin (\emph{J01FA01}),
|
||||
\strong{fosf}: fosfomycin (\emph{J01XX01}),
|
||||
\strong{fusi}: fusidic acid (\emph{J01XC01}),
|
||||
\strong{gent}: gentamicin (\emph{J01GB03}),
|
||||
\strong{imip}: imipenem and cilastatin (\emph{J01DH51}),
|
||||
\strong{kana}: kanamycin (\emph{J01GB04}),
|
||||
\strong{levo}: levofloxacin (\emph{J01MA12}),
|
||||
\strong{linc}: lincomycin (\emph{J01FF02}),
|
||||
\strong{line}: linezolid (\emph{J01XX08}),
|
||||
\strong{mero}: meropenem (\emph{J01DH02}),
|
||||
\strong{mezl}: mezlocillin (\emph{J01CA10}),
|
||||
\strong{mino}: minocycline (\emph{J01AA08}),
|
||||
\strong{moxi}: moxifloxacin (\emph{J01MA14}),
|
||||
\strong{nali}: nalidixic acid (\emph{J01MB02}),
|
||||
\strong{neom}: neomycin (\emph{J01GB05}),
|
||||
\strong{neti}: netilmicin (\emph{J01GB07}),
|
||||
\strong{nitr}: nitrofurantoin (\emph{J01XE01}),
|
||||
\strong{norf}: norfloxacin (\emph{J01MA06}),
|
||||
\strong{novo}: novobiocin (an ATCvet code: \emph{QJ01XX95}),
|
||||
\strong{oflo}: ofloxacin (\emph{J01MA01}),
|
||||
\strong{peni}: penicillins, combinations with other antibacterials (\emph{J01RA01}),
|
||||
\strong{pita}: piperacillin and beta-lactamase inhibitor (\emph{J01CR05}),
|
||||
\strong{poly}: polymyxin B (\emph{J01XB02}),
|
||||
\strong{pris}: pristinamycin (\emph{J01FG01}),
|
||||
\strong{qida}: quinupristin/dalfopristin (\emph{J01FG02}),
|
||||
\strong{rifa}: rifampicin (\emph{J04AB02}),
|
||||
\strong{roxi}: roxithromycin (\emph{J01FA06}),
|
||||
\strong{siso}: sisomicin (\emph{J01GB08}),
|
||||
\strong{teic}: teicoplanin (\emph{J01XA02}),
|
||||
\strong{tetr}: tetracycline (\emph{J01AA07}),
|
||||
\strong{tica}: ticarcillin (\emph{J01CA13}),
|
||||
\strong{tige}: tigecycline (\emph{J01AA12}),
|
||||
\strong{tobr}: tobramycin (\emph{J01GB01}),
|
||||
\strong{trim}: trimethoprim (\emph{J01EA01}),
|
||||
\strong{trsu}: sulfamethoxazole and trimethoprim (\emph{J01EE01}),
|
||||
\strong{vanc}: vancomycin (\emph{J01XA01}).
|
||||
}
|
||||
|
||||
\examples{
|
||||
a <- EUCAST_rules(septic_patients)
|
||||
a <- data.frame(bactid = c("STAAUR", # Staphylococcus aureus
|
||||
|
|
@ -67,3 +134,7 @@ a
|
|||
b <- EUCAST_rules(a)
|
||||
b
|
||||
}
|
||||
\keyword{eucast}
|
||||
\keyword{interpretive}
|
||||
\keyword{reading}
|
||||
\keyword{resistance}
|
||||
|
|
|
|||
|
|
@ -92,13 +92,13 @@ A <- my_patients \%>\%
|
|||
resistance = resistance(gent))
|
||||
|
||||
B <- my_patients \%>\%
|
||||
filter(first_isolate == TRUE) \%>\%
|
||||
filter(first_isolate == TRUE) \%>\% # the 1st isolate filter
|
||||
group_by(hospital_id) \%>\%
|
||||
summarise(count = n_rsi(gent), # gentamicin
|
||||
summarise(count = n_rsi(gent),
|
||||
resistance = resistance(gent))
|
||||
|
||||
# Have a look at A and B. B is more reliable because every isolate is
|
||||
# counted once. Gentamicin resitance in hospital D seems to be 5\%
|
||||
# counted once. Gentamicin resitance in hospital D appears to be 5\%
|
||||
# higher than originally thought.
|
||||
|
||||
## OTHER EXAMPLES:
|
||||
|
|
|
|||
|
|
@ -5,53 +5,64 @@
|
|||
\alias{rsi_predict}
|
||||
\title{Predict antimicrobial resistance}
|
||||
\usage{
|
||||
resistance_predict(tbl, col_ab, col_date,
|
||||
year_max = as.integer(format(as.Date(Sys.Date()), "\%Y")) + 15,
|
||||
year_every = 1, model = "binomial", I_as_R = TRUE,
|
||||
resistance_predict(tbl, col_ab, col_date, year_min = NULL, year_max = NULL,
|
||||
year_every = 1, minimum = 30, model = "binomial", I_as_R = TRUE,
|
||||
preserve_measurements = TRUE, info = TRUE)
|
||||
|
||||
rsi_predict(tbl, col_ab, col_date,
|
||||
year_max = as.integer(format(as.Date(Sys.Date()), "\%Y")) + 15,
|
||||
year_every = 1, model = "binomial", I_as_R = TRUE,
|
||||
rsi_predict(tbl, col_ab, col_date, year_min = NULL, year_max = NULL,
|
||||
year_every = 1, minimum = 30, model = "binomial", I_as_R = TRUE,
|
||||
preserve_measurements = TRUE, info = TRUE)
|
||||
}
|
||||
\arguments{
|
||||
\item{tbl}{table that contains columns \code{col_ab} and \code{col_date}}
|
||||
\item{tbl}{a \code{data.frame} containing isolates.}
|
||||
|
||||
\item{col_ab}{column name of \code{tbl} with antimicrobial interpretations (\code{R}, \code{I} and \code{S}), supports tidyverse-like quotation}
|
||||
\item{col_ab}{column name of \code{tbl} with antimicrobial interpretations (\code{R}, \code{I} and \code{S})}
|
||||
|
||||
\item{col_date}{column name of the date, will be used to calculate years if this column doesn't consist of years already, supports tidyverse-like quotation}
|
||||
\item{col_date}{column name of the date, will be used to calculate years if this column doesn't consist of years already}
|
||||
|
||||
\item{year_max}{highest year to use in the prediction model, deafults to 15 years after today}
|
||||
\item{year_min}{lowest year to use in the prediction model, dafaults the lowest year in \code{col_date}}
|
||||
|
||||
\item{year_max}{highest year to use in the prediction model, defaults to 15 years after today}
|
||||
|
||||
\item{year_every}{unit of sequence between lowest year found in the data and \code{year_max}}
|
||||
|
||||
\item{minimum}{minimal amount of available isolates per year to include. Years containing less observations will be estimated by the model.}
|
||||
|
||||
\item{model}{the statistical model of choice. Valid values are \code{"binomial"} (or \code{"binom"} or \code{"logit"}) or \code{"loglin"} or \code{"linear"} (or \code{"lin"}).}
|
||||
|
||||
\item{I_as_R}{treat \code{I} as \code{R}}
|
||||
|
||||
\item{preserve_measurements}{overwrite predictions of years that are actually available in the data, with the original data. The standard errors of those years will be \code{NA}.}
|
||||
\item{preserve_measurements}{logical to indicate whether predictions of years that are actually available in the data should be overwritten with the original data. The standard errors of those years will be \code{NA}.}
|
||||
|
||||
\item{info}{print textual analysis with the name and \code{\link{summary}} of the model.}
|
||||
}
|
||||
\value{
|
||||
\code{data.frame} with columns \code{year}, \code{probR}, \code{se_min} and \code{se_max}.
|
||||
\code{data.frame} with columns:
|
||||
\itemize{
|
||||
\item{\code{year}}
|
||||
\item{\code{resistance}, the same as \code{estimated} when \code{preserve_measurements = FALSE}, and a combination of \code{observed} and \code{estimated} otherwise}
|
||||
\item{\code{se_min}, the lower bound of the standard error with a minimum of \code{0}}
|
||||
\item{\code{se_max} the upper bound of the standard error with a maximum of \code{1}}
|
||||
\item{\code{observations}, the total number of observations, i.e. S + I + R}
|
||||
\item{\code{observed}, the original observed values}
|
||||
\item{\code{estimated}, the estimated values, calculated by the model}
|
||||
}
|
||||
}
|
||||
\description{
|
||||
Create a prediction model to predict antimicrobial resistance for the next years on statistical solid ground. Standard errors (SE) will be returned as columns \code{se_min} and \code{se_max}. See Examples for a real live example.
|
||||
}
|
||||
\examples{
|
||||
\dontrun{
|
||||
# use it directly:
|
||||
rsi_predict(tbl = tbl[which(first_isolate == TRUE & genus == "Haemophilus"),],
|
||||
col_ab = "amcl", col_date = "date")
|
||||
# use it with base R:
|
||||
resistance_predict(tbl = tbl[which(first_isolate == TRUE & genus == "Haemophilus"),],
|
||||
col_ab = "amcl", col_date = "date")
|
||||
|
||||
# or with dplyr so you can actually read it:
|
||||
# or use dplyr so you can actually read it:
|
||||
library(dplyr)
|
||||
tbl \%>\%
|
||||
filter(first_isolate == TRUE,
|
||||
genus == "Haemophilus") \%>\%
|
||||
rsi_predict(amcl, date)
|
||||
resistance_predict(amcl, date)
|
||||
}
|
||||
|
||||
|
||||
|
|
@ -73,11 +84,38 @@ septic_patients \%>\%
|
|||
species == "coli",
|
||||
first_isolate == TRUE) \%>\%
|
||||
# predict resistance of cefotaxime for next years
|
||||
rsi_predict(col_ab = "cfot",
|
||||
col_date = "date",
|
||||
year_max = 2025,
|
||||
preserve_measurements = FALSE)
|
||||
resistance_predict(col_ab = "cfot",
|
||||
col_date = "date",
|
||||
year_max = 2025,
|
||||
preserve_measurements = TRUE,
|
||||
minimum = 0)
|
||||
|
||||
# create nice plots with ggplot
|
||||
if (!require(ggplot2)) {
|
||||
|
||||
data <- septic_patients \%>\%
|
||||
filter(bactid == "ESCCOL") \%>\%
|
||||
resistance_predict(col_ab = "amox",
|
||||
col_date = "date",
|
||||
info = FALSE,
|
||||
minimum = 15)
|
||||
|
||||
ggplot(data,
|
||||
aes(x = year)) +
|
||||
geom_col(aes(y = resistance),
|
||||
fill = "grey75") +
|
||||
geom_errorbar(aes(ymin = se_min,
|
||||
ymax = se_max),
|
||||
colour = "grey50") +
|
||||
scale_y_continuous(limits = c(0, 1),
|
||||
breaks = seq(0, 1, 0.1),
|
||||
labels = paste0(seq(0, 100, 10), "\%")) +
|
||||
labs(title = expression(paste("Forecast of amoxicillin resistance in ",
|
||||
italic("E. coli"))),
|
||||
y = "\%IR",
|
||||
x = "Year") +
|
||||
theme_minimal(base_size = 13)
|
||||
}
|
||||
}
|
||||
\seealso{
|
||||
\code{\link{resistance}} \cr \code{\link{lm}} \code{\link{glm}}
|
||||
|
|
|
|||
|
|
@ -11,10 +11,9 @@ test_that("EUCAST rules work", {
|
|||
amox = "-", # Amoxicillin
|
||||
stringsAsFactors = FALSE)
|
||||
b <- data.frame(bactid =
|
||||
as.bactid(
|
||||
c("KLEPNE", # Klebsiella pneumoniae
|
||||
"PSEAER", # Pseudomonas aeruginosa
|
||||
"ENTAER")), # Enterobacter aerogenes
|
||||
c("KLEPNE", # Klebsiella pneumoniae
|
||||
"PSEAER", # Pseudomonas aeruginosa
|
||||
"ENTAER"), # Enterobacter aerogenes
|
||||
amox = "R", # Amoxicillin
|
||||
stringsAsFactors = FALSE)
|
||||
expect_identical(EUCAST_rules(a, info = FALSE), b)
|
||||
|
|
@ -26,9 +25,8 @@ test_that("EUCAST rules work", {
|
|||
coli = "-", # Colistin
|
||||
stringsAsFactors = FALSE)
|
||||
b <- data.frame(bactid =
|
||||
as.bactid(
|
||||
c("STAAUR", # Staphylococcus aureus
|
||||
"STCGRA")), # Streptococcus pyognenes (Lancefield Group A)
|
||||
c("STAAUR", # Staphylococcus aureus
|
||||
"STCGRA"), # Streptococcus pyognenes (Lancefield Group A)
|
||||
coli = "R", # Colistin
|
||||
stringsAsFactors = FALSE)
|
||||
expect_equal(EUCAST_rules(a, info = FALSE), b)
|
||||
|
|
|
|||
|
|
@ -84,8 +84,9 @@ test_that("prediction of rsi works", {
|
|||
filter(bactid == "ESCCOL") %>%
|
||||
rsi_predict(col_ab = "amox",
|
||||
col_date = "date",
|
||||
minimum = 10,
|
||||
info = TRUE) %>%
|
||||
pull("probR")
|
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pull("resistance")
|
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# amox resistance will increase according to data set `septic_patients`
|
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expect_true(amox_R[3] < amox_R[20])
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue