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Update clinical breakpoints and fix some as.mo() bugs (#117)

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* Updates clinical breakpoints EUCAST/CLSI 2023, fixes #102, fixes #112, fixes #113, fixes #114, fixes #115
* docs
* implement ecoffs
* unit tests
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Dr. Matthijs Berends 2023-06-22 15:10:59 +02:00 committed by GitHub
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4
DESCRIPTION
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@ -1,6 +1,6 @@
Package: AMR
Version: 2.0.0.9023
Date: 2023-05-27
Version: 2.0.0.9024
Date: 2023-06-22
Title: Antimicrobial Resistance Data Analysis
Description: Functions to simplify and standardise antimicrobial resistance (AMR)
data analysis and to work with microbial and antimicrobial properties by

14
NEWS.md
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@ -1,18 +1,26 @@
# AMR 2.0.0.9023
# AMR 2.0.0.9024
## Changed
## New
* Clinical breakpoints and intrinsic resistance of EUCAST 2023 and CLSI 2023 have been added for `as.sir()`. EUCAST 2023 (v13.0) is now the new default guideline for all MIC and disks diffusion interpretations, and for `eucast_rules()` to apply EUCAST Expert Rules.
* The EUCAST dosage guideline of v13.0 has been added to the `dosage` data set
* ECOFF: the `clinical_breakpoints` data set now contains the new column `ecoff`, in which the epidemiological cut-off (ECOFF) are available. These ECOFFs can be used for MIC/disk interpretation using `as.sir(..., ecoff = TRUE)`, which is an important new addition for veterinary microbiology.
* Added oxygen tolerance from BacDive to over 25,000 bacteria in the `microorganisms` data set
* Added `mo_oxygen_tolerance()` to retrieve the values
* Added `mo_is_anaerobic()` to determine which genera/species are obligate anaerobic bacteria
* Added LPSN and GBIF identifiers, and oxygen tolerance to `mo_info()`
* Added SAS Transport files (file extension `.xpt`) to [our download page](https://msberends.github.io/AMR/articles/datasets.html) to use in SAS software
* Added microbial codes for Gram-negative/positive anaerobic bacteria
## Changed
* `mo_rank()` now returns `NA` for 'unknown' microorganisms (`B_ANAER`, `B_ANAER-NEG`, `B_ANAER-POS`, `B_GRAMN`, `B_GRAMP`, `F_FUNGUS`, `F_YEAST`, and `UNKNOWN`)
* Fixed formatting for `sir_interpretation_history()`
* Fixed some WHONET codes for microorganisms and consequently a couple of entries in `clinical_breakpoints`
* Fixed a bug for `as.mo()` that led to coercion of `NA` values when using custom microorganism codes
* Fixed usage of `icu_exclude` in `first_isolates()`
* Improved `as.mo()` algorithm for searching on only species names
* Improved `as.mo()` algorithm:
* Now allows searching on only species names
* Fix for using the `keep_synonyms` argument when using MO codes as input
* Fix for using the `minimum_matching_score` argument
* Updated the code table in `microorganisms.codes`

2
R/aa_amr-package.R
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@ -36,7 +36,7 @@
#'
#' This work was published in the Journal of Statistical Software (Volume 104(3); \doi{jss.v104.i03}) and formed the basis of two PhD theses (\doi{10.33612/diss.177417131} and \doi{10.33612/diss.192486375}).
#'
#' After installing this package, R knows [**`r format_included_data_number(AMR::microorganisms)` microorganisms**](https://msberends.github.io/AMR/reference/microorganisms.html) (updated `r format(TAXONOMY_VERSION$GBIF$accessed_date, "%B %Y")`) and all [**`r format_included_data_number(nrow(AMR::antibiotics) + nrow(AMR::antivirals))` antibiotic, antimycotic and antiviral drugs**](https://msberends.github.io/AMR/reference/antibiotics.html) by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral breakpoint guidelines from CLSI and EUCAST are included from the last 10 years. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl), in collaboration with non-profit organisations [Certe Medical Diagnostics and Advice Foundation](https://www.certe.nl) and [University Medical Center Groningen](https://www.umcg.nl).
#' After installing this package, R knows [**`r format_included_data_number(AMR::microorganisms)` microorganisms**](https://msberends.github.io/AMR/reference/microorganisms.html) (updated `r format(TAXONOMY_VERSION$GBIF$accessed_date, "%B %Y")`) and all [**`r format_included_data_number(nrow(AMR::antibiotics) + nrow(AMR::antivirals))` antibiotic, antimycotic and antiviral drugs**](https://msberends.github.io/AMR/reference/antibiotics.html) by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI and EUCAST are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl), in collaboration with non-profit organisations [Certe Medical Diagnostics and Advice Foundation](https://www.certe.nl) and [University Medical Center Groningen](https://www.umcg.nl).
#'
#' The `AMR` package is available in `r vector_and(vapply(FUN.VALUE = character(1), LANGUAGES_SUPPORTED_NAMES, function(x) x$exonym), quotes = FALSE, sort = FALSE)`. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages.
#' @section Reference Data Publicly Available:

30
R/aa_globals.R
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@ -30,6 +30,12 @@
# add new version numbers here, and add the rules themselves to "data-raw/eucast_rules.tsv" and clinical_breakpoints
# (sourcing "data-raw/_pre_commit_hook.R" will process the TSV file)
EUCAST_VERSION_BREAKPOINTS <- list(
"13.0" = list(
version_txt = "v13.0",
year = 2023,
title = "'EUCAST Clinical Breakpoint Tables'",
url = "https://www.eucast.org/clinical_breakpoints/"
),
"12.0" = list(
version_txt = "v12.0",
year = 2022,
@ -50,10 +56,10 @@ EUCAST_VERSION_BREAKPOINTS <- list(
)
)
EUCAST_VERSION_EXPERT_RULES <- list(
"3.1" = list(
version_txt = "v3.1",
year = 2016,
title = "'EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes'",
"3.3" = list(
version_txt = "v3.3",
year = 2021,
title = "'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes'",
url = "https://www.eucast.org/expert_rules_and_expected_phenotypes/"
),
"3.2" = list(
@ -62,10 +68,18 @@ EUCAST_VERSION_EXPERT_RULES <- list(
title = "'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes'",
url = "https://www.eucast.org/expert_rules_and_expected_phenotypes/"
),
"3.3" = list(
version_txt = "v3.3",
year = 2021,
title = "'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes'",
"3.1" = list(
version_txt = "v3.1",
year = 2016,
title = "'EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes'",
url = "https://www.eucast.org/expert_rules_and_expected_phenotypes/"
)
)
EUCAST_VERSION_RESISTANTPHENOTYPES <- list(
"1.2" = list(
version_txt = "v1.2",
year = 2023,
title = "'Expected Resistant Phenotypes'",
url = "https://www.eucast.org/expert_rules_and_expected_phenotypes/"
)
)

1
R/aa_options.R
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@ -37,6 +37,7 @@
#' * `AMR_guideline` \cr Used for setting the default guideline for interpreting MIC values and disk diffusion diameters with [as.sir()]. Can be only the guideline name (e.g., `"CLSI"`) or the name with a year (e.g. `"CLSI 2019"`). The default to the latest implemented EUCAST guideline, currently \code{"`r clinical_breakpoints$guideline[1]`"}. Supported guideline are currently EUCAST (`r min(as.integer(gsub("[^0-9]", "", subset(clinical_breakpoints, guideline %like% "EUCAST")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(clinical_breakpoints, guideline %like% "EUCAST")$guideline)))`) and CLSI (`r min(as.integer(gsub("[^0-9]", "", subset(clinical_breakpoints, guideline %like% "CLSI")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(clinical_breakpoints, guideline %like% "CLSI")$guideline)))`).
#' * `AMR_ignore_pattern` \cr A [regular expression][base::regex] to ignore (i.e., make `NA`) any match given in [as.mo()] and all [`mo_*`][mo_property()] functions.
#' * `AMR_include_PKPD` \cr A [logical] to use in [as.sir()], to indicate that PK/PD clinical breakpoints must be applied as a last resort - the default is `TRUE`.
#' * `AMR_ecoff` \cr A [logical] use in [as.sir()], to indicate that ECOFF (Epidemiological Cut-Off) values must be used - the default is `FALSE`.
#' * `AMR_include_screening` \cr A [logical] to use in [as.sir()], to indicate that clinical breakpoints for screening are allowed - the default is `FALSE`.
#' * `AMR_keep_synonyms` \cr A [logical] to use in [as.mo()] and all [`mo_*`][mo_property()] functions, to indicate if old, previously valid taxonomic names must be preserved and not be corrected to currently accepted names. The default is `FALSE`.
#' * `AMR_cleaning_regex` \cr A [regular expression][base::regex] (case-insensitive) to use in [as.mo()] and all [`mo_*`][mo_property()] functions, to clean the user input. The default is the outcome of [mo_cleaning_regex()], which removes texts between brackets and texts such as "species" and "serovar".

2
R/ab_selectors.R
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@ -620,7 +620,7 @@ administrable_iv <- function(only_sir_columns = FALSE, ...) {
#' @rdname antibiotic_class_selectors
#' @inheritParams eucast_rules
#' @details The [not_intrinsic_resistant()] function can be used to only select antibiotic columns that pose no intrinsic resistance for the microorganisms in the data set. For example, if a data set contains only microorganism codes or names of *E. coli* and *K. pneumoniae* and contains a column "vancomycin", this column will be removed (or rather, unselected) using this function. It currently applies `r format_eucast_version_nr(names(EUCAST_VERSION_EXPERT_RULES[length(EUCAST_VERSION_EXPERT_RULES)]))` to determine intrinsic resistance, using the [eucast_rules()] function internally. Because of this determination, this function is quite slow in terms of performance.
#' @details The [not_intrinsic_resistant()] function can be used to only select antibiotic columns that pose no intrinsic resistance for the microorganisms in the data set. For example, if a data set contains only microorganism codes or names of *E. coli* and *K. pneumoniae* and contains a column "vancomycin", this column will be removed (or rather, unselected) using this function. It currently applies `r format_eucast_version_nr(names(EUCAST_VERSION_EXPERT_RULES[1]))` to determine intrinsic resistance, using the [eucast_rules()] function internally. Because of this determination, this function is quite slow in terms of performance.
#' @export
not_intrinsic_resistant <- function(only_sir_columns = FALSE, col_mo = NULL, version_expertrules = 3.3, ...) {
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)

8
R/data.R
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@ -257,8 +257,11 @@
#' - `disk_dose`\cr Dose of the used disk diffusion method
#' - `breakpoint_S`\cr Lowest MIC value or highest number of millimetres that leads to "S"
#' - `breakpoint_R`\cr Highest MIC value or lowest number of millimetres that leads to "R"
#' - `ecoff`\cr Epidemiological cut-off (ECOFF) value, used in antimicrobial susceptibility testing to differentiate between wild-type and non-wild-type strains of bacteria or fungi (use [`as.sir(..., ecoff = TRUE)`][as.sir()] to interpret raw data using ECOFF values)
#' - `uti`\cr A [logical] value (`TRUE`/`FALSE`) to indicate whether the rule applies to a urinary tract infection (UTI)
#' @details
#' Clinical breakpoints are validated through [WHONET](https://whonet.org), a free desktop Windows application developed and supported by the WHO Collaborating Centre for Surveillance of Antimicrobial Resistance. More can be read on [their website](https://whonet.org).
#'
#' Like all data sets in this package, this data set is publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
#'
#' They **allow for machine reading EUCAST and CLSI guidelines**, which is almost impossible with the MS Excel and PDF files distributed by EUCAST and CLSI.
@ -296,11 +299,8 @@
#' - `administration`\cr Route of administration, either `r vector_or(dosage$administration)`
#' - `notes`\cr Additional dosage notes
#' - `original_txt`\cr Original text in the PDF file of EUCAST
#' - `eucast_version`\cr Version number of the EUCAST Clinical Breakpoints guideline to which these dosages apply
#' - `eucast_version`\cr Version number of the EUCAST Clinical Breakpoints guideline to which these dosages apply, either `r vector_or(dosage$eucast_version, quotes = FALSE, sort = TRUE, reverse = TRUE)`
#' @details
#' This data set is based on `r format_eucast_version_nr(12.0)` and `r format_eucast_version_nr(11.0)`.
#'
#' ### Direct download
#' Like all data sets in this package, this data set is publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
#' @examples
#' dosage

10
R/eucast_rules.R
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@ -63,8 +63,9 @@ format_eucast_version_nr <- function(version, markdown = TRUE) {
#' @param info a [logical] to indicate whether progress should be printed to the console - the default is only print while in interactive sessions
#' @param rules a [character] vector that specifies which rules should be applied. Must be one or more of `"breakpoints"`, `"expert"`, `"other"`, `"custom"`, `"all"`, and defaults to `c("breakpoints", "expert")`. The default value can be set to another value using the [package option][AMR-options] [`AMR_eucastrules`][AMR-options]: `options(AMR_eucastrules = "all")`. If using `"custom"`, be sure to fill in argument `custom_rules` too. Custom rules can be created with [custom_eucast_rules()].
#' @param verbose a [logical] to turn Verbose mode on and off (default is off). In Verbose mode, the function does not apply rules to the data, but instead returns a data set in logbook form with extensive info about which rows and columns would be effected and in which way. Using Verbose mode takes a lot more time.
#' @param version_breakpoints the version number to use for the EUCAST Clinical Breakpoints guideline. Can be either `r vector_or(names(EUCAST_VERSION_BREAKPOINTS), reverse = TRUE)`.
#' @param version_expertrules the version number to use for the EUCAST Expert Rules and Intrinsic Resistance guideline. Can be either `r vector_or(names(EUCAST_VERSION_EXPERT_RULES), reverse = TRUE)`.
#' @param version_breakpoints the version number to use for the EUCAST Clinical Breakpoints guideline. Can be `r vector_or(names(EUCAST_VERSION_BREAKPOINTS), reverse = TRUE)`.
#' @param version_expertrules the version number to use for the EUCAST Expert Rules and Intrinsic Resistance guideline. Can be `r vector_or(names(EUCAST_VERSION_EXPERT_RULES), reverse = TRUE)`.
#' @param version_resistant_phenotypes the version number to use for the EUCAST Expected Resistant Phenotypes. Can be `r vector_or(names(EUCAST_VERSION_RESISTANTPHENOTYPES), reverse = TRUE)`.
#' @param ampc_cephalosporin_resistance a [character] value that should be applied to cefotaxime, ceftriaxone and ceftazidime for AmpC de-repressed cephalosporin-resistant mutants - the default is `NA`. Currently only works when `version_expertrules` is `3.2` and higher; these version of '*EUCAST Expert Rules on Enterobacterales*' state that results of cefotaxime, ceftriaxone and ceftazidime should be reported with a note, or results should be suppressed (emptied) for these three drugs. A value of `NA` (the default) for this argument will remove results for these three drugs, while e.g. a value of `"R"` will make the results for these drugs resistant. Use `NULL` or `FALSE` to not alter results for these three drugs of AmpC de-repressed cephalosporin-resistant mutants. Using `TRUE` is equal to using `"R"`. \cr For *EUCAST Expert Rules* v3.2, this rule applies to: `r vector_and(gsub("[^a-zA-Z ]+", "", unlist(strsplit(EUCAST_RULES_DF[which(EUCAST_RULES_DF$reference.version %in% c(3.2, 3.3) & EUCAST_RULES_DF$reference.rule %like% "ampc"), "this_value"][1], "|", fixed = TRUE))), quotes = "*")`.
#' @param ... column name of an antibiotic, see section *Antibiotics* below
#' @param ab any (vector of) text that can be coerced to a valid antibiotic drug code with [as.ab()]
@ -167,6 +168,7 @@ eucast_rules <- function(x,
verbose = FALSE,
version_breakpoints = 12.0,
version_expertrules = 3.3,
version_resistant_phenotypes = 1.2,
ampc_cephalosporin_resistance = NA,
only_sir_columns = FALSE,
custom_rules = NULL,
@ -178,6 +180,7 @@ eucast_rules <- function(x,
meet_criteria(verbose, allow_class = "logical", has_length = 1)
meet_criteria(version_breakpoints, allow_class = c("numeric", "integer"), has_length = 1, is_in = as.double(names(EUCAST_VERSION_BREAKPOINTS)))
meet_criteria(version_expertrules, allow_class = c("numeric", "integer"), has_length = 1, is_in = as.double(names(EUCAST_VERSION_EXPERT_RULES)))
meet_criteria(version_resistant_phenotypes, allow_class = c("numeric", "integer"), has_length = 1, is_in = as.double(names(EUCAST_VERSION_RESISTANTPHENOTYPES)))
meet_criteria(ampc_cephalosporin_resistance, allow_class = c("logical", "character", "sir"), has_length = 1, allow_NA = TRUE, allow_NULL = TRUE)
meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
meet_criteria(custom_rules, allow_class = "custom_eucast_rules", allow_NULL = TRUE)
@ -205,6 +208,7 @@ eucast_rules <- function(x,
breakpoints_info <- EUCAST_VERSION_BREAKPOINTS[[which(as.double(names(EUCAST_VERSION_BREAKPOINTS)) == version_breakpoints)]]
expertrules_info <- EUCAST_VERSION_EXPERT_RULES[[which(as.double(names(EUCAST_VERSION_EXPERT_RULES)) == version_expertrules)]]
resistantphenotypes_info <- EUCAST_VERSION_RESISTANTPHENOTYPES[[which(as.double(names(EUCAST_VERSION_RESISTANTPHENOTYPES)) == version_resistant_phenotypes)]]
# support old setting (until AMR v1.3.0)
if (missing(rules) && !is.null(getOption("AMR.eucast_rules"))) {
@ -1185,7 +1189,7 @@ edit_sir <- function(x,
#' @rdname eucast_rules
#' @export
eucast_dosage <- function(ab, administration = "iv", version_breakpoints = 12.0) {
eucast_dosage <- function(ab, administration = "iv", version_breakpoints = 13.0) {
meet_criteria(ab, allow_class = c("character", "numeric", "integer", "factor"))
meet_criteria(administration, allow_class = "character", is_in = dosage$administration[!is.na(dosage$administration)], has_length = 1)
meet_criteria(version_breakpoints, allow_class = c("numeric", "integer"), has_length = 1, is_in = as.double(names(EUCAST_VERSION_BREAKPOINTS)))

48
R/mo.R
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@ -172,9 +172,10 @@ as.mo <- function(x,
add_MO_lookup_to_AMR_env()
if (tryCatch(all(x %in% c(AMR_env$MO_lookup$mo, NA)) &&
if (tryCatch(all(x %in% c(AMR_env$MO_lookup$mo, NA)), error = function(e) FALSE) &&
isFALSE(Becker) &&
isFALSE(Lancefield), error = function(e) FALSE)) {
isFALSE(Lancefield) &&
isTRUE(keep_synonyms)) {
# don't look into valid MO codes, just return them
# is.mo() won't work - MO codes might change between package versions
return(set_clean_class(x, new_class = c("mo", "character")))
@ -266,6 +267,8 @@ as.mo <- function(x,
x_out <- trimws2(gsub(" +", " ", x_out, perl = TRUE))
x_search_cleaned <- x_out
x_out <- tolower(x_out)
# when x_search_cleaned are only capitals (such as in codes), make them lowercase to increase matching score
x_search_cleaned[x_search_cleaned == toupper(x_search_cleaned)] <- x_out[x_search_cleaned == toupper(x_search_cleaned)]
# first check if cleaning led to an exact result, case-insensitive
if (x_out %in% AMR_env$MO_lookup$fullname_lower) {
@ -297,15 +300,19 @@ as.mo <- function(x,
} else if (length(x_parts) > 3) {
first_chars <- paste0("(^| )[", paste(substr(x_parts, 1, 1), collapse = ""), "]")
filtr <- which(AMR_env$MO_lookup$full_first %like_case% first_chars)
} else if (nchar(x_out) == 3) {
# no space and 3 characters - probably a code such as SAU or ECO
msg <<- c(msg, paste0("Input \"", x_search, "\" was assumed to be a microorganism code - tried to match on \"", substr(x_out, 1, 1), AMR_env$dots, " ", substr(x_out, 2, 3), AMR_env$dots, "\""))
filtr <- which(AMR_env$MO_lookup$fullname_lower %like_case% paste0("(^| )", substr(x_out, 1, 1), ".* ", substr(x_out, 2, 3)))
} else if (nchar(x_out) == 4) {
# no space and 4 characters - probably a code such as STAU or ESCO
msg <- c(msg, paste0("Input \"", x_search, "\" was assumed to be a microorganism code - tried to match on ", vector_and(c(substr(x_out, 1, 2), substr(x_out, 3, 4)), sort = FALSE)))
msg <<- c(msg, paste0("Input \"", x_search, "\" was assumed to be a microorganism code - tried to match on \"", substr(x_out, 1, 2), AMR_env$dots, " ", substr(x_out, 3, 4), AMR_env$dots, "\""))
filtr <- which(AMR_env$MO_lookup$fullname_lower %like_case% paste0("(^| )", substr(x_out, 1, 2), ".* ", substr(x_out, 3, 4)))
} else if (nchar(x_out) <= 6) {
# no space and 5-6 characters - probably a code such as STAAUR or ESCCOL
first_part <- paste0(substr(x_out, 1, 2), "[a-z]*", substr(x_out, 3, 3))
second_part <- substr(x_out, 4, nchar(x_out))
msg <- c(msg, paste0("Input \"", x_search, "\" was assumed to be a microorganism code - tried to match on ", vector_and(c(gsub("[a-z]*", "(...)", first_part, fixed = TRUE), second_part), sort = FALSE)))
msg <<- c(msg, paste0("Input \"", x_search, "\" was assumed to be a microorganism code - tried to match on \"", gsub("[a-z]*", AMR_env$dots, first_part, fixed = TRUE), " ", second_part, AMR_env$dots, "\""))
filtr <- which(AMR_env$MO_lookup$fullname_lower %like_case% paste0("(^| )", first_part, ".* ", second_part))
} else {
# for genus or species or subspecies
@ -328,13 +335,16 @@ as.mo <- function(x,
minimum_matching_score_current <- minimum_matching_score_current / AMR_env$MO_lookup$prevalence[match(mo_to_search, AMR_env$MO_lookup$fullname)]
# correct back for kingdom
minimum_matching_score_current <- minimum_matching_score_current / AMR_env$MO_lookup$kingdom_index[match(mo_to_search, AMR_env$MO_lookup$fullname)]
} else {
minimum_matching_score_current <- minimum_matching_score
minimum_matching_score_current <- pmax(minimum_matching_score_current, m)
if (length(m) > 1 && all(m <= 0.55, na.rm = TRUE)) {
# if the highest score is 0.5, we have nothing serious - 0.5 is the lowest for pathogenic group 1
# make everything NA so the results will get removed below
m[seq_len(length(m))] <- NA_real_
}
if (sum(m >= minimum_matching_score_current) > 10) {
# at least 10 are left over, make the ones under `m` NA
m[m < minimum_matching_score_current] <- NA_real_
} else {
# minimum_matching_score was set, so remove everything below it
m[m < minimum_matching_score] <- NA_real_
minimum_matching_score_current <- minimum_matching_score
}
top_hits <- mo_to_search[order(m, decreasing = TRUE, na.last = NA)] # na.last = NA will remove the NAs
@ -815,7 +825,7 @@ rep.mo <- function(x, ...) {
print.mo_uncertainties <- function(x, n = 10, ...) {
more_than_50 <- FALSE
if (NROW(x) == 0) {
cat(word_wrap("No uncertainties to show. Only uncertainties of the last call of `as.mo()` or any `mo_*()` function are stored.\n\n", add_fn = font_blue))
cat(word_wrap("No uncertainties to show. Only uncertainties of the last call to `as.mo()` or any `mo_*()` function are stored.\n\n", add_fn = font_blue))
return(invisible(NULL))
} else if (NROW(x) > 50) {
more_than_50 <- TRUE
@ -833,20 +843,20 @@ print.mo_uncertainties <- function(x, n = 10, ...) {
if (has_colour()) {
cat(word_wrap("Colour keys: ",
col_red(" 0.000-0.499 "),
col_orange(" 0.500-0.599 "),
col_yellow(" 0.600-0.699 "),
col_green(" 0.700-1.000"),
col_red(" 0.000-0.549 "),
col_orange(" 0.550-0.649 "),
col_yellow(" 0.650-0.749 "),
col_green(" 0.750-1.000"),
add_fn = font_blue
), font_green_bg(" "), "\n", sep = "")
}
score_set_colour <- function(text, scores) {
# set colours to scores
text[scores >= 0.7] <- col_green(text[scores >= 0.7])
text[scores >= 0.6 & scores < 0.7] <- col_yellow(text[scores >= 0.6 & scores < 0.7])
text[scores >= 0.5 & scores < 0.6] <- col_orange(text[scores >= 0.5 & scores < 0.6])
text[scores < 0.5] <- col_red(text[scores < 0.5])
text[scores >= 0.75] <- col_green(text[scores >= 0.75])
text[scores >= 0.65 & scores < 0.75] <- col_yellow(text[scores >= 0.65 & scores < 0.75])
text[scores >= 0.55 & scores < 0.65] <- col_orange(text[scores >= 0.55 & scores < 0.65])
text[scores < 0.55] <- col_red(text[scores < 0.55])
text
}

8
R/random.R
View File

@ -136,11 +136,11 @@ random_exec <- function(type, size, mo = NULL, ab = NULL) {
# get highest/lowest +/- random 1 to 3 higher factors of two
max_range <- mic_range[min(
length(mic_range),
which(mic_range == max(df$breakpoint_R)) + sample(c(1:3), 1)
which(mic_range == max(df$breakpoint_R, na.rm = TRUE)) + sample(c(1:3), 1)
)]
min_range <- mic_range[max(
1,
which(mic_range == min(df$breakpoint_S)) - sample(c(1:3), 1)
which(mic_range == min(df$breakpoint_S, na.rm = TRUE)) - sample(c(1:3), 1)
)]
mic_range_new <- mic_range[mic_range <= max_range & mic_range >= min_range]
@ -158,8 +158,8 @@ random_exec <- function(type, size, mo = NULL, ab = NULL) {
return(out)
} else if (type == "DISK") {
set_range <- seq(
from = as.integer(min(df$breakpoint_R) / 1.25),
to = as.integer(max(df$breakpoint_S) * 1.25),
from = as.integer(min(df$breakpoint_R, na.rm = TRUE) / 1.25),
to = as.integer(max(df$breakpoint_S, na.rm = TRUE) * 1.25),
by = 1
)
out <- sample(set_range, size = size, replace = TRUE)

31
R/sir.R
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@ -43,6 +43,7 @@
#' @param add_intrinsic_resistance *(only useful when using a EUCAST guideline)* a [logical] to indicate whether intrinsic antibiotic resistance must also be considered for applicable bug-drug combinations, meaning that e.g. ampicillin will always return "R" in *Klebsiella* species. Determination is based on the [intrinsic_resistant] data set, that itself is based on `r format_eucast_version_nr(3.3)`.
#' @param include_screening a [logical] to indicate that clinical breakpoints for screening are allowed - the default is `FALSE`. Can also be set with the [package option][AMR-options] [`AMR_include_screening`][AMR-options].
#' @param include_PKPD a [logical] to indicate that PK/PD clinical breakpoints must be applied as a last resort - the default is `TRUE`. Can also be set with the [package option][AMR-options] [`AMR_include_PKPD`][AMR-options].
#' @param ecoff a [logical] to indicate that ECOFF (Epidemiological Cut-Off) values must be used **instead** of other clinical breakpoints - the default is `FALSE`. Can also be set with the [package option][AMR-options] [`AMR_ecoff`][AMR-options].
#' @param reference_data a [data.frame] to be used for interpretation, which defaults to the [clinical_breakpoints] data set. Changing this argument allows for using own interpretation guidelines. This argument must contain a data set that is equal in structure to the [clinical_breakpoints] data set (same column names and column types). Please note that the `guideline` argument will be ignored when `reference_data` is manually set.
#' @param threshold maximum fraction of invalid antimicrobial interpretations of `x`, see *Examples*
#' @param ... for using on a [data.frame]: names of columns to apply [as.sir()] on (supports tidy selection such as `column1:column4`). Otherwise: arguments passed on to methods.
@ -428,6 +429,7 @@ as.sir.mic <- function(x,
reference_data = AMR::clinical_breakpoints,
include_screening = getOption("AMR_include_screening", FALSE),
include_PKPD = getOption("AMR_include_PKPD", TRUE),
ecoff = getOption("AMR_ecoff", FALSE),
...) {
as_sir_method(
method_short = "mic",
@ -442,6 +444,7 @@ as.sir.mic <- function(x,
reference_data = reference_data,
include_screening = include_screening,
include_PKPD = include_PKPD,
ecoff = ecoff,
...
)
}
@ -457,6 +460,7 @@ as.sir.disk <- function(x,
reference_data = AMR::clinical_breakpoints,
include_screening = getOption("AMR_include_screening", FALSE),
include_PKPD = getOption("AMR_include_PKPD", TRUE),
ecoff = getOption("AMR_ecoff", FALSE),
...) {
as_sir_method(
method_short = "disk",
@ -471,6 +475,7 @@ as.sir.disk <- function(x,
reference_data = reference_data,
include_screening = include_screening,
include_PKPD = include_PKPD,
ecoff = ecoff,
...
)
}
@ -486,7 +491,8 @@ as.sir.data.frame <- function(x,
add_intrinsic_resistance = FALSE,
reference_data = AMR::clinical_breakpoints,
include_screening = getOption("AMR_include_screening", FALSE),
include_PKPD = getOption("AMR_include_PKPD", TRUE)) {
include_PKPD = getOption("AMR_include_PKPD", TRUE),
ecoff = getOption("AMR_ecoff", FALSE)) {
meet_criteria(x, allow_class = "data.frame") # will also check for dimensions > 0
meet_criteria(col_mo, allow_class = "character", is_in = colnames(x), allow_NULL = TRUE)
meet_criteria(guideline, allow_class = "character", has_length = 1)
@ -494,6 +500,9 @@ as.sir.data.frame <- function(x,
meet_criteria(conserve_capped_values, allow_class = "logical", has_length = 1)
meet_criteria(add_intrinsic_resistance, allow_class = "logical", has_length = 1)
meet_criteria(reference_data, allow_class = "data.frame")
meet_criteria(include_screening, allow_class = "logical", has_length = 1)
meet_criteria(include_PKPD, allow_class = "logical", has_length = 1)
meet_criteria(ecoff, allow_class = "logical", has_length = 1)
x.bak <- x
for (i in seq_len(ncol(x))) {
@ -625,6 +634,7 @@ as.sir.data.frame <- function(x,
reference_data = reference_data,
include_screening = include_screening,
include_PKPD = include_PKPD,
ecoff = ecoff,
is_data.frame = TRUE
)
} else if (types[i] == "disk") {
@ -642,6 +652,7 @@ as.sir.data.frame <- function(x,
reference_data = reference_data,
include_screening = include_screening,
include_PKPD = include_PKPD,
ecoff = ecoff,
is_data.frame = TRUE
)
} else if (types[i] == "sir") {
@ -711,6 +722,7 @@ as_sir_method <- function(method_short,
reference_data,
include_screening,
include_PKPD,
ecoff,
...) {
meet_criteria(x, allow_NA = TRUE, .call_depth = -2)
meet_criteria(mo, allow_class = c("mo", "character"), allow_NULL = TRUE, .call_depth = -2)
@ -722,6 +734,7 @@ as_sir_method <- function(method_short,
meet_criteria(reference_data, allow_class = "data.frame", .call_depth = -2)
meet_criteria(include_screening, allow_class = "logical", has_length = 1, .call_depth = -2)
meet_criteria(include_PKPD, allow_class = "logical", has_length = 1, .call_depth = -2)
meet_criteria(ecoff, allow_class = "logical", has_length = 1, .call_depth = -2)
check_reference_data(reference_data, .call_depth = -2)
# for dplyr's across()
@ -820,7 +833,11 @@ as_sir_method <- function(method_short,
message_("=> Interpreting ", method_long, " of ", ifelse(isTRUE(list(...)$is_data.frame), "column ", ""),
agent_formatted,
mo_var_found,
" according to ", ifelse(identical(reference_data, AMR::clinical_breakpoints),
" according to ",
ifelse(isTRUE(ecoff),
"ECOFF values of ",
""),
ifelse(identical(reference_data, AMR::clinical_breakpoints),
font_bold(guideline_coerced),
"manually defined 'reference_data'"
),
@ -887,6 +904,16 @@ as_sir_method <- function(method_short,
breakpoints <- breakpoints %pm>%
subset(mo != "UNKNOWN" & ref_tbl %unlike% "PK.*PD")
}
if (isFALSE(ecoff)) {
# remove ECOFF interpretations from the breakpoints table
breakpoints <- breakpoints %pm>%
subset(ref_tbl != "ECOFF")
} else {
# keep only ECOFF interpretations from the breakpoints table
breakpoints <- breakpoints %pm>%
subset(ref_tbl == "ECOFF") %pm>%
pm_mutate(breakpoint_S = ecoff, breakpoint_R = ecoff)
}
msgs <- character(0)
if (nrow(breakpoints) == 0) {

2
R/zzz.R
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@ -83,9 +83,11 @@ if (utf8_supported && !is_latex) {
# \u2139 is a symbol officially named 'information source'
AMR_env$info_icon <- "\u2139"
AMR_env$bullet_icon <- "\u2022"
AMR_env$dots <- "\u2026"
} else {
AMR_env$info_icon <- "i"
AMR_env$bullet_icon <- "*"
AMR_env$dots <- "..."
}
.onLoad <- function(lib, pkg) {

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data-raw/dosage.txt
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@ -1,4 +1,171 @@
"ab" "name" "type" "dose" "dose_times" "administration" "notes" "original_txt" "eucast_version"
"AMK" "Amikacin" "standard_dosage" "25-30 mg/kg" 1 "iv" "" "25-30 mg/kg x 1 iv" 13
"AMX" "Amoxicillin" "high_dosage" "2 g" 6 "iv" "" "2 g x 6 iv" 13
"AMX" "Amoxicillin" "standard_dosage" "1 g" 3 "iv" "" "1 g x 3-4 iv" 13
"AMX" "Amoxicillin" "high_dosage" "0.75-1 g" 3 "oral" "" "0.75-1 g x 3 oral" 13
"AMX" "Amoxicillin" "standard_dosage" "0.5 g" 3 "oral" "" "0.5 g x 3 oral" 13
"AMX" "Amoxicillin" "uncomplicated_uti" "0.5 g" 3 "oral" "" "0.5 g x 3 oral" 13
"AMC" "Amoxicillin/clavulanic acid" "high_dosage" "2 g + 0.2 g" 3 "iv" "" "(2 g amoxicillin + 0.2 g clavulanic acid) x 3 iv" 13
"AMC" "Amoxicillin/clavulanic acid" "standard_dosage" "1 g + 0.2 g" 3 "iv" "" "(1 g amoxicillin + 0.2 g clavulanic acid) x 3-4 iv" 13
"AMC" "Amoxicillin/clavulanic acid" "high_dosage" "0.875 g + 0.125 g" 3 "oral" "" "(0.875 g amoxicillin + 0.125 g clavulanic acid) x 3 oral" 13
"AMC" "Amoxicillin/clavulanic acid" "standard_dosage" "0.5 g + 0.125 g" 3 "oral" "" "(0.5 g amoxicillin + 0.125 g clavulanic acid) x 3 oral" 13
"AMC" "Amoxicillin/clavulanic acid" "uncomplicated_uti" "0.5 g + 0.125 g" 3 "oral" "" "(0.5 g amoxicillin + 0.125 g clavulanic acid) x 3 oral" 13
"AMP" "Ampicillin" "high_dosage" "2 g" 4 "iv" "" "2 g x 4 iv" 13
"AMP" "Ampicillin" "standard_dosage" "2 g" 3 "iv" "" "2 g x 3 iv" 13
"SAM" "Ampicillin/sulbactam" "high_dosage" "2 g + 1 g" 4 "iv" "" "(2 g ampicillin + 1 g sulbactam) x 4 iv" 13
"SAM" "Ampicillin/sulbactam" "standard_dosage" "2 g + 1 g" 3 "iv" "" "(2 g ampicillin + 1 g sulbactam) x 3 iv" 13
"AZM" "Azithromycin" "standard_dosage" "0.5 g" 1 "iv" "" "0.5 g x 1 iv" 13
"AZM" "Azithromycin" "standard_dosage" "0.5 g" 1 "oral" "" "0.5 g x 1 oral" 13
"ATM" "Aztreonam" "high_dosage" "2 g" 4 "iv" "" "2 g x 4 iv" 13
"ATM" "Aztreonam" "standard_dosage" "1 g" 3 "iv" "" "1 g x 3 iv" 13
"PEN" "Benzylpenicillin" "high_dosage" "1.2 g" 4 "iv" "" "1.2 g (2 MU) x 4-6 iv" 13
"PEN" "Benzylpenicillin" "standard_dosage" "0.6 g" 4 "iv" "" "0.6 g (1 MU) x 4 iv" 13
"CEC" "Cefaclor" "high_dosage" "1 g" 3 "oral" "" "1 g x 3 oral" 13
"CEC" "Cefaclor" "standard_dosage" "0.25-0.5 g" 3 "oral" "" "0.25-0.5 g x 3 oral" 13
"CFR" "Cefadroxil" "standard_dosage" "0.5-1 g" 2 "oral" "" "0.5-1 g x 2 oral" 13
"CFR" "Cefadroxil" "uncomplicated_uti" "0.5-1 g" 2 "oral" "" "0.5-1 g x 2 oral" 13
"LEX" "Cefalexin" "standard_dosage" "0.25-1 g" 2 "oral" "" "0.25-1 g x 2-3 oral" 13
"LEX" "Cefalexin" "uncomplicated_uti" "0.25-1 g" 2 "oral" "" "0.25-1 g x 2-3 oral" 13
"CZO" "Cefazolin" "high_dosage" "2 g" 3 "iv" "" "2 g x 3 iv" 13
"CZO" "Cefazolin" "standard_dosage" "1 g" 3 "iv" "" "1 g x 3 iv" 13
"FEP" "Cefepime" "high_dosage" "2 g" 3 "iv" "" "2 g x 3 iv" 13
"FEP" "Cefepime" "standard_dosage" "2 g" 2 "iv" "" "2 g x 2 iv" 13
"FDC" "Cefiderocol" "standard_dosage" "2 g" 3 "iv" "over 3 hours" "2 g x 3 iv over 3 hours" 13
"CFM" "Cefixime" "standard_dosage" "0.2-0.4 g" 2 "oral" "" "0.2-0.4 g x 2 oral" 13
"CFM" "Cefixime" "uncomplicated_uti" "0.2-0.4 g" 2 "oral" "" "0.2-0.4 g x 2 oral" 13
"CTX" "Cefotaxime" "high_dosage" "2 g" 3 "iv" "" "2 g x 3 iv" 13
"CTX" "Cefotaxime" "standard_dosage" "1 g" 3 "iv" "" "1 g x 3 iv" 13
"CPD" "Cefpodoxime" "standard_dosage" "0.1-0.2 g" 2 "oral" "" "0.1-0.2 g x 2 oral" 13
"CPD" "Cefpodoxime" "uncomplicated_uti" "0.1-0.2 g" 2 "oral" "" "0.1-0.2 g x 2 oral" 13
"CPT" "Ceftaroline" "high_dosage" "0.6 g" 3 "iv" "over 2 hours" "0.6 g x 3 iv over 2 hours" 13
"CPT" "Ceftaroline" "standard_dosage" "0.6 g" 2 "iv" "over 1 hour" "0.6 g x 2 iv over 1 hour" 13
"CAZ" "Ceftazidime" "high_dosage" "1 g" 6 "iv" "" "1 g x 6 iv" 13
"CAZ" "Ceftazidime" "standard_dosage" "1 g" 3 "iv" "" "1 g x 3 iv" 13
"CZA" "Ceftazidime/avibactam" "standard_dosage" "2 g + 0.5 g" 3 "iv" "over 2 hours" "(2 g ceftazidime + 0.5 g avibactam) x 3 iv over 2 hours" 13
"CTB" "Ceftibuten" "standard_dosage" "0.4 g" 1 "oral" "" "0.4 g x 1 oral" 13
"BPR" "Ceftobiprole" "standard_dosage" "0.5 g" 3 "iv" "over 2 hours" "0.5 g x 3 iv over 2 hours" 13
"CZT" "Ceftolozane/tazobactam" "standard_dosage" "1 g + 0.5 g" 3 "iv" "over 1 hour" "(1 g ceftolozane + 0.5 g tazobactam) x 3 iv over 1 hour" 13
"CZT" "Ceftolozane/tazobactam" "standard_dosage" "2 g + 1 g" 3 "iv" "over 1 hour" "(2 g ceftolozane + 1 g tazobactam) x 3 iv over 1 hour" 13
"CRO" "Ceftriaxone" "high_dosage" "4 g" 1 "iv" "" "4 g x 1 iv" 13
"CRO" "Ceftriaxone" "standard_dosage" "2 g" 1 "iv" "" "2 g x 1 iv" 13
"CXM" "Cefuroxime" "high_dosage" "1.5 g" 3 "iv" "" "1.5 g x 3 iv" 13
"CXM" "Cefuroxime" "standard_dosage" "0.75 g" 3 "iv" "" "0.75 g x 3 iv" 13
"CXM" "Cefuroxime" "high_dosage" "0.5 g" 2 "oral" "" "0.5 g x 2 oral" 13
"CXM" "Cefuroxime" "standard_dosage" "0.25 g" 2 "oral" "" "0.25 g x 2 oral" 13
"CXM" "Cefuroxime" "uncomplicated_uti" "0.25 g" 2 "oral" "" "0.25 g x 2 oral" 13
"CHL" "Chloramphenicol" "high_dosage" "2 g" 4 "iv" "" "2 g x 4 iv" 13
"CHL" "Chloramphenicol" "standard_dosage" "1 g" 4 "iv" "" "1 g x 4 iv" 13
"CHL" "Chloramphenicol" "high_dosage" "2 g" 4 "oral" "" "2 g x 4 oral" 13
"CHL" "Chloramphenicol" "standard_dosage" "1 g" 4 "oral" "" "1 g x 4 oral" 13
"CIP" "Ciprofloxacin" "high_dosage" "0.4 g" 3 "iv" "" "0.4 g x 3 iv" 13
"CIP" "Ciprofloxacin" "standard_dosage" "0.4 g" 2 "iv" "" "0.4 g x 2 iv" 13
"CIP" "Ciprofloxacin" "high_dosage" "0.75 g" 2 "oral" "" "0.75 g x 2 oral" 13
"CIP" "Ciprofloxacin" "standard_dosage" "0.5 g" 2 "oral" "" "0.5 g x 2 oral" 13
"CLR" "Clarithromycin" "high_dosage" "0.5 g" 2 "oral" "" "0.5 g x 2 oral" 13
"CLR" "Clarithromycin" "standard_dosage" "0.25 g" 2 "oral" "" "0.25 g x 2 oral" 13
"CLI" "Clindamycin" "high_dosage" "0.9 g" 3 "iv" "" "0.9 g x 3 iv" 13
"CLI" "Clindamycin" "standard_dosage" "0.6 g" 3 "iv" "" "0.6 g x 3 iv" 13
"CLI" "Clindamycin" "high_dosage" "0.3 g" 4 "oral" "" "0.3 g x 4 oral" 13
"CLI" "Clindamycin" "standard_dosage" "0.3 g" 2 "oral" "" "0.3 g x 2 oral" 13
"CLO" "Cloxacillin" "high_dosage" "2 g" 6 "iv" "" "2 g x 6 iv" 13
"CLO" "Cloxacillin" "standard_dosage" "1 g" 4 "iv" "" "1 g x 4 iv" 13
"CLO" "Cloxacillin" "high_dosage" "1 g" 4 "oral" "" "1 g x 4 oral" 13
"CLO" "Cloxacillin" "standard_dosage" "0.5 g" 4 "oral" "" "0.5 g x 4 oral" 13
"COL" "Colistin" "standard_dosage" "4.5 MU" 2 "iv" "loading dose of 9 MU" "4.5 MU x 2 iv with a loading dose of 9 MU" 13
"DAL" "Dalbavancin" "standard_dosage" "1 g" 1 "iv" "over 30 minutes on day 8" "1 g x 1 iv over 30 minutes on day 1 If needed, 0.5 g x 1 iv over 30 minutes on day 8" 13
"DAP" "Daptomycin" "standard_dosage" "4 mg/kg" 1 "iv" "" "4 mg/kg x 1 iv" 13
"DAP" "Daptomycin" "standard_dosage" "6 mg/kg" 1 "iv" "" "6 mg/kg x 1 iv" 13
"DFX" "Delafloxacin" "standard_dosage" "0.3 g" 2 "iv" "" "0.3 g x 2 iv" 13
"DFX" "Delafloxacin" "standard_dosage" "0.45 g" 2 "oral" "" "0.45 g x 2 oral" 13
"DIC" "Dicloxacillin" "high_dosage" "2 g" 6 "iv" "" "2 g x 6 iv" 13
"DIC" "Dicloxacillin" "standard_dosage" "1 g" 4 "iv" "" "1 g x 4 iv" 13
"DIC" "Dicloxacillin" "high_dosage" "2 g" 4 "oral" "" "2 g x 4 oral" 13
"DIC" "Dicloxacillin" "standard_dosage" "0.5-1 g" 4 "oral" "" "0.5-1 g x 4 oral" 13
"DOR" "Doripenem" "high_dosage" "1 g" 3 "iv" "over 1 hour" "1 g x 3 iv over 1 hour" 13
"DOR" "Doripenem" "standard_dosage" "0.5 g" 3 "iv" "over 1 hour" "0.5 g x 3 iv over 1 hour" 13
"DOX" "Doxycycline" "high_dosage" "0.2 g" 1 "oral" "" "0.2 g x 1 oral" 13
"DOX" "Doxycycline" "standard_dosage" "0.1 g" 1 "oral" "" "0.1 g x 1 oral" 13
"ERV" "Eravacycline" "standard_dosage" "1 mg/kg" 2 "iv" "" "1 mg/kg x 2 iv" 13
"ETP" "Ertapenem" "standard_dosage" "1 g" 1 "iv" "over 30 minutes" "1 g x 1 iv over 30 minutes" 13
"ERY" "Erythromycin" "high_dosage" "1 g" 4 "iv" "" "1 g x 4 iv" 13
"ERY" "Erythromycin" "standard_dosage" "0.5 g" 2 "iv" "" "0.5 g x 2-4 iv" 13
"ERY" "Erythromycin" "high_dosage" "1 g" 4 "oral" "" "1 g x 4 oral" 13
"ERY" "Erythromycin" "standard_dosage" "0.5 g" 2 "oral" "" "0.5 g x 2-4 oral" 13
"FDX" "Fidaxomicin" "standard_dosage" "0.2 g" 2 "oral" "" "0.2 g x 2 oral" 13
"FLC" "Flucloxacillin" "high_dosage" "2 g" 6 "iv" "" "2 g x 6 iv" 13
"FLC" "Flucloxacillin" "standard_dosage" "2 g" 4 "iv" "" "2 g x 4 iv (or 1 g x 6 iv)" 13
"FLC" "Flucloxacillin" "high_dosage" "1 g" 4 "oral" "" "1 g x 4 oral" 13
"FLC" "Flucloxacillin" "standard_dosage" "1 g" 3 "oral" "" "1 g x 3 oral" 13
"FOS" "Fosfomycin" "high_dosage" "8 g" 3 "iv" "" "8 g x 3 iv" 13
"FOS" "Fosfomycin" "standard_dosage" "4 g" 3 "iv" "" "4 g x 3 iv" 13
"FUS" "Fusidic acid" "high_dosage" "0.5 g" 3 "iv" "" "0.5 g x 3 iv" 13
"FUS" "Fusidic acid" "standard_dosage" "0.5 g" 2 "iv" "" "0.5 g x 2 iv" 13
"FUS" "Fusidic acid" "high_dosage" "0.5 g" 3 "oral" "" "0.5 g x 3 oral" 13
"FUS" "Fusidic acid" "standard_dosage" "0.5 g" 2 "oral" "" "0.5 g x 2 oral" 13
"GEN" "Gentamicin" "standard_dosage" "6-7 mg/kg" 1 "iv" "" "6-7 mg/kg x 1 iv" 13
"IPM" "Imipenem" "high_dosage" "1 g" 4 "iv" "over 30 minutes" "1 g x 4 iv over 30 minutes" 13
"IPM" "Imipenem" "standard_dosage" "0.5 g" 4 "iv" "over 30 minutes" "0.5 g x 4 iv over 30 minutes" 13
"IMR" "Imipenem/relebactam" "standard_dosage" "0.5 g + 0.25 g" 4 "iv" "over 30 minutes" "(0.5 g imipenem + 0.25 g relebactam) x 4 iv over 30 minutes" 13
"IMR" "Imipenem/relebactam" "standard_dosage" "0.5 g + 0.25 g" 4 "iv" "over 30 minutes" "(0.5 g imipenem + 0.25 g relebactam) x 4 iv over 30 minutes" 13
"LMU" "Lefamulin" "standard_dosage" "0.15 g" 2 "iv" "or 0.6 g x 2 oral" "0.15 g x 2 iv or 0.6 g x 2 oral" 13
"LMU" "Lefamulin" "standard_dosage" "0.6 g" 2 "oral" "" "0.6 g x 2 oral" 13
"LVX" "Levofloxacin" "high_dosage" "0.5 g" 2 "iv" "" "0.5 g x 2 iv" 13
"LVX" "Levofloxacin" "standard_dosage" "0.5 g" 1 "iv" "" "0.5 g x 1 iv" 13
"LVX" "Levofloxacin" "high_dosage" "0.5 g" 2 "oral" "" "0.5 g x 2 oral" 13
"LVX" "Levofloxacin" "standard_dosage" "0.5 g" 1 "oral" "" "0.5 g x 1 oral" 13
"LNZ" "Linezolid" "standard_dosage" "0.6 g" 2 "iv" "" "0.6 g x 2 iv" 13
"LNZ" "Linezolid" "standard_dosage" "0.6 g" 2 "oral" "" "0.6 g x 2 oral" 13
"MEM" "Meropenem" "high_dosage" "2 g" 3 "iv" "over 3 hours" "2 g x 3 iv over 3 hours" 13
"MEM" "Meropenem" "standard_dosage" "1 g" 3 "iv" "over 30 minutes" "1 g x 3 iv over 30 minutes" 13
"MEV" "Meropenem/vaborbactam" "standard_dosage" "2 g + 2 g" 3 "iv" "over 3 hours" "(2 g meropenem + 2 g vaborbactam) x 3 iv over 3 hours" 13
"MTR" "Metronidazole" "high_dosage" "0.5 g" 3 "iv" "" "0.5 g x 3 iv" 13
"MTR" "Metronidazole" "standard_dosage" "0.4 g" 3 "iv" "" "0.4 g x 3 iv" 13
"MTR" "Metronidazole" "high_dosage" "0.5 g" 3 "oral" "" "0.5 g x 3 oral" 13
"MTR" "Metronidazole" "standard_dosage" "0.4 g" 3 "oral" "" "0.4 g x 3 oral" 13
"MNO" "Minocycline" "standard_dosage" "0.1 g" 2 "oral" "" "0.1 g x 2 oral" 13
"MFX" "Moxifloxacin" "standard_dosage" "0.4 g" 1 "iv" "" "0.4 g x 1 iv" 13
"MFX" "Moxifloxacin" "standard_dosage" "0.4 g" 1 "oral" "" "0.4 g x 1 oral" 13
"OFX" "Ofloxacin" "high_dosage" "0.4 g" 2 "iv" "" "0.4 g x 2 iv" 13
"OFX" "Ofloxacin" "standard_dosage" "0.2 g" 2 "iv" "" "0.2 g x 2 iv" 13
"OFX" "Ofloxacin" "high_dosage" "0.4 g" 2 "oral" "" "0.4 g x 2 oral" 13
"OFX" "Ofloxacin" "standard_dosage" "0.2 g" 2 "oral" "" "0.2 g x 2 oral" 13
"ORI" "Oritavancin" "standard_dosage" "1.2 g" 1 "iv" "" "1.2 g x 1 (single dose) iv over 3 hours" 13
"OXA" "Oxacillin" "high_dosage" "1 g" 6 "iv" "" "1 g x 6 iv" 13
"OXA" "Oxacillin" "standard_dosage" "1 g" 4 "iv" "" "1 g x 4 iv" 13
"PHN" "Phenoxymethylpenicillin" "standard_dosage" "0.5-2 g" 3 "oral" "" "0.5-2 g x 3-4 oral" 13
"PIP" "Piperacillin" "high_dosage" "4 g" 4 "iv" "" "4 g x 4 iv by extended 3-hour infusion" 13
"PIP" "Piperacillin" "standard_dosage" "4 g" 4 "iv" "" "4 g x 4 iv" 13
"TZP" "Piperacillin/tazobactam" "high_dosage" "4 g + 0.5 g" 4 "iv" "" "(4 g piperacillin + 0.5 g tazobactam) x 4 iv by extended 3-hour infusion" 13
"TZP" "Piperacillin/tazobactam" "standard_dosage" "4 g + 0.5 g" 4 "iv" "" "(4 g piperacillin + 0.5 g tazobactam) x 4 iv 30-minute infusion or x 3 iv by extended 4-hour infusion" 13
"QDA" "Quinupristin/dalfopristin" "high_dosage" "7.5 mg/kg" 3 "iv" "" "7.5 mg/kg x 3 iv" 13
"QDA" "Quinupristin/dalfopristin" "standard_dosage" "7.5 mg/kg" 2 "iv" "" "7.5 mg/kg x 2 iv" 13
"RIF" "Rifampicin" "standard_dosage" "0.6 g" 1 "iv" "" "0.6 g x 1 iv" 13
"RIF" "Rifampicin" "standard_dosage" "0.6 g" 1 "oral" "" "0.6 g x 1 oral" 13
"RXT" "Roxithromycin" "standard_dosage" "0.15 g" 2 "oral" "" "0.15 g x 2 oral" 13
"SPT" "Spectinomycin" "standard_dosage" "2 g" 1 "im" "" "2 g x 1 im" 13
"TZD" "Tedizolid" "standard_dosage" "0.2 g" 1 "iv" "" "0.2 g x 1 iv" 13
"TZD" "Tedizolid" "standard_dosage" "0.2 g" 1 "oral" "" "0.2 g x 1 oral" 13
"TEC" "Teicoplanin" "high_dosage" "0.8 g" 1 "iv" "" "0.8 g x 1 iv" 13
"TEC" "Teicoplanin" "standard_dosage" "0.4 g" 1 "iv" "" "0.4 g x 1 iv" 13
"TLV" "Telavancin" "standard_dosage" "10 mg/kg" 1 "iv" "over 1 hour" "10 mg/kg x 1 iv over 1 hour" 13
"TLT" "Telithromycin" "standard_dosage" "0.8 g" 1 "oral" "" "0.8 g x 1 oral" 13
"TEM" "Temocillin" "high_dosage" "2 g" 3 "iv" "" "2 g x 3 iv" 13
"TEM" "Temocillin" "standard_dosage" "2 g" 2 "iv" "" "2 g x 2 iv" 13
"TCY" "Tetracycline" "high_dosage" "0.5 g" 4 "oral" "" "0.5 g x 4 oral" 13
"TCY" "Tetracycline" "standard_dosage" "0.25 g" 4 "oral" "" "0.25 g x 4 oral" 13
"TIC" "Ticarcillin" "high_dosage" "3 g" 6 "iv" "" "3 g x 6 iv" 13
"TIC" "Ticarcillin" "standard_dosage" "3 g" 4 "iv" "" "3 g x 4 iv" 13
"TCC" "Ticarcillin/clavulanic acid" "high_dosage" "3 g + 0.1 g" 6 "iv" "" "(3 g ticarcillin + 0.1 g clavulanic acid) x 6 iv" 13
"TCC" "Ticarcillin/clavulanic acid" "standard_dosage" "3 g + 0.1-0.2 g" 4 "iv" "" "(3 g ticarcillin + 0.1-0.2 g clavulanic acid) x 4 iv" 13
"TGC" "Tigecycline" "standard_dosage" "0.1 g" "loading dose followed by 50 mg x 2 iv" "0.1 g loading dose followed by 50 mg x 2 iv" 13
"TOB" "Tobramycin" "standard_dosage" "6-7 mg/kg" 1 "iv" "" "6-7 mg/kg x 1 iv" 13
"SXT" "Trimethoprim/sulfamethoxazole" "high_dosage" "0.24 g + 1.2 g" 2 "oral" "" "(0.24 g trimethoprim + 1.2 g sulfamethoxazole) x 2 oral" 13
"SXT" "Trimethoprim/sulfamethoxazole" "high_dosage" "0.24 g + 1.2 g" 2 "oral" "" "(0.24 g trimethoprim + 1.2 g sulfamethoxazole) x 2 oral or (0.24 g trimethoprim + 1.2 g sulfamethoxazole) x 2 iv" 13
"SXT" "Trimethoprim/sulfamethoxazole" "standard_dosage" "0.16 g + 0.8 g" 2 "oral" "" "(0.16 g trimethoprim + 0.8 g sulfamethoxazole) x 2 oral" 13
"SXT" "Trimethoprim/sulfamethoxazole" "standard_dosage" "0.16 g + 0.8 g" 2 "oral" "" "(0.16 g trimethoprim + 0.8 g sulfamethoxazole) x 2 oral or (0.16 g trimethoprim + 0.8 g sulfamethoxazole) x 2 iv" 13
"SXT" "Trimethoprim/sulfamethoxazole" "uncomplicated_uti" "0.16 g + 0.8 g" 2 "oral" "" "(0.16 g trimethoprim + 0.8 g sulfamethoxazole) x 2 oral" 13
"SXT" "Trimethoprim/sulfamethoxazole" "uncomplicated_uti" "0.16 g + 0.8 g" 2 "oral" "" "(0.16 g trimethoprim + 0.8 g sulfamethoxazole) x 2 oral" 13
"VAN" "Vancomycin" "standard_dosage" "1 g" 2 "iv" "" "1 g x 2 iv or 2 g x 1 by continuous infusion" 13
"AMK" "Amikacin" "standard_dosage" "25-30 mg/kg" 1 "iv" "" "25-30 mg/kg x 1 iv" 12
"AMX" "Amoxicillin" "high_dosage" "2 g" 6 "iv" "" "2 g x 6 iv" 12
"AMX" "Amoxicillin" "standard_dosage" "1 g" 3 "iv" "" "1 g x 3-4 iv" 12

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data-raw/reproduction_of_clinical_breakpoints.R
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@ -73,6 +73,7 @@ whonet_organisms.bak <- whonet_organisms
# generate the mo codes and add their names
whonet_organisms <- whonet_organisms.bak %>%
mutate(mo = as.mo(gsub("(sero[a-z]*| complex| nontypable| non[-][a-zA-Z]+|var[.]| not .*|sp[.],.*|, .*variant.*|, .*toxin.*|, microaer.*| beta-haem[.])", "", ORGANISM),
minimum_matching_score = 0.6,
keep_synonyms = TRUE,
language = "en"),
mo = case_when(ORGANISM %like% "Anaerobic" & ORGANISM %like% "negative" ~ as.mo("B_ANAER-NEG"),
@ -86,17 +87,21 @@ whonet_organisms <- whonet_organisms.bak %>%
new_mo_codes <- whonet_organisms %>%
mutate(
first_part = sapply(ORGANISM, function(x) strsplit(gsub("[^a-zA-Z _-]+", "", x), " ")[[1]][1], USE.NAMES = FALSE),
keep = mo_name %like_case% first_part | ORGANISM %like% "Gram " | ORGANISM == "Other" | ORGANISM %like% "anaerobic")
# update microorganisms.codes with the latest WHONET codes
microorganisms.codes <- microorganisms.codes %>%
# remove all old WHONET codes, whether we (in the end) keep them or not
filter(!toupper(code) %in% toupper(whonet_organisms$ORGANISM_CODE)) %>%
# and add the new ones
bind_rows(new_mo_codes %>%
keep = mo_name %like_case% first_part | ORGANISM %like% "Gram " | ORGANISM == "Other" | ORGANISM %like% "anaerobic") %>%
filter(keep == TRUE) %>%
transmute(code = toupper(ORGANISM_CODE),
mo = mo)) %>%
mo = mo)
# update microorganisms.codes with the latest WHONET codes
microorganisms.codes2 <- microorganisms.codes %>%
# remove all old WHONET codes, whether we (in the end) keep them or not
filter(!toupper(code) %in% toupper(new_mo_codes$code)) %>%
# and add the new ones
bind_rows(new_mo_codes) %>%
arrange(code)
# new codes:
microorganisms.codes2$code[which(!microorganisms.codes2$code %in% microorganisms.codes$code)]
mo_name(microorganisms.codes2$mo[which(!microorganisms.codes2$code %in% microorganisms.codes$code)], keep_synonyms = TRUE)
microorganisms.codes <- microorganisms.codes2
# Run this part to update ASIARS-Net:
# start
@ -140,11 +145,11 @@ whonet_antibiotics <- read_tsv("data-raw/WHONET/Resources/Antibiotics.txt", na =
breakpoints <- whonet_breakpoints %>%
mutate(code = toupper(ORGANISM_CODE)) %>%
left_join(bind_rows(microorganisms.codes,
left_join(bind_rows(microorganisms.codes %>% filter(!code %in% c("ALL", "GEN")),
# GEN (Generic) and ALL (All) are PK/PD codes
data.frame(code = c("ALL", "GEN"),
mo = rep(as.mo("UNKNOWN"), 2))))
# these ones lack a MO name, they cannot be used:
# these ones lack an MO name, they cannot be used:
unknown <- breakpoints %>%
filter(is.na(mo)) %>%
pull(code) %>%
@ -159,9 +164,13 @@ breakpoints %>%
filter(!WHONET_ABX_CODE %in% whonet_antibiotics$WHONET_ABX_CODE) %>%
count(YEAR, GUIDELINES, WHONET_ABX_CODE) %>%
arrange(desc(YEAR))
breakpoints %>%
filter(!WHONET_ABX_CODE %in% whonet_antibiotics$WHONET_ABX_CODE) %>%
pull(WHONET_ABX_CODE) %>%
unique()
# we cannot use them
breakpoints <- breakpoints %>%
filter(WHONET_ABX_CODE %in% whonet_antibiotics$WHONET_ABX_CODE)
# breakpoints <- breakpoints %>%
# filter(WHONET_ABX_CODE %in% whonet_antibiotics$WHONET_ABX_CODE)
# now check with our own antibiotics
breakpoints %>%
filter(!toupper(WHONET_ABX_CODE) %in% antibiotics$ab) %>%
@ -171,19 +180,19 @@ breakpoints %>%
breakpoints_new <- breakpoints %>%
# only last available 10 years
filter(YEAR > max(YEAR) - 10) %>%
# filter(YEAR > max(YEAR) - 10) %>%
transmute(
guideline = paste(GUIDELINES, YEAR),
method = TEST_METHOD,
site = gsub(".*(UTI|urinary|urine).*", "UTI", SITE_OF_INFECTION, ignore.case = TRUE),
site = SITE_OF_INFECTION,
mo,
rank_index = case_when(
is.na(mo_rank(mo)) ~ 6, # for UNKNOWN, B_GRAMN, B_ANAER, B_ANAER-NEG, etc.
mo_rank(mo) %like% "(infra|sub)" ~ 1,
mo_rank(mo) == "species" ~ 2,
mo_rank(mo) == "genus" ~ 3,
mo_rank(mo) == "family" ~ 4,
mo_rank(mo) == "order" ~ 5,
is.na(mo_rank(mo, keep_synonyms = TRUE)) ~ 6, # for UNKNOWN, B_GRAMN, B_ANAER, B_ANAER-NEG, etc.
mo_rank(mo, keep_synonyms = TRUE) %like% "(infra|sub)" ~ 1,
mo_rank(mo, keep_synonyms = TRUE) == "species" ~ 2,
mo_rank(mo, keep_synonyms = TRUE) == "genus" ~ 3,
mo_rank(mo, keep_synonyms = TRUE) == "family" ~ 4,
mo_rank(mo, keep_synonyms = TRUE) == "order" ~ 5,
TRUE ~ 6
),
ab = as.ab(WHONET_ABX_CODE),
@ -191,7 +200,7 @@ breakpoints_new <- breakpoints %>%
disk_dose = POTENCY,
breakpoint_S = S,
breakpoint_R = R,
uti = ifelse(is.na(site), FALSE, site == "UTI")
uti = ifelse(is.na(site), FALSE, gsub(".*(UTI|urinary|urine).*", "UTI", site) == "UTI")
) %>%
# Greek symbols and EM dash symbols are not allowed by CRAN, so replace them with ASCII:
mutate(disk_dose = disk_dose %>%
@ -206,8 +215,18 @@ breakpoints_new <- breakpoints %>%
breakpoints_new %>%
mutate(id = paste(guideline, ab, mo, method, site)) %>%
filter(id %in% .$id[which(duplicated(id))])
# remove duplicates
breakpoints_new <- breakpoints_new %>%
distinct(guideline, ab, mo, method, site, .keep_all = TRUE)
# clean disk zones and MICs
# fix reference table names
breakpoints_new %>% filter(guideline %like% "EUCAST", is.na(ref_tbl))
breakpoints_new <- breakpoints_new %>%
mutate(ref_tbl = case_when(is.na(ref_tbl) & guideline %like% "EUCAST 202" ~ lead(ref_tbl),
is.na(ref_tbl) ~ "Unknown",
TRUE ~ ref_tbl))
# clean disk zones
breakpoints_new[which(breakpoints_new$method == "DISK"), "breakpoint_S"] <- as.double(as.disk(breakpoints_new[which(breakpoints_new$method == "DISK"), "breakpoint_S", drop = TRUE]))
breakpoints_new[which(breakpoints_new$method == "DISK"), "breakpoint_R"] <- as.double(as.disk(breakpoints_new[which(breakpoints_new$method == "DISK"), "breakpoint_R", drop = TRUE]))
@ -227,7 +246,7 @@ breakpoints_new[which(breakpoints_new$breakpoint_R == 1025), "breakpoint_R"] <-
# WHONET adds one log2 level to the R breakpoint for their software, e.g. in AMC in Enterobacterales:
# EUCAST 2022 guideline: S <= 8 and R > 8
# WHONET file: S <= 8 and R >= 16
breakpoints_new %>% filter(guideline == "EUCAST 2022", ab == "AMC", mo == "B_[ORD]_ENTRBCTR", method == "MIC")
breakpoints_new %>% filter(guideline == "EUCAST 2023", ab == "AMC", mo == "B_[ORD]_ENTRBCTR", method == "MIC")
# this will make an MIC of 12 I, which should be R, so:
breakpoints_new <- breakpoints_new %>%
mutate(breakpoint_R = ifelse(guideline %like% "EUCAST" & method == "MIC" & log2(breakpoint_R) - log2(breakpoint_S) != 0,
@ -235,6 +254,7 @@ breakpoints_new <- breakpoints_new %>%
breakpoint_R
))
# fix disks as well
breakpoints_new %>% filter(guideline == "EUCAST 2023", ab == "AMC", mo == "B_[ORD]_ENTRBCTR", method == "DISK")
breakpoints_new <- breakpoints_new %>%
mutate(breakpoint_R = ifelse(guideline %like% "EUCAST" & method == "DISK" & breakpoint_S - breakpoint_R != 0,
breakpoint_R + 1,
@ -244,7 +264,7 @@ breakpoints_new <- breakpoints_new %>%
breakpoints_new[which(is.na(breakpoints_new$breakpoint_R)), "breakpoint_R"] <- breakpoints_new[which(is.na(breakpoints_new$breakpoint_R)), "breakpoint_S"]
# check again
breakpoints_new %>% filter(guideline == "EUCAST 2022", ab == "AMC", mo == "B_[ORD]_ENTRBCTR", method == "MIC")
breakpoints_new %>% filter(guideline == "EUCAST 2023", ab == "AMC", mo == "B_[ORD]_ENTRBCTR", method == "MIC")
# compare with current version
clinical_breakpoints %>% filter(guideline == "EUCAST 2022", ab == "AMC", mo == "B_[ORD]_ENTRBCTR", method == "MIC")
@ -252,6 +272,72 @@ clinical_breakpoints %>% filter(guideline == "EUCAST 2022", ab == "AMC", mo == "
dim(breakpoints_new)
dim(clinical_breakpoints)
# ECOFFs ----
# ECOFF = Epidemiological Cut-Off
whonet_ecoff <- read_tsv("data-raw/WHONET/Resources/Breakpoints.txt", na = c("", "NA", "-"), show_col_types = FALSE) %>%
filter(BREAKPOINT_TYPE == "ECOFF", GUIDELINES %in% c("CLSI", "EUCAST"))
ecoff <- whonet_ecoff %>%
filter(!ORGANISM_CODE %in% c("clu", "BFX", "PFX", "kma", "cdh")) %>%
transmute(guideline = paste(GUIDELINES, YEAR),
mo = as.mo(ORGANISM_CODE, keep_synonyms = TRUE),
ab = as.ab(WHONET_ABX_CODE),
method = TEST_METHOD,
ecoff = as.double(ECV_ECOFF)) %>%
filter(!is.na(ecoff)) %>%
distinct()
# join to breakpoints
breakpoints_new <- breakpoints_new %>%
bind_rows(breakpoints_new %>%
right_join(ecoff, by = c("guideline", "mo", "ab", "method"))) %>%
mutate(ref_tbl = ifelse(is.na(ref_tbl), "ECOFF", ref_tbl)) %>%
distinct(guideline, ab, mo, method, site, .keep_all = TRUE) %>%
arrange(desc(guideline), ab, mo, method) %>%
mutate(rank_index = case_when(
is.na(mo_rank(mo, keep_synonyms = TRUE)) ~ 6, # for UNKNOWN, B_GRAMN, B_ANAER, B_ANAER-NEG, etc.
mo_rank(mo, keep_synonyms = TRUE) %like% "(infra|sub)" ~ 1,
mo_rank(mo, keep_synonyms = TRUE) == "species" ~ 2,
mo_rank(mo, keep_synonyms = TRUE) == "genus" ~ 3,
mo_rank(mo, keep_synonyms = TRUE) == "family" ~ 4,
mo_rank(mo, keep_synonyms = TRUE) == "order" ~ 5,
TRUE ~ 6
)) %>%
mutate(uti = ifelse(is.na(uti), FALSE, uti)) %>%
relocate(ecoff, .after = breakpoint_R)
breakpoints_new.bak <- mutate(uti = ifelse(is.na(uti), FALSE, uti), .after = ecoff)
# EXTEND CoNS/CoPS/GAS/GBS ----
# extend all coagulase-postive/-negative staphylococci
CoNS <- breakpoints_new %>% filter(mo == as.mo("CoNS"))
for (m in MO_CONS[mo_subspecies(MO_CONS, keep_synonyms = TRUE) == ""]) {
breakpoints_new <- breakpoints_new %>%
bind_rows(CoNS %>%
mutate(mo = m))
}
CoPS <- breakpoints_new %>% filter(mo == as.mo("CoPS"))
for (m in MO_COPS[mo_subspecies(MO_COPS, keep_synonyms = TRUE) == ""]) {
breakpoints_new <- breakpoints_new %>%
bind_rows(CoPS %>%
mutate(mo = m))
}
# do the same for group A and B streptococci
breakpoints_new <- breakpoints_new %>%
bind_rows(breakpoints_new %>%
filter(mo == as.mo("Streptococcus Group A")) %>%
mutate(mo = as.mo("Streptococcus pyogenes"))) %>%
bind_rows(breakpoints_new %>%
filter(mo == as.mo("Streptococcus Group B")) %>%
mutate(mo = as.mo("Streptococcus agalactiae")))
# remove duplicates again for CoNS/CoPS/GBS and arrange
breakpoints_new <- breakpoints_new %>%
mutate(mo = as.mo(mo, keep_synonyms = TRUE)) %>%
distinct(guideline, ab, mo, method, site, .keep_all = TRUE) %>%
arrange(desc(guideline), ab, mo, method)
# Save to package ----

13
data-raw/reproduction_of_dosage.R
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@ -32,9 +32,9 @@ library(readxl)
library(cleaner)
# URL:
# https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Breakpoint_tables/Dosages_v_11.0_Breakpoint_Tables.pdf
# https://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Breakpoint_tables/Dosages_v_13.0_Breakpoint_Tables.pdf
# download the PDF file, open in Adobe Acrobat and export as Excel workbook
breakpoints_version <- 12
breakpoints_version <- 13
dosage_source <- read_excel("data-raw/Dosages_v_12.0_Breakpoint_Tables.xlsx", skip = 4, na = "None") %>%
format_names(snake_case = TRUE, penicillins = "drug") %>%
@ -170,10 +170,9 @@ dosage_new <- bind_rows(
) %>%
arrange(name, administration, type) %>%
filter(!is.na(dose), dose != ".") %>%
as.data.frame(stringsAsFactors = FALSE)
rownames(dosage_new) <- NULL
dosage <- bind_rows(dosage_new, AMR::dosage) %>%
# this makes it a tibble as well:
dataset_UTF8_to_ASCII()
usethis::use_data(dosage, internal = FALSE, overwrite = TRUE, version = 2)
dosage <- bind_rows(dosage_new, AMR::dosage)
usethis::use_data(dosage, internal = FALSE, overwrite = TRUE, version = 2, compress = "xz")

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4
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@ -20,7 +20,7 @@ The `AMR` package is a [free and open-source](#copyright) R package with [zero d
This work was published in the Journal of Statistical Software (Volume 104(3); [DOI 10.18637/jss.v104.i03](https://doi.org/10.18637/jss.v104.i03)) and formed the basis of two PhD theses ([DOI 10.33612/diss.177417131](https://doi.org/10.33612/diss.177417131) and [DOI 10.33612/diss.192486375](https://doi.org/10.33612/diss.192486375)).
After installing this package, R knows [**~52,000 distinct microbial species**](./reference/microorganisms.html) (updated December 2022) and all [**~600 antibiotic, antimycotic and antiviral drugs**](./reference/antibiotics.html) by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral breakpoint guidelines from CLSI and EUCAST are included from the last 10 years. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl), in collaboration with non-profit organisations [Certe Medical Diagnostics and Advice Foundation](https://www.certe.nl) and [University Medical Center Groningen](https://www.umcg.nl).
After installing this package, R knows [**~52,000 distinct microbial species**](./reference/microorganisms.html) (updated December 2022) and all [**~600 antibiotic, antimycotic and antiviral drugs**](./reference/antibiotics.html) by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI and EUCAST are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl), in collaboration with non-profit organisations [Certe Medical Diagnostics and Advice Foundation](https://www.certe.nl) and [University Medical Center Groningen](https://www.umcg.nl).
##### Used in over 175 countries, translated into 20 languages
@ -205,7 +205,7 @@ out %>% set_ab_names(property = "atc")
This package was intended as a comprehensive toolbox for integrated AMR data analysis. This package can be used for:
* Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the List of Prokaryotic names with Standing in Nomenclature ([LPSN]((https://lpsn.dsmz.de))) and the Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) ([manual](./reference/mo_property.html))
* Interpreting raw MIC and disk diffusion values, based on any CLSI or EUCAST guideline from the last 10 years ([manual](./reference/as.sir.html))
* Interpreting raw MIC and disk diffusion values, based on any CLSI or EUCAST guideline ([manual](./reference/as.sir.html))
* Retrieving antimicrobial drug names, doses and forms of administration from clinical health care records ([manual](./reference/ab_from_text.html))
* Determining first isolates to be used for AMR data analysis ([manual](./reference/first_isolate.html))
* Calculating antimicrobial resistance ([tutorial](./articles/AMR.html))

8
inst/tinytest/test-data.R
View File

@ -47,10 +47,10 @@ expect_false(any(is.na(microorganisms.codes$mo)))
expect_true(all(dosage$ab %in% antibiotics$ab))
expect_true(all(dosage$name %in% antibiotics$name))
# check valid disks/MICs
expect_false(any(is.na(as.mic(clinical_breakpoints[which(clinical_breakpoints$method == "MIC"), "breakpoint_S", drop = TRUE]))))
expect_false(any(is.na(as.mic(clinical_breakpoints[which(clinical_breakpoints$method == "MIC"), "breakpoint_R", drop = TRUE]))))
expect_false(any(is.na(as.disk(clinical_breakpoints[which(clinical_breakpoints$method == "DISK"), "breakpoint_S", drop = TRUE]))))
expect_false(any(is.na(as.disk(clinical_breakpoints[which(clinical_breakpoints$method == "DISK"), "breakpoint_R", drop = TRUE]))))
expect_false(any(is.na(as.mic(clinical_breakpoints[which(clinical_breakpoints$method == "MIC" & clinical_breakpoints$ref_tbl != "ECOFF"), "breakpoint_S", drop = TRUE]))))
expect_false(any(is.na(as.mic(clinical_breakpoints[which(clinical_breakpoints$method == "MIC" & clinical_breakpoints$ref_tbl != "ECOFF"), "breakpoint_R", drop = TRUE]))))
expect_false(any(is.na(as.disk(clinical_breakpoints[which(clinical_breakpoints$method == "DISK" & clinical_breakpoints$ref_tbl != "ECOFF"), "breakpoint_S", drop = TRUE]))))
expect_false(any(is.na(as.disk(clinical_breakpoints[which(clinical_breakpoints$method == "DISK" & clinical_breakpoints$ref_tbl != "ECOFF"), "breakpoint_R", drop = TRUE]))))
# antibiotic names must always be coercible to their original AB code
expect_identical(as.ab(antibiotics$name), antibiotics$ab)

0
inst/tinytest/test-ggplot_rsi.R → inst/tinytest/test-ggplot_sir.R
View File

2
inst/tinytest/test-mo.R
View File

@ -28,7 +28,7 @@
# ==================================================================== #
MOs <- subset(microorganisms, !is.na(mo) & nchar(mo) > 3)
expect_identical(as.character(MOs$mo), as.character(as.mo(MOs$mo)))
expect_identical(as.character(MOs$mo), as.character(as.mo(MOs$mo, keep_synonyms = TRUE)))
expect_identical(
as.character(as.mo(c("E. coli", "H. influenzae"), keep_synonyms = FALSE)),

4
inst/tinytest/test-mo_property.R
View File

@ -166,15 +166,13 @@ expect_identical(mo_ref("Chlamydia psittaci"), "Garcia-Lopez et al., 2019")
expect_identical(mo_ref("Chlamydophila psittaci", keep_synonyms = TRUE), "Everett et al., 1999")
expect_true(112283007 %in% mo_snomed("Escherichia coli")[[1]])
# old codes must throw a warning in mo_* family
# expect_warning(mo_name(c("B_ESCHR_COL", "B_STPHY_AUR")))
# outcome of mo_fullname must always return the fullname from the data set
x <- data.frame(
mo = microorganisms$mo,
# fullname from the original data:
f1 = microorganisms$fullname,
# newly created fullname based on MO code:
f2 = mo_fullname(microorganisms$mo, language = "en"),
f2 = mo_fullname(microorganisms$mo, language = "en", keep_synonyms = TRUE),
stringsAsFactors = FALSE
)
expect_equal(nrow(subset(x, f1 != f2)), 0)

4
inst/tinytest/test-rsi.R → inst/tinytest/test-sir.R
View File

@ -142,6 +142,10 @@ expect_equal(
suppressMessages(as.sir(as.mic(2), "E. coli", "ampicillin", guideline = "EUCAST 2020")),
as.sir("S")
)
expect_equal(
suppressMessages(as.sir(as.mic(2), "E. coli", "ampicillin", guideline = "EUCAST 2020", ecoff = TRUE)),
as.sir("S")
)
expect_equal(
suppressMessages(as.sir(as.mic(32), "E. coli", "ampicillin", guideline = "EUCAST 2020")),
as.sir("R")

3
man/AMR-options.Rd
View File

@ -12,9 +12,10 @@ This is an overview of all the package-specific \code{\link[=options]{options()}
\item \code{AMR_custom_ab} \cr Allows to use custom antimicrobial drugs with this package. This is explained in \code{\link[=add_custom_antimicrobials]{add_custom_antimicrobials()}}.
\item \code{AMR_custom_mo} \cr Allows to use custom microorganisms with this package. This is explained in \code{\link[=add_custom_microorganisms]{add_custom_microorganisms()}}.
\item \code{AMR_eucastrules} \cr Used for setting the default types of rules for \code{\link[=eucast_rules]{eucast_rules()}} function, must be one or more of: \code{"breakpoints"}, \code{"expert"}, \code{"other"}, \code{"custom"}, \code{"all"}, and defaults to \code{c("breakpoints", "expert")}.
\item \code{AMR_guideline} \cr Used for setting the default guideline for interpreting MIC values and disk diffusion diameters with \code{\link[=as.sir]{as.sir()}}. Can be only the guideline name (e.g., \code{"CLSI"}) or the name with a year (e.g. \code{"CLSI 2019"}). The default to the latest implemented EUCAST guideline, currently \code{"EUCAST 2022"}. Supported guideline are currently EUCAST (2013-2022) and CLSI (2013-2022).
\item \code{AMR_guideline} \cr Used for setting the default guideline for interpreting MIC values and disk diffusion diameters with \code{\link[=as.sir]{as.sir()}}. Can be only the guideline name (e.g., \code{"CLSI"}) or the name with a year (e.g. \code{"CLSI 2019"}). The default to the latest implemented EUCAST guideline, currently \code{"EUCAST 2023"}. Supported guideline are currently EUCAST (2011-2023) and CLSI (2011-2023).
\item \code{AMR_ignore_pattern} \cr A \link[base:regex]{regular expression} to ignore (i.e., make \code{NA}) any match given in \code{\link[=as.mo]{as.mo()}} and all \code{\link[=mo_property]{mo_*}} functions.
\item \code{AMR_include_PKPD} \cr A \link{logical} to use in \code{\link[=as.sir]{as.sir()}}, to indicate that PK/PD clinical breakpoints must be applied as a last resort - the default is \code{TRUE}.
\item \code{AMR_ecoff} \cr A \link{logical} use in \code{\link[=as.sir]{as.sir()}}, to indicate that ECOFF (Epidemiological Cut-Off) values must be used - the default is \code{FALSE}.
\item \code{AMR_include_screening} \cr A \link{logical} to use in \code{\link[=as.sir]{as.sir()}}, to indicate that clinical breakpoints for screening are allowed - the default is \code{FALSE}.
\item \code{AMR_keep_synonyms} \cr A \link{logical} to use in \code{\link[=as.mo]{as.mo()}} and all \code{\link[=mo_property]{mo_*}} functions, to indicate if old, previously valid taxonomic names must be preserved and not be corrected to currently accepted names. The default is \code{FALSE}.
\item \code{AMR_cleaning_regex} \cr A \link[base:regex]{regular expression} (case-insensitive) to use in \code{\link[=as.mo]{as.mo()}} and all \code{\link[=mo_property]{mo_*}} functions, to clean the user input. The default is the outcome of \code{\link[=mo_cleaning_regex]{mo_cleaning_regex()}}, which removes texts between brackets and texts such as "species" and "serovar".

2
man/AMR.Rd
View File

@ -32,7 +32,7 @@ The \code{AMR} package is a \href{https://msberends.github.io/AMR/#copyright}{fr
This work was published in the Journal of Statistical Software (Volume 104(3); \doi{jss.v104.i03}) and formed the basis of two PhD theses (\doi{10.33612/diss.177417131} and \doi{10.33612/diss.192486375}).
After installing this package, R knows \href{https://msberends.github.io/AMR/reference/microorganisms.html}{\strong{~52 000 microorganisms}} (updated December 2022) and all \href{https://msberends.github.io/AMR/reference/antibiotics.html}{\strong{~600 antibiotic, antimycotic and antiviral drugs}} by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral breakpoint guidelines from CLSI and EUCAST are included from the last 10 years. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). \strong{It was designed to work in any setting, including those with very limited resources}. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the \href{https://www.rug.nl}{University of Groningen}, in collaboration with non-profit organisations \href{https://www.certe.nl}{Certe Medical Diagnostics and Advice Foundation} and \href{https://www.umcg.nl}{University Medical Center Groningen}.
After installing this package, R knows \href{https://msberends.github.io/AMR/reference/microorganisms.html}{\strong{~52 000 microorganisms}} (updated December 2022) and all \href{https://msberends.github.io/AMR/reference/antibiotics.html}{\strong{~600 antibiotic, antimycotic and antiviral drugs}} by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI and EUCAST are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). \strong{It was designed to work in any setting, including those with very limited resources}. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the \href{https://www.rug.nl}{University of Groningen}, in collaboration with non-profit organisations \href{https://www.certe.nl}{Certe Medical Diagnostics and Advice Foundation} and \href{https://www.umcg.nl}{University Medical Center Groningen}.
The \code{AMR} package is available in English, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Italian, Japanese, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swedish, Turkish, and Ukrainian. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages.
}

2
man/antibiotic_class_selectors.Rd
View File

@ -112,7 +112,7 @@ not_intrinsic_resistant(
\item{col_mo}{column name of the names or codes of the microorganisms (see \code{\link[=as.mo]{as.mo()}}) - the default is the first column of class \code{\link{mo}}. Values will be coerced using \code{\link[=as.mo]{as.mo()}}.}
\item{version_expertrules}{the version number to use for the EUCAST Expert Rules and Intrinsic Resistance guideline. Can be either "3.3", "3.2", or "3.1".}
\item{version_expertrules}{the version number to use for the EUCAST Expert Rules and Intrinsic Resistance guideline. Can be "3.3", "3.2", or "3.1".}
}
\value{
(internally) a \link{character} vector of column names, with additional class \code{"ab_selector"}

2
man/as.mic.Rd
View File

@ -33,7 +33,7 @@ Ordered \link{factor} with additional class \code{\link{mic}}, that in mathemati
This transforms vectors to a new class \code{\link{mic}}, which treats the input as decimal numbers, while maintaining operators (such as ">=") and only allowing valid MIC values known to the field of (medical) microbiology.
}
\details{
To interpret MIC values as SIR values, use \code{\link[=as.sir]{as.sir()}} on MIC values. It supports guidelines from EUCAST (2013-2022) and CLSI (2013-2022).
To interpret MIC values as SIR values, use \code{\link[=as.sir]{as.sir()}} on MIC values. It supports guidelines from EUCAST (2011-2023) and CLSI (2011-2023).
This class for MIC values is a quite a special data type: formally it is an ordered \link{factor} with valid MIC values as \link{factor} levels (to make sure only valid MIC values are retained), but for any mathematical operation it acts as decimal numbers:

21
man/as.sir.Rd
View File

@ -18,9 +18,9 @@ An object of class \code{sir} (inherits from \code{ordered}, \code{factor}) of l
\source{
For interpretations of minimum inhibitory concentration (MIC) values and disk diffusion diameters:
\itemize{
\item \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data}, 2013-2022, \emph{Clinical and Laboratory Standards Institute} (CLSI). \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
\item \strong{M100 Performance Standard for Antimicrobial Susceptibility Testing}, 2013-2022, \emph{Clinical and Laboratory Standards Institute} (CLSI). \url{https://clsi.org/standards/products/microbiology/documents/m100/}.
\item \strong{Breakpoint tables for interpretation of MICs and zone diameters}, 2013-2022, \emph{European Committee on Antimicrobial Susceptibility Testing} (EUCAST). \url{https://www.eucast.org/clinical_breakpoints}.
\item \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data}, 2011-2023, \emph{Clinical and Laboratory Standards Institute} (CLSI). \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
\item \strong{M100 Performance Standard for Antimicrobial Susceptibility Testing}, 2011-2023, \emph{Clinical and Laboratory Standards Institute} (CLSI). \url{https://clsi.org/standards/products/microbiology/documents/m100/}.
\item \strong{Breakpoint tables for interpretation of MICs and zone diameters}, 2011-2023, \emph{European Committee on Antimicrobial Susceptibility Testing} (EUCAST). \url{https://www.eucast.org/clinical_breakpoints}.
}
}
\usage{
@ -43,6 +43,7 @@ is_sir_eligible(x, threshold = 0.05)
reference_data = AMR::clinical_breakpoints,
include_screening = getOption("AMR_include_screening", FALSE),
include_PKPD = getOption("AMR_include_PKPD", TRUE),
ecoff = getOption("AMR_ecoff", FALSE),
...
)
@ -56,6 +57,7 @@ is_sir_eligible(x, threshold = 0.05)
reference_data = AMR::clinical_breakpoints,
include_screening = getOption("AMR_include_screening", FALSE),
include_PKPD = getOption("AMR_include_PKPD", TRUE),
ecoff = getOption("AMR_ecoff", FALSE),
...
)
@ -69,7 +71,8 @@ is_sir_eligible(x, threshold = 0.05)
add_intrinsic_resistance = FALSE,
reference_data = AMR::clinical_breakpoints,
include_screening = getOption("AMR_include_screening", FALSE),
include_PKPD = getOption("AMR_include_PKPD", TRUE)
include_PKPD = getOption("AMR_include_PKPD", TRUE),
ecoff = getOption("AMR_ecoff", FALSE)
)
sir_interpretation_history(clean = FALSE)
@ -85,7 +88,7 @@ sir_interpretation_history(clean = FALSE)
\item{ab}{any (vector of) text that can be coerced to a valid antimicrobial drug code with \code{\link[=as.ab]{as.ab()}}}
\item{guideline}{defaults to EUCAST 2022 (the latest implemented EUCAST guideline in the \link{clinical_breakpoints} data set), but can be set with the \link[=AMR-options]{package option} \code{\link[=AMR-options]{AMR_guideline}}. Currently supports EUCAST (2013-2022) and CLSI (2013-2022), see \emph{Details}.}
\item{guideline}{defaults to EUCAST 2023 (the latest implemented EUCAST guideline in the \link{clinical_breakpoints} data set), but can be set with the \link[=AMR-options]{package option} \code{\link[=AMR-options]{AMR_guideline}}. Currently supports EUCAST (2011-2023) and CLSI (2011-2023), see \emph{Details}.}
\item{uti}{(Urinary Tract Infection) A vector with \link{logical}s (\code{TRUE} or \code{FALSE}) to specify whether a UTI specific interpretation from the guideline should be chosen. For using \code{\link[=as.sir]{as.sir()}} on a \link{data.frame}, this can also be a column containing \link{logical}s or when left blank, the data set will be searched for a column 'specimen', and rows within this column containing 'urin' (such as 'urine', 'urina') will be regarded isolates from a UTI. See \emph{Examples}.}
@ -99,6 +102,8 @@ sir_interpretation_history(clean = FALSE)
\item{include_PKPD}{a \link{logical} to indicate that PK/PD clinical breakpoints must be applied as a last resort - the default is \code{TRUE}. Can also be set with the \link[=AMR-options]{package option} \code{\link[=AMR-options]{AMR_include_PKPD}}.}
\item{ecoff}{a \link{logical} to indicate that ECOFF (Epidemiological Cut-Off) values must be used \strong{instead} of other clinical breakpoints - the default is \code{FALSE}. Can also be set with the \link[=AMR-options]{package option} \code{\link[=AMR-options]{AMR_ecoff}}.}
\item{col_mo}{column name of the names or codes of the microorganisms (see \code{\link[=as.mo]{as.mo()}}) - the default is the first column of class \code{\link{mo}}. Values will be coerced using \code{\link[=as.mo]{as.mo()}}.}
\item{clean}{a \link{logical} to indicate whether previously stored results should be forgotten after returning the 'logbook' with results}
@ -142,9 +147,9 @@ your_data \%>\% mutate(across(where(is.disk), as.sir))
\subsection{Supported Guidelines}{
For interpreting MIC values as well as disk diffusion diameters, currently implemented guidelines are EUCAST (2013-2022) and CLSI (2013-2022).
For interpreting MIC values as well as disk diffusion diameters, currently implemented guidelines are EUCAST (2011-2023) and CLSI (2011-2023).
Thus, the \code{guideline} argument must be set to e.g., \code{"EUCAST 2022"} or \code{"CLSI 2022"}. By simply using \code{"EUCAST"} (the default) or \code{"CLSI"} as input, the latest included version of that guideline will automatically be selected. You can set your own data set using the \code{reference_data} argument. The \code{guideline} argument will then be ignored.
Thus, the \code{guideline} argument must be set to e.g., \code{"EUCAST 2023"} or \code{"CLSI 2023"}. By simply using \code{"EUCAST"} (the default) or \code{"CLSI"} as input, the latest included version of that guideline will automatically be selected. You can set your own data set using the \code{reference_data} argument. The \code{guideline} argument will then be ignored.
You can set the default guideline with the \link[=AMR-options]{package option} \code{\link[=AMR-options]{AMR_guideline}} (e.g. in your \code{.Rprofile} file), such as:
@ -163,7 +168,7 @@ After using \code{\link[=as.sir]{as.sir()}}, you can use the \code{\link[=eucast
\subsection{Machine-Readable Clinical Breakpoints}{
The repository of this package \href{https://github.com/msberends/AMR/blob/main/data-raw/clinical_breakpoints.txt}{contains a machine-readable version} of all guidelines. This is a CSV file consisting of 18 271 rows and 11 columns. This file is machine-readable, since it contains one row for every unique combination of the test method (MIC or disk diffusion), the antimicrobial drug and the microorganism. \strong{This allows for easy implementation of these rules in laboratory information systems (LIS)}. Note that it only contains interpretation guidelines for humans - interpretation guidelines from CLSI for animals were removed.
The repository of this package \href{https://github.com/msberends/AMR/blob/main/data-raw/clinical_breakpoints.txt}{contains a machine-readable version} of all guidelines. This is a CSV file consisting of 42 599 rows and 12 columns. This file is machine-readable, since it contains one row for every unique combination of the test method (MIC or disk diffusion), the antimicrobial drug and the microorganism. \strong{This allows for easy implementation of these rules in laboratory information systems (LIS)}. Note that it only contains interpretation guidelines for humans - interpretation guidelines from CLSI for animals were removed.
}
\subsection{Other}{

7
man/clinical_breakpoints.Rd
View File

@ -5,7 +5,7 @@
\alias{clinical_breakpoints}
\title{Data Set with Clinical Breakpoints for SIR Interpretation}
\format{
A \link[tibble:tibble]{tibble} with 18 271 observations and 11 variables:
A \link[tibble:tibble]{tibble} with 42 599 observations and 12 variables:
\itemize{
\item \code{guideline}\cr Name of the guideline
\item \code{method}\cr Either "DISK" or "MIC"
@ -17,6 +17,7 @@ A \link[tibble:tibble]{tibble} with 18 271 observations and 11 variables:
\item \code{disk_dose}\cr Dose of the used disk diffusion method
\item \code{breakpoint_S}\cr Lowest MIC value or highest number of millimetres that leads to "S"
\item \code{breakpoint_R}\cr Highest MIC value or lowest number of millimetres that leads to "R"
\item \code{ecoff}\cr Epidemiological cut-off (ECOFF) value, used in antimicrobial susceptibility testing to differentiate between wild-type and non-wild-type strains of bacteria or fungi (use \code{\link[=as.sir]{as.sir(..., ecoff = TRUE)}} to interpret raw data using ECOFF values)
\item \code{uti}\cr A \link{logical} value (\code{TRUE}/\code{FALSE}) to indicate whether the rule applies to a urinary tract infection (UTI)
}
}
@ -24,9 +25,11 @@ A \link[tibble:tibble]{tibble} with 18 271 observations and 11 variables:
clinical_breakpoints
}
\description{
Data set containing clinical breakpoints to interpret MIC and disk diffusion to SIR values, according to international guidelines. Currently implemented guidelines are EUCAST (2013-2022) and CLSI (2013-2022). Use \code{\link[=as.sir]{as.sir()}} to transform MICs or disks measurements to SIR values.
Data set containing clinical breakpoints to interpret MIC and disk diffusion to SIR values, according to international guidelines. Currently implemented guidelines are EUCAST (2011-2023) and CLSI (2011-2023). Use \code{\link[=as.sir]{as.sir()}} to transform MICs or disks measurements to SIR values.
}
\details{
Clinical breakpoints are validated through \href{https://whonet.org}{WHONET}, a free desktop Windows application developed and supported by the WHO Collaborating Centre for Surveillance of Antimicrobial Resistance. More can be read on \href{https://whonet.org}{their website}.
Like all data sets in this package, this data set is publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit \href{https://msberends.github.io/AMR/articles/datasets.html}{our website for the download links}. The actual files are of course available on \href{https://github.com/msberends/AMR/tree/main/data-raw}{our GitHub repository}.
They \strong{allow for machine reading EUCAST and CLSI guidelines}, which is almost impossible with the MS Excel and PDF files distributed by EUCAST and CLSI.

8
man/dosage.Rd
View File

@ -5,7 +5,7 @@
\alias{dosage}
\title{Data Set with Treatment Dosages as Defined by EUCAST}
\format{
A \link[tibble:tibble]{tibble} with 336 observations and 9 variables:
A \link[tibble:tibble]{tibble} with 503 observations and 9 variables:
\itemize{
\item \code{ab}\cr Antibiotic ID as used in this package (such as \code{AMC}), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available
\item \code{name}\cr Official name of the antimicrobial drug as used by WHONET/EARS-Net or the WHO
@ -15,7 +15,7 @@ A \link[tibble:tibble]{tibble} with 336 observations and 9 variables:
\item \code{administration}\cr Route of administration, either "im", "iv", or "oral"
\item \code{notes}\cr Additional dosage notes
\item \code{original_txt}\cr Original text in the PDF file of EUCAST
\item \code{eucast_version}\cr Version number of the EUCAST Clinical Breakpoints guideline to which these dosages apply
\item \code{eucast_version}\cr Version number of the EUCAST Clinical Breakpoints guideline to which these dosages apply, either 13, 12, or 11
}
}
\usage{
@ -25,12 +25,8 @@ dosage
EUCAST breakpoints used in this package are based on the dosages in this data set. They can be retrieved with \code{\link[=eucast_dosage]{eucast_dosage()}}.
}
\details{
This data set is based on \href{https://www.eucast.org/clinical_breakpoints/}{'EUCAST Clinical Breakpoint Tables' v12.0} (2022) and \href{https://www.eucast.org/clinical_breakpoints/}{'EUCAST Clinical Breakpoint Tables' v11.0} (2021).
\subsection{Direct download}{
Like all data sets in this package, this data set is publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit \href{https://msberends.github.io/AMR/articles/datasets.html}{our website for the download links}. The actual files are of course available on \href{https://github.com/msberends/AMR/tree/main/data-raw}{our GitHub repository}.
}
}
\examples{
dosage
}

9
man/eucast_rules.Rd
View File

@ -27,13 +27,14 @@ eucast_rules(
verbose = FALSE,
version_breakpoints = 12,
version_expertrules = 3.3,
version_resistant_phenotypes = 1.2,
ampc_cephalosporin_resistance = NA,
only_sir_columns = FALSE,
custom_rules = NULL,
...
)
eucast_dosage(ab, administration = "iv", version_breakpoints = 12)
eucast_dosage(ab, administration = "iv", version_breakpoints = 13)
}
\arguments{
\item{x}{a data set with antibiotic columns, such as \code{amox}, \code{AMX} and \code{AMC}}
@ -46,9 +47,11 @@ eucast_dosage(ab, administration = "iv", version_breakpoints = 12)
\item{verbose}{a \link{logical} to turn Verbose mode on and off (default is off). In Verbose mode, the function does not apply rules to the data, but instead returns a data set in logbook form with extensive info about which rows and columns would be effected and in which way. Using Verbose mode takes a lot more time.}
\item{version_breakpoints}{the version number to use for the EUCAST Clinical Breakpoints guideline. Can be either "12.0", "11.0", or "10.0".}
\item{version_breakpoints}{the version number to use for the EUCAST Clinical Breakpoints guideline. Can be "13.0", "12.0", "11.0", or "10.0".}
\item{version_expertrules}{the version number to use for the EUCAST Expert Rules and Intrinsic Resistance guideline. Can be either "3.3", "3.2", or "3.1".}
\item{version_expertrules}{the version number to use for the EUCAST Expert Rules and Intrinsic Resistance guideline. Can be "3.3", "3.2", or "3.1".}
\item{version_resistant_phenotypes}{the version number to use for the EUCAST Expected Resistant Phenotypes. Can be "1.2".}
\item{ampc_cephalosporin_resistance}{a \link{character} value that should be applied to cefotaxime, ceftriaxone and ceftazidime for AmpC de-repressed cephalosporin-resistant mutants - the default is \code{NA}. Currently only works when \code{version_expertrules} is \code{3.2} and higher; these version of '\emph{EUCAST Expert Rules on Enterobacterales}' state that results of cefotaxime, ceftriaxone and ceftazidime should be reported with a note, or results should be suppressed (emptied) for these three drugs. A value of \code{NA} (the default) for this argument will remove results for these three drugs, while e.g. a value of \code{"R"} will make the results for these drugs resistant. Use \code{NULL} or \code{FALSE} to not alter results for these three drugs of AmpC de-repressed cephalosporin-resistant mutants. Using \code{TRUE} is equal to using \code{"R"}. \cr For \emph{EUCAST Expert Rules} v3.2, this rule applies to: \emph{Citrobacter braakii}, \emph{Citrobacter freundii}, \emph{Citrobacter gillenii}, \emph{Citrobacter murliniae}, \emph{Citrobacter rodenticum}, \emph{Citrobacter sedlakii}, \emph{Citrobacter werkmanii}, \emph{Citrobacter youngae}, \emph{Enterobacter}, \emph{Hafnia alvei}, \emph{Klebsiella aerogenes}, \emph{Morganella morganii}, \emph{Providencia}, and \emph{Serratia}.}

4
man/microorganisms.codes.Rd
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@ -3,9 +3,9 @@
\docType{data}
\name{microorganisms.codes}
\alias{microorganisms.codes}
\title{Data Set with 4 909 Common Microorganism Codes}
\title{Data Set with 4 942 Common Microorganism Codes}
\format{
A \link[tibble:tibble]{tibble} with 4 909 observations and 2 variables:
A \link[tibble:tibble]{tibble} with 4 942 observations and 2 variables:
\itemize{
\item \code{code}\cr Commonly used code of a microorganism
\item \code{mo}\cr ID of the microorganism in the \link{microorganisms} data set

2
man/plot.Rd
View File

@ -126,7 +126,7 @@ Functions to plot classes \code{sir}, \code{mic} and \code{disk}, with support f
\details{
The interpretation of "I" will be named "Increased exposure" for all EUCAST guidelines since 2019, and will be named "Intermediate" in all other cases.
For interpreting MIC values as well as disk diffusion diameters, supported guidelines to be used as input for the \code{guideline} argument are: "EUCAST 2022", "EUCAST 2021", "EUCAST 2020", "EUCAST 2019", "EUCAST 2018", "EUCAST 2017", "EUCAST 2016", "EUCAST 2015", "EUCAST 2014", "EUCAST 2013", "CLSI 2022", "CLSI 2021", "CLSI 2020", "CLSI 2019", "CLSI 2018", "CLSI 2017", "CLSI 2016", "CLSI 2015", "CLSI 2014", and "CLSI 2013".
For interpreting MIC values as well as disk diffusion diameters, supported guidelines to be used as input for the \code{guideline} argument are: "EUCAST 2023", "EUCAST 2022", "EUCAST 2021", "EUCAST 2020", "EUCAST 2019", "EUCAST 2018", "EUCAST 2017", "EUCAST 2016", "EUCAST 2015", "EUCAST 2014", "EUCAST 2013", "EUCAST 2012", "EUCAST 2011", "CLSI 2023", "CLSI 2022", "CLSI 2021", "CLSI 2020", "CLSI 2019", "CLSI 2018", "CLSI 2017", "CLSI 2016", "CLSI 2015", "CLSI 2014", "CLSI 2013", "CLSI 2012", and "CLSI 2011".
Simply using \code{"CLSI"} or \code{"EUCAST"} as input will automatically select the latest version of that guideline.
}

2
man/random.Rd
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@ -33,7 +33,7 @@ These functions can be used for generating random MIC values and disk diffusion
\details{
The base \R function \code{\link[=sample]{sample()}} is used for generating values.
Generated values are based on the EUCAST 2022 guideline as implemented in the \link{clinical_breakpoints} data set. To create specific generated values per bug or drug, set the \code{mo} and/or \code{ab} argument.
Generated values are based on the EUCAST 2023 guideline as implemented in the \link{clinical_breakpoints} data set. To create specific generated values per bug or drug, set the \code{mo} and/or \code{ab} argument.
}
\examples{
random_mic(25)