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(v2.1.1.9163) cleanup

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dr. M.S. (Matthijs) Berends
2025-02-27 14:04:29 +01:00
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R/sir.R
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@ -30,12 +30,12 @@
#' Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data
#'
#' @description Clean up existing SIR values, or interpret minimum inhibitory concentration (MIC) values and disk diffusion diameters according to EUCAST or CLSI. [as.sir()] transforms the input to a new class [`sir`], which is an ordered [factor] containing the levels `S`, `SDD`, `I`, `R`, `NI`.
#'
#'
#' These breakpoints are currently implemented:
#' - For **clinical microbiology**: EUCAST `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST" & type == "human")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST" & type == "human")$guideline)))` and CLSI `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI" & type == "human")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI" & type == "human")$guideline)))`;
#' - For **veterinary microbiology**: EUCAST `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST" & type == "animal")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST" & type == "animal")$guideline)))` and CLSI `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI" & type == "animal")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI" & type == "animal")$guideline)))`;
#' - For **ECOFFs** (Epidemiological Cut-off Values): EUCAST `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST" & type == "ECOFF")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST" & type == "ECOFF")$guideline)))` and CLSI `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI" & type == "ECOFF")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI" & type == "ECOFF")$guideline)))`.
#'
#'
#' All breakpoints used for interpretation are available in our [clinical_breakpoints] data set.
#' @rdname as.sir
#' @param x vector of values (for class [`mic`]: MIC values in mg/L, for class [`disk`]: a disk diffusion radius in millimetres)
@ -56,7 +56,7 @@
#' @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.
#' @details
#' *Note: The clinical breakpoints in this package were validated through, and imported from, [WHONET](https://whonet.org). The public use of this `AMR` package has been endorsed by both CLSI and EUCAST. See [clinical_breakpoints] for more information.*
#'
#'
#' ### How it Works
#'
#' The [as.sir()] function can work in four ways:
@ -70,7 +70,7 @@
#' your_data %>% mutate(across(where(is.mic), as.sir))
#' your_data %>% mutate_if(is.mic, as.sir, ab = "column_with_antibiotics", mo = "column_with_microorganisms")
#' your_data %>% mutate_if(is.mic, as.sir, ab = c("cipro", "ampicillin", ...), mo = c("E. coli", "K. pneumoniae", ...))
#'
#'
#' # for veterinary breakpoints, also set `host`:
#' your_data %>% mutate_if(is.mic, as.sir, host = "column_with_animal_species", guideline = "CLSI")
#' ```
@ -82,7 +82,7 @@
#' your_data %>% mutate(across(where(is.disk), as.sir))
#' your_data %>% mutate_if(is.disk, as.sir, ab = "column_with_antibiotics", mo = "column_with_microorganisms")
#' your_data %>% mutate_if(is.disk, as.sir, ab = c("cipro", "ampicillin", ...), mo = c("E. coli", "K. pneumoniae", ...))
#'
#'
#' # for veterinary breakpoints, also set `host`:
#' your_data %>% mutate_if(is.disk, as.sir, host = "column_with_animal_species", guideline = "CLSI")
#' ```
@ -105,20 +105,20 @@
#' # or to reset:
#' options(AMR_guideline = NULL)
#' ```
#'
#'
#' For veterinary guidelines, these might be the best options:
#'
#'
#' ```
#' options(AMR_guideline = "CLSI")
#' options(AMR_breakpoint_type = "animal")
#' ```
#'
#'
#' When applying veterinary breakpoints (by setting `host` or by setting `breakpoint_type = "animal"`), the [CLSI VET09 guideline](https://clsi.org/standards/products/veterinary-medicine/documents/vet09/) will be applied to cope with missing animal species-specific breakpoints.
#'
#' ### After Interpretation
#'
#' After using [as.sir()], you can use the [eucast_rules()] defined by EUCAST to (1) apply inferred susceptibility and resistance based on results of other antimicrobials and (2) apply intrinsic resistance based on taxonomic properties of a microorganism.
#'
#'
#' To determine which isolates are multi-drug resistant, be sure to run [mdro()] (which applies the MDR/PDR/XDR guideline from 2012 at default) on a data set that contains S/I/R values. Read more about [interpreting multidrug-resistant organisms here][mdro()].
#'
#' ### Machine-Readable Clinical Breakpoints
@ -128,7 +128,7 @@
#' ### Other
#'
#' The function [is.sir()] detects if the input contains class `sir`. If the input is a [data.frame], it iterates over all columns and returns a [logical] vector.
#'
#'
#' The base R function [as.double()] can be used to retrieve quantitative values from a `sir` object: `"S"` = 1, `"I"`/`"SDD"` = 2, `"R"` = 3. All other values are rendered `NA` . **Note:** Do not use `as.integer()`, since that (because of how R works internally) will return the factor level indices, and not these aforementioned quantitative values.
#'
#' The function [is_sir_eligible()] returns `TRUE` when a column contains at most 5% invalid antimicrobial interpretations (not S and/or I and/or R and/or NI and/or SDD), and `FALSE` otherwise. The threshold of 5% can be set with the `threshold` argument. If the input is a [data.frame], it iterates over all columns and returns a [logical] vector.
@ -158,14 +158,14 @@
#' - **CLSI VET09: Understanding Susceptibility Test Data as a Component of Antimicrobial Stewardship in Veterinary Settings**, `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI" & type == "animal")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "CLSI" & type == "animal")$guideline)))`, *Clinical and Laboratory Standards Institute* (CLSI). <https://clsi.org/standards/products/veterinary-medicine/documents/vet09/>.
#' - **EUCAST Breakpoint tables for interpretation of MICs and zone diameters**, `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, guideline %like% "EUCAST")$guideline)))`, *European Committee on Antimicrobial Susceptibility Testing* (EUCAST). <https://www.eucast.org/clinical_breakpoints>.
#' - **WHONET** as a source for machine-reading the clinical breakpoints ([read more here](https://msberends.github.io/AMR/reference/clinical_breakpoints.html#imported-from-whonet)), 1989-`r max(as.integer(gsub("[^0-9]", "", AMR::clinical_breakpoints$guideline)))`, *WHO Collaborating Centre for Surveillance of Antimicrobial Resistance*. <https://whonet.org/>.
#'
#'
#' @inheritSection AMR Reference Data Publicly Available
#' @examples
#' example_isolates
#' summary(example_isolates) # see all SIR results at a glance
#'
#' # For INTERPRETING disk diffusion and MIC values -----------------------
#'
#'
#' # example data sets, with combined MIC values and disk zones
#' df_wide <- data.frame(
#' microorganism = "Escherichia coli",
@ -191,69 +191,97 @@
#' df_wide %>% mutate(across(where(is.mic), as.sir))
#' df_wide %>% mutate_at(vars(amoxicillin:tobra), as.sir)
#' df_wide %>% mutate(across(amoxicillin:tobra, as.sir))
#'
#'
#' # approaches that all work with additional arguments:
#' df_long %>%
#' # given a certain data type, e.g. MIC values
#' mutate_if(is.mic, as.sir,
#' mo = "bacteria",
#' ab = "antibiotic",
#' guideline = "CLSI")
#' mo = "bacteria",
#' ab = "antibiotic",
#' guideline = "CLSI"
#' )
#' df_long %>%
#' mutate(across(where(is.mic),
#' function(x) as.sir(x,
#' mo = "bacteria",
#' ab = "antibiotic",
#' guideline = "CLSI")))
#' mutate(across(
#' where(is.mic),
#' function(x) {
#' as.sir(x,
#' mo = "bacteria",
#' ab = "antibiotic",
#' guideline = "CLSI"
#' )
#' }
#' ))
#' df_wide %>%
#' # given certain columns, e.g. from 'cipro' to 'genta'
#' mutate_at(vars(cipro:genta), as.sir,
#' mo = "bacteria",
#' guideline = "CLSI")
#' mo = "bacteria",
#' guideline = "CLSI"
#' )
#' df_wide %>%
#' mutate(across(cipro:genta,
#' function(x) as.sir(x,
#' mo = "bacteria",
#' guideline = "CLSI")))
#'
#' mutate(across(
#' cipro:genta,
#' function(x) {
#' as.sir(x,
#' mo = "bacteria",
#' guideline = "CLSI"
#' )
#' }
#' ))
#'
#' # for veterinary breakpoints, add 'host':
#' df_long$animal_species <- c("cats", "dogs", "horses", "cattle")
#' df_long %>%
#' # given a certain data type, e.g. MIC values
#' mutate_if(is.mic, as.sir,
#' mo = "bacteria",
#' ab = "antibiotic",
#' host = "animal_species",
#' guideline = "CLSI")
#' mo = "bacteria",
#' ab = "antibiotic",
#' host = "animal_species",
#' guideline = "CLSI"
#' )
#' df_long %>%
#' mutate(across(where(is.mic),
#' function(x) as.sir(x,
#' mo = "bacteria",
#' ab = "antibiotic",
#' host = "animal_species",
#' guideline = "CLSI")))
#' mutate(across(
#' where(is.mic),
#' function(x) {
#' as.sir(x,
#' mo = "bacteria",
#' ab = "antibiotic",
#' host = "animal_species",
#' guideline = "CLSI"
#' )
#' }
#' ))
#' df_wide %>%
#' mutate_at(vars(cipro:genta), as.sir,
#' mo = "bacteria",
#' ab = "antibiotic",
#' host = "animal_species",
#' guideline = "CLSI")
#' mo = "bacteria",
#' ab = "antibiotic",
#' host = "animal_species",
#' guideline = "CLSI"
#' )
#' df_wide %>%
#' mutate(across(cipro:genta,
#' function(x) as.sir(x,
#' mo = "bacteria",
#' host = "animal_species",
#' guideline = "CLSI")))
#'
#' mutate(across(
#' cipro:genta,
#' function(x) {
#' as.sir(x,
#' mo = "bacteria",
#' host = "animal_species",
#' guideline = "CLSI"
#' )
#' }
#' ))
#'
#' # to include information about urinary tract infections (UTI)
#' data.frame(mo = "E. coli",
#' nitrofuratoin = c("<= 2", 32),
#' from_the_bladder = c(TRUE, FALSE)) %>%
#' data.frame(
#' mo = "E. coli",
#' nitrofuratoin = c("<= 2", 32),
#' from_the_bladder = c(TRUE, FALSE)
#' ) %>%
#' as.sir(uti = "from_the_bladder")
#'
#' data.frame(mo = "E. coli",
#' nitrofuratoin = c("<= 2", 32),
#' specimen = c("urine", "blood")) %>%
#' data.frame(
#' mo = "E. coli",
#' nitrofuratoin = c("<= 2", 32),
#' specimen = c("urine", "blood")
#' ) %>%
#' as.sir() # automatically determines urine isolates
#'
#' df_wide %>%
@ -292,12 +320,12 @@
#' is.sir(sir_data)
#' plot(sir_data) # for percentages
#' barplot(sir_data) # for frequencies
#'
#'
#' # as common in R, you can use as.integer() to return factor indices:
#' as.integer(as.sir(c("S", "SDD", "I", "R", "NI", NA)))
#' # but for computational use, as.double() will return 1 for S, 2 for I/SDD, and 3 for R:
#' as.double(as.sir(c("S", "SDD", "I", "R", "NI", NA)))
#'
#'
#' # the dplyr way
#' if (require("dplyr")) {
#' example_isolates %>%
@ -326,16 +354,19 @@ as_sir_structure <- function(x,
method = NULL,
ref_tbl = NULL,
ref_breakpoints = NULL) {
out <- structure(factor(as.character(unlist(unname(x))),
levels = c("S", "SDD", "I", "R", "NI"),
ordered = TRUE),
guideline = guideline,
mo = mo,
ab = ab,
method = method,
ref_tbl = ref_tbl,
ref_breakpoints = ref_breakpoints,
class = c("sir", "ordered", "factor"))
out <- structure(
factor(as.character(unlist(unname(x))),
levels = c("S", "SDD", "I", "R", "NI"),
ordered = TRUE
),
guideline = guideline,
mo = mo,
ab = ab,
method = method,
ref_tbl = ref_tbl,
ref_breakpoints = ref_breakpoints,
class = c("sir", "ordered", "factor")
)
}
#' @rdname as.sir
@ -633,7 +664,7 @@ as.sir.data.frame <- function(x,
if (is.null(col_mo)) {
col_mo <- search_type_in_df(x = x, type = "mo", info = FALSE)
}
# -- host
if (missing(breakpoint_type) && any(host %in% clinical_breakpoints$host[!clinical_breakpoints$host %in% c("human", "ECOFF")], na.rm = TRUE)) {
message_("Assuming `breakpoint_type = \"animal\"` since `host` contains animal species.")
@ -651,7 +682,7 @@ as.sir.data.frame <- function(x,
} else {
host <- breakpoint_type
}
# -- UTIs
col_uti <- uti
if (is.null(col_uti)) {
@ -861,7 +892,7 @@ convert_host <- function(x, lang = get_AMR_locale()) {
x_out[is.na(x_out) & (x %like% "horse|equine|Equus ferus" | x %like% translate_AMR("horse|horses|equine", lang))] <- "horse"
x_out[is.na(x_out) & (x %like% "aqua|fish|Pisces" | x %like% translate_AMR("aquatic|fish", lang))] <- "aquatic"
x_out[is.na(x_out) & (x %like% "bird|chicken|poultry|avia|Gallus gallus" | x %like% translate_AMR("bird|birds|poultry", lang))] <- "poultry"
# additional animals, not necessarily currently in breakpoint guidelines:
x_out[is.na(x_out) & (x %like% "camel|camelid|Camelus dromedarius" | x %like% translate_AMR("camel|camels|camelid", lang))] <- "camels"
x_out[is.na(x_out) & (x %like% "deer|cervine|Cervidae" | x %like% translate_AMR("deer|deers|cervine", lang))] <- "deer"
@ -878,8 +909,8 @@ convert_host <- function(x, lang = get_AMR_locale()) {
x_out[is.na(x_out) & (x %like% "sheep|ovine|Ovis aries" | x %like% translate_AMR("sheep|sheeps|ovine", lang))] <- "sheep"
x_out[is.na(x_out) & (x %like% "snake|serpentine|Serpentes" | x %like% translate_AMR("snake|snakes|serpentine", lang))] <- "snakes"
x_out[is.na(x_out) & (x %like% "turkey|meleagrine|Meleagris gallopavo" | x %like% translate_AMR("turkey|turkeys|meleagrine", lang))] <- "turkey"
x_out[x_out == "ecoff"] <- "ECOFF"
x_out
}
@ -914,29 +945,29 @@ as_sir_method <- function(method_short,
meet_criteria(breakpoint_type, allow_class = "character", is_in = reference_data$type, has_length = 1, .call_depth = -2)
meet_criteria(host, allow_class = c("character", "factor"), allow_NULL = TRUE, allow_NA = TRUE, .call_depth = -2)
meet_criteria(verbose, allow_class = "logical", has_length = 1, .call_depth = -2)
# backward compatibilty
dots <- list(...)
dots <- dots[which(!names(dots) %in% c("warn", "mo.bak", "is_data.frame"))]
if (length(dots) != 0) {
warning_("These arguments in `as.sir()` are no longer used: ", vector_and(names(dots), quotes = "`"), ".", call = FALSE)
}
guideline_coerced <- get_guideline(guideline, reference_data)
if (message_not_thrown_before("as.sir", "sir_interpretation_history")) {
message()
message_("Run `sir_interpretation_history()` afterwards to retrieve a logbook with all the details of the breakpoint interpretations.\n\n", add_fn = font_green)
}
current_df <- tryCatch(get_current_data(NA, 0), error = function(e) NULL)
# get host
if (breakpoint_type == "animal") {
if (is.null(host)) {
host <- "dogs"
if (message_not_thrown_before("as.sir", "host_missing")) {
message_("Animal hosts not set in `host`, assuming `host = \"dogs\"`, since these have the highest breakpoint availability.\n\n")
message_("Animal hosts not set in `host`, assuming `host = \"dogs\"`, since these have the highest breakpoint availability.\n\n")
}
}
} else {
@ -949,7 +980,7 @@ as_sir_method <- function(method_short,
host <- breakpoint_type
}
}
if (!is.null(host) && !all(toupper(as.character(host)) %in% c("HUMAN", "ECOFF"))) {
if (!is.null(current_df) && length(host) == 1 && host %in% colnames(current_df) && any(current_df[[host]] %like% "[A-Z]", na.rm = TRUE)) {
host <- current_df[[host]]
@ -959,7 +990,7 @@ as_sir_method <- function(method_short,
if (!is.null(cur_column_dplyr) && is.data.frame(current_df)) {
# try to get current column, which will only be available when in across()
host <- tryCatch(cur_column_dplyr(),
error = function(e) host
error = function(e) host
)
}
}
@ -976,7 +1007,7 @@ as_sir_method <- function(method_short,
message_("Please note that in the absence of specific veterinary breakpoints for certain animal hosts, the CLSI guideline VET09 will be applied where possible.\n\n")
}
}
# get ab
if (!is.null(current_df) && length(ab) == 1 && ab %in% colnames(current_df) && any(current_df[[ab]] %like% "[A-Z]", na.rm = TRUE)) {
ab <- current_df[[ab]]
@ -986,11 +1017,11 @@ as_sir_method <- function(method_short,
if (!is.null(cur_column_dplyr) && is.data.frame(current_df)) {
# try to get current column, which will only be available when in across()
ab <- tryCatch(cur_column_dplyr(),
error = function(e) ab
error = function(e) ab
)
}
}
# get mo
if (!is.null(current_df) && length(mo) == 1 && mo %in% colnames(current_df)) {
mo_var_found <- paste0(" based on column '", font_bold(mo), "'")
@ -1028,7 +1059,7 @@ as_sir_method <- function(method_short,
call = FALSE
)
}
# get uti
if (!is.null(current_df) && length(uti) == 1 && uti %in% colnames(current_df)) {
uti <- current_df[[uti]]
@ -1055,7 +1086,7 @@ as_sir_method <- function(method_short,
}
}
# TODO set uti to specimen column here
if (length(ab) == 1 && ab %like% paste0("as.", method_short)) {
stop_("No unambiguous name was supplied about the antibiotic (argument `ab`). See ?as.sir.", call = FALSE)
@ -1100,27 +1131,33 @@ as_sir_method <- function(method_short,
warning_("in `as.sir()`: using 'add_intrinsic_resistance' is only useful when using EUCAST guidelines, since the rules for intrinsic resistance are based on EUCAST.")
}
}
# format agents ----
agent_formatted <- paste0("'", font_bold(ab.bak, collapse = NULL), "'")
agent_name <- ab_name(ab, tolower = TRUE, language = NULL)
same_ab <- generalise_antibiotic_name(ab) == generalise_antibiotic_name(agent_name)
same_ab.bak <- generalise_antibiotic_name(ab.bak) == generalise_antibiotic_name(agent_name)
agent_formatted[same_ab.bak] <- paste0(agent_formatted[same_ab.bak], " (", ab[same_ab.bak], ")")
agent_formatted[!same_ab.bak & !same_ab] <- paste0(agent_formatted[!same_ab.bak & !same_ab],
" (", ifelse(ab.bak[!same_ab.bak & !same_ab] == ab[!same_ab.bak & !same_ab],
"",
paste0(ab[!same_ab.bak & !same_ab], ", ")),
agent_name[!same_ab.bak & !same_ab],
")")
agent_formatted[!same_ab.bak & !same_ab] <- paste0(
agent_formatted[!same_ab.bak & !same_ab],
" (", ifelse(ab.bak[!same_ab.bak & !same_ab] == ab[!same_ab.bak & !same_ab],
"",
paste0(ab[!same_ab.bak & !same_ab], ", ")
),
agent_name[!same_ab.bak & !same_ab],
")"
)
# this intro text will also be printed in the progress bar if the `progress` package is installed
intro_txt <- paste0("Interpreting ", method_long, ": ", ifelse(isTRUE(list(...)$is_data.frame), "column ", ""),
ifelse(length(unique(agent_formatted)) == 1, unique(agent_formatted), paste0(vector_and(agent_formatted, quotes = FALSE, sort = FALSE))),
mo_var_found,
ifelse(identical(reference_data, AMR::clinical_breakpoints),
paste0(", ", font_bold(guideline_coerced)),
""),
"... ")
intro_txt <- paste0(
"Interpreting ", method_long, ": ", ifelse(isTRUE(list(...)$is_data.frame), "column ", ""),
ifelse(length(unique(agent_formatted)) == 1, unique(agent_formatted), paste0(vector_and(agent_formatted, quotes = FALSE, sort = FALSE))),
mo_var_found,
ifelse(identical(reference_data, AMR::clinical_breakpoints),
paste0(", ", font_bold(guideline_coerced)),
""
),
"... "
)
# prepare used arguments ----
method <- method_short
@ -1131,7 +1168,7 @@ as_sir_method <- function(method_short,
rise_notes <- FALSE
method_coerced <- toupper(method)
ab_coerced <- as.ab(ab)
if (identical(reference_data, AMR::clinical_breakpoints)) {
breakpoints <- reference_data %pm>%
subset(guideline == guideline_coerced & method == method_coerced & ab %in% ab_coerced)
@ -1144,7 +1181,7 @@ as_sir_method <- function(method_short,
breakpoints <- reference_data %pm>%
subset(method == method_coerced & ab %in% ab_coerced)
}
# create the unique data frame to be filled to save time
df <- data.frame(
values = x,
@ -1162,9 +1199,9 @@ as_sir_method <- function(method_short,
# when as.sir.disk is called directly
df$values <- as.disk(df$values)
}
df_unique <- unique(df[ , c("mo", "ab", "uti", "host"), drop = FALSE])
df_unique <- unique(df[, c("mo", "ab", "uti", "host"), drop = FALSE])
# get all breakpoints, use humans as backup for animals
breakpoint_type_lookup <- breakpoint_type
if (breakpoint_type == "animal") {
@ -1172,7 +1209,7 @@ as_sir_method <- function(method_short,
}
breakpoints <- breakpoints %pm>%
subset(type %in% breakpoint_type_lookup)
if (isFALSE(include_screening)) {
# remove screening rules from the breakpoints table
breakpoints <- breakpoints %pm>%
@ -1190,7 +1227,7 @@ as_sir_method <- function(method_short,
any_is_intrinsic_resistant <- FALSE
add_intrinsic_resistance_to_AMR_env()
}
if (nrow(df_unique) < 10 || nrow(breakpoints) == 0) {
# only print intro under 10 items, otherwise progressbar will print this and then it will be printed double
message_(intro_txt, appendLF = FALSE, as_note = FALSE)
@ -1198,19 +1235,22 @@ as_sir_method <- function(method_short,
p <- progress_ticker(n = nrow(df_unique), n_min = 10, title = font_blue(intro_txt), only_bar_percent = TRUE)
has_progress_bar <- !is.null(import_fn("progress_bar", "progress", error_on_fail = FALSE)) && nrow(df_unique) >= 10
on.exit(close(p))
if (nrow(breakpoints) == 0) {
# apparently no breakpoints found
message(
paste0(font_rose_bg(" WARNING "), "\n"),
font_black(paste0(" ", AMR_env$bullet_icon, " No ", guideline_coerced, " ", method_coerced, " breakpoints available for ",
suppressMessages(suppressWarnings(ab_name(unique(ab_coerced), language = NULL, tolower = TRUE))),
" (", unique(ab_coerced), ")."), collapse = "\n"))
font_black(paste0(
" ", AMR_env$bullet_icon, " No ", guideline_coerced, " ", method_coerced, " breakpoints available for ",
suppressMessages(suppressWarnings(ab_name(unique(ab_coerced), language = NULL, tolower = TRUE))),
" (", unique(ab_coerced), ")."
), collapse = "\n")
)
load_mo_uncertainties(metadata_mo)
return(rep(NA_sir_, nrow(df)))
}
vectorise_log_entry <- function(x, len) {
if (length(x) == 1 && len > 1) {
rep(x, len)
@ -1218,7 +1258,7 @@ as_sir_method <- function(method_short,
x
}
}
# run the rules (df_unique is a row combination per mo/ab/uti/host) ----
for (i in seq_len(nrow(df_unique))) {
p$tick()
@ -1265,7 +1305,7 @@ as_sir_method <- function(method_short,
suppressMessages(suppressWarnings(ab_name(ab_current, language = NULL, tolower = TRUE))),
" (", ab_current, ")"
)
# gather all available breakpoints for current MO
# TODO for VET09 do not filter out E. coli and such
breakpoints_current <- breakpoints %pm>%
@ -1276,16 +1316,16 @@ as_sir_method <- function(method_short,
mo_current_species_group,
mo_current_other
))
# TODO are operators considered??
# This seems to not work well: as.sir(as.mic(c(4, ">4", ">=4", 8, ">8", ">=8")), ab = "AMC", mo = "E. coli", breakpoint_type = "animal", host = "dogs", guideline = "CLSI 2024")
## fall-back methods for veterinary guidelines ----
if (breakpoint_type == "animal" && !host_current %in% breakpoints_current$host) {
if (guideline_coerced %like% "CLSI") {
# VET09 says that staph/strep/enterococcus BP can be extrapolated to all Gr+ cocci except for intrinsic resistance, so take all Gr+ cocci:
gram_plus_cocci_vet09 <- microorganisms$mo[microorganisms$genus %in% c("Staphylococcus", "Streptococcus", "Peptostreptococcus", "Aerococcus", "Micrococcus") & microorganisms$rank == "genus"] # TODO should probably include genera that were either of these before
# HUMAN SUBSTITUTES
if (ab_current == "AZM" && mo_current_genus %in% gram_plus_cocci_vet09 && host_current %in% c("dogs", "cats", "horse")) {
# azithro can take human breakpoints for these agents
@ -1331,22 +1371,19 @@ as_sir_method <- function(method_short,
# vancomycin can take human breakpoints in these hosts
breakpoints_current <- breakpoints_current %pm>% subset(host == "human")
notes_current <- c(notes_current, paste0("Using ", font_bold("human"), " breakpoints for ", ab_formatted, " based on CLSI VET09."))
} else if (host_current %in% c("dogs", "cats") && (mo_current_genus %in% c("B_AMYCS", "B_NOCRD", "B_CMPYL", "B_CRYNB", "B_ENTRC", "B_MYCBC", "B_PSDMN", "B_AERMN") | mo_current_class == "B_[CLS]_BTPRTBCT" | mo_current == "B_LISTR_MNCY")) {
# dog breakpoints if no canine/feline
# TODO do we still have dogs breakpoints at this point???
breakpoints_current <- breakpoints_current %pm>% subset(host == "human") # WRONG
notes_current <- c(notes_current, paste0("Using ", font_bold("human"), " breakpoints for ", mo_formatted, " based on CLSI VET09."))
} else {
# no specific CLSI solution for this, so only filter on current host (if no breakpoints available -> too bad)
breakpoints_current <- breakpoints_current %pm>%
subset(host == host_current)
}
}
}
if (NROW(breakpoints_current) == 0) {
AMR_env$sir_interpretation_history <- rbind_AMR(
AMR_env$sir_interpretation_history,
@ -1374,16 +1411,18 @@ as_sir_method <- function(method_short,
notes <- c(notes, notes_current)
next
}
# sort on host and taxonomic rank
# (this will e.g. prefer 'species' breakpoints over 'order' breakpoints)
if (is.na(uti_current)) {
breakpoints_current <- breakpoints_current %pm>%
# `uti` is a column in the data set
# this will put UTI = FALSE first, then UTI = NA, then UTI = TRUE
pm_mutate(uti_index = ifelse(!is.na(uti) & uti == FALSE, 1,
ifelse(is.na(uti), 2,
3))) %pm>%
pm_mutate(uti_index = ifelse(!is.na(uti) & uti == FALSE, 1,
ifelse(is.na(uti), 2,
3
)
)) %pm>%
# be as specific as possible (i.e. prefer species over genus):
pm_arrange(rank_index, uti_index)
} else if (uti_current == TRUE) {
@ -1392,7 +1431,7 @@ as_sir_method <- function(method_short,
# be as specific as possible (i.e. prefer species over genus):
pm_arrange(rank_index)
}
# throw messages for different body sites
site <- breakpoints_current[1L, "site", drop = FALSE] # this is the one we'll take
if (is.na(site)) {
@ -1412,7 +1451,7 @@ as_sir_method <- function(method_short,
# breakpoints for multiple body sites available
notes_current <- c(notes_current, paste0("Multiple breakpoints available for ", font_bold(ab_formatted), " in ", mo_formatted, " - assuming ", site, "."))
}
# first check if mo is intrinsic resistant
if (isTRUE(add_intrinsic_resistance) && guideline_coerced %like% "EUCAST" && paste(mo_current, ab_current) %in% AMR_env$intrinsic_resistant) {
notes_current <- c(notes_current, paste0("Intrinsic resistance applied for ", ab_formatted, " in ", mo_formatted, ""))
@ -1464,7 +1503,7 @@ as_sir_method <- function(method_short,
TRUE ~ NA_sir_
)
}
# write to verbose output
AMR_env$sir_interpretation_history <- rbind_AMR(
AMR_env$sir_interpretation_history,
@ -1494,7 +1533,7 @@ as_sir_method <- function(method_short,
notes <- c(notes, notes_current)
df[rows, "result"] <- new_sir
}
close(p)
# printing messages
if (has_progress_bar == TRUE) {
@ -1518,9 +1557,9 @@ as_sir_method <- function(method_short,
} else {
message(font_green_bg(" OK "))
}
load_mo_uncertainties(metadata_mo)
df$result
}
@ -1536,11 +1575,11 @@ sir_interpretation_history <- function(clean = FALSE) {
# sort descending on time
out <- out[order(format(out$datetime, "%Y%m%d%H%M"), out$index, decreasing = TRUE), , drop = FALSE]
}
if (isTRUE(clean)) {
AMR_env$sir_interpretation_history <- AMR_env$sir_interpretation_history[0, , drop = FALSE]
}
if (pkg_is_available("tibble")) {
out <- import_fn("as_tibble", "tibble")(out)
}
@ -1757,7 +1796,7 @@ summary.sir <- function(object, ...) {
#' @noRd
c.sir <- function(...) {
lst <- list(...)
# TODO for #170
# guideline <- vapply(FUN.VALUE = character(1), lst, function(x) attributes(x)$guideline %or% NA_character_)
# mo <- vapply(FUN.VALUE = character(1), lst, function(x) attributes(x)$mo %or% NA_character_)
@ -1765,9 +1804,9 @@ c.sir <- function(...) {
# method <- vapply(FUN.VALUE = character(1), lst, function(x) attributes(x)$method %or% NA_character_)
# ref_tbl <- vapply(FUN.VALUE = character(1), lst, function(x) attributes(x)$ref_tbl %or% NA_character_)
# ref_breakpoints <- vapply(FUN.VALUE = character(1), lst, function(x) attributes(x)$ref_breakpoints %or% NA_character_)
out <- as.sir(unlist(lapply(list(...), as.character)))
# TODO for #170
# if (!all(is.na(guideline))) {
# attributes(out)$guideline <- guideline
@ -1777,7 +1816,7 @@ c.sir <- function(...) {
# attributes(out)$ref_tbl <- ref_tbl
# attributes(out)$ref_breakpoints <- ref_breakpoints
# }
out
}