P281424/AMR
1
0
Fork 0
mirror of https://github.com/msberends/AMR.git synced 2024-12-26 04:46:11 +01:00
Code Releases Activity
77656a676c
AMR/R/first_isolate.R

545 lines
23 KiB
R
Executable File
Raw Blame History

# ==================================================================== #
# TITLE #
# Antimicrobial Resistance (AMR) Analysis #
# #
# SOURCE #
# https://gitlab.com/msberends/AMR #
# #
# LICENCE #
# (c) 2018-2020 Berends MS, Luz CF et al. #
# #
# This R package is free software; you can freely use and distribute #
# it for both personal and commercial purposes under the terms of the #
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
# the Free Software Foundation. #
# #
# We created this package for both routine data analysis and academic #
# research and it was publicly released in the hope that it will be #
# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
# Visit our website for more info: https://msberends.gitlab.io/AMR. #
# ==================================================================== #
#' Determine first (weighted) isolates
#'
#' Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type.
#' @inheritSection lifecycle Stable lifecycle
#' @param x a [`data.frame`] containing isolates.
#' @param col_date column name of the result date (or date that is was received on the lab), defaults to the first column of with a date class
#' @param col_patient_id column name of the unique IDs of the patients, defaults to the first column that starts with 'patient' or 'patid' (case insensitive)
#' @param col_mo column name of the IDs of the microorganisms (see [as.mo()]), defaults to the first column of class [`mo`]. Values will be coerced using [as.mo()].
#' @param col_testcode column name of the test codes. Use `col_testcode = NULL` to **not** exclude certain test codes (like test codes for screening). In that case `testcodes_exclude` will be ignored.
#' @param col_specimen column name of the specimen type or group
#' @param col_icu column name of the logicals (`TRUE`/`FALSE`) whether a ward or department is an Intensive Care Unit (ICU)
#' @param col_keyantibiotics column name of the key antibiotics to determine first *weighted* isolates, see [key_antibiotics()]. Defaults to the first column that starts with 'key' followed by 'ab' or 'antibiotics' (case insensitive). Use `col_keyantibiotics = FALSE` to prevent this.
#' @param episode_days episode in days after which a genus/species combination will be determined as 'first isolate' again. The default of 365 days is based on the guideline by CLSI, see Source.
#' @param testcodes_exclude character vector with test codes that should be excluded (case-insensitive)
#' @param icu_exclude logical whether ICU isolates should be excluded (rows with value `TRUE` in column `col_icu`)
#' @param specimen_group value in column `col_specimen` to filter on
#' @param type type to determine weighed isolates; can be `"keyantibiotics"` or `"points"`, see Details
#' @param ignore_I logical to determine whether antibiotic interpretations with `"I"` will be ignored when `type = "keyantibiotics"`, see Details
#' @param points_threshold points until the comparison of key antibiotics will lead to inclusion of an isolate when `type = "points"`, see Details
#' @param info print progress
#' @param include_unknown logical to determine whether 'unknown' microorganisms should be included too, i.e. microbial code `"UNKNOWN"`, which defaults to `FALSE`. For WHONET users, this means that all records with organism code `"con"` (*contamination*) will be excluded at default. Isolates with a microbial ID of `NA` will always be excluded as first isolate.
#' @param ... parameters passed on to the [first_isolate()] function
#' @details **WHY THIS IS SO IMPORTANT** \cr
#' To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode [(ref)](https://www.ncbi.nlm.nih.gov/pubmed/17304462). If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all *S. aureus* isolates would be overestimated, because you included this MRSA more than once. It would be [selection bias](https://en.wikipedia.org/wiki/Selection_bias).
#'
#' All isolates with a microbial ID of `NA` will be excluded as first isolate.
#'
#' The functions [filter_first_isolate()] and [filter_first_weighted_isolate()] are helper functions to quickly filter on first isolates. The function [filter_first_isolate()] is essentially equal to:
#' ```
#' x %>%
#' mutate(only_firsts = first_isolate(x, ...)) %>%
#' filter(only_firsts == TRUE) %>%
#' select(-only_firsts)
#' ```
#' The function [filter_first_weighted_isolate()] is essentially equal to:
#' ```
#' x %>%
#' mutate(keyab = key_antibiotics(.)) %>%
#' mutate(only_weighted_firsts = first_isolate(x,
#' col_keyantibiotics = "keyab", ...)) %>%
#' filter(only_weighted_firsts == TRUE) %>%
#' select(-only_weighted_firsts)
#' ```
#' @section Key antibiotics:
#' There are two ways to determine whether isolates can be included as first *weighted* isolates which will give generally the same results:
#'
#' 1. Using `type = "keyantibiotics"` and parameter `ignore_I`
#'
#' Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With `ignore_I = FALSE`, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. Read more about this in the [key_antibiotics()] function.
#'
#' 2. Using `type = "points"` and parameter `points_threshold`
#'
#' A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds `points_threshold`, which default to `2`, an isolate will be (re)selected as a first weighted isolate.
#' @rdname first_isolate
#' @seealso [key_antibiotics()]
#' @export
#' @importFrom dplyr arrange_at lag between row_number filter mutate arrange pull ungroup
#' @importFrom crayon blue bold silver
# @importFrom clean percentage
#' @return A [`logical`] vector
#' @source Methodology of this function is based on:
#'
#' **M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition**, 2014, *Clinical and Laboratory Standards Institute (CLSI)*. <https://clsi.org/standards/products/microbiology/documents/m39/>.
#' @inheritSection AMR Read more on our website!
#' @examples
#' # `example_isolates` is a dataset available in the AMR package.
#' # See ?example_isolates.
#'
#' library(dplyr)
#' # Filter on first isolates:
#' example_isolates %>%
#' mutate(first_isolate = first_isolate(.,
#' col_date = "date",
#' col_patient_id = "patient_id",
#' col_mo = "mo")) %>%
#' filter(first_isolate == TRUE)
#'
#' # Which can be shortened to:
#' example_isolates %>%
#' filter_first_isolate()
#' # or for first weighted isolates:
#' example_isolates %>%
#' filter_first_weighted_isolate()
#'
#' # Now let's see if first isolates matter:
#' A <- example_isolates %>%
#' group_by(hospital_id) %>%
#' summarise(count = n_rsi(GEN), # gentamicin availability
#' resistance = resistance(GEN)) # gentamicin resistance
#'
#' B <- example_isolates %>%
#' filter_first_weighted_isolate() %>% # the 1st isolate filter
#' group_by(hospital_id) %>%
#' summarise(count = n_rsi(GEN), # gentamicin availability
#' resistance = resistance(GEN)) # gentamicin resistance
#'
#' # Have a look at A and B.
#' # B is more reliable because every isolate is only counted once.
#' # Gentamicin resitance in hospital D appears to be 3.1% higher than
#' # when you (erroneously) would have used all isolates for analysis.
#'
#'
#' ## OTHER EXAMPLES:
#'
#' \dontrun{
#'
#' # set key antibiotics to a new variable
#' x$keyab <- key_antibiotics(x)
#'
#' x$first_isolate <- first_isolate(x)
#'
#' x$first_isolate_weighed <- first_isolate(x, col_keyantibiotics = 'keyab')
#'
#' x$first_blood_isolate <- first_isolate(x, specimen_group = "Blood")
#' }
first_isolate <- function(x,
col_date = NULL,
col_patient_id = NULL,
col_mo = NULL,
col_testcode = NULL,
col_specimen = NULL,
col_icu = NULL,
col_keyantibiotics = NULL,
episode_days = 365,
testcodes_exclude = NULL,
icu_exclude = FALSE,
specimen_group = NULL,
type = "keyantibiotics",
ignore_I = TRUE,
points_threshold = 2,
info = interactive(),
include_unknown = FALSE,
...) {
if (!is.data.frame(x)) {
stop("`x` must be a data.frame.", call. = FALSE)
}
dots <- unlist(list(...))
if (length(dots) != 0) {
# backwards compatibility with old parameters
dots.names <- dots %>% names()
if ("filter_specimen" %in% dots.names) {
specimen_group <- dots[which(dots.names == "filter_specimen")]
}
if ("tbl" %in% dots.names) {
x <- dots[which(dots.names == "tbl")]
}
}
# try to find columns based on type
# -- mo
if (is.null(col_mo)) {
col_mo <- search_type_in_df(x = x, type = "mo")
}
if (is.null(col_mo)) {
stop("`col_mo` must be set.", call. = FALSE)
}
# -- date
if (is.null(col_date)) {
col_date <- search_type_in_df(x = x, type = "date")
}
if (is.null(col_date)) {
stop("`col_date` must be set.", call. = FALSE)
}
# convert to Date (pipes/pull for supporting tibbles too)
dates <- x %>% pull(col_date) %>% as.Date()
dates[is.na(dates)] <- as.Date("1970-01-01")
x[, col_date] <- dates
# -- patient id
if (is.null(col_patient_id)) {
if (all(c("First name", "Last name", "Sex") %in% colnames(x))) {
# WHONET support
x <- x %>% mutate(patient_id = paste(`First name`, `Last name`, Sex))
col_patient_id <- "patient_id"
message(blue(paste0("NOTE: Using combined columns `", bold("First name"), "`, `", bold("Last name"), "` and `", bold("Sex"), "` as input for `col_patient_id`")))
} else {
col_patient_id <- search_type_in_df(x = x, type = "patient_id")
}
}
if (is.null(col_patient_id)) {
stop("`col_patient_id` must be set.", call. = FALSE)
}
# -- key antibiotics
if (is.null(col_keyantibiotics)) {
col_keyantibiotics <- search_type_in_df(x = x, type = "keyantibiotics")
}
if (isFALSE(col_keyantibiotics)) {
col_keyantibiotics <- NULL
}
# -- specimen
if (is.null(col_specimen) & !is.null(specimen_group)) {
col_specimen <- search_type_in_df(x = x, type = "specimen")
}
if (isFALSE(col_specimen)) {
col_specimen <- NULL
}
# check if columns exist
check_columns_existance <- function(column, tblname = x) {
if (NROW(tblname) <= 1 | NCOL(tblname) <= 1) {
stop("Please check tbl for existance.")
}
if (!is.null(column)) {
if (!(column %in% colnames(tblname))) {
stop("Column `", column, "` not found.")
}
}
}
check_columns_existance(col_date)
check_columns_existance(col_patient_id)
check_columns_existance(col_mo)
check_columns_existance(col_testcode)
check_columns_existance(col_icu)
check_columns_existance(col_keyantibiotics)
# create new dataframe with original row index
x <- x %>%
mutate(newvar_row_index = seq_len(nrow(x)),
newvar_mo = x %>% pull(col_mo) %>% as.mo(),
newvar_genus_species = paste(mo_genus(newvar_mo), mo_species(newvar_mo)),
newvar_date = x %>% pull(col_date),
newvar_patient_id = x %>% pull(col_patient_id))
if (is.null(col_testcode)) {
testcodes_exclude <- NULL
}
# remove testcodes
if (!is.null(testcodes_exclude) & info == TRUE) {
message(blue(paste0("[Criterion] Excluded test codes: ", toString(testcodes_exclude))))
}
if (is.null(col_icu)) {
icu_exclude <- FALSE
} else {
x <- x %>%
mutate(col_icu = x %>% pull(col_icu) %>% as.logical())
}
if (is.null(col_specimen)) {
specimen_group <- NULL
}
# filter on specimen group and keyantibiotics when they are filled in
if (!is.null(specimen_group)) {
check_columns_existance(col_specimen, x)
if (info == TRUE) {
message(blue(paste0("[Criterion] Excluded other than specimen group '", specimen_group, "'")))
}
}
if (!is.null(col_keyantibiotics)) {
x <- x %>% mutate(key_ab = x %>% pull(col_keyantibiotics))
}
if (is.null(testcodes_exclude)) {
testcodes_exclude <- ""
}
# arrange data to the right sorting
if (is.null(specimen_group)) {
# not filtering on specimen
if (icu_exclude == FALSE) {
if (info == TRUE & !is.null(col_icu)) {
message(blue("[Criterion] Included isolates from ICU"))
}
x <- x %>%
arrange(newvar_patient_id,
newvar_genus_species,
newvar_date)
row.start <- 1
row.end <- nrow(x)
} else {
if (info == TRUE) {
message(blue("[Criterion] Excluded isolates from ICU"))
}
x <- x %>%
arrange_at(c(col_icu,
"newvar_patient_id",
"newvar_genus_species",
"newvar_date"))
suppressWarnings(
row.start <- which(x %>% pull(col_icu) == FALSE) %>% min(na.rm = TRUE)
)
suppressWarnings(
row.end <- which(x %>% pull(col_icu) == FALSE) %>% max(na.rm = TRUE)
)
}
} else {
# filtering on specimen and only analyse these row to save time
if (icu_exclude == FALSE) {
if (info == TRUE & !is.null(col_icu)) {
message(blue("[Criterion] Included isolates from ICU.\n"))
}
x <- x %>%
arrange_at(c(col_specimen,
"newvar_patient_id",
"newvar_genus_species",
"newvar_date"))
suppressWarnings(
row.start <- which(x %>% pull(col_specimen) == specimen_group) %>% min(na.rm = TRUE)
)
suppressWarnings(
row.end <- which(x %>% pull(col_specimen) == specimen_group) %>% max(na.rm = TRUE)
)
} else {
if (info == TRUE) {
message(blue("[Criterion] Excluded isolates from ICU"))
}
x <- x %>%
arrange_at(c(col_icu,
col_specimen,
"newvar_patient_id",
"newvar_genus_species",
"newvar_date"))
suppressWarnings(
row.start <- min(which(x %>% pull(col_specimen) == specimen_group
& x %>% pull(col_icu) == FALSE),
na.rm = TRUE)
)
suppressWarnings(
row.end <- max(which(x %>% pull(col_specimen) == specimen_group &
x %>% pull(col_icu) == FALSE),
na.rm = TRUE)
)
}
}
# no isolates found
if (abs(row.start) == Inf | abs(row.end) == Inf) {
if (info == TRUE) {
message(paste("=> Found", bold("no isolates")))
}
return(rep(FALSE, nrow(x)))
}
# did find some isolates - add new index numbers of rows
x <- x %>% mutate(newvar_row_index_sorted = seq_len(nrow(.)))
scope.size <- row.end - row.start + 1
identify_new_year <- function(x, episode_days) {
# I asked on StackOverflow:
# https://stackoverflow.com/questions/42122245/filter-one-row-every-year
if (length(x) == 1) {
return(TRUE)
}
indices <- integer(0)
start <- x[1]
ind <- 1
indices[ind] <- ind
for (i in 2:length(x)) {
if (isTRUE(as.numeric(x[i] - start) >= episode_days)) {
ind <- ind + 1
indices[ind] <- i
start <- x[i]
}
}
result <- rep(FALSE, length(x))
result[indices] <- TRUE
return(result)
}
# Analysis of first isolate ----
all_first <- x %>%
mutate(other_pat_or_mo = if_else(newvar_patient_id == lag(newvar_patient_id)
& newvar_genus_species == lag(newvar_genus_species),
FALSE,
TRUE)) %>%
group_by(newvar_patient_id,
newvar_genus_species) %>%
mutate(more_than_episode_ago = identify_new_year(x = newvar_date,
episode_days = episode_days)) %>%
ungroup()
weighted.notice <- ""
if (!is.null(col_keyantibiotics)) {
weighted.notice <- "weighted "
if (info == TRUE) {
if (type == "keyantibiotics") {
message(blue(paste0("[Criterion] Inclusion based on key antibiotics, ",
ifelse(ignore_I == FALSE, "not ", ""),
"ignoring I")))
}
if (type == "points") {
message(blue(paste0("[Criterion] Inclusion based on key antibiotics, using points threshold of "
, points_threshold)))
}
}
type_param <- type
all_first <- all_first %>%
mutate(key_ab_lag = lag(key_ab)) %>%
mutate(key_ab_other = !key_antibiotics_equal(y = key_ab,
z = key_ab_lag,
type = type_param,
ignore_I = ignore_I,
points_threshold = points_threshold,
info = info)) %>%
mutate(
real_first_isolate =
if_else(
newvar_row_index_sorted %>% between(row.start, row.end)
& newvar_genus_species != ""
& (other_pat_or_mo | more_than_episode_ago | key_ab_other),
TRUE,
FALSE))
} else {
# no key antibiotics
all_first <- all_first %>%
mutate(
real_first_isolate =
if_else(
newvar_row_index_sorted %>% between(row.start, row.end)
& newvar_genus_species != ""
& (other_pat_or_mo | more_than_episode_ago),
TRUE,
FALSE))
}
# first one as TRUE
all_first[row.start, "real_first_isolate"] <- TRUE
# no tests that should be included, or ICU
if (!is.null(col_testcode)) {
all_first[which(all_first[, col_testcode] %in% tolower(testcodes_exclude)), "real_first_isolate"] <- FALSE
}
if (icu_exclude == TRUE) {
all_first[which(all_first[, col_icu] == TRUE), "real_first_isolate"] <- FALSE
}
decimal.mark <- getOption("OutDec")
big.mark <- ifelse(decimal.mark != ",", ",", ".")
# handle empty microorganisms
if (any(all_first$newvar_mo == "UNKNOWN", na.rm = TRUE) & info == TRUE) {
message(blue(paste0("NOTE: ", ifelse(include_unknown == TRUE, "Included ", "Excluded "),
format(sum(all_first$newvar_mo == "UNKNOWN"),
decimal.mark = decimal.mark, big.mark = big.mark),
" isolates with a microbial ID 'UNKNOWN' (column `", bold(col_mo), "`)")))
}
all_first[which(all_first$newvar_mo == "UNKNOWN"), "real_first_isolate"] <- include_unknown
# exclude all NAs
if (any(is.na(all_first$newvar_mo)) & info == TRUE) {
message(blue(paste0("NOTE: Excluded ", format(sum(is.na(all_first$newvar_mo)),
decimal.mark = decimal.mark, big.mark = big.mark),
" isolates with a microbial ID 'NA' (column `", bold(col_mo), "`)")))
}
all_first[which(is.na(all_first$newvar_mo)), "real_first_isolate"] <- FALSE
# arrange back according to original sorting again
all_first <- all_first %>%
arrange(newvar_row_index) %>%
pull(real_first_isolate)
if (info == TRUE) {
n_found <- base::sum(all_first, na.rm = TRUE)
p_found_total <- percentage(n_found / nrow(x))
p_found_scope <- percentage(n_found / scope.size)
# mark up number of found
n_found <- base::format(n_found, big.mark = big.mark, decimal.mark = decimal.mark)
if (p_found_total != p_found_scope) {
msg_txt <- paste0("=> Found ",
bold(paste0(n_found, " first ", weighted.notice, "isolates")),
" (", p_found_scope, " within scope and ", p_found_total, " of total)")
} else {
msg_txt <- paste0("=> Found ",
bold(paste0(n_found, " first ", weighted.notice, "isolates")),
" (", p_found_total, " of total)")
}
base::message(msg_txt)
}
all_first
}
#' @rdname first_isolate
#' @importFrom dplyr filter
#' @export
filter_first_isolate <- function(x,
col_date = NULL,
col_patient_id = NULL,
col_mo = NULL,
...) {
filter(x, first_isolate(x = x,
col_date = col_date,
col_patient_id = col_patient_id,
col_mo = col_mo,
...))
}
#' @rdname first_isolate
#' @importFrom dplyr %>% mutate filter
#' @export
filter_first_weighted_isolate <- function(x,
col_date = NULL,
col_patient_id = NULL,
col_mo = NULL,
col_keyantibiotics = NULL,
...) {
tbl_keyab <- x %>%
mutate(keyab = suppressMessages(key_antibiotics(.,
col_mo = col_mo,
...))) %>%
mutate(firsts = first_isolate(.,
col_date = col_date,
col_patient_id = col_patient_id,
col_mo = col_mo,
col_keyantibiotics = "keyab",
...))
x[which(tbl_keyab$firsts == TRUE), ]
}
View Git Blame Copy Permalink