mirror of https://github.com/msberends/AMR.git
475 lines
21 KiB
R
Executable File
475 lines
21 KiB
R
Executable File
# ==================================================================== #
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# TITLE #
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# Antimicrobial Resistance (AMR) Analysis #
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# #
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# SOURCE #
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# https://gitlab.com/msberends/AMR #
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# #
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# LICENCE #
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# (c) 2018-2020 Berends MS, Luz CF et al. #
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# #
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# This R package is free software; you can freely use and distribute #
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# it for both personal and commercial purposes under the terms of the #
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# GNU General Public License version 2.0 (GNU GPL-2), as published by #
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# the Free Software Foundation. #
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# #
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# We created this package for both routine data analysis and academic #
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# research and it was publicly released in the hope that it will be #
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# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
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# Visit our website for more info: https://msberends.gitlab.io/AMR. #
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# ==================================================================== #
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#' Determine first (weighted) isolates
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#'
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#' Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type.
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#' @inheritSection lifecycle Stable lifecycle
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#' @param x a [`data.frame`] containing isolates.
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#' @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
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#' @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)
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#' @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()].
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#' @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.
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#' @param col_specimen column name of the specimen type or group
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#' @param col_icu column name of the logicals (`TRUE`/`FALSE`) whether a ward or department is an Intensive Care Unit (ICU)
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#' @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.
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#' @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.
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#' @param testcodes_exclude character vector with test codes that should be excluded (case-insensitive)
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#' @param icu_exclude logical whether ICU isolates should be excluded (rows with value `TRUE` in column `col_icu`)
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#' @param specimen_group value in column `col_specimen` to filter on
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#' @param type type to determine weighed isolates; can be `"keyantibiotics"` or `"points"`, see Details
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#' @param ignore_I logical to determine whether antibiotic interpretations with `"I"` will be ignored when `type = "keyantibiotics"`, see Details
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#' @param points_threshold points until the comparison of key antibiotics will lead to inclusion of an isolate when `type = "points"`, see Details
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#' @param info print progress
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#' @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.
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#' @param ... parameters passed on to the [first_isolate()] function
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#' @details **WHY THIS IS SO IMPORTANT** \cr
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#' 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).
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#'
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#' All isolates with a microbial ID of `NA` will be excluded as first isolate.
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#'
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#' 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 one of:
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#' ```
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#' x %>% filter(first_isolate(., ...))
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#' ```
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#' The function [filter_first_weighted_isolate()] is essentially equal to:
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#' ```
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#' x %>%
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#' mutate(keyab = key_antibiotics(.)) %>%
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#' mutate(only_weighted_firsts = first_isolate(x,
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#' col_keyantibiotics = "keyab", ...)) %>%
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#' filter(only_weighted_firsts == TRUE) %>%
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#' select(-only_weighted_firsts, -keyab)
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#' ```
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#' @section Key antibiotics:
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#' There are two ways to determine whether isolates can be included as first *weighted* isolates which will give generally the same results:
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#'
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#' 1. Using `type = "keyantibiotics"` and parameter `ignore_I`
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#'
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#' 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.
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#'
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#' 2. Using `type = "points"` and parameter `points_threshold`
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#'
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#' 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.
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#' @rdname first_isolate
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#' @seealso [key_antibiotics()]
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#' @export
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#' @return A [`logical`] vector
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#' @source Methodology of this function is strictly based on:
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#'
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#' **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/>.
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#' @inheritSection AMR Read more on our website!
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#' @examples
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#' # `example_isolates` is a dataset available in the AMR package.
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#' # See ?example_isolates.
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#'
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#' \dontrun{
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#' library(dplyr)
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#' # Filter on first isolates:
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#' example_isolates %>%
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#' mutate(first_isolate = first_isolate(.)) %>%
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#' filter(first_isolate == TRUE)
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#'
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#' # Now let's see if first isolates matter:
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#' A <- example_isolates %>%
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#' group_by(hospital_id) %>%
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#' summarise(count = n_rsi(GEN), # gentamicin availability
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#' resistance = resistance(GEN)) # gentamicin resistance
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#'
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#' B <- example_isolates %>%
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#' filter_first_weighted_isolate() %>% # the 1st isolate filter
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#' group_by(hospital_id) %>%
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#' summarise(count = n_rsi(GEN), # gentamicin availability
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#' resistance = resistance(GEN)) # gentamicin resistance
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#'
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#' # Have a look at A and B.
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#' # B is more reliable because every isolate is counted only once.
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#' # Gentamicin resistance in hospital D appears to be 3.7% higher than
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#' # when you (erroneously) would have used all isolates for analysis.
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#'
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#'
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#' ## OTHER EXAMPLES:
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#'
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#' # Short-hand versions:
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#' example_isolates %>%
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#' filter_first_isolate()
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#'
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#' example_isolates %>%
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#' filter_first_weighted_isolate()
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#'
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#'
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#' # set key antibiotics to a new variable
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#' x$keyab <- key_antibiotics(x)
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#'
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#' x$first_isolate <- first_isolate(x)
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#'
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#' x$first_isolate_weighed <- first_isolate(x, col_keyantibiotics = 'keyab')
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#'
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#' x$first_blood_isolate <- first_isolate(x, specimen_group = "Blood")
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#' }
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first_isolate <- function(x,
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col_date = NULL,
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col_patient_id = NULL,
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col_mo = NULL,
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col_testcode = NULL,
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col_specimen = NULL,
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col_icu = NULL,
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col_keyantibiotics = NULL,
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episode_days = 365,
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testcodes_exclude = NULL,
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icu_exclude = FALSE,
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specimen_group = NULL,
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type = "keyantibiotics",
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ignore_I = TRUE,
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points_threshold = 2,
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info = interactive(),
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include_unknown = FALSE,
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...) {
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dots <- unlist(list(...))
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if (length(dots) != 0) {
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# backwards compatibility with old parameters
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dots.names <- dots %>% names()
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if ("filter_specimen" %in% dots.names) {
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specimen_group <- dots[which(dots.names == "filter_specimen")]
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}
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if ("tbl" %in% dots.names) {
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x <- dots[which(dots.names == "tbl")]
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}
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}
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stop_ifnot(is.data.frame(x), "`x` must be a data.frame")
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stop_if(any(dim(x) == 0), "`x` must contain rows and columns")
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# remove data.table, grouping from tibbles, etc.
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x <- as.data.frame(x, stringsAsFactors = FALSE)
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# try to find columns based on type
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# -- mo
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if (is.null(col_mo)) {
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col_mo <- search_type_in_df(x = x, type = "mo")
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stop_if(is.null(col_mo), "`col_mo` must be set")
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}
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# -- date
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if (is.null(col_date)) {
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col_date <- search_type_in_df(x = x, type = "date")
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stop_if(is.null(col_date), "`col_date` must be set")
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}
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# convert to Date
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dates <- as.Date(x[, col_date, drop = TRUE])
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dates[is.na(dates)] <- as.Date("1970-01-01")
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x[, col_date] <- dates
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# -- patient id
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if (is.null(col_patient_id)) {
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if (all(c("First name", "Last name", "Sex") %in% colnames(x))) {
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# WHONET support
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x$patient_id <- paste(x$`First name`, x$`Last name`, x$Sex)
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col_patient_id <- "patient_id"
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message(font_blue(paste0("NOTE: Using combined columns `", font_bold("First name"), "`, `", font_bold("Last name"), "` and `", font_bold("Sex"), "` as input for `col_patient_id`")))
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} else {
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col_patient_id <- search_type_in_df(x = x, type = "patient_id")
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}
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stop_if(is.null(col_patient_id), "`col_patient_id` must be set")
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}
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# -- key antibiotics
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if (is.null(col_keyantibiotics)) {
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col_keyantibiotics <- search_type_in_df(x = x, type = "keyantibiotics")
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}
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if (isFALSE(col_keyantibiotics)) {
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col_keyantibiotics <- NULL
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}
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# -- specimen
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if (is.null(col_specimen) & !is.null(specimen_group)) {
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col_specimen <- search_type_in_df(x = x, type = "specimen")
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}
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if (isFALSE(col_specimen)) {
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col_specimen <- NULL
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}
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# check if columns exist
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check_columns_existance <- function(column, tblname = x) {
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if (!is.null(column)) {
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stop_ifnot(column %in% colnames(tblname),
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"Column `", column, "` not found.", call = FALSE)
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}
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}
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check_columns_existance(col_date)
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check_columns_existance(col_patient_id)
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check_columns_existance(col_mo)
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check_columns_existance(col_testcode)
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check_columns_existance(col_icu)
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check_columns_existance(col_keyantibiotics)
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# create original row index
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x$newvar_row_index <- seq_len(nrow(x))
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x$newvar_mo <- x %>% pull(col_mo) %>% as.mo()
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x$newvar_genus_species <- paste(mo_genus(x$newvar_mo), mo_species(x$newvar_mo))
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x$newvar_date <- x %>% pull(col_date)
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x$newvar_patient_id <- x %>% pull(col_patient_id)
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if (is.null(col_testcode)) {
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testcodes_exclude <- NULL
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}
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# remove testcodes
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if (!is.null(testcodes_exclude) & info == TRUE) {
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message(font_black(paste0("[Criterion] Exclude test codes: ", toString(paste0("'", testcodes_exclude, "'")))))
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}
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if (is.null(col_specimen)) {
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specimen_group <- NULL
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}
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# filter on specimen group and keyantibiotics when they are filled in
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if (!is.null(specimen_group)) {
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check_columns_existance(col_specimen, x)
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if (info == TRUE) {
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message(font_black(paste0("[Criterion] Exclude other than specimen group '", specimen_group, "'")))
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}
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}
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if (!is.null(col_keyantibiotics)) {
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x$newvar_key_ab <- x[, col_keyantibiotics, drop = TRUE]
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}
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if (is.null(testcodes_exclude)) {
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testcodes_exclude <- ""
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}
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# arrange data to the right sorting
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if (is.null(specimen_group)) {
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x <- x[order(x$newvar_patient_id,
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x$newvar_genus_species,
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x$newvar_date), ]
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rownames(x) <- NULL
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row.start <- 1
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row.end <- nrow(x)
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} else {
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# filtering on specimen and only analyse these rows to save time
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x <- x[order(pull(x, col_specimen),
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x$newvar_patient_id,
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x$newvar_genus_species,
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x$newvar_date), ]
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rownames(x) <- NULL
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suppressWarnings(
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row.start <- which(x %>% pull(col_specimen) == specimen_group) %>% min(na.rm = TRUE)
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)
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suppressWarnings(
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row.end <- which(x %>% pull(col_specimen) == specimen_group) %>% max(na.rm = TRUE)
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)
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}
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# no isolates found
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if (abs(row.start) == Inf | abs(row.end) == Inf) {
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if (info == TRUE) {
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message(paste("=> Found", font_bold("no isolates")))
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}
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return(rep(FALSE, nrow(x)))
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}
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# did find some isolates - add new index numbers of rows
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x$newvar_row_index_sorted <- seq_len(nrow(x))
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scope.size <- row.end - row.start + 1
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identify_new_year <- function(x, episode_days) {
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# I asked on StackOverflow:
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# https://stackoverflow.com/questions/42122245/filter-one-row-every-year
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if (length(x) == 1) {
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return(TRUE)
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}
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indices <- integer(0)
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start <- x[1]
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ind <- 1
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indices[ind] <- ind
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for (i in 2:length(x)) {
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if (isTRUE(as.numeric(x[i] - start) >= episode_days)) {
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ind <- ind + 1
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indices[ind] <- i
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start <- x[i]
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}
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}
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result <- rep(FALSE, length(x))
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result[indices] <- TRUE
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return(result)
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}
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# Analysis of first isolate ----
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x$other_pat_or_mo <- if_else(x$newvar_patient_id == lag(x$newvar_patient_id) &
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x$newvar_genus_species == lag(x$newvar_genus_species),
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FALSE,
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TRUE)
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x$episode_group <- paste(x$newvar_patient_id, x$newvar_genus_species)
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x$more_than_episode_ago <- unname(unlist(lapply(unique(x$episode_group),
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function(g,
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df = x,
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days = episode_days) {
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identify_new_year(x = df[which(df$episode_group == g), "newvar_date"],
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episode_days = days)
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})))
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weighted.notice <- ""
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if (!is.null(col_keyantibiotics)) {
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weighted.notice <- "weighted "
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if (info == TRUE) {
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if (type == "keyantibiotics") {
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message(font_black(paste0("[Criterion] Base inclusion on key antibiotics, ",
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ifelse(ignore_I == FALSE, "not ", ""),
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"ignoring I")))
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}
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if (type == "points") {
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message(font_black(paste0("[Criterion] Base inclusion on key antibiotics, using points threshold of "
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, points_threshold)))
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}
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}
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type_param <- type
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x$other_key_ab <- !key_antibiotics_equal(y = x$newvar_key_ab,
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z = lag(x$newvar_key_ab),
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type = type_param,
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ignore_I = ignore_I,
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points_threshold = points_threshold,
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info = info)
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# with key antibiotics
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x$newvar_first_isolate <- if_else(x$newvar_row_index_sorted >= row.start &
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x$newvar_row_index_sorted <= row.end &
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x$newvar_genus_species != "" &
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(x$other_pat_or_mo | x$more_than_episode_ago | x$other_key_ab),
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TRUE,
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FALSE)
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} else {
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# no key antibiotics
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x$newvar_first_isolate <- if_else(x$newvar_row_index_sorted >= row.start &
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x$newvar_row_index_sorted <= row.end &
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x$newvar_genus_species != "" &
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(x$other_pat_or_mo | x$more_than_episode_ago),
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TRUE,
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FALSE)
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}
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# first one as TRUE
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x[row.start, "newvar_first_isolate"] <- TRUE
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# no tests that should be included, or ICU
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if (!is.null(col_testcode)) {
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x[which(x[, col_testcode] %in% tolower(testcodes_exclude)), "newvar_first_isolate"] <- FALSE
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}
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if (!is.null(col_icu)) {
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if (icu_exclude == TRUE) {
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message(font_black("[Criterion] Exclude isolates from ICU.\n"))
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x[which(as.logical(x[, col_icu, drop = TRUE])), "newvar_first_isolate"] <- FALSE
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} else {
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message(font_black("[Criterion] Include isolates from ICU.\n"))
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}
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}
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decimal.mark <- getOption("OutDec")
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big.mark <- ifelse(decimal.mark != ",", ",", ".")
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# handle empty microorganisms
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if (any(x$newvar_mo == "UNKNOWN", na.rm = TRUE) & info == TRUE) {
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message(font_blue(paste0("NOTE: ", ifelse(include_unknown == TRUE, "Included ", "Excluded "),
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format(sum(x$newvar_mo == "UNKNOWN"),
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decimal.mark = decimal.mark, big.mark = big.mark),
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" isolates with a microbial ID 'UNKNOWN' (column `", font_bold(col_mo), "`)")))
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}
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x[which(x$newvar_mo == "UNKNOWN"), "newvar_first_isolate"] <- include_unknown
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# exclude all NAs
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if (any(is.na(x$newvar_mo)) & info == TRUE) {
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message(font_blue(paste0("NOTE: Excluded ", format(sum(is.na(x$newvar_mo)),
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decimal.mark = decimal.mark, big.mark = big.mark),
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" isolates with a microbial ID 'NA' (column `", font_bold(col_mo), "`)")))
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}
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x[which(is.na(x$newvar_mo)), "newvar_first_isolate"] <- FALSE
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# arrange back according to original sorting again
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x <- x[order(x$newvar_row_index), ]
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rownames(x) <- NULL
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if (info == TRUE) {
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n_found <- base::sum(x$newvar_first_isolate, na.rm = TRUE)
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p_found_total <- percentage(n_found / nrow(x))
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p_found_scope <- percentage(n_found / scope.size)
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# mark up number of found
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n_found <- base::format(n_found, big.mark = big.mark, decimal.mark = decimal.mark)
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if (p_found_total != p_found_scope) {
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msg_txt <- paste0("=> Found ",
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font_bold(paste0(n_found, " first ", weighted.notice, "isolates")),
|
|
" (", p_found_scope, " within scope and ", p_found_total, " of total)")
|
|
} else {
|
|
msg_txt <- paste0("=> Found ",
|
|
font_bold(paste0(n_found, " first ", weighted.notice, "isolates")),
|
|
" (", p_found_total, " of total)")
|
|
}
|
|
message(font_black(msg_txt))
|
|
}
|
|
|
|
x$newvar_first_isolate
|
|
|
|
}
|
|
|
|
#' @rdname first_isolate
|
|
#' @export
|
|
filter_first_isolate <- function(x,
|
|
col_date = NULL,
|
|
col_patient_id = NULL,
|
|
col_mo = NULL,
|
|
...) {
|
|
subset(x, first_isolate(x = x,
|
|
col_date = col_date,
|
|
col_patient_id = col_patient_id,
|
|
col_mo = col_mo,
|
|
...))
|
|
}
|
|
|
|
#' @rdname first_isolate
|
|
#' @export
|
|
filter_first_weighted_isolate <- function(x,
|
|
col_date = NULL,
|
|
col_patient_id = NULL,
|
|
col_mo = NULL,
|
|
col_keyantibiotics = NULL,
|
|
...) {
|
|
y <- x
|
|
if (is.null(col_keyantibiotics)) {
|
|
# first try to look for it
|
|
col_keyantibiotics <- search_type_in_df(x = x, type = "keyantibiotics")
|
|
# still NULL? Then create it since we are calling filter_first_WEIGHTED_isolate()
|
|
if (is.null(col_keyantibiotics)) {
|
|
y$keyab <- suppressMessages(key_antibiotics(x,
|
|
col_mo = col_mo,
|
|
...))
|
|
col_keyantibiotics <- "keyab"
|
|
}
|
|
}
|
|
|
|
subset(x, first_isolate(x = y,
|
|
col_date = col_date,
|
|
col_patient_id = col_patient_id,
|
|
col_mo = col_mo,
|
|
col_keyantibiotics = col_keyantibiotics,
|
|
...))
|
|
}
|