mirror of https://github.com/msberends/AMR.git
528 lines
25 KiB
R
Executable File
528 lines
25 KiB
R
Executable File
# ==================================================================== #
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# TITLE #
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# Antimicrobial Resistance (AMR) Analysis for R #
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# #
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# SOURCE #
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# https://github.com/msberends/AMR #
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# #
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# LICENCE #
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# (c) 2018-2021 Berends MS, Luz CF et al. #
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# Developed at the University of Groningen, the Netherlands, in #
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# collaboration with non-profit organisations Certe Medical #
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# Diagnostics & Advice, and University Medical Center Groningen. #
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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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# 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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# #
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# Visit our website for the full manual and a complete tutorial about #
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# how to conduct AMR analysis: https://msberends.github.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. To determine patient episodes not necessarily based on microorganisms, use [is_new_episode()] that also supports grouping with the `dplyr` package.
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#' @inheritSection lifecycle Stable lifecycle
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#' @param x a [data.frame] containing isolates. Can be left blank for automatic determination.
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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 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 (such as 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 the column set with `col_icu`)
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#' @param specimen_group value in the column set with `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 ... arguments passed on to [first_isolate()] when using [filter_first_isolate()], or arguments passed on to [key_antibiotics()] when using [filter_first_weighted_isolate()]
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#' @details
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#' These functions are context-aware when used inside `dplyr` verbs, such as `filter()`, `mutate()` and `summarise()`. This means that then the `x` argument can be left blank, please see *Examples*.
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#'
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#' The [first_isolate()] function is a wrapper around the [is_new_episode()] function, but more efficient for data sets containing microorganism codes or names.
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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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#' ### Why this is so important
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#' To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode [(Hindler *et al.* 2007)](https://pubmed.ncbi.nlm.nih.gov/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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#' ### `filter_*()` shortcuts
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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.
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#'
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#' The function [filter_first_isolate()] is essentially equal to either:
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#'
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#' ```
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#' x[first_isolate(x, ...), ]
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#'
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#' x %>% filter(first_isolate(...))
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#' ```
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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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#' ```
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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 argument `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 argument `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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#' # basic filtering on first isolates
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#' example_isolates[first_isolate(example_isolates), ]
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#'
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#' # filtering based on isolates ----------------------------------------------
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#' \donttest{
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#' if (require("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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#' # short-hand versions:
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#' example_isolates %>%
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#' filter(first_isolate())
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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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#' # 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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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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if (missing(x)) {
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x <- get_current_data(arg_name = "x", call = -2)
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}
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meet_criteria(x, allow_class = "data.frame") # also checks dimensions to be >0
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meet_criteria(col_date, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
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meet_criteria(col_patient_id, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
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meet_criteria(col_mo, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
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meet_criteria(col_testcode, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
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if (isFALSE(col_specimen)) {
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col_specimen <- NULL
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}
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meet_criteria(col_specimen, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
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meet_criteria(col_icu, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
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if (isFALSE(col_keyantibiotics)) {
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col_keyantibiotics <- NULL
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}
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meet_criteria(col_keyantibiotics, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
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meet_criteria(episode_days, allow_class = c("numeric", "integer"), has_length = 1)
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meet_criteria(testcodes_exclude, allow_class = "character", allow_NULL = TRUE)
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meet_criteria(icu_exclude, allow_class = "logical", has_length = 1)
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meet_criteria(specimen_group, allow_class = "character", has_length = 1, allow_NULL = TRUE)
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meet_criteria(type, allow_class = "character", has_length = 1)
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meet_criteria(ignore_I, allow_class = "logical", has_length = 1)
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meet_criteria(points_threshold, allow_class = c("numeric", "integer"), has_length = 1)
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meet_criteria(info, allow_class = "logical", has_length = 1)
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meet_criteria(include_unknown, allow_class = "logical", has_length = 1)
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dots <- unlist(list(...))
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if (length(dots) != 0) {
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# backwards compatibility with old arguments
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dots.names <- dots %pm>% 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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# 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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stop_ifnot(col_mo %in% colnames(x), "column '", col_mo, "' (`col_mo`) not found")
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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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# -- 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_("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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# -- 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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# 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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# convert dates 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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# create original row index
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x$newvar_row_index <- seq_len(nrow(x))
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x$newvar_mo <- x[, col_mo, drop = TRUE]
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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[, col_date, drop = TRUE]
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x$newvar_patient_id <- x[, col_patient_id, drop = TRUE]
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if (is.null(col_testcode)) {
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testcodes_exclude <- NULL
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}
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# remove testcodes
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if (!is.null(testcodes_exclude) & info == TRUE) {
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message_("[Criterion] Exclude test codes: ", toString(paste0("'", testcodes_exclude, "'")),
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add_fn = font_black,
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as_note = FALSE)
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}
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if (is.null(col_specimen)) {
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specimen_group <- NULL
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}
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# filter on specimen group and keyantibiotics when they are filled in
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if (!is.null(specimen_group)) {
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check_columns_existance(col_specimen, x)
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if (info == TRUE) {
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message_("[Criterion] Exclude other than specimen group '", specimen_group, "'",
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add_fn = font_black,
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as_note = FALSE)
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}
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}
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if (!is.null(col_keyantibiotics)) {
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x$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$newvar_date), ]
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rownames(x) <- NULL
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row.start <- 1
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row.end <- nrow(x)
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} else {
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# filtering on specimen and only analyse these rows to save time
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x <- x[order(pm_pull(x, col_specimen),
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x$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 %pm>% pm_pull(col_specimen) == specimen_group) %pm>% min(na.rm = TRUE)
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)
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suppressWarnings(
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row.end <- which(x %pm>% pm_pull(col_specimen) == specimen_group) %pm>% max(na.rm = TRUE)
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)
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}
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# speed up - return immediately if obvious
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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_("=> Found ", font_bold("no isolates"),
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add_fn = font_black,
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as_note = FALSE)
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}
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return(rep(FALSE, nrow(x)))
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}
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if (row.start == row.end) {
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if (info == TRUE) {
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message_("=> Found ", font_bold("1 isolate"), ", as the data only contained 1 row",
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add_fn = font_black,
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as_note = FALSE)
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}
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return(TRUE)
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}
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if (length(c(row.start:row.end)) == pm_n_distinct(x[c(row.start:row.end), col_mo, drop = TRUE])) {
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if (info == TRUE) {
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message_("=> Found ", font_bold(paste(length(c(row.start:row.end)), "isolates")),
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", as all isolates were different microorganisms",
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add_fn = font_black,
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as_note = FALSE)
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}
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return(rep(TRUE, length(c(row.start:row.end))))
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}
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# did find some isolates - add new index numbers of rows
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x$newvar_row_index_sorted <- seq_len(nrow(x))
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scope.size <- nrow(x[which(x$newvar_row_index_sorted %in% c(row.start + 1:row.end) &
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!is.na(x$newvar_mo)), , drop = FALSE])
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# Analysis of first isolate ----
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x$other_pat_or_mo <- ifelse(x$newvar_patient_id == pm_lag(x$newvar_patient_id) &
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x$newvar_genus_species == pm_lag(x$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 <- unlist(lapply(unique(x$episode_group),
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function(g,
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df = x,
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days = episode_days) {
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is_new_episode(x = df[which(df$episode_group == g), ]$newvar_date,
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episode_days = days)
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}))
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weighted.notice <- ""
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if (!is.null(col_keyantibiotics)) {
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weighted.notice <- "weighted "
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if (info == TRUE) {
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if (type == "keyantibiotics") {
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message_("[Criterion] Base inclusion on key antibiotics, ",
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ifelse(ignore_I == FALSE, "not ", ""),
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"ignoring I",
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add_fn = font_black,
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as_note = FALSE)
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}
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if (type == "points") {
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message_("[Criterion] Base inclusion on key antibiotics, using points threshold of "
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, points_threshold,
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add_fn = font_black,
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as_note = FALSE)
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}
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}
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type_param <- type
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x$other_key_ab <- !key_antibiotics_equal(y = x$newvar_key_ab,
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z = pm_lag(x$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$newvar_first_isolate <- pm_if_else(x$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$other_key_ab),
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TRUE,
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FALSE)
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} else {
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# no key antibiotics
|
|
x$newvar_first_isolate <- pm_if_else(x$newvar_row_index_sorted >= row.start &
|
|
x$newvar_row_index_sorted <= row.end &
|
|
x$newvar_genus_species != "" &
|
|
(x$other_pat_or_mo | x$more_than_episode_ago),
|
|
TRUE,
|
|
FALSE)
|
|
}
|
|
|
|
# first one as TRUE
|
|
x[row.start, "newvar_first_isolate"] <- TRUE
|
|
# no tests that should be included, or ICU
|
|
if (!is.null(col_testcode)) {
|
|
x[which(x[, col_testcode] %in% tolower(testcodes_exclude)), "newvar_first_isolate"] <- FALSE
|
|
}
|
|
if (!is.null(col_icu)) {
|
|
if (icu_exclude == TRUE) {
|
|
message_("[Criterion] Exclude isolates from ICU.",
|
|
add_fn = font_black,
|
|
as_note = FALSE)
|
|
x[which(as.logical(x[, col_icu, drop = TRUE])), "newvar_first_isolate"] <- FALSE
|
|
} else {
|
|
message_("[Criterion] Include isolates from ICU.",
|
|
add_fn = font_black,
|
|
as_note = FALSE)
|
|
}
|
|
}
|
|
|
|
decimal.mark <- getOption("OutDec")
|
|
big.mark <- ifelse(decimal.mark != ",", ",", ".")
|
|
|
|
# handle empty microorganisms
|
|
if (any(x$newvar_mo == "UNKNOWN", na.rm = TRUE) & info == TRUE) {
|
|
message_(ifelse(include_unknown == TRUE, "Included ", "Excluded "),
|
|
format(sum(x$newvar_mo == "UNKNOWN", na.rm = TRUE),
|
|
decimal.mark = decimal.mark, big.mark = big.mark),
|
|
" isolates with a microbial ID 'UNKNOWN' (column '", font_bold(col_mo), "')")
|
|
}
|
|
x[which(x$newvar_mo == "UNKNOWN"), "newvar_first_isolate"] <- include_unknown
|
|
|
|
# exclude all NAs
|
|
if (any(is.na(x$newvar_mo)) & info == TRUE) {
|
|
message_("Excluded ", format(sum(is.na(x$newvar_mo), na.rm = TRUE),
|
|
decimal.mark = decimal.mark, big.mark = big.mark),
|
|
" isolates with a microbial ID 'NA' (column '", font_bold(col_mo), "')")
|
|
}
|
|
x[which(is.na(x$newvar_mo)), "newvar_first_isolate"] <- FALSE
|
|
|
|
# arrange back according to original sorting again
|
|
x <- x[order(x$newvar_row_index), ]
|
|
rownames(x) <- NULL
|
|
|
|
if (info == TRUE) {
|
|
n_found <- sum(x$newvar_first_isolate, na.rm = TRUE)
|
|
p_found_total <- percentage(n_found / nrow(x[which(!is.na(x$newvar_mo)), , drop = FALSE]), digits = 1)
|
|
p_found_scope <- percentage(n_found / scope.size, digits = 1)
|
|
if (!p_found_total %like% "[.]") {
|
|
p_found_total <- gsub("%", ".0%", p_found_total, fixed = TRUE)
|
|
}
|
|
if (!p_found_scope %like% "[.]") {
|
|
p_found_scope <- gsub("%", ".0%", p_found_scope, fixed = TRUE)
|
|
}
|
|
# mark up number of found
|
|
n_found <- format(n_found, big.mark = big.mark, decimal.mark = decimal.mark)
|
|
if (p_found_total != p_found_scope) {
|
|
msg_txt <- paste0("=> Found ",
|
|
font_bold(paste0(n_found, " first ", weighted.notice, "isolates")),
|
|
" (", p_found_scope, " within scope and ", p_found_total, " of total where a microbial ID was available)")
|
|
} else {
|
|
msg_txt <- paste0("=> Found ",
|
|
font_bold(paste0(n_found, " first ", weighted.notice, "isolates")),
|
|
" (", p_found_total, " of total where a microbial ID was available)")
|
|
}
|
|
message_(msg_txt, add_fn = font_black, as_note = FALSE)
|
|
}
|
|
|
|
x$newvar_first_isolate
|
|
|
|
}
|
|
|
|
#' @rdname first_isolate
|
|
#' @export
|
|
filter_first_isolate <- function(x,
|
|
col_date = NULL,
|
|
col_patient_id = NULL,
|
|
col_mo = NULL,
|
|
...) {
|
|
meet_criteria(x, allow_class = "data.frame")
|
|
meet_criteria(col_date, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
|
|
meet_criteria(col_patient_id, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
|
|
meet_criteria(col_mo, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
|
|
subset(x, first_isolate(x = x,
|
|
col_date = col_date,
|
|
col_patient_id = col_patient_id,
|
|
col_mo = col_mo,
|
|
...))
|
|
}
|
|
|
|
#' @rdname first_isolate
|
|
#' @export
|
|
filter_first_weighted_isolate <- function(x,
|
|
col_date = NULL,
|
|
col_patient_id = NULL,
|
|
col_mo = NULL,
|
|
col_keyantibiotics = NULL,
|
|
...) {
|
|
meet_criteria(x, allow_class = "data.frame")
|
|
meet_criteria(col_date, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
|
|
meet_criteria(col_patient_id, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
|
|
meet_criteria(col_mo, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
|
|
meet_criteria(col_keyantibiotics, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x))
|
|
y <- x
|
|
if (is.null(col_keyantibiotics)) {
|
|
# first try to look for it
|
|
col_keyantibiotics <- search_type_in_df(x = x, type = "keyantibiotics")
|
|
# still NULL? Then create it since we are calling filter_first_WEIGHTED_isolate()
|
|
if (is.null(col_keyantibiotics)) {
|
|
y$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))
|
|
}
|