# ==================================================================== # # TITLE # # Antimicrobial Resistance (AMR) Analysis for R # # # # SOURCE # # https://github.com/msberends/AMR # # # # LICENCE # # (c) 2018-2020 Berends MS, Luz CF et al. # # Developed at the University of Groningen, the Netherlands, in # # collaboration with non-profit organisations Certe Medical # # Diagnostics & Advice, and University Medical Center Groningen. # # # # 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 the full manual and a complete tutorial about # # how to conduct AMR analysis: https://msberends.github.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:/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). #' #' 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 either: #' ``` #' x[first_isolate(x, ...), ] #' x %>% filter(first_isolate(x, ...)) #' ``` #' 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, -keyab) #' ``` #' @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 #' @return A [`logical`] vector #' @source Methodology of this function is strictly based on: #' #' **M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition**, 2014, *Clinical and Laboratory Standards Institute (CLSI)*. . #' @inheritSection AMR Read more on our website! #' @examples #' # `example_isolates` is a dataset available in the AMR package. #' # See ?example_isolates. #' #' # basic filtering on first isolates #' example_isolates[first_isolate(example_isolates), ] #' #' \donttest{ #' if (require("dplyr")) { #' # Filter on first isolates: #' example_isolates %>% #' mutate(first_isolate = first_isolate(.)) %>% #' filter(first_isolate == TRUE) #' #' # Short-hand versions: #' example_isolates %>% #' filter_first_isolate() #' #' 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 counted only once. #' # Gentamicin resistance in hospital D appears to be 3.7% higher than #' # when you (erroneously) would have used all isolates for analysis. #' } #' } 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, ...) { 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_testcode, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x)) meet_criteria(col_specimen, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x)) meet_criteria(col_icu, 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)) meet_criteria(episode_days, allow_class = c("numeric", "integer"), has_length = 1) meet_criteria(testcodes_exclude, allow_class = "character", allow_NULL = TRUE) meet_criteria(icu_exclude, allow_class = "logical", has_length = 1) meet_criteria(specimen_group, allow_class = "character", has_length = 1, allow_NULL = TRUE) meet_criteria(type, allow_class = "character", has_length = 1) meet_criteria(ignore_I, allow_class = "logical", has_length = 1) meet_criteria(points_threshold, allow_class = c("numeric", "integer"), has_length = 1) meet_criteria(info, allow_class = "logical", has_length = 1) meet_criteria(include_unknown, allow_class = "logical", has_length = 1) dots <- unlist(list(...)) if (length(dots) != 0) { # backwards compatibility with old parameters dots.names <- dots %pm>% 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")] } } stop_ifnot(is.data.frame(x), "`x` must be a data.frame") stop_if(any(dim(x) == 0), "`x` must contain rows and columns") # remove data.table, grouping from tibbles, etc. x <- as.data.frame(x, stringsAsFactors = FALSE) # try to find columns based on type # -- mo if (is.null(col_mo)) { col_mo <- search_type_in_df(x = x, type = "mo") stop_if(is.null(col_mo), "`col_mo` must be set") stop_ifnot(col_mo %in% colnames(x), "column '", col_mo, "' (`col_mo`) not found") } # -- date if (is.null(col_date)) { col_date <- search_type_in_df(x = x, type = "date") stop_if(is.null(col_date), "`col_date` must be set") } # -- patient id if (is.null(col_patient_id)) { if (all(c("First name", "Last name", "Sex") %in% colnames(x))) { # WHONET support x$patient_id <- paste(x$`First name`, x$`Last name`, x$Sex) col_patient_id <- "patient_id" 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`"))) } else { col_patient_id <- search_type_in_df(x = x, type = "patient_id") } stop_if(is.null(col_patient_id), "`col_patient_id` must be set") } # -- 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 (!is.null(column)) { stop_ifnot(column %in% colnames(tblname), "Column `", column, "` not found.", call = FALSE) } } 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) # convert dates to Date dates <- as.Date(x[, col_date, drop = TRUE]) dates[is.na(dates)] <- as.Date("1970-01-01") x[, col_date] <- dates # create original row index x$newvar_row_index <- seq_len(nrow(x)) x$newvar_mo <- x[, col_mo, drop = TRUE] x$newvar_genus_species <- paste(mo_genus(x$newvar_mo), mo_species(x$newvar_mo)) x$newvar_date <- x[, col_date, drop = TRUE] x$newvar_patient_id <- x[, col_patient_id, drop = TRUE] if (is.null(col_testcode)) { testcodes_exclude <- NULL } # remove testcodes if (!is.null(testcodes_exclude) & info == TRUE) { message(font_black(paste0("[Criterion] Exclude test codes: ", toString(paste0("'", testcodes_exclude, "'"))))) } if (is.null(col_specimen)) { specimen_group <- NULL } # filter on specimen group and keyantibiotics when they are filled in if (!is.null(specimen_group)) { check_columns_existance(col_specimen, x) if (info == TRUE) { message(font_black(paste0("[Criterion] Exclude other than specimen group '", specimen_group, "'"))) } } if (!is.null(col_keyantibiotics)) { x$newvar_key_ab <- x[, col_keyantibiotics, drop = TRUE] } if (is.null(testcodes_exclude)) { testcodes_exclude <- "" } # arrange data to the right sorting if (is.null(specimen_group)) { x <- x[order(x$newvar_patient_id, x$newvar_genus_species, x$newvar_date), ] rownames(x) <- NULL row.start <- 1 row.end <- nrow(x) } else { # filtering on specimen and only analyse these rows to save time x <- x[order(pm_pull(x, col_specimen), x$newvar_patient_id, x$newvar_genus_species, x$newvar_date), ] rownames(x) <- NULL suppressWarnings( row.start <- which(x %pm>% pm_pull(col_specimen) == specimen_group) %pm>% min(na.rm = TRUE) ) suppressWarnings( row.end <- which(x %pm>% pm_pull(col_specimen) == specimen_group) %pm>% max(na.rm = TRUE) ) } # no isolates found if (abs(row.start) == Inf | abs(row.end) == Inf) { if (info == TRUE) { message(paste("=> Found", font_bold("no isolates"))) } return(rep(FALSE, nrow(x))) } # did find some isolates - add new index numbers of rows x$newvar_row_index_sorted <- seq_len(nrow(x)) scope.size <- nrow(x[which(x$newvar_row_index_sorted %in% c(row.start + 1:row.end) & !is.na(x$newvar_mo)), , drop = FALSE]) identify_new_year <- function(x, episode_days) { # I asked on StackOverflow: # https://stackoverflow.com/questions/42122245/filter-one-row-every-year if (length(x) == 1) { return(TRUE) } indices <- integer(0) start <- x[1] ind <- 1 indices[ind] <- ind for (i in 2:length(x)) { if (isTRUE(as.numeric(x[i] - start) >= episode_days)) { ind <- ind + 1 indices[ind] <- i start <- x[i] } } result <- rep(FALSE, length(x)) result[indices] <- TRUE return(result) } # Analysis of first isolate ---- x$other_pat_or_mo <- ifelse(x$newvar_patient_id == pm_lag(x$newvar_patient_id) & x$newvar_genus_species == pm_lag(x$newvar_genus_species), FALSE, TRUE) x$episode_group <- paste(x$newvar_patient_id, x$newvar_genus_species) x$more_than_episode_ago <- unlist(lapply(unique(x$episode_group), function(g, df = x, days = episode_days) { identify_new_year(x = df[which(df$episode_group == g), "newvar_date", drop = TRUE], episode_days = days) })) weighted.notice <- "" if (!is.null(col_keyantibiotics)) { weighted.notice <- "weighted " if (info == TRUE) { if (type == "keyantibiotics") { message(font_black(paste0("[Criterion] Base inclusion on key antibiotics, ", ifelse(ignore_I == FALSE, "not ", ""), "ignoring I"))) } if (type == "points") { message(font_black(paste0("[Criterion] Base inclusion on key antibiotics, using points threshold of " , points_threshold))) } } type_param <- type x$other_key_ab <- !key_antibiotics_equal(y = x$newvar_key_ab, z = pm_lag(x$newvar_key_ab), type = type_param, ignore_I = ignore_I, points_threshold = points_threshold, info = info) # with key antibiotics x$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$more_than_episode_ago | x$other_key_ab), TRUE, FALSE) } else { # 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(font_black("[Criterion] Exclude isolates from ICU.\n")) x[which(as.logical(x[, col_icu, drop = TRUE])), "newvar_first_isolate"] <- FALSE } else { message(font_black("[Criterion] Include isolates from ICU.\n")) } } decimal.mark <- getOption("OutDec") big.mark <- ifelse(decimal.mark != ",", ",", ".") # handle empty microorganisms if (any(x$newvar_mo == "UNKNOWN", na.rm = TRUE) & info == TRUE) { message(font_blue(paste0("NOTE: ", ifelse(include_unknown == TRUE, "Included ", "Excluded "), format(sum(x$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(font_blue(paste0("NOTE: 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$newvar_mo)), , drop = FALSE]), digits = 1) p_found_scope <- percentage(n_found / scope.size, digits = 1) if (!p_found_total %like% "[.]") { p_found_total <- gsub("%", ".0%", p_found_total, fixed = TRUE) } if (!p_found_scope %like% "[.]") { p_found_scope <- gsub("%", ".0%", p_found_scope, fixed = TRUE) } # mark up number of found n_found <- format(n_found, big.mark = big.mark, decimal.mark = decimal.mark) if (p_found_total != p_found_scope) { msg_txt <- paste0("=> Found ", font_bold(paste0(n_found, " first ", weighted.notice, "isolates")), " (", p_found_scope, " within scope and ", p_found_total, " of total where a microbial ID was available)") } else { msg_txt <- paste0("=> Found ", font_bold(paste0(n_found, " first ", weighted.notice, "isolates")), " (", p_found_total, " of total where a microbial ID was available)") } message(font_black(msg_txt)) } x$newvar_first_isolate } #' @rdname first_isolate #' @export filter_first_isolate <- function(x, col_date = NULL, col_patient_id = NULL, col_mo = NULL, ...) { 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, col_mo = col_mo, col_keyantibiotics = col_keyantibiotics, ...)) }