# ==================================================================== # # TITLE # # Antimicrobial Resistance (AMR) Analysis # # # # SOURCE # # https://gitlab.com/msberends/AMR # # # # LICENCE # # (c) 2019 Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) # # # # 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. # # # # This R package was created for academic research and was publicly # # released in the hope that it will be useful, but it comes WITHOUT # # ANY WARRANTY OR LIABILITY. # # Visit our website for more info: https://msberends.gitab.io/AMR. # # ==================================================================== # #' Determine first (weighted) isolates #' #' Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type. #' @param tbl a \code{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 unique IDs of the microorganisms (see \code{\link{mo}}), defaults to the first column of class \code{mo}. Values will be coerced using \code{\link{as.mo}}. #' @param col_testcode column name of the test codes. Use \code{col_testcode = NULL} to \strong{not} exclude certain test codes (like test codes for screening). In that case \code{testcodes_exclude} will be ignored. #' @param col_specimen column name of the specimen type or group #' @param col_icu column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU) #' @param col_keyantibiotics column name of the key antibiotics to determine first \emph{weighted} isolates, see \code{\link{key_antibiotics}}. Defaults to the first column that starts with 'key' followed by 'ab' or 'antibiotics' (case insensitive). Use \code{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 #' @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 \code{TRUE} in column \code{col_icu}) #' @param specimen_group value in column \code{col_specimen} to filter on #' @param type type to determine weighed isolates; can be \code{"keyantibiotics"} or \code{"points"}, see Details #' @param ignore_I logical to determine whether antibiotic interpretations with \code{"I"} will be ignored when \code{type = "keyantibiotics"}, see Details #' @param points_threshold points until the comparison of key antibiotics will lead to inclusion of an isolate when \code{type = "points"}, see Details #' @param info print progress #' @param ... parameters passed on to the \code{first_isolate} function #' @details \strong{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 \href{https://www.ncbi.nlm.nih.gov/pubmed/17304462}{[1]}. 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 \emph{S. aureus} isolates would be overestimated, because you included this MRSA more than once. It would be \href{https://en.wikipedia.org/wiki/Selection_bias}{selection bias}. #' #' The function \code{filter_first_isolate} is essentially equal to: #' \preformatted{ #' tbl \%>\% #' mutate(only_firsts = first_isolate(tbl, ...)) \%>\% #' filter(only_firsts == TRUE) \%>\% #' select(-only_firsts) #' } #' The function \code{filter_first_weighted_isolate} is essentially equal to: #' \preformatted{ #' tbl \%>\% #' mutate(keyab = key_antibiotics(.)) \%>\% #' mutate(only_weighted_firsts = first_isolate(tbl, #' col_keyantibiotics = "keyab", ...)) \%>\% #' filter(only_weighted_firsts == TRUE) \%>\% #' select(-only_weighted_firsts) #' } #' @section Key antibiotics: #' There are two ways to determine whether isolates can be included as first \emph{weighted} isolates which will give generally the same results: \cr #' #' \strong{1. Using} \code{type = "keyantibiotics"} \strong{and parameter} \code{ignore_I} \cr #' Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With \code{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. \cr #' #' \strong{2. Using} \code{type = "points"} \strong{and parameter} \code{points_threshold} \cr #' 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 \code{points_threshold}, an isolate will be (re)selected as a first weighted isolate. #' @rdname first_isolate #' @keywords isolate isolates first #' @seealso \code{\link{key_antibiotics}} #' @export #' @importFrom dplyr arrange_at lag between row_number filter mutate arrange #' @importFrom crayon blue bold silver #' @return Logical vector #' @source Methodology of this function is based on: \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}. #' @inheritSection AMR Read more on our website! #' @examples #' # septic_patients is a dataset available in the AMR package. It is true, genuine data. #' ?septic_patients #' #' library(dplyr) #' # Filter on first isolates: #' septic_patients %>% #' mutate(first_isolate = first_isolate(., #' col_date = "date", #' col_patient_id = "patient_id", #' col_mo = "mo")) %>% #' filter(first_isolate == TRUE) #' #' # Which can be shortened to: #' septic_patients %>% #' filter_first_isolate() #' # or for first weighted isolates: #' septic_patients %>% #' filter_first_weighted_isolate() #' #' # Now let's see if first isolates matter: #' A <- septic_patients %>% #' group_by(hospital_id) %>% #' summarise(count = n_rsi(gent), # gentamicin availability #' resistance = portion_IR(gent)) # gentamicin resistance #' #' B <- septic_patients %>% #' filter_first_weighted_isolate() %>% # the 1st isolate filter #' group_by(hospital_id) %>% #' summarise(count = n_rsi(gent), # gentamicin availability #' resistance = portion_IR(gent)) # gentamicin resistance #' #' # Have a look at A and B. #' # B is more reliable because every isolate is only counted once. #' # Gentamicin resitance in hospital D appears to be 5.4% higher than #' # when you (erroneously) would have used all isolates! #' #' #' ## OTHER EXAMPLES: #' #' \dontrun{ #' #' # set key antibiotics to a new variable #' tbl$keyab <- key_antibiotics(tbl) #' #' tbl$first_isolate <- #' first_isolate(tbl) #' #' tbl$first_isolate_weighed <- #' first_isolate(tbl, #' col_keyantibiotics = 'keyab') #' #' tbl$first_blood_isolate <- #' first_isolate(tbl, #' specimen_group = 'Blood') #' #' tbl$first_blood_isolate_weighed <- #' first_isolate(tbl, #' specimen_group = 'Blood', #' col_keyantibiotics = 'keyab') #' #' tbl$first_urine_isolate <- #' first_isolate(tbl, #' specimen_group = 'Urine') #' #' tbl$first_urine_isolate_weighed <- #' first_isolate(tbl, #' specimen_group = 'Urine', #' col_keyantibiotics = 'keyab') #' #' tbl$first_resp_isolate <- #' first_isolate(tbl, #' specimen_group = 'Respiratory') #' #' tbl$first_resp_isolate_weighed <- #' first_isolate(tbl, #' specimen_group = 'Respiratory', #' col_keyantibiotics = 'keyab') #' } first_isolate <- function(tbl, 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 = TRUE, ...) { if (!is.data.frame(tbl)) { stop("`tbl` must be a data.frame.", call. = FALSE) } dots <- unlist(list(...)) if (length(dots) != 0) { # backwards compatibility with old parameters dots.names <- dots %>% names() if ('filter_specimen' %in% dots.names) { specimen_group <- dots[which(dots.names == 'filter_specimen')] } } # try to find columns based on type # -- mo if (is.null(col_mo)) { col_mo <- search_type_in_df(tbl = tbl, type = "mo") } if (is.null(col_mo)) { stop("`col_mo` must be set.", call. = FALSE) } # -- date if (is.null(col_date)) { col_date <- search_type_in_df(tbl = tbl, type = "date") } if (is.null(col_date)) { stop("`col_date` must be set.", call. = FALSE) } # convert to Date (pipes/pull for supporting tibbles too) tbl[, col_date] <- tbl %>% pull(col_date) %>% as.Date() # -- patient id if (is.null(col_patient_id)) { if (all(c("First name", "Last name", "Sex", "Identification number") %in% colnames(tbl))) { # WHONET support tbl <- tbl %>% mutate(patient_id = paste(`First name`, `Last name`, Sex)) col_patient_id <- "patient_id" message(blue(paste0("NOTE: Using combined columns ", bold("`First name`, `Last name` and `Sex`"), " as input for `col_patient_id`."))) } else { col_patient_id <- search_type_in_df(tbl = tbl, type = "patient_id") } } if (is.null(col_patient_id)) { stop("`col_patient_id` must be set.", call. = FALSE) } # -- key antibiotics if (is.null(col_keyantibiotics)) { col_keyantibiotics <- search_type_in_df(tbl = tbl, type = "keyantibiotics") } if (isFALSE(col_keyantibiotics)) { col_keyantibiotics <- NULL } # -- specimen if (is.null(col_specimen)) { col_specimen <- search_type_in_df(tbl = tbl, type = "specimen") } if (isFALSE(col_specimen)) { col_specimen <- NULL } # check if columns exist check_columns_existance <- function(column, tblname = tbl) { if (NROW(tblname) <= 1 | NCOL(tblname) <= 1) { stop('Please check tbl for existance.') } if (!is.null(column)) { if (!(column %in% colnames(tblname))) { stop('Column `', column, '` not found.') } } } check_columns_existance(col_date) check_columns_existance(col_patient_id) check_columns_existance(col_mo) check_columns_existance(col_testcode) check_columns_existance(col_icu) check_columns_existance(col_keyantibiotics) # join to microorganisms data set tbl <- tbl %>% mutate_at(vars(col_mo), as.mo) %>% left_join_microorganisms(by = col_mo) col_genus <- "genus" col_species <- "species" if (is.null(col_testcode)) { testcodes_exclude <- NULL } # remove testcodes if (!is.null(testcodes_exclude) & info == TRUE) { cat('[Criterion] Excluded test codes:\n', toString(testcodes_exclude), '\n') } if (is.null(col_icu)) { icu_exclude <- FALSE } else { tbl <- tbl %>% mutate(col_icu = tbl %>% pull(col_icu) %>% as.logical()) } if (is.null(col_specimen)) { specimen_group <- NULL } # filter on specimen group and keyantibiotics when they are filled in if (!is.null(specimen_group)) { check_columns_existance(col_specimen, tbl) if (info == TRUE) { cat('[Criterion] Excluded other than specimen group \'', specimen_group, '\'\n', sep = '') } } if (!is.null(col_keyantibiotics)) { tbl <- tbl %>% mutate(key_ab = tbl %>% pull(col_keyantibiotics)) } if (is.null(testcodes_exclude)) { testcodes_exclude <- '' } # create new dataframe with original row index and right sorting tbl <- tbl %>% mutate(first_isolate_row_index = 1:nrow(tbl), date_lab = tbl %>% pull(col_date), patient_id = tbl %>% pull(col_patient_id), species = tbl %>% pull(col_species), genus = tbl %>% pull(col_genus)) %>% mutate(species = if_else(is.na(species) | species == "(no MO)", "", species), genus = if_else(is.na(genus) | genus == "(no MO)", "", genus)) if (is.null(specimen_group)) { # not filtering on specimen if (icu_exclude == FALSE) { if (info == TRUE & !is.null(col_icu)) { cat('[Criterion] Included isolates from ICU.\n') } tbl <- tbl %>% arrange_at(c(col_patient_id, col_genus, col_species, col_date)) row.start <- 1 row.end <- nrow(tbl) } else { if (info == TRUE) { cat('[Criterion] Excluded isolates from ICU.\n') } tbl <- tbl %>% arrange_at(c(col_icu, col_patient_id, col_genus, col_species, col_date)) suppressWarnings( row.start <- which(tbl %>% pull(col_icu) == FALSE) %>% min(na.rm = TRUE) ) suppressWarnings( row.end <- which(tbl %>% pull(col_icu) == FALSE) %>% max(na.rm = TRUE) ) } } else { # filtering on specimen and only analyse these row to save time if (icu_exclude == FALSE) { if (info == TRUE & !is.null(col_icu)) { cat('[Criterion] Included isolates from ICU.\n') } tbl <- tbl %>% arrange_at(c(col_specimen, col_patient_id, col_genus, col_species, col_date)) suppressWarnings( row.start <- which(tbl %>% pull(col_specimen) == specimen_group) %>% min(na.rm = TRUE) ) suppressWarnings( row.end <- which(tbl %>% pull(col_specimen) == specimen_group) %>% max(na.rm = TRUE) ) } else { if (info == TRUE) { cat('[Criterion] Excluded isolates from ICU.\n') } tbl <- tbl %>% arrange_at(c(col_icu, col_specimen, col_patient_id, col_genus, col_species, col_date)) suppressWarnings( row.start <- which(tbl %>% pull(col_specimen) == specimen_group & tbl %>% pull(col_icu) == FALSE) %>% min(na.rm = TRUE) ) suppressWarnings( row.end <- which(tbl %>% pull(col_specimen) == specimen_group & tbl %>% pull(col_icu) == FALSE) %>% max(na.rm = TRUE) ) } } if (abs(row.start) == Inf | abs(row.end) == Inf) { if (info == TRUE) { message(paste("=> Found", bold("no isolates"))) } # NAs where genus is unavailable return(tbl %>% mutate(real_first_isolate = if_else(genus == '', NA, FALSE)) %>% pull(real_first_isolate) ) } # suppress warnings because dplyr wants us to use library(dplyr) when using filter(row_number()) suppressWarnings( scope.size <- tbl %>% filter( row_number() %>% between(row.start, row.end), genus != "", species != "") %>% nrow() ) 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 (as.numeric(x[i] - start >= episode_days)) { ind = ind + 1 indices[ind] = i start = x[i] } } result <- rep(FALSE, length(x)) result[indices] <- TRUE return(result) } # Analysis of first isolate ---- all_first <- tbl %>% mutate(other_pat_or_mo = if_else(patient_id == lag(patient_id) & genus == lag(genus) & species == lag(species), FALSE, TRUE)) %>% group_by_at(vars(patient_id, genus, species)) %>% mutate(more_than_episode_ago = identify_new_year(x = date_lab, episode_days = episode_days)) %>% ungroup() weighted.notice <- '' if (!is.null(col_keyantibiotics)) { weighted.notice <- 'weighted ' if (info == TRUE) { if (type == 'keyantibiotics') { cat('[Criterion] Inclusion based on key antibiotics, ') if (ignore_I == FALSE) { cat('not ') } cat('ignoring I.\n') } if (type == 'points') { cat(paste0('[Criterion] Inclusion based on key antibiotics, using points threshold of ' , points_threshold, '.\n')) } } type_param <- type # suppress warnings because dplyr want us to use library(dplyr) when using filter(row_number()) suppressWarnings( all_first <- all_first %>% mutate(key_ab_lag = lag(key_ab)) %>% mutate(key_ab_other = !key_antibiotics_equal(x = key_ab, y = key_ab_lag, type = type_param, ignore_I = ignore_I, points_threshold = points_threshold, info = info)) %>% mutate( real_first_isolate = if_else( between(row_number(), row.start, row.end) & genus != "" & species != "" & (other_pat_or_mo | more_than_episode_ago | key_ab_other), TRUE, FALSE)) ) } else { # no key antibiotics # suppress warnings because dplyr want us to use library(dplyr) when using filter(row_number()) suppressWarnings( all_first <- all_first %>% mutate( real_first_isolate = if_else( between(row_number(), row.start, row.end) & genus != "" & species != "" & (other_pat_or_mo | more_than_episode_ago), TRUE, FALSE)) ) } # first one as TRUE all_first[row.start, 'real_first_isolate'] <- TRUE # no tests that should be included, or ICU if (!is.null(col_testcode)) { all_first[which(all_first[, col_testcode] %in% tolower(testcodes_exclude)), 'real_first_isolate'] <- FALSE } if (icu_exclude == TRUE) { all_first[which(all_first[, col_icu] == TRUE), 'real_first_isolate'] <- FALSE } # NAs where genus is unavailable all_first <- all_first %>% mutate(real_first_isolate = if_else(genus %in% c('', '(no MO)', NA), NA, real_first_isolate)) all_first <- all_first %>% arrange(first_isolate_row_index) %>% pull(real_first_isolate) if (info == TRUE) { decimal.mark <- getOption("OutDec") big.mark <- ifelse(decimal.mark != ",", ",", ".") n_found <- base::sum(all_first, na.rm = TRUE) p_found_total <- percent(n_found / nrow(tbl), force_zero = TRUE) p_found_scope <- percent(n_found / scope.size, force_zero = TRUE) # mark up number of found n_found <- base::format(n_found, big.mark = big.mark, decimal.mark = decimal.mark) if (p_found_total != p_found_scope) { msg_txt <- paste0("=> Found ", bold(paste0(n_found, " first ", weighted.notice, "isolates")), " (", p_found_scope, " within scope and ", p_found_total, " of total)") } else { msg_txt <- paste0("=> Found ", bold(paste0(n_found, " first ", weighted.notice, "isolates")), " (", p_found_total, " of total)") } base::message(msg_txt) } all_first } #' @rdname first_isolate #' @importFrom dplyr filter #' @export filter_first_isolate <- function(tbl, col_date = NULL, col_patient_id = NULL, col_mo = NULL, ...) { filter(tbl, first_isolate(tbl = tbl, col_date = col_date, col_patient_id = col_patient_id, col_mo = col_mo, ...)) } #' @rdname first_isolate #' @importFrom dplyr %>% mutate filter #' @export filter_first_weighted_isolate <- function(tbl, col_date = NULL, col_patient_id = NULL, col_mo = NULL, col_keyantibiotics = NULL, ...) { tbl_keyab <- tbl %>% mutate(keyab = suppressMessages(key_antibiotics(., col_mo = col_mo, ...))) %>% mutate(firsts = first_isolate(., col_date = col_date, col_patient_id = col_patient_id, col_mo = col_mo, col_keyantibiotics = "keyab", ...)) tbl[which(tbl_keyab$firsts == TRUE),] }