# ==================================================================== # # TITLE # # Antimicrobial Resistance (AMR) Data Analysis for R # # # # SOURCE # # https://github.com/msberends/AMR # # # # LICENCE # # (c) 2018-2021 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 data 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. To determine patient episodes not necessarily based on microorganisms, use [is_new_episode()] that also supports grouping with the `dplyr` package. #' @inheritSection lifecycle Stable Lifecycle #' @param x a [data.frame] containing isolates. Can be left blank for automatic determination, see *Examples*. #' @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 #' @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 (such as 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. Can also be the output of [key_antibiotics()]. #' @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 to indicate whether ICU isolates should be excluded (rows with value `TRUE` in the column set with `col_icu`) #' @param specimen_group value in the column set with `col_specimen` to filter on #' @param type type to determine weighed isolates; can be `"keyantibiotics"` or `"points"`, see *Details* #' @param ignore_I logical to indicate 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 a [logical] to indicate info should be printed, defaults to `TRUE` only in interactive mode #' @param include_unknown logical to indicate 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 include_untested_rsi logical to indicate whether also rows without antibiotic results are still eligible for becoming a first isolate. Use `include_untested_rsi = FALSE` to always return `FALSE` for such rows. This checks the data set for columns of class `` and consequently requires transforming columns with antibiotic results using [as.rsi()] first. #' @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()] #' @details #' These functions are context-aware. This means that then the `x` argument can be left blank, see *Examples*. #' #' The [first_isolate()] function is a wrapper around the [is_new_episode()] function, but more efficient for data sets containing microorganism codes or names. #' #' All isolates with a microbial ID of `NA` will be excluded as first isolate. #' #' ## Why this is so Important #' 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). #' #' ## `filter_*()` Shortcuts #' #' 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(...)) #' ``` #' #' 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 argument `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 argument `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 defaults 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 data set available in the AMR package. #' # See ?example_isolates. #' #' example_isolates[first_isolate(example_isolates), ] #' #' \donttest{ #' # faster way, only works in R 3.2 and later: #' example_isolates[first_isolate(), ] #' #' # get all first Gram-negatives #' example_isolates[which(first_isolate() & mo_is_gram_negative()), ] #' #' if (require("dplyr")) { #' # filter on first isolates using dplyr: #' example_isolates %>% #' filter(first_isolate()) #' #' # short-hand versions: #' example_isolates %>% #' filter_first_isolate() #' example_isolates %>% #' filter_first_weighted_isolate() #' #' # grouped determination of first isolates (also prints group names): #' example_isolates %>% #' group_by(hospital_id) %>% #' mutate(first = first_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 = NULL, 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, include_untested_rsi = TRUE, ...) { if (is_null_or_grouped_tbl(x)) { # when `x` is left blank, auto determine it (get_current_data() also contains dplyr::cur_data_all()) # is also fix for using a grouped df as input (a dot as first argument) x <- tryCatch(get_current_data(arg_name = "x", call = -2), error = function(e) x) } meet_criteria(x, allow_class = "data.frame") # also checks dimensions to be >0 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)) if (isFALSE(col_specimen)) { col_specimen <- NULL } 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)) if (length(col_keyantibiotics) > 1) { meet_criteria(col_keyantibiotics, allow_class = "character", has_length = nrow(x)) x$keyabcol <- col_keyantibiotics col_keyantibiotics <- "keyabcol" } else { if (isFALSE(col_keyantibiotics)) { col_keyantibiotics <- NULL } meet_criteria(col_keyantibiotics, allow_class = "character", has_length = 1, allow_NULL = TRUE, is_in = colnames(x)) } meet_criteria(episode_days, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = FALSE) meet_criteria(testcodes_exclude, allow_class = "character", allow_NULL = TRUE) meet_criteria(icu_exclude, allow_class = "logical", has_length = 1) meet_criteria(specimen_group, allow_class = "character", has_length = 1, allow_NULL = TRUE) meet_criteria(type, allow_class = "character", has_length = 1) meet_criteria(ignore_I, allow_class = "logical", has_length = 1) meet_criteria(points_threshold, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE) meet_criteria(info, allow_class = "logical", has_length = 1) meet_criteria(include_unknown, allow_class = "logical", has_length = 1) meet_criteria(include_untested_rsi, allow_class = "logical", has_length = 1) # remove data.table, grouping from tibbles, etc. x <- as.data.frame(x, stringsAsFactors = FALSE) dots <- unlist(list(...)) if (length(dots) != 0) { # backwards compatibility with old arguments dots.names <- names(dots) if ("filter_specimen" %in% dots.names) { specimen_group <- dots[which(dots.names == "filter_specimen")] } if ("tbl" %in% dots.names) { x <- dots[which(dots.names == "tbl")] } } # try to find columns based on type # -- mo if (is.null(col_mo)) { col_mo <- search_type_in_df(x = x, type = "mo") stop_if(is.null(col_mo), "`col_mo` must be set") } # -- date if (is.null(col_date)) { col_date <- search_type_in_df(x = x, type = "date") stop_if(is.null(col_date), "`col_date` must be set") } # -- patient id if (is.null(col_patient_id)) { if (all(c("First name", "Last name", "Sex") %in% colnames(x))) { # WHONET support x$patient_id <- paste(x$`First name`, x$`Last name`, x$Sex) col_patient_id <- "patient_id" message_("Using combined columns '", font_bold("First name"), "', '", font_bold("Last name"), "' and '", font_bold("Sex"), "' as input for `col_patient_id`") } else { col_patient_id <- search_type_in_df(x = x, type = "patient_id") } stop_if(is.null(col_patient_id), "`col_patient_id` must be set") } # -- key antibiotics if (is.null(col_keyantibiotics)) { col_keyantibiotics <- search_type_in_df(x = x, type = "keyantibiotics") } # -- specimen if (is.null(col_specimen) & !is.null(specimen_group)) { col_specimen <- search_type_in_df(x = x, type = "specimen") } # check if columns exist check_columns_existance <- function(column, tblname = x) { if (!is.null(column)) { stop_ifnot(column %in% colnames(tblname), "Column '", column, "' not found.", call = FALSE) } } check_columns_existance(col_date) check_columns_existance(col_patient_id) check_columns_existance(col_mo) check_columns_existance(col_testcode) check_columns_existance(col_icu) check_columns_existance(col_keyantibiotics) # convert dates to Date dates <- as.Date(x[, col_date, drop = TRUE]) dates[is.na(dates)] <- as.Date("1970-01-01") x[, col_date] <- dates # create original row index x$newvar_row_index <- seq_len(nrow(x)) x$newvar_mo <- as.mo(x[, col_mo, drop = TRUE]) x$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_("[Criterion] Exclude test codes: ", toString(paste0("'", testcodes_exclude, "'")), add_fn = font_black, as_note = FALSE) } 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_("[Criterion] Exclude other than specimen group '", specimen_group, "'", add_fn = font_black, as_note = FALSE) } } 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$newvar_date), ] rownames(x) <- NULL suppressWarnings( row.start <- which(x %pm>% pm_pull(col_specimen) == specimen_group) %pm>% min(na.rm = TRUE) ) suppressWarnings( row.end <- which(x %pm>% pm_pull(col_specimen) == specimen_group) %pm>% max(na.rm = TRUE) ) } # speed up - return immediately if obvious if (abs(row.start) == Inf | abs(row.end) == Inf) { if (info == TRUE) { message_("=> Found ", font_bold("no isolates"), add_fn = font_black, as_note = FALSE) } return(rep(FALSE, nrow(x))) } if (row.start == row.end) { if (info == TRUE) { message_("=> Found ", font_bold("1 isolate"), ", as the data only contained 1 row", add_fn = font_black, as_note = FALSE) } return(TRUE) } if (length(c(row.start:row.end)) == pm_n_distinct(x[c(row.start:row.end), col_mo, drop = TRUE])) { if (info == TRUE) { message_("=> Found ", font_bold(paste(length(c(row.start:row.end)), "isolates")), ", as all isolates were different microorganisms", add_fn = font_black, as_note = FALSE) } return(rep(TRUE, length(c(row.start:row.end)))) } # did find some isolates - add new index numbers of rows x$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]) # 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) { is_new_episode(x = df[which(df$episode_group == g), ]$newvar_date, episode_days = days) })) weighted.notice <- "" if (!is.null(col_keyantibiotics)) { weighted.notice <- "weighted " if (info == TRUE) { if (type == "keyantibiotics") { message_("[Criterion] Base inclusion on key antibiotics, ", ifelse(ignore_I == FALSE, "not ", ""), "ignoring I", add_fn = font_black, as_note = FALSE) } if (type == "points") { message_("[Criterion] Base inclusion on key antibiotics, using points threshold of " , points_threshold, add_fn = font_black, as_note = FALSE) } } type_param <- type x$other_key_ab <- !key_antibiotics_equal(y = x$newvar_key_ab, z = pm_lag(x$newvar_key_ab), type = type_param, ignore_I = ignore_I, points_threshold = points_threshold, na.rm = TRUE) # with key antibiotics x$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_("[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 != ",", ",", ".") if (info == TRUE) { # print group name if used in dplyr::group_by() cur_group <- import_fn("cur_group", "dplyr", error_on_fail = FALSE) if (!is.null(cur_group)) { group_df <- tryCatch(cur_group(), error = function(e) data.frame()) if (NCOL(group_df) > 0) { # transform factors to characters group <- vapply(FUN.VALUE = character(1), group_df, function(x) { if (is.numeric(x)) { format(x) } else if (is.logical(x)) { as.character(x) } else { paste0('"', x, '"') } }) cat("\nGroup: ", paste0(names(group), " = ", group, collapse = ", "), "\n", sep = "") } } } # 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' (in 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$newvar_mo), na.rm = TRUE), decimal.mark = decimal.mark, big.mark = big.mark), " isolates with a microbial ID 'NA' (in column '", font_bold(col_mo), "')") } x[which(is.na(x$newvar_mo)), "newvar_first_isolate"] <- FALSE # handle isolates without antibiogram if (include_untested_rsi == FALSE && any(is.rsi(x))) { rsi_all_NA <- which(unname(vapply(FUN.VALUE = logical(1), as.data.frame(t(x[, is.rsi(x), drop = FALSE])), function(rsi_values) all(is.na(rsi_values))))) x[rsi_all_NA, "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$newvar_mo)), , drop = FALSE]), digits = 1) p_found_scope <- percentage(n_found / scope.size, digits = 1) if (p_found_total %unlike% "[.]") { p_found_total <- gsub("%", ".0%", p_found_total, fixed = TRUE) } if (p_found_scope %unlike% "[.]") { p_found_scope <- gsub("%", ".0%", p_found_scope, fixed = TRUE) } # mark up number of found n_found <- format(n_found, big.mark = big.mark, decimal.mark = decimal.mark) if (p_found_total != p_found_scope) { msg_txt <- paste0("=> Found ", font_bold(paste0(n_found, " first ", weighted.notice, "isolates")), " (", p_found_scope, " within scope and ", p_found_total, " of total where a microbial ID was available)") } else { msg_txt <- paste0("=> Found ", font_bold(paste0(n_found, " first ", weighted.notice, "isolates")), " (", p_found_total, " of total where a microbial ID was available)") } message_(msg_txt, add_fn = font_black, as_note = FALSE) } x$newvar_first_isolate } #' @rdname first_isolate #' @export filter_first_isolate <- function(x = NULL, col_date = NULL, col_patient_id = NULL, col_mo = NULL, ...) { if (is_null_or_grouped_tbl(x)) { # when `x` is left blank, auto determine it (get_current_data() also contains dplyr::cur_data_all()) # is also fix for using a grouped df as input (a dot as first argument) x <- tryCatch(get_current_data(arg_name = "x", call = -2), error = function(e) x) } meet_criteria(x, allow_class = "data.frame") # also checks dimensions to be >0 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 = NULL, col_date = NULL, col_patient_id = NULL, col_mo = NULL, col_keyantibiotics = NULL, ...) { if (is_null_or_grouped_tbl(x)) { # when `x` is left blank, auto determine it (get_current_data() also contains dplyr::cur_data_all()) # is also fix for using a grouped df as input (a dot as first argument) x <- tryCatch(get_current_data(arg_name = "x", call = -2), error = function(e) x) } meet_criteria(x, allow_class = "data.frame") # also checks dimensions to be >0 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)) }