mirror of https://github.com/msberends/AMR.git
- Added new algorithm to determine weighted isolates, can now be `points` or `keyantibiotics, see `?first_isolate`
- Function `first_isolate` supports tidyverse-like evaluation of parameters (no need to quote them anymore) - Functions `as.rsi` and `as.mic` now add the package name and version as attribute
This commit is contained in:
parent
2db25b3b38
commit
dd2517ecb7
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@ -73,4 +73,5 @@ importFrom(graphics,text)
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importFrom(reshape2,dcast)
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importFrom(rvest,html_nodes)
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importFrom(rvest,html_table)
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importFrom(utils,packageDescription)
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importFrom(xml2,read_html)
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3
NEWS
3
NEWS
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@ -3,6 +3,9 @@
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- Renamed `ablist` to `antibiotics`
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- Added support for character vector in join functions
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- Altered `%like%` to make it case insensitive
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- Added new algorithm to determine weighted isolates, can now be `points` or `keyantibiotics, see `?first_isolate`
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- Function `first_isolate` supports tidyverse-like evaluation of parameters (no need to quote them anymore)
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- Functions `as.rsi` and `as.mic` now add the package name and version as attribute
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## 0.1.1
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- `EUCAST_rules` applies for amoxicillin even if ampicillin is missing
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10
R/classes.R
10
R/classes.R
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@ -24,6 +24,7 @@
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#' @return New class \code{rsi}
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#' @export
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#' @importFrom dplyr %>%
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#' @importFrom utils packageDescription
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#' @examples
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#' rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370)))
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#' rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370), "A", "B", "C"))
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@ -54,13 +55,15 @@ as.rsi <- function(x) {
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sort()
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list_missing <- paste0('"', list_missing , '"', collapse = ", ")
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warning(na_after - na_before, ' results truncated (',
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round(((na_after - na_before) / length(x)) / 100),
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round(((na_after - na_before) / length(x)) * 100),
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'%) that were invalid antimicrobial interpretations: ',
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list_missing, call. = FALSE)
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}
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x <- x %>% toupper() %>% factor(levels = c("S", "I", "R"), ordered = TRUE)
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class(x) <- c('rsi', 'ordered', 'factor')
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attr(x, 'package') <- 'AMR'
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attr(x, 'package.version') <- packageDescription('AMR')$Version
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x
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}
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}
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@ -192,6 +195,7 @@ barplot.rsi <- function(height, ...) {
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#' @return New class \code{mic}
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#' @export
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#' @importFrom dplyr %>%
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#' @importFrom utils packageDescription
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#' @examples
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#' mic_data <- as.mic(c(">=32", "1.0", "1", "1.00", 8, "<=0.128", "8", "16", "16"))
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#' is.mic(mic_data)
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@ -289,7 +293,7 @@ as.mic <- function(x, na.rm = FALSE) {
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sort()
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list_missing <- paste0('"', list_missing , '"', collapse = ", ")
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warning(na_after - na_before, ' results truncated (',
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round(((na_after - na_before) / length(x)) / 100),
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round(((na_after - na_before) / length(x)) * 100),
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'%) that were invalid MICs: ',
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list_missing, call. = FALSE)
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}
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@ -298,6 +302,8 @@ as.mic <- function(x, na.rm = FALSE) {
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levels = lvls,
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ordered = TRUE)
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class(x) <- c('mic', 'ordered', 'factor')
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attr(x, 'package') <- 'AMR'
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attr(x, 'package.version') <- packageDescription('AMR')$Version
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x
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}
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}
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@ -18,33 +18,50 @@
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#' Determine first (weighted) isolates
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#'
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#' Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type.
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#' Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type.
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#' @param tbl a \code{data.frame} containing isolates.
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#' @param col_date column name of the result date (or date that is was received on the lab)
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#' @param col_patient_id column name of the unique IDs of the patients
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#' @param col_genus column name of the genus of the microorganisms
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#' @param col_species column name of the species of the microorganisms
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#' @param col_testcode column name of the test codes. Use \code{col_testcode = NA} to \strong{not} exclude certain test codes (like test codes for screening). In that case \code{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 (\code{TRUE}/\code{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 \emph{weighted} isolates, see \code{\link{key_antibiotics}}.
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#' @param col_date column name of the result date (or date that is was received on the lab), supports tidyverse-like quotation
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#' @param col_patient_id column name of the unique IDs of the patients, supports tidyverse-like quotation
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#' @param col_genus column name of the genus of the microorganisms, supports tidyverse-like quotation
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#' @param col_species column name of the species of the microorganisms, supports tidyverse-like quotation
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#' @param col_testcode column name of the test codes. Use \code{col_testcode = NA} to \strong{not} exclude certain test codes (like test codes for screening). In that case \code{testcodes_exclude} will be ignored. Supports tidyverse-like quotation.
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#' @param col_specimen column name of the specimen type or group, supports tidyverse-like quotation
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#' @param col_icu column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU), supports tidyverse-like quotation
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#' @param col_keyantibiotics column name of the key antibiotics to determine first \emph{weighted} isolates, see \code{\link{key_antibiotics}}. Supports tidyverse-like quotation.
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#' @param episode_days episode in days after which a genus/species combination will be determined as 'first isolate' again
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#' @param testcodes_exclude character vector with test codes that should be excluded (caseINsensitive)
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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
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#' @param filter_specimen specimen group or type that should be excluded
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#' @param output_logical return output as \code{logical} (will else the values \code{0} or \code{1})
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#' @param points_threshold points until the comparison of key antibiotics will lead to inclusion of an isolate, see Details
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#' @param output_logical return output as \code{logical} (will else be the values \code{0} or \code{1})
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#' @param type type to determine weighed isolates; can be \code{"keyantibiotics"} or \code{"points"}, see Details
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#' @param ignore_I logical to determine whether antibiotic interpretations with \code{"I"} will be ignored when \code{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 \code{type = "points"}, see Details
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#' @param info print progress
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#' @details \strong{Why this is so important} \cr
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#' @details \strong{WHY THIS IS SO IMPORTANT} \cr
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#' To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode \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}.
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#'
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#' \strong{Using parameter \code{points_threshold}} \cr
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#' To compare key antibiotics, the difference between antimicrobial interpretations will be measured. 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.
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#' \strong{DETERMINING WEIGHTED ISOLATES} \cr
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#' \strong{1. Using \code{type = "keyantibiotics"} and parameter \code{ignore_I}} \cr
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#' To determine weighted isolates, the difference between key antibiotics will be checked. 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 and fast method. \cr
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#' \strong{2. Using \code{type = "points"} and parameter \code{points_threshold}} \cr
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#' To determine weighted isolates, difference between antimicrobial interpretations will be measured with points. 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. This method is being used by the Infection Prevention department (Dr M. Lokate) of the University Medical Center Groningen (UMCG).
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#' @keywords isolate isolates first
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#' @export
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#' @importFrom dplyr arrange_at lag between row_number filter mutate arrange
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#' @return A vector to add to table, see Examples.
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#' @examples
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#' # septic_patients is a dataset available in the AMR package
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#' ?septic_patients
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#' my_patients <- septic_patients
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#'
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#' library(dplyr)
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#' my_patients$first_isolate <- my_patients %>%
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#' left_join_bactlist() %>%
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#' first_isolate(col_date = date,
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#' col_patient_id = patient_id,
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#' col_genus = genus,
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#' col_species = species)
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#'
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#' \dontrun{
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#'
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#' # set key antibiotics to a new variable
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@ -90,18 +107,30 @@ first_isolate <- function(tbl,
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col_genus,
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col_species,
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col_testcode = NA,
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col_specimen,
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col_icu,
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col_specimen = NA,
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col_icu = NA,
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col_keyantibiotics = NA,
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episode_days = 365,
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testcodes_exclude = '',
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icu_exclude = FALSE,
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filter_specimen = NA,
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output_logical = TRUE,
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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 = TRUE) {
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# controleren of kolommen wel bestaan
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# support tidyverse-like quotation
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col_date <- quasiquotate(deparse(substitute(col_date)), col_date)
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col_patient_id <- quasiquotate(deparse(substitute(col_patient_id)), col_patient_id)
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col_genus <- quasiquotate(deparse(substitute(col_genus)), col_genus)
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col_species <- quasiquotate(deparse(substitute(col_species)), col_species)
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col_testcode <- quasiquotate(deparse(substitute(col_testcode)), col_testcode)
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col_specimen <- quasiquotate(deparse(substitute(col_specimen)), col_specimen)
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col_icu <- quasiquotate(deparse(substitute(col_icu)), col_icu)
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col_keyantibiotics <- quasiquotate(deparse(substitute(col_keyantibiotics)), col_keyantibiotics)
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# check if columns exist
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check_columns_existance <- function(column, tblname = tbl) {
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if (NROW(tblname) <= 1 | NCOL(tblname) <= 1) {
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stop('Please check tbl for existance.')
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@ -125,7 +154,7 @@ first_isolate <- function(tbl,
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if (is.na(col_testcode)) {
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testcodes_exclude <- NA
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}
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# testcodes verwijderen die ingevuld zijn
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# remove testcodes
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if (!is.na(testcodes_exclude[1]) & testcodes_exclude[1] != '' & info == TRUE) {
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cat('Isolates from these test codes will be ignored:\n', toString(testcodes_exclude), '\n')
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}
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@ -137,9 +166,13 @@ first_isolate <- function(tbl,
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mutate(col_icu = tbl %>% pull(col_icu) %>% as.logical())
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}
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if (is.na(col_specimen)) {
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filter_specimen <- ''
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}
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specgroup.notice <- ''
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weighted.notice <- ''
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# filteren op materiaalgroep en sleutelantibiotica gebruiken wanneer deze ingevuld zijn
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# filter on specimen group and keyantibiotics when they are filled in
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if (!is.na(filter_specimen) & filter_specimen != '') {
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check_columns_existance(col_specimen, tbl)
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if (info == TRUE) {
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@ -158,8 +191,7 @@ first_isolate <- function(tbl,
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testcodes_exclude <- ''
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}
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# nieuwe dataframe maken met de oorspronkelijke rij-index, 0-bepaling en juiste sortering
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#cat('Sorting table...')
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# create new dataframe with original row index and right sorting
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tbl <- tbl %>%
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mutate(first_isolate_row_index = 1:nrow(tbl),
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eersteisolaatbepaling = 0,
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@ -203,7 +235,7 @@ first_isolate <- function(tbl,
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}
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} else {
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# sorteren op materiaal en alleen die rijen analyseren om tijd te besparen
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# sort on specimen and only analyse these row to save time
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if (icu_exclude == FALSE) {
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if (info == TRUE) {
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cat('Isolates from ICU will *NOT* be ignored.\n')
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@ -247,7 +279,7 @@ first_isolate <- function(tbl,
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if (info == TRUE) {
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cat('No isolates found.\n')
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}
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# NA's maken waar genus niet beschikbaar is
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# NA's where genus is unavailable
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tbl <- tbl %>%
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mutate(real_first_isolate = if_else(genus == '', NA, FALSE))
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if (output_logical == FALSE) {
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@ -263,7 +295,7 @@ first_isolate <- function(tbl,
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genus != '') %>%
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nrow()
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# Analyse van eerste isolaat ----
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# Analysis of first isolate ----
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all_first <- tbl %>%
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mutate(other_pat_or_mo = if_else(patient_id == lag(patient_id)
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& genus == lag(genus)
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@ -277,13 +309,24 @@ first_isolate <- function(tbl,
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if (col_keyantibiotics != '') {
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if (info == TRUE) {
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cat(paste0('Comparing key antibiotics for first weighted isolates (using points threshold of '
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, points_threshold, ')...\n'))
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if (type == 'keyantibiotics') {
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cat('Comparing key antibiotics for first weighted isolates (')
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if (ignore_I == FALSE) {
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cat('NOT ')
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}
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cat('ignoring I)...\n')
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}
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if (type == 'points') {
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cat(paste0('Comparing antibiotics for first weighted isolates (using points threshold of '
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, points_threshold, ')...\n'))
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}
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}
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all_first <- all_first %>%
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mutate(key_ab_lag = lag(key_ab)) %>%
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mutate(key_ab_other = !key_antibiotics_equal(x = key_ab,
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y = key_ab_lag,
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type = type,
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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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mutate(
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@ -312,9 +355,9 @@ first_isolate <- function(tbl,
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FALSE))
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}
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# allereerst isolaat als TRUE
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# first one as TRUE
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all_first[row.start, 'real_first_isolate'] <- TRUE
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# geen testen die uitgesloten moeten worden, of ICU
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# no tests that should be included, or ICU
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if (!is.na(col_testcode)) {
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all_first[which(all_first[, col_testcode] %in% tolower(testcodes_exclude)), 'real_first_isolate'] <- FALSE
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}
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@ -322,7 +365,7 @@ first_isolate <- function(tbl,
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all_first[which(all_first[, col_icu] == TRUE), 'real_first_isolate'] <- FALSE
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}
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# NA's maken waar genus niet beschikbaar is
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# NA's where genus is unavailable
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all_first <- all_first %>%
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mutate(real_first_isolate = if_else(genus == '', NA, real_first_isolate))
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@ -353,7 +396,7 @@ first_isolate <- function(tbl,
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#' @param tbl table with antibiotics coloms, like \code{amox} and \code{amcl}.
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#' @param col_bactcode column of bacteria IDs in \code{tbl}; these should occur in \code{bactlist$bactid}, see \code{\link{bactlist}}
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#' @param info print warnings
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#' @param amcl,amox,cfot,cfta,cftr,cfur,cipr,clar,clin,clox,doxy,gent,line,mero,peni,pita,rifa,teic,trsu,vanc column names of antibiotics.
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#' @param amcl,amox,cfot,cfta,cftr,cfur,cipr,clar,clin,clox,doxy,gent,line,mero,peni,pita,rifa,teic,trsu,vanc column names of antibiotics, case-insensitive
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#' @export
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#' @importFrom dplyr %>% mutate if_else
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#' @return Character of length 1.
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@ -394,6 +437,13 @@ key_antibiotics <- function(tbl,
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clin, clox, doxy, gent, line, mero, peni,
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pita, rifa, teic, trsu, vanc)
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col.list <- col.list[!is.na(col.list)]
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for (i in 1:length(col.list)) {
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if (toupper(col.list[i]) %in% colnames(tbl)) {
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col.list[i] <- toupper(col.list[i])
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} else if (tolower(col.list[i]) %in% colnames(tbl)) {
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col.list[i] <- tolower(col.list[i])
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}
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}
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if (!all(col.list %in% colnames(tbl))) {
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if (info == TRUE) {
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warning('These columns do not exist and will be ignored:\n',
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@ -402,6 +452,25 @@ key_antibiotics <- function(tbl,
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call. = FALSE)
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}
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}
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amox <- col.list[1]
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cfot <- col.list[2]
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cfta <- col.list[3]
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cftr <- col.list[4]
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cfur <- col.list[5]
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cipr <- col.list[6]
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clar <- col.list[7]
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clin <- col.list[8]
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clox <- col.list[9]
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doxy <- col.list[10]
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gent <- col.list[11]
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line <- col.list[12]
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mero <- col.list[13]
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peni <- col.list[14]
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pita <- col.list[15]
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rifa <- col.list[16]
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teic <- col.list[17]
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trsu <- col.list[18]
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vanc <- col.list[19]
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# join bactlist
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tbl <- tbl %>% left_join_bactlist(col_bactcode)
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@ -448,8 +517,15 @@ key_antibiotics <- function(tbl,
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#' @importFrom dplyr progress_estimated %>%
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#' @noRd
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key_antibiotics_equal <- function(x, y, points_threshold = 2, info = FALSE) {
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key_antibiotics_equal <- function(x,
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y,
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type = c("keyantibiotics", "points"),
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ignore_I = TRUE,
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points_threshold = 2,
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info = FALSE) {
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# x is active row, y is lag
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type <- type[1]
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if (length(x) != length(y)) {
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stop('Length of `x` and `y` must be equal.')
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@ -484,17 +560,42 @@ key_antibiotics_equal <- function(x, y, points_threshold = 2, info = FALSE) {
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} else {
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# count points for every single character:
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# - no change is 0 points
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# - I <-> S|R is 0.5 point
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||||
# - S|R <-> R|S is 1 point
|
||||
# use the levels of as.rsi (S = 1, I = 2, R = 3)
|
||||
|
||||
x2 <- strsplit(x[i], "")[[1]] %>% as.rsi() %>% as.double()
|
||||
y2 <- strsplit(y[i], "")[[1]] %>% as.rsi() %>% as.double()
|
||||
x2 <- strsplit(x[i], "")[[1]]
|
||||
y2 <- strsplit(y[i], "")[[1]]
|
||||
|
||||
points <- (x2 - y2) %>% abs() %>% sum(na.rm = TRUE)
|
||||
result[i] <- ((points / 2) >= points_threshold)
|
||||
if (type == 'points') {
|
||||
# count points for every single character:
|
||||
# - no change is 0 points
|
||||
# - I <-> S|R is 0.5 point
|
||||
# - S|R <-> R|S is 1 point
|
||||
# use the levels of as.rsi (S = 1, I = 2, R = 3)
|
||||
|
||||
suppressWarnings(x2 <- x2 %>% as.rsi() %>% as.double())
|
||||
suppressWarnings(y2 <- y2 %>% as.rsi() %>% as.double())
|
||||
|
||||
points <- (x2 - y2) %>% abs() %>% sum(na.rm = TRUE)
|
||||
result[i] <- ((points / 2) >= points_threshold)
|
||||
|
||||
} else if (type == 'keyantibiotics') {
|
||||
# check if key antibiotics are exactly the same
|
||||
# also possible to ignore I, so only S <-> R and S <-> R are counted
|
||||
if (ignore_I == TRUE) {
|
||||
valid_chars <- c('S', 's', 'R', 'r')
|
||||
} else {
|
||||
valid_chars <- c('S', 's', 'I', 'i', 'R', 'r')
|
||||
}
|
||||
|
||||
# remove invalid values (like "-", NA) on both locations
|
||||
x2[which(!x2 %in% valid_chars)] <- '?'
|
||||
x2[which(!y2 %in% valid_chars)] <- '?'
|
||||
y2[which(!x2 %in% valid_chars)] <- '?'
|
||||
y2[which(!y2 %in% valid_chars)] <- '?'
|
||||
|
||||
result[i] <- all(x2 == y2)
|
||||
|
||||
} else {
|
||||
stop('No valid value for type, must be `points` or `keyantibiotics`. See ?first_isolate.')
|
||||
}
|
||||
}
|
||||
}
|
||||
if (info == TRUE) {
|
||||
|
|
|
|||
3
R/join.R
3
R/join.R
|
|
@ -13,6 +13,9 @@
|
|||
#' @examples
|
||||
#' left_join_bactlist("STAAUR")
|
||||
#'
|
||||
#' library(dplyr)
|
||||
#' septic_patients %>% left_join_bactlist()
|
||||
#'
|
||||
#' df <- data.frame(date = seq(from = as.Date("2018-01-01"),
|
||||
#' to = as.Date("2018-01-07"),
|
||||
#' by = 1),
|
||||
|
|
|
|||
21
README.md
21
README.md
|
|
@ -18,18 +18,27 @@ This package is available on CRAN and also here on GitHub.
|
|||
### From CRAN (recommended, latest stable version)
|
||||
[](http://cran.r-project.org/package=AMR)
|
||||
[](http://cran.r-project.org/package=AMR)
|
||||
[](http://cran.r-project.org/package=AMR)
|
||||
|
||||
- RStudio:
|
||||
- Click on `Tools` and then `Install Packages...`
|
||||
- Type in `AMR` and press <kbd>Install</kbd>
|
||||
|
||||
- R console:
|
||||
- <img src="https://cran.r-project.org/favicon.ico" alt="R favicon" height="20px">In R:
|
||||
- `install.packages("AMR")`
|
||||
|
||||
- <img src="http://www.rstudio.com/favicon.ico" alt="RStudio favicon" height="20px"> In RStudio:
|
||||
- Click on `Tools` and then `Install Packages...`
|
||||
- Type in `AMR` and press <kbd>Install</kbd>
|
||||
|
||||
- <img src="https://exploratory.io/favicon.ico" alt="Exploratory favicon" height="20px"> In Exploratory.io:
|
||||
- Click on your username at the right hand side top
|
||||
- Click on `R Packages`
|
||||
- Click on the `Install` tab
|
||||
- Type in `AMR` and press <kbd>Install</kbd>
|
||||
- Once it’s installed it will show up in the `User Packages` section under the `Packages` tab.
|
||||
|
||||
### From GitHub (latest development version)
|
||||
[](https://travis-ci.org/msberends/AMR)
|
||||
[](https://github.com/msberends/AMR/commits/master)
|
||||
[](https://github.com/msberends/AMR/commits/master)
|
||||
[](https://github.com/msberends/AMR/commits/master)
|
||||
[](https://codecov.io/gh/msberends/AMR)
|
||||
|
||||
```r
|
||||
install.packages("devtools")
|
||||
|
|
|
|||
|
|
@ -5,41 +5,46 @@
|
|||
\title{Determine first (weighted) isolates}
|
||||
\usage{
|
||||
first_isolate(tbl, col_date, col_patient_id, col_genus, col_species,
|
||||
col_testcode = NA, col_specimen, col_icu, col_keyantibiotics = NA,
|
||||
episode_days = 365, testcodes_exclude = "", icu_exclude = FALSE,
|
||||
filter_specimen = NA, output_logical = TRUE, points_threshold = 2,
|
||||
col_testcode = NA, col_specimen = NA, col_icu = NA,
|
||||
col_keyantibiotics = NA, episode_days = 365, testcodes_exclude = "",
|
||||
icu_exclude = FALSE, filter_specimen = NA, output_logical = TRUE,
|
||||
type = "keyantibiotics", ignore_I = TRUE, points_threshold = 2,
|
||||
info = TRUE)
|
||||
}
|
||||
\arguments{
|
||||
\item{tbl}{a \code{data.frame} containing isolates.}
|
||||
|
||||
\item{col_date}{column name of the result date (or date that is was received on the lab)}
|
||||
\item{col_date}{column name of the result date (or date that is was received on the lab), supports tidyverse-like quotation}
|
||||
|
||||
\item{col_patient_id}{column name of the unique IDs of the patients}
|
||||
\item{col_patient_id}{column name of the unique IDs of the patients, supports tidyverse-like quotation}
|
||||
|
||||
\item{col_genus}{column name of the genus of the microorganisms}
|
||||
\item{col_genus}{column name of the genus of the microorganisms, supports tidyverse-like quotation}
|
||||
|
||||
\item{col_species}{column name of the species of the microorganisms}
|
||||
\item{col_species}{column name of the species of the microorganisms, supports tidyverse-like quotation}
|
||||
|
||||
\item{col_testcode}{column name of the test codes. Use \code{col_testcode = NA} to \strong{not} exclude certain test codes (like test codes for screening). In that case \code{testcodes_exclude} will be ignored.}
|
||||
\item{col_testcode}{column name of the test codes. Use \code{col_testcode = NA} to \strong{not} exclude certain test codes (like test codes for screening). In that case \code{testcodes_exclude} will be ignored. Supports tidyverse-like quotation.}
|
||||
|
||||
\item{col_specimen}{column name of the specimen type or group}
|
||||
\item{col_specimen}{column name of the specimen type or group, supports tidyverse-like quotation}
|
||||
|
||||
\item{col_icu}{column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU)}
|
||||
\item{col_icu}{column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU), supports tidyverse-like quotation}
|
||||
|
||||
\item{col_keyantibiotics}{column name of the key antibiotics to determine first \emph{weighted} isolates, see \code{\link{key_antibiotics}}.}
|
||||
\item{col_keyantibiotics}{column name of the key antibiotics to determine first \emph{weighted} isolates, see \code{\link{key_antibiotics}}. Supports tidyverse-like quotation.}
|
||||
|
||||
\item{episode_days}{episode in days after which a genus/species combination will be determined as 'first isolate' again}
|
||||
|
||||
\item{testcodes_exclude}{character vector with test codes that should be excluded (caseINsensitive)}
|
||||
\item{testcodes_exclude}{character vector with test codes that should be excluded (case-insensitive)}
|
||||
|
||||
\item{icu_exclude}{logical whether ICU isolates should be excluded}
|
||||
|
||||
\item{filter_specimen}{specimen group or type that should be excluded}
|
||||
|
||||
\item{output_logical}{return output as \code{logical} (will else the values \code{0} or \code{1})}
|
||||
\item{output_logical}{return output as \code{logical} (will else be the values \code{0} or \code{1})}
|
||||
|
||||
\item{points_threshold}{points until the comparison of key antibiotics will lead to inclusion of an isolate, see Details}
|
||||
\item{type}{type to determine weighed isolates; can be \code{"keyantibiotics"} or \code{"points"}, see Details}
|
||||
|
||||
\item{ignore_I}{logical to determine whether antibiotic interpretations with \code{"I"} will be ignored when \code{type = "keyantibiotics"}, see Details}
|
||||
|
||||
\item{points_threshold}{points until the comparison of key antibiotics will lead to inclusion of an isolate when \code{type = "points"}, see Details}
|
||||
|
||||
\item{info}{print progress}
|
||||
}
|
||||
|
|
@ -50,13 +55,28 @@ A vector to add to table, see Examples.
|
|||
Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type.
|
||||
}
|
||||
\details{
|
||||
\strong{Why this is so important} \cr
|
||||
\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}.
|
||||
|
||||
\strong{Using parameter \code{points_threshold}} \cr
|
||||
To compare key antibiotics, the difference between antimicrobial interpretations will be measured. 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.
|
||||
\strong{DETERMINING WEIGHTED ISOLATES} \cr
|
||||
\strong{1. Using \code{type = "keyantibiotics"} and parameter \code{ignore_I}} \cr
|
||||
To determine weighted isolates, the difference between key antibiotics will be checked. 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 and fast method. \cr
|
||||
\strong{2. Using \code{type = "points"} and parameter \code{points_threshold}} \cr
|
||||
To determine weighted isolates, difference between antimicrobial interpretations will be measured with points. 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. This method is being used by the Infection Prevention department (Dr M. Lokate) of the University Medical Center Groningen (UMCG).
|
||||
}
|
||||
\examples{
|
||||
# septic_patients is a dataset available in the AMR package
|
||||
?septic_patients
|
||||
my_patients <- septic_patients
|
||||
|
||||
library(dplyr)
|
||||
my_patients$first_isolate <- my_patients \%>\%
|
||||
left_join_bactlist() \%>\%
|
||||
first_isolate(col_date = date,
|
||||
col_patient_id = patient_id,
|
||||
col_genus = genus,
|
||||
col_species = species)
|
||||
|
||||
\dontrun{
|
||||
|
||||
# set key antibiotics to a new variable
|
||||
|
|
|
|||
|
|
@ -41,6 +41,9 @@ As opposed to the \code{\link[dplyr]{join}} functions of \code{dplyr}, character
|
|||
\examples{
|
||||
left_join_bactlist("STAAUR")
|
||||
|
||||
library(dplyr)
|
||||
septic_patients \%>\% left_join_bactlist()
|
||||
|
||||
df <- data.frame(date = seq(from = as.Date("2018-01-01"),
|
||||
to = as.Date("2018-01-07"),
|
||||
by = 1),
|
||||
|
|
|
|||
|
|
@ -18,7 +18,7 @@ key_antibiotics(tbl, col_bactcode = "bactid", info = TRUE, amcl = "amcl",
|
|||
|
||||
\item{info}{print warnings}
|
||||
|
||||
\item{amcl, amox, cfot, cfta, cftr, cfur, cipr, clar, clin, clox, doxy, gent, line, mero, peni, pita, rifa, teic, trsu, vanc}{column names of antibiotics.}
|
||||
\item{amcl, amox, cfot, cfta, cftr, cfur, cipr, clar, clin, clox, doxy, gent, line, mero, peni, pita, rifa, teic, trsu, vanc}{column names of antibiotics, case-insensitive}
|
||||
}
|
||||
\value{
|
||||
Character of length 1.
|
||||
|
|
|
|||
Loading…
Reference in New Issue