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use guess_bactid for GLIMS codes

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dr. M.S. (Matthijs) Berends 2018-03-19 21:23:21 +01:00
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4
R/data.R
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@ -85,14 +85,14 @@
#' Translation table for UMCG with ~1100 microorganisms #' Translation table for UMCG with ~1100 microorganisms
#' #'
#' A dataset containing all bacteria codes of UMCG MMB. These codes can be joined to data with an ID from \code{\link{bactlist}$bactid}, using \code{\link{left_join_bactlist}}. #' A dataset containing all bacteria codes of UMCG MMB. These codes can be joined to data with an ID from \code{\link{bactlist}$bactid} (using \code{\link{left_join_bactlist}}). GLIMS codes can also be translated to valid \code{bactid}'s with \code{\link{guess_bactid}}.
#' @format A data.frame with 1090 observations and 2 variables: #' @format A data.frame with 1090 observations and 2 variables:
#' \describe{ #' \describe{
#' \item{\code{mocode}}{Code of microorganism according to UMCG MMB} #' \item{\code{mocode}}{Code of microorganism according to UMCG MMB}
#' \item{\code{bactid}}{Code of microorganism in \code{\link{bactlist}}} #' \item{\code{bactid}}{Code of microorganism in \code{\link{bactlist}}}
#' } #' }
#' @source MOLIS (LIS of Certe) - \url{https://www.certe.nl} \cr \cr GLIMS (LIS of UMCG) - \url{https://www.umcg.nl} #' @source MOLIS (LIS of Certe) - \url{https://www.certe.nl} \cr \cr GLIMS (LIS of UMCG) - \url{https://www.umcg.nl}
#' @seealso \code{\link{bactlist}} #' @seealso \code{\link{guess_bactid}} \code{\link{bactlist}}
"bactlist.umcg" "bactlist.umcg"
#' Dataset with 2000 blood culture isolates of septic patients #' Dataset with 2000 blood culture isolates of septic patients

14
R/first_isolates.R
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@ -41,10 +41,10 @@
#' 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}. #' 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{DETERMINING WEIGHTED ISOLATES} \cr #' \strong{DETERMINING WEIGHTED ISOLATES} \cr
#' \strong{1. Using \code{type = "keyantibiotics"} and parameter \code{ignore_I}} \cr #' \strong{1. Using} \code{type = "keyantibiotics"} \strong{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 #' 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 method and 30-35 times faster than method 2. \cr
#' \strong{2. Using \code{type = "points"} and parameter \code{points_threshold}} \cr #' \strong{2. Using} \code{type = "points"} \strong{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). #' 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).
#' @keywords isolate isolates first #' @keywords isolate isolates first
#' @export #' @export
#' @importFrom dplyr arrange_at lag between row_number filter mutate arrange #' @importFrom dplyr arrange_at lag between row_number filter mutate arrange
@ -676,6 +676,12 @@ guess_bactid <- function(x) {
# try only genus, with 'species' attached # try only genus, with 'species' attached
found <- AMR::bactlist %>% filter(fullname %like% x_species[i]) found <- AMR::bactlist %>% filter(fullname %like% x_species[i])
} }
if (nrow(found) == 0) {
# search for GLIMS code
if (toupper(x.bak[i]) %in% toupper(AMR::bactlist.umcg$mocode)) {
found <- AMR::bactlist.umcg %>% filter(toupper(mocode) == toupper(x.bak[i]))
}
}
if (nrow(found) == 0) { if (nrow(found) == 0) {
# try splitting of characters and then find ID # try splitting of characters and then find ID
# like esco = E. coli, klpn = K. pneumoniae, stau = S. aureus # like esco = E. coli, klpn = K. pneumoniae, stau = S. aureus

1
R/globals.R
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@ -30,6 +30,7 @@ globalVariables(c('.',
'key_ab_lag', 'key_ab_lag',
'key_ab_other', 'key_ab_other',
'mic', 'mic',
'mocode',
'n', 'n',
'other_pat_or_mo', 'other_pat_or_mo',
'patient_id', 'patient_id',

4
man/bactlist.umcg.Rd
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@ -16,9 +16,9 @@ MOLIS (LIS of Certe) - \url{https://www.certe.nl} \cr \cr GLIMS (LIS of UMCG) -
bactlist.umcg bactlist.umcg
} }
\description{ \description{
A dataset containing all bacteria codes of UMCG MMB. These codes can be joined to data with an ID from \code{\link{bactlist}$bactid}, using \code{\link{left_join_bactlist}}. A dataset containing all bacteria codes of UMCG MMB. These codes can be joined to data with an ID from \code{\link{bactlist}$bactid} (using \code{\link{left_join_bactlist}}). GLIMS codes can also be translated to valid \code{bactid}'s with \code{\link{guess_bactid}}.
} }
\seealso{ \seealso{
\code{\link{bactlist}} \code{\link{guess_bactid}} \code{\link{bactlist}}
} }
\keyword{datasets} \keyword{datasets}

8
man/first_isolate.Rd
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@ -59,10 +59,10 @@ Determine first (weighted) isolates of all microorganisms of every patient per e
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}. 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{DETERMINING WEIGHTED ISOLATES} \cr \strong{DETERMINING WEIGHTED ISOLATES} \cr
\strong{1. Using \code{type = "keyantibiotics"} and parameter \code{ignore_I}} \cr \strong{1. Using} \code{type = "keyantibiotics"} \strong{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 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 method and 30-35 times faster than method 2. \cr
\strong{2. Using \code{type = "points"} and parameter \code{points_threshold}} \cr \strong{2. Using} \code{type = "points"} \strong{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). 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{ \examples{
# septic_patients is a dataset available in the AMR package # septic_patients is a dataset available in the AMR package