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155 lines
8.4 KiB
R
155 lines
8.4 KiB
R
# ==================================================================== #
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# TITLE #
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# Antimicrobial Resistance (AMR) Analysis #
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# #
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# AUTHORS #
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# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
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# #
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# LICENCE #
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# This program is free software; you can redistribute it and/or modify #
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# it under the terms of the GNU General Public License version 2.0, #
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# as published by the Free Software Foundation. #
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# #
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# This program is distributed in the hope that it will be useful, #
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# but WITHOUT ANY WARRANTY; without even the implied warranty of #
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
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# GNU General Public License for more details. #
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# ==================================================================== #
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#' Dataset with 420 antibiotics
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#'
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#' A dataset containing all antibiotics with a J0 code, with their DDD's. Properties were downloaded from the WHO, see Source.
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#' @format A data.frame with 420 observations and 16 variables:
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#' \describe{
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#' \item{\code{atc}}{ATC code, like \code{J01CR02}}
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#' \item{\code{molis}}{MOLIS code, like \code{amcl}}
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#' \item{\code{umcg}}{UMCG code, like \code{AMCL}}
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#' \item{\code{official}}{Official name by the WHO, like \code{"Amoxicillin and enzyme inhibitor"}}
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#' \item{\code{official_nl}}{Official name in the Netherlands, like \code{"Amoxicilline met enzymremmer"}}
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#' \item{\code{trivial_nl}}{Trivial name in Dutch, like \code{"Amoxicilline/clavulaanzuur"}}
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#' \item{\code{oral_ddd}}{Defined Daily Dose (DDD), oral treatment}
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#' \item{\code{oral_units}}{Units of \code{ddd_units}}
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#' \item{\code{iv_ddd}}{Defined Daily Dose (DDD), parenteral treatment}
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#' \item{\code{iv_units}}{Units of \code{iv_ddd}}
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#' \item{\code{atc_group1}}{ATC group, like \code{"Macrolides, lincosamides and streptogramins"}}
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#' \item{\code{atc_group2}}{Subgroup of \code{atc_group1}, like \code{"Macrolides"}}
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#' \item{\code{atc_group1_nl}}{ATC group in Dutch, like \code{"Macroliden, lincosamiden en streptograminen"}}
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#' \item{\code{atc_group2_nl}}{Subgroup of \code{atc_group1} in Dutch, like \code{"Macroliden"}}
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#' \item{\code{useful_gramnegative}}{\code{FALSE} if not useful according to EUCAST, \code{NA} otherwise (see Source)}
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#' \item{\code{useful_grampositive}}{\code{FALSE} if not useful according to EUCAST, \code{NA} otherwise (see Source)}
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#' }
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#' @source - World Health Organization: \url{https://www.whocc.no/atc_ddd_index/} \cr - EUCAST - Expert rules intrinsic exceptional V3.1 \cr - MOLIS (LIS of Certe): \url{https://www.certe.nl} \cr - GLIMS (LIS of UMCG): \url{https://www.umcg.nl}
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#' @seealso \code{\link{microorganisms}}
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# last two columns created with:
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# antibiotics %>%
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# mutate(useful_gramnegative =
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# if_else(
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# atc_group1 %like% '(fusidic|glycopeptide|macrolide|lincosamide|daptomycin|linezolid)' |
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# atc_group2 %like% '(fusidic|glycopeptide|macrolide|lincosamide|daptomycin|linezolid)' |
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# official %like% '(fusidic|glycopeptide|macrolide|lincosamide|daptomycin|linezolid)',
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# FALSE,
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# NA
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# ),
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# useful_grampositive =
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# if_else(
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# atc_group1 %like% '(aztreonam|temocillin|polymyxin|colistin|nalidixic)' |
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# atc_group2 %like% '(aztreonam|temocillin|polymyxin|colistin|nalidixic)' |
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# official %like% '(aztreonam|temocillin|polymyxin|colistin|nalidixic)',
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# FALSE,
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# NA
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# )
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# )
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"antibiotics"
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#' Dataset with ~2500 microorganisms
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#'
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#' A dataset containing 2500 microorganisms. MO codes of the UMCG can be looked up using \code{\link{microorganisms.umcg}}.
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#' @format A data.frame with 2507 observations and 12 variables:
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#' \describe{
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#' \item{\code{bactid}}{ID of microorganism}
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#' \item{\code{bactsys}}{Bactsyscode of microorganism}
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#' \item{\code{family}}{Family name of microorganism}
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#' \item{\code{genus}}{Genus name of microorganism, like \code{"Echerichia"}}
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#' \item{\code{species}}{Species name of microorganism, like \code{"coli"}}
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#' \item{\code{subspecies}}{Subspecies name of bio-/serovar of microorganism, like \code{"EHEC"}}
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#' \item{\code{fullname}}{Full name, like \code{"Echerichia coli (EHEC)"}}
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#' \item{\code{type}}{Type of microorganism, like \code{"Bacteria"} and \code{"Fungus/yeast"}}
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#' \item{\code{gramstain}}{Gram of microorganism, like \code{"Negative rods"}}
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#' \item{\code{aerobic}}{Logical whether bacteria is aerobic}
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#' \item{\code{type_nl}}{Type of microorganism in Dutch, like \code{"Bacterie"} and \code{"Schimmel/gist"}}
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#' \item{\code{gramstain_nl}}{Gram of microorganism in Dutch, like \code{"Negatieve staven"}}
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#' }
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#' @source MOLIS (LIS of Certe) - \url{https://www.certe.nl}
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#' @seealso \code{\link{guess_bactid}} \code{\link{antibiotics}} \code{\link{microorganisms.umcg}}
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"microorganisms"
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#' Translation table for UMCG with ~1100 microorganisms
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#'
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#' A dataset containing all bacteria codes of UMCG MMB. These codes can be joined to data with an ID from \code{\link{microorganisms}$bactid} (using \code{\link{left_join_microorganisms}}). GLIMS codes can also be translated to valid \code{bactid}'s with \code{\link{guess_bactid}}.
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#' @format A data.frame with 1090 observations and 2 variables:
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#' \describe{
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#' \item{\code{mocode}}{Code of microorganism according to UMCG MMB}
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#' \item{\code{bactid}}{Code of microorganism in \code{\link{microorganisms}}}
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#' }
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#' @source MOLIS (LIS of Certe) - \url{https://www.certe.nl} \cr \cr GLIMS (LIS of UMCG) - \url{https://www.umcg.nl}
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#' @seealso \code{\link{guess_bactid}} \code{\link{microorganisms}}
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"microorganisms.umcg"
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#' Dataset with 2000 blood culture isolates of septic patients
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#'
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#' An anonymised dataset containing 2000 microbial blood culture isolates with their antibiogram of septic patients found in 5 different hospitals in the Netherlands, between 2001 and 2017. This data.frame can be used to practice AMR analysis. For examples, press F1.
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#' @format A data.frame with 2000 observations and 47 variables:
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#' \describe{
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#' \item{\code{date}}{date of receipt at the laboratory}
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#' \item{\code{hospital_id}}{ID of the hospital}
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#' \item{\code{ward_icu}}{logical to determine if ward is an intensive care unit}
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#' \item{\code{ward_clinical}}{logical to determine if ward is a regular clinical ward}
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#' \item{\code{ward_outpatient}}{logical to determine if ward is an outpatient clinic}
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#' \item{\code{age}}{age of the patient}
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#' \item{\code{sex}}{sex of the patient}
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#' \item{\code{patient_id}}{ID of the patient, first 10 characters of an SHA hash containing irretrievable information}
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#' \item{\code{bactid}}{ID of microorganism, see \code{\link{microorganisms}}}
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#' \item{\code{peni:mupi}}{38 different antibiotics with class \code{rsi} (see \code{\link{as.rsi}}); these column names occur in \code{\link{antibiotics}} and can be translated with \code{\link{abname}}}
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#' }
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#' @source MOLIS (LIS of Certe) - \url{https://www.certe.nl}
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#' @examples
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#' # ----------- #
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#' # PREPARATION #
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#' # ----------- #
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#'
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#' # Save this example dataset to an object, so we can edit it:
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#' my_data <- septic_patients
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#'
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#' # load the dplyr package to make data science A LOT easier
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#' library(dplyr)
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#'
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#' # Add first isolates to our dataset:
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#' my_data <- my_data %>%
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#' mutate(first_isolates = first_isolate(my_data, "date", "patient_id", "bactid"))
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#'
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#' # -------- #
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#' # ANALYSIS #
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#' # -------- #
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#'
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#' # 1. Get the amoxicillin resistance percentages
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#' # of E. coli, divided by hospital:
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#'
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#' my_data %>%
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#' filter(bactid == "ESCCOL",
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#' first_isolates == TRUE) %>%
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#' group_by(hospital_id) %>%
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#' summarise(n = n(),
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#' amoxicillin_resistance = rsi(amox))
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#'
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#'
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#' # 2. Get the amoxicillin/clavulanic acid resistance
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#' # percentages of E. coli, trend over the years:
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#'
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#' my_data %>%
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#' filter(bactid == guess_bactid("E. coli"),
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#' first_isolates == TRUE) %>%
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#' group_by(year = format(date, "%Y")) %>%
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#' summarise(n = n(),
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#' amoxclav_resistance = rsi(amcl, minimum = 20))
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"septic_patients"
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