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139 lines
7.4 KiB
R
139 lines
7.4 KiB
R
# ==================================================================== #
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# TITLE #
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# Antimicrobial Resistance (AMR) Data Analysis for R #
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# #
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# SOURCE #
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# https://github.com/msberends/AMR #
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# #
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# LICENCE #
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# (c) 2018-2022 Berends MS, Luz CF et al. #
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# Developed at the University of Groningen, the Netherlands, in #
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# collaboration with non-profit organisations Certe Medical #
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# Diagnostics & Advice, and University Medical Center Groningen. #
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# #
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# This R package is free software; you can freely use and distribute #
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# it for both personal and commercial purposes under the terms of the #
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# GNU General Public License version 2.0 (GNU GPL-2), as published by #
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# the Free Software Foundation. #
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# We created this package for both routine data analysis and academic #
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# research and it was publicly released in the hope that it will be #
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# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
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# #
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# Visit our website for the full manual and a complete tutorial about #
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# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
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# ==================================================================== #
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# This script runs in under a minute and renews all guidelines of CLSI and EUCAST!
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library(dplyr)
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library(readr)
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library(tidyr)
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library(AMR)
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# Install the WHONET software on Windows (http://www.whonet.org/software.html),
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# and copy the folder C:\WHONET\Codes to data-raw/WHONET/Codes
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DRGLST <- readr::read_tsv("data-raw/WHONET/Codes/DRGLST.txt", na = c("", "NA", "-"))
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DRGLST1 <- readr::read_tsv("data-raw/WHONET/Codes/DRGLST1.txt", na = c("", "NA", "-"))
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ORGLIST <- readr::read_tsv("data-raw/WHONET/Codes/ORGLIST.txt", na = c("", "NA", "-"))
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# create data set for generic rules (i.e., AB-specific but not MO-specific)
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rsi_generic <- DRGLST %>%
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filter(CLSI == "X" | EUCST == "X") %>%
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select(ab = ANTIBIOTIC, disk_dose = POTENCY, matches("^(CLSI|EUCST)[0-9]")) %>%
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mutate(ab = as.ab(ab),
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across(matches("(CLSI|EUCST)"), as.double)) %>%
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pivot_longer(-c(ab, disk_dose), names_to = "method") %>%
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separate(method, into = c("guideline", "method"), sep = "_") %>%
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mutate(method = ifelse(method %like% "D",
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gsub("D", "DISK_", method, fixed = TRUE),
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gsub("M", "MIC_", method, fixed = TRUE))) %>%
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separate(method, into = c("method", "rsi"), sep = "_") %>%
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# I is in the middle, so we only need R and S (saves data)
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filter(rsi %in% c("R", "S")) %>%
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pivot_wider(names_from = rsi, values_from = value) %>%
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transmute(guideline = gsub("([0-9]+)$", " 20\\1", gsub("EUCST", "EUCAST", guideline)),
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method,
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site = NA_character_,
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mo = as.mo("UNKNOWN"),
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ab,
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ref_tbl = "Generic rules",
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disk_dose,
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breakpoint_S = S,
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breakpoint_R = R,
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uti = FALSE) %>%
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filter(!(is.na(breakpoint_S) & is.na(breakpoint_R)), !is.na(mo), !is.na(ab))
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rsi_generic
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# create data set for AB-specific and MO-specific rules
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rsi_specific <- DRGLST1 %>%
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# only support guidelines for humans (for now)
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filter(HOST == "Human" & SITE_INF %unlike% "canine|feline",
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# only CLSI and EUCAST
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GUIDELINES %like% "(CLSI|EUCST)") %>%
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# get microorganism names from another WHONET table
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mutate(ORG_CODE = tolower(ORG_CODE)) %>%
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left_join(ORGLIST %>%
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transmute(ORG_CODE = tolower(ORG),
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SCT_TEXT = case_when(is.na(SCT_TEXT) & is.na(ORGANISM) ~ ORG_CODE,
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is.na(SCT_TEXT) ~ ORGANISM,
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TRUE ~ SCT_TEXT)) %>%
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# WHO for 'Generic'
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bind_rows(tibble(ORG_CODE = "gen", SCT_TEXT = "Unknown")) %>%
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# WHO for 'Enterobacterales'
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bind_rows(tibble(ORG_CODE = "ebc", SCT_TEXT = "Enterobacterales"))
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) %>%
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# still some manual cleaning required
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filter(!SCT_TEXT %in% c("Anaerobic Actinomycetes")) %>%
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transmute(guideline = gsub("([0-9]+)$", " 20\\1", gsub("EUCST", "EUCAST", GUIDELINES)),
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method = toupper(TESTMETHOD),
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site = SITE_INF,
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mo = as.mo(SCT_TEXT),
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ab = as.ab(WHON5_CODE),
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ref_tbl = REF_TABLE,
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disk_dose = POTENCY,
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breakpoint_S = as.double(ifelse(method == "DISK", DISK_S, MIC_S)),
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breakpoint_R = as.double(ifelse(method == "DISK", DISK_R, MIC_R)),
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uti = site %like% "(UTI|urinary|urine)") %>%
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filter(!(is.na(breakpoint_S) & is.na(breakpoint_R)), !is.na(mo), !is.na(ab))
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rsi_specific
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rsi_translation <- rsi_generic %>%
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bind_rows(rsi_specific) %>%
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# add the taxonomic rank index, used for sorting (so subspecies match first, order matches last)
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mutate(rank_index = case_when(mo_rank(mo) %like% "(infra|sub)" ~ 1,
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mo_rank(mo) == "species" ~ 2,
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mo_rank(mo) == "genus" ~ 3,
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mo_rank(mo) == "family" ~ 4,
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mo_rank(mo) == "order" ~ 5,
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TRUE ~ 6),
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.after = mo) %>%
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arrange(desc(guideline), ab, mo, method) %>%
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distinct(guideline, ab, mo, method, site, .keep_all = TRUE) %>%
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as.data.frame(stringsAsFactors = FALSE)
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# disks MUST be 6-50 mm, so correct where that is wrong:
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rsi_translation[which(rsi_translation$method == "DISK" &
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(is.na(rsi_translation$breakpoint_S) | rsi_translation$breakpoint_S > 50)), "breakpoint_S"] <- 50
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rsi_translation[which(rsi_translation$method == "DISK" &
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(is.na(rsi_translation$breakpoint_R) | rsi_translation$breakpoint_R < 6)), "breakpoint_R"] <- 6
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m <- unique(as.double(as.mic(levels(as.mic(1)))))
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rsi_translation[which(rsi_translation$method == "MIC" &
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is.na(rsi_translation$breakpoint_S)), "breakpoint_S"] <- min(m)
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rsi_translation[which(rsi_translation$method == "MIC" &
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is.na(rsi_translation$breakpoint_R)), "breakpoint_R"] <- max(m)
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# WHONET has no >1024 but instead uses 1025, 513, etc, so raise these one higher valid MIC factor level:
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rsi_translation[which(rsi_translation$breakpoint_R == 129), "breakpoint_R"] <- m[which(m == 128) + 1]
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rsi_translation[which(rsi_translation$breakpoint_R == 257), "breakpoint_R"] <- m[which(m == 256) + 1]
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rsi_translation[which(rsi_translation$breakpoint_R == 513), "breakpoint_R"] <- m[which(m == 512) + 1]
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rsi_translation[which(rsi_translation$breakpoint_R == 1025), "breakpoint_R"] <- m[which(m == 1024) + 1]
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# Greek symbols and EM dash symbols are not allowed by CRAN, so replace them with ASCII:
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rsi_translation$disk_dose <- gsub("μ", "u", rsi_translation$disk_dose, fixed = TRUE)
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rsi_translation$disk_dose <- gsub("–", "-", rsi_translation$disk_dose, fixed = TRUE)
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# save to package
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usethis::use_data(rsi_translation, overwrite = TRUE)
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rm(rsi_translation)
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devtools::load_all(".")
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