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312 lines
14 KiB
R
312 lines
14 KiB
R
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library(dplyr)
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# got EARS-Net codes (= ECDC/WHO codes) from here:
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# Installed WHONET 2019 software on Windows (http://www.whonet.org/software.html),
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# opened C:\WHONET\Codes\WHONETCodes.mdb in MS Access
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# and exported table 'DRGLST' to MS Excel
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library(readxl)
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DRGLST <- read_excel("DRGLST.xlsx")
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abx <- DRGLST %>%
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select(ab = WHON5_CODE,
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name = ANTIBIOTIC) %>%
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# remove the ones without WHONET code
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filter(!is.na(ab)) %>%
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distinct(name, .keep_all = TRUE) %>%
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# add the ones without WHONET code
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bind_rows(
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DRGLST %>%
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select(ab = WHON5_CODE,
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name = ANTIBIOTIC) %>%
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filter(is.na(ab)) %>%
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distinct(name, .keep_all = TRUE)
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# add new ab code later
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) %>%
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arrange(name)
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# add old ATC codes
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ab_old <- AMR::antibiotics %>%
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mutate(official = gsub("( and |, )", "/", official),
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abbr = tolower(paste(ifelse(is.na(abbr), "", abbr),
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ifelse(is.na(certe), "", certe),
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ifelse(is.na(umcg), "", umcg),
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sep = "|")))
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for (i in 1:nrow(ab_old)) {
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abbr <- ab_old[i, "abbr"]
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abbr <- strsplit(abbr, "|", fixed = TRUE) %>% unlist() %>% unique()
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abbr <- abbr[abbr != ""]
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#print(abbr)
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if (length(abbr) == 0) {
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ab_old[i, "abbr"] <- NA_character_
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} else {
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ab_old[i, "abbr"] <- paste(abbr, collapse = "|")
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}
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}
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# create reference data set: to be able to map ab to atc
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abx_atc1 <- abx %>%
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mutate(name_lower = tolower(name)) %>%
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left_join(ab_old %>%
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select(ears_net, atc), by = c(ab = "ears_net")) %>%
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rename(atc1 = atc) %>%
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left_join(ab_old %>%
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mutate(official = gsub(", combinations", "", official, fixed = TRUE)) %>%
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transmute(official = tolower(official), atc), by = c(name_lower = "official")) %>%
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rename(atc2 = atc) %>%
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left_join(ab_old %>%
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mutate(official = gsub(", combinations", "", official, fixed = TRUE)) %>%
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mutate(official = gsub("f", "ph", official)) %>%
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transmute(official = tolower(official), atc), by = c(name_lower = "official")) %>%
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rename(atc3 = atc) %>%
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left_join(ab_old %>%
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mutate(official = gsub(", combinations", "", official, fixed = TRUE)) %>%
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mutate(official = gsub("t", "th", official)) %>%
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transmute(official = tolower(official), atc), by = c(name_lower = "official")) %>%
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rename(atc4 = atc) %>%
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left_join(ab_old %>%
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mutate(official = gsub(", combinations", "", official, fixed = TRUE)) %>%
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mutate(official = gsub("f", "ph", official)) %>%
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mutate(official = gsub("t", "th", official)) %>%
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transmute(official = tolower(official), atc), by = c(name_lower = "official")) %>%
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rename(atc5 = atc) %>%
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left_join(ab_old %>%
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mutate(official = gsub(", combinations", "", official, fixed = TRUE)) %>%
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mutate(official = gsub("f", "ph", official)) %>%
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mutate(official = gsub("t", "th", official)) %>%
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mutate(official = gsub("ine$", "in", official)) %>%
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transmute(official = tolower(official), atc), by = c(name_lower = "official")) %>%
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rename(atc6 = atc) %>%
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mutate(atc = case_when(!is.na(atc1) ~ atc1,
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!is.na(atc2) ~ atc2,
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!is.na(atc3) ~ atc3,
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!is.na(atc4) ~ atc4,
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!is.na(atc4) ~ atc5,
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TRUE ~ atc6)) %>%
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distinct(ab, name, .keep_all = TRUE) %>%
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select(ab, atc, name)
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abx_atc2 <- ab_old %>%
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filter(!atc %in% abx_atc1$atc,
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is.na(ears_net),
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!is.na(atc_group1),
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!atc_group1 %like% ("virus|vaccin|viral|immun"),
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!official %like% "(combinations| with )") %>%
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mutate(ab = NA_character_) %>%
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as.data.frame(stringsAsFactors = FALSE) %>%
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select(ab, atc, name = official)
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abx2 <- bind_rows(abx_atc1, abx_atc2)
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rm(abx_atc1)
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rm(abx_atc2)
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abx2$ab[is.na(abx2$ab)] <- toupper(abbreviate(gsub("[/0-9-]",
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" ",
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abx2$name[is.na(abx2$ab)]),
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minlength = 3,
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method = "left.kept",
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strict = TRUE))
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n_distinct(abx2$ab)
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abx2 <- abx2 %>% arrange(ab)
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seqnr <- 0
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# add follow up nrs
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for (i in 2:nrow(abx2)) {
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if (abx2[i, "ab"] == abx2[i - 1, "ab"]) {
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seqnr <- seqnr + 1
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abx2[i, "seqnr"] <- seqnr
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} else {
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seqnr <- 0
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}
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}
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for (i in 2:nrow(abx2)) {
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if (!is.na(abx2[i, "seqnr"])) {
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abx2[i, "ab"] <- paste0(abx2[i, "ab"], abx2[i, "seqnr"])
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}
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}
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abx2 <- abx2 %>% select(-seqnr) %>% arrange(name)
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# everything unique??
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nrow(abx2) == n_distinct(abx2$ab)
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# get ATC properties
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abx2 <- abx2 %>%
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left_join(ab_old %>%
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select(atc, abbr, atc_group1, atc_group2,
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oral_ddd, oral_units, iv_ddd, iv_units))
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abx2$abbr <- lapply(as.list(abx2$abbr), function(x) unlist(strsplit(x, "|", fixed = TRUE)))
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# vector with official names, returns vector with CIDs
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get_CID <- function(ab) {
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CID <- rep(NA_integer_, length(ab))
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p <- progress_estimated(n = length(ab), min_time = 0)
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for (i in 1:length(ab)) {
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p$tick()$print()
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CID[i] <- tryCatch(
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data.table::fread(paste0("https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/",
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URLencode(ab[i], reserved = TRUE),
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"/cids/TXT?name_type=complete"),
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showProgress = FALSE)[[1]][1],
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error = function(e) NA_integer_)
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if (is.na(CID[i])) {
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# try with removing the text in brackets
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CID[i] <- tryCatch(
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data.table::fread(paste0("https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/",
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URLencode(trimws(gsub("[(].*[)]", "", ab[i])), reserved = TRUE),
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"/cids/TXT?name_type=complete"),
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showProgress = FALSE)[[1]][1],
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error = function(e) NA_integer_)
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}
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if (is.na(CID[i])) {
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# try match on word and take the lowest CID value (sorted)
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ab[i] <- gsub("[^a-z0-9]+", " ", ab[i], ignore.case = TRUE)
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CID[i] <- tryCatch(
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data.table::fread(paste0("https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/",
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URLencode(ab[i], reserved = TRUE),
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"/cids/TXT?name_type=word"),
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showProgress = FALSE)[[1]][1],
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error = function(e) NA_integer_)
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}
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Sys.sleep(0.1)
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}
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CID
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}
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# get CIDs (2-3 min)
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CIDs <- get_CID(abx2$name)
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# These could not be found:
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abx2[is.na(CIDs),] %>% View()
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# returns list with synonyms (brand names), with CIDs as names
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get_synonyms <- function(CID, clean = TRUE) {
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synonyms <- rep(NA_character_, length(CID))
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p <- progress_estimated(n = length(CID), min_time = 0)
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for (i in 1:length(CID)) {
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p$tick()$print()
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synonyms_txt <- ""
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if (is.na(CID[i])) {
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next
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}
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synonyms_txt <- tryCatch(
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data.table::fread(paste0("https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/fastidentity/cid/",
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CID[i],
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"/synonyms/TXT"),
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sep = "\n",
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showProgress = FALSE)[[1]],
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error = function(e) NA_character_)
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Sys.sleep(0.1)
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if (clean == TRUE) {
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# remove text between brackets
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synonyms_txt <- trimws(gsub("[(].*[)]", "",
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gsub("[[].*[]]", "",
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gsub("[(].*[]]", "",
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gsub("[[].*[)]", "", synonyms_txt)))))
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synonyms_txt <- gsub("Co-", "Co", synonyms_txt, fixed = TRUE)
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# only length 6 to 20 and no txt with reading marks or numbers and must start with capital letter (= brand)
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synonyms_txt <- synonyms_txt[nchar(synonyms_txt) %in% c(6:20)
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& !grepl("[-&{},_0-9/]", synonyms_txt)
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& grepl("^[A-Z]", synonyms_txt, ignore.case = FALSE)]
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synonyms_txt <- unlist(strsplit(synonyms_txt, ";", fixed = TRUE))
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}
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synonyms_txt <- unique(trimws(synonyms_txt[tolower(synonyms_txt) %in% unique(tolower(synonyms_txt))]))
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synonyms[i] <- list(sort(synonyms_txt))
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}
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names(synonyms) <- CID
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synonyms
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}
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# get brand names from PubChem (2-3 min)
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synonyms <- get_synonyms(CIDs)
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synonyms <- lapply(synonyms,
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function(x) {
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if (length(x) == 0 | all(is.na(x))) {
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""
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} else {
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x
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}})
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# add them to data set
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antibiotics <- abx2 %>%
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left_join(DRGLST %>%
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select(ab = WHON5_CODE, CLASS, SUBCLASS) %>%
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distinct(ab, .keep_all = TRUE), by = "ab") %>%
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transmute(ab,
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atc,
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cid = CIDs,
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# no capital after a slash: Ampicillin/Sulbactam -> Ampicillin/sulbactam
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name = name %>%
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gsub("([/-])([A-Z])", "\\1\\L\\2", ., perl = TRUE) %>%
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gsub("edta", "EDTA", ., ignore.case = TRUE),
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group = case_when(
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paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "am(ph|f)enicol" ~ "Amphenicols",
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paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "aminoglycoside" ~ "Aminoglycosides",
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paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "carbapenem" | name %like% "(imipenem|meropenem)" ~ "Carbapenems",
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paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "First-generation cephalosporin" ~ "Cephalosporins (1st gen.)",
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paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "Second-generation cephalosporin" ~ "Cephalosporins (2nd gen.)",
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paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "Third-generation cephalosporin" ~ "Cephalosporins (3rd gen.)",
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paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "Fourth-generation cephalosporin" ~ "Cephalosporins (4th gen.)",
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paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "(tuberculosis|mycobacter)" ~ "Antimycobacterials",
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paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "cephalosporin" ~ "Cephalosporins",
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name %like% "^Ce" & is.na(atc_group1) & paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "beta-?lactam" ~ "Cephalosporins",
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paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "(beta-?lactam|penicillin)" ~ "Beta-lactams/penicillins",
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paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "quinolone" ~ "Quinolones",
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paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "glycopeptide" ~ "Glycopeptides",
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paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "macrolide" ~ "Macrolides/lincosamides",
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paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "tetracycline" ~ "Tetracyclines",
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paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "trimethoprim" ~ "Trimethoprims",
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paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "polymyxin" ~ "Polymyxins",
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paste(atc_group1, atc_group2, CLASS, SUBCLASS) %like% "(fungal|mycot)" ~ "Antifungals/antimycotics",
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TRUE ~ "Other antibacterials"
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),
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atc_group1, atc_group2,
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abbreviations = unname(abbr),
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synonyms = unname(synonyms),
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oral_ddd, oral_units,
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iv_ddd, iv_units) %>%
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as.data.frame(stringsAsFactors = FALSE)
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# some exceptions
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antibiotics[which(antibiotics$ab == "DOX"), "abbreviations"][[1]] <- list(c("dox", "doxy"))
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antibiotics[which(antibiotics$ab == "FLC"), "abbreviations"][[1]] <- list(c("clox"))
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antibiotics[which(antibiotics$ab == "CEC"), "abbreviations"][[1]] <- list(c(antibiotics[which(antibiotics$ab == "CEC"), "abbreviations"][[1]], "CFC")) # cefaclor old WHONET4 code
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# 'Polymixin B' (POL) and 'Polymyxin B' (PLB) both exist, so:
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antibiotics[which(antibiotics$ab == "PLB"), "abbreviations"][[1]] <- list(c(antibiotics[which(antibiotics$ab == "PLB"), "abbreviations"][[1]], "POL", "Polymixin", "Polymixin B"))
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antibiotics <- filter(antibiotics, ab != "POL")
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# 'Latamoxef' (LTM) and 'Moxalactam (Latamoxef)' (MOX) both exist, so:
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antibiotics[which(antibiotics$ab == "LTM"), "abbreviations"][[1]] <- list(c("MOX", "moxa"))
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antibiotics <- filter(antibiotics, ab != "MOX")
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# RFP and RFP1 (the J0 one) both mean 'rifapentine', although 'rifp' is not recognised, so:
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antibiotics <- filter(antibiotics, ab != "RFP")
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antibiotics[which(antibiotics$ab == "RFP1"), "ab"] <- "RFP"
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antibiotics[which(antibiotics$ab == "RFP"), "abbreviations"][[1]] <- list(c("rifp"))
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# Rifampicin is better known as a drug than Rifampin (Rifampin is still listed as a brand name), so:
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antibiotics[which(antibiotics$ab == "RIF"), "name"] <- "Rifampicin"
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# PME and PVM1 (the J0 one) both mean 'Pivmecillinam', so:
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antibiotics <- filter(antibiotics, ab != "PME")
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antibiotics[which(antibiotics$ab == "PVM1"), "ab"] <- "PME"
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# ESBL E-test codes:
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antibiotics[which(antibiotics$ab == "CCV"), "abbreviations"][[1]] <- list(c("xtzl"))
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antibiotics[which(antibiotics$ab == "CAZ"), "abbreviations"][[1]] <- list(c(antibiotics[which(antibiotics$ab == "CAZ"), "abbreviations"][[1]], "xtz", "cefta"))
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antibiotics[which(antibiotics$ab == "CPC"), "abbreviations"][[1]] <- list(c("xpml"))
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antibiotics[which(antibiotics$ab == "FEP"), "abbreviations"][[1]] <- list(c(antibiotics[which(antibiotics$ab == "FEP"), "abbreviations"][[1]], "xpm"))
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antibiotics[which(antibiotics$ab == "CTC"), "abbreviations"][[1]] <- list(c("xctl"))
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antibiotics[which(antibiotics$ab == "CTX"), "abbreviations"][[1]] <- list(c(antibiotics[which(antibiotics$ab == "CTX"), "abbreviations"][[1]], "xct"))
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antibiotics <- antibiotics %>% arrange(name)
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class(antibiotics$ab) <- "ab"
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dim(antibiotics) # for R/data.R
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usethis::use_data(antibiotics, overwrite = TRUE)
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rm(antibiotics)
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