P281424/AMR
1
0 Fork 0
mirror of https://github.com/msberends/AMR.git synced 2026-11-16 00:10:57 +01:00
Code Releases Activity
Files
0293d296aa24d81208182465e8e4fea5f7e1076b
AMR/data-raw/wisca_test/parameters_amr.R
T

623 lines
32 KiB
R
Raw Blame History

# Copyright (c) [2022] [Larisse Bolton]
wisca_params <- function(x, antibiotic_in, pathogen_in, analysis, exclude, isolate_first, susceptible_I, infection_in, infection_full) {
# Isolate the type of syndrome you would like to investigate
if (infection_in != "") {
x <- x %>%
filter(infection_type == infection_in)
} else {
x <- x
}
# Define antibiotic regimens under investigation from "antimicrobials" dataset
antibiotic_df <- function(abo_in) {
mono_ab <- str_to_title(abo_in[!str_detect(abo_in, fixed("+"))]) # identify single antimicrobials
mono_antibiotic_x_set <- data.frame()
for (j in 1:length(mono_ab)) { # generate dataframe for single antimicrobials with abbreviations and fullnames
mono_x_name <- ab_name(mono_ab[j], only_first = TRUE)
mono_x_ab <- as.ab(mono_x_name)
mono_antibiotic <- subset(antimicrobials, antimicrobials$ab %in% mono_x_ab)[, c("ab", "name")]
mono_antibiotic_x_set <- rbind.data.frame(mono_antibiotic_x_set, mono_antibiotic, row.names = NULL)
}
names(mono_antibiotic_x_set) <- c("ab1", "namerx")
mono_antibiotic_x_set$ab1 <- as.character(mono_antibiotic_x_set$ab1)
comb_ab <- str_to_title(abo_in[str_detect(abo_in, fixed("+"))]) # identify combination regimens
if (length(comb_ab) > 0) {
comb_antibiotic_x_total <- data.frame()
for (jj in 1:length(comb_ab)) { # generate dataframe for combination antimicrobials with abbreviations and fullnames
comb_ab_sep <- unlist(str_split(comb_ab[jj], fixed("+")))
comb_antibiotic_x_pre <- data.frame(x = rep(0, times = 2), y = rep(0, times = 2))
for (ii in 1:length(comb_ab_sep)) {
comb_x_name <- ab_name(comb_ab_sep[ii], only_first = TRUE)
comb_x_ab <- as.ab(comb_x_name)
comb_antibiotic <- subset(antimicrobials, antimicrobials$ab %in% comb_x_ab)[, c("ab", "name")]
comb_antibiotic_x_pre[ii, ] <- comb_antibiotic
names(comb_antibiotic_x_pre) <- names(comb_antibiotic)
}
comb_antibiotic_x <- pivot_wider(comb_antibiotic_x_pre, names_from = name, values_from = ab)
comb_antibiotic_x_set <- comb_antibiotic_x %>%
mutate(namerx = str_flatten(str_c(names(comb_antibiotic_x), collapse = "+")))
new_ab <- sapply(X = as.character(1:(ncol(comb_antibiotic_x_set) - 1)), FUN = function(x) {
str_flatten(str_c("ab", x))
})
names(comb_antibiotic_x_set)[1:(ncol(comb_antibiotic_x_set) - 1)] <- new_ab
names(comb_antibiotic_x_set)[ncol(comb_antibiotic_x_set)] <- "namerx"
comb_antibiotic_x_total <- rbind.data.frame(comb_antibiotic_x_total, comb_antibiotic_x_set, row.names = NULL)
}
antibiotic_x_set <- full_join(x = mono_antibiotic_x_set, y = comb_antibiotic_x_total) # generate full antibiotic dataset for analysis
antibiotic_x_set <- select(antibiotic_x_set, starts_with("ab"), starts_with("namerx"))
} else {
antibiotic_x_set <- mono_antibiotic_x_set
}
return(antibiotic_x_set)
}
antibiotic_rx <- antibiotic_df(abo_in = antibiotic_in) # full dataframe with antibiotic regimens
# if the dataset names are in different format to built-in set
if ((length(names(x)[sapply(names(x), function(x1) {
any(str_detect(x1, "[A-Z]$") & !(x1 %in% antimicrobials$ab) & !(x1 %in% antimicrobials$name))
})]) > 0)) {
names(x)[which(str_detect(names(x), "[a-z]+") & !str_detect(names(x), "fullname") & !str_detect(names(x), "mo"))] <- str_to_upper(names(x)[which(str_detect(names(x), "[a-z]+") & !str_detect(names(x), "fullname") & !str_detect(names(x), "mo"))])
names(x)[which(str_detect(names(x), "^ORG") & str_detect(names(x), "NAME$"))] <- "fullname"
names(x)[which(str_detect(names(x), "^ORG") & str_detect(names(x), "CODE$"))] <- "organism"
names(x)[which(str_detect(names(x), "^DATE") & str_detect(names(x), "CULTURE$") | str_detect(names(x), "^DATE") & str_detect(names(x), "SPECIMEN"))] <- "date"
names(x)[which(str_detect(names(x), "^EPISODE") | str_detect(names(x), "^PAT"))] <- "patient"
names(x)[which(str_detect(names(x), "^WARD"))] <- "ward"
names(x)[which(str_detect(names(x), "^HOSP"))] <- "hospital"
names(x)[which(str_detect(names(x), "DATE") & (str_detect(names(x), "REGISTRATION")))] <- "date"
}
# remove any duplicated variables
if (any(duplicated(names(x)))) {
x <- x[, -max(which(names(x) == names(x)[which(duplicated(names(x)))]))]
} else {
x <- x
}
x <- arrange(x, "date") # arrange in increasing date
# if your dataset does not have an episode identifier
if (length(names(x)[sapply(names(x), function(x1) {
any(str_detect(x1, "patient"))
})]) == 0) {
x$patient <- seq(1, nrow(x), 1)
}
# find demographic data within dataset
micro_df_ind <- which(sapply(x, function(y) any(str_detect(y, "^S$")) | any(str_detect(y, "^R$")) | any(str_detect(y, "^SENSITIVE$")) | any(str_detect(y, "^RESISTANT$"))) == TRUE)
micro_df <- names(x)[micro_df_ind] # column names for susceptibility data
demograph_df <- names(x)[which(!names(x) %in% micro_df)] # column names for demographic or organism data
# Dataset required to contain both mo and fullname
if ("mo" %in% names(x)) {
# If mo included but not fullname:
x <- x %>%
mutate(mo = as.mo(mo), keep_synonyms = TRUE)
test_isolates_pre <- x %>%
mutate(across(.cols = all_of(demograph_df), ~ str_to_title(.x))) %>%
mutate(mo = toupper(mo))
if ("fullname" %in% names(x)) {
test_isolates <- test_isolates_pre[, c(demograph_df)]
} else {
test_isolates <- left_join_microorganisms(x = test_isolates_pre, by = "mo")[, c(demograph_df, "fullname")] # add in fullname
}
test_isolates <- test_isolates %>% # remove bugs to be excluded and final style conversions
filter(!(fullname %in% exclude)) %>%
mutate(fullname = str_to_title(fullname))
rx_abo <- x %>% # from orginal dataset
filter(mo %in% test_isolates$mo) %>% # select only those bugs that should be included
distinct(date, patient, mo, .keep_all = TRUE) # ensure that there are no duplications
rx_regs <- names(rx_abo)[!(names(rx_abo) %in% demograph_df)] # extract susceptibility data
if (any(str_detect(rx_regs, fixed("+")))) { # if combinations have already been accounted for in susceptibility profiling
rx_regs_ind <- which(!(names(rx_abo) %in% demograph_df)) # extract susceptibility data
rx_regs_comb <- rx_regs[str_detect(rx_regs, fixed("+"))] # find combination regimen variables
rx_regs_comb_x_total <- data.frame()
for (jj in 1:length(rx_regs_comb)) { # for every combination regimen
rx_regs_comb_sep <- str_remove_all(unlist(str_split(rx_regs_comb[jj], fixed("+"))), "[[:punct:]]") # extract separate antimicrobials included in regimen
rx_regs_comb_pre <- data.frame(x = rep(0, times = 2), y = rep(0, times = 2))
for (ii in 1:length(rx_regs_comb_sep)) { # for every antibiotic in the combination regimen
rx_regs_comb_name <- ab_name(rx_regs_comb_sep[ii], only_first = TRUE) # extract their antibiotic name
rx_regs_comb_ab <- as.ab(rx_regs_comb_name) # convert antibiotic name to abbreviation
rx_regs_comb_antibiotic <- subset(antimicrobials, antimicrobials$ab %in% rx_regs_comb_ab)[, c("ab", "name")]
rx_regs_comb_pre[ii, ] <- rx_regs_comb_antibiotic
names(rx_regs_comb_pre) <- names(rx_regs_comb_antibiotic)
}
rx_regs_comb_x <- pivot_wider(rx_regs_comb_pre, names_from = name, values_from = ab)
rx_regs_comb_x_set <- rx_regs_comb_x %>%
mutate(namerx = str_flatten(str_c(names(rx_regs_comb_x), collapse = "+"))) # generate new antimicrobials table
rx_regs_new_ab <- sapply(X = as.character(1:(ncol(rx_regs_comb_x_set) - 1)), FUN = function(x) {
str_flatten(str_c("ab", x))
})
names(rx_regs_comb_x_set)[1:(ncol(rx_regs_comb_x_set) - 1)] <- rx_regs_new_ab
names(rx_regs_comb_x_set)[ncol(rx_regs_comb_x_set)] <- "namerx"
rx_regs_comb_x_total <- rbind.data.frame(rx_regs_comb_x_total, rx_regs_comb_x_set, row.names = NULL)
}
rx_regs_comb_rename <- select(rx_regs_comb_x_total, starts_with("ab")) # extract all antibiotic abbreviations
# regenerate variable names for dataset
suppressWarnings({
for (k in 1:nrow(rx_regs_comb_x_total)) {
rx_regs_comb_rename_2 <- unlist(rx_regs_comb_rename[k, ])
rx_regs_comb_rename_vec <- str_flatten(str_c(rx_regs_comb_rename_2, collapse = "+"))
names(rx_abo)[names(rx_abo) == rx_regs_comb[k]] <- rx_regs_comb_rename_vec
}
})
names(rx_abo)[!(str_detect(names(rx_abo), fixed("+"))) & !(names(rx_abo) %in% demograph_df)] <-
as.ab(names(rx_abo)[!(str_detect(names(rx_abo), fixed("+"))) & !(names(rx_abo) %in% demograph_df)])
names(rx_abo)[which(duplicated(names(rx_abo)))] <- str_c(names(rx_abo)[which(duplicated(names(rx_abo)))], ".x")
rx_abo_upd <- left_join_microorganisms(x = rx_abo, by = "mo")[, c(names(rx_abo), "fullname")]
} else {
names(rx_abo)[!(names(rx_abo) %in% demograph_df)] <- as.ab(names(rx_abo)[!(names(rx_abo) %in% demograph_df)])
names(rx_abo)[which(duplicated(names(rx_abo)))] <- str_c(names(rx_abo)[which(duplicated(names(rx_abo)))], ".x")
rx_abo_upd <- left_join_microorganisms(x = rx_abo, by = "mo")[, c(names(rx_abo), "fullname")]
}
} else {
# If fullname included in dataset:
test_isolates_pre <- x %>%
mutate(across(.cols = all_of(demograph_df), ~ str_to_title(.x))) %>%
filter(!(fullname %in% unique(exclude)))
print(nrow(test_isolates_pre))
test_isolates <- left_join_microorganisms(x = test_isolates_pre, by = "fullname")[, c(demograph_df, "mo")]
test_isolates$fullname <- str_to_title(test_isolates$fullname)
test_isolates$mo <- toupper(test_isolates$mo)
rx_abo <- x %>%
mutate(fullname = str_to_title(fullname)) %>%
filter(!(fullname %in% unique(exclude))) %>%
distinct(date, patient, fullname, .keep_all = TRUE)
rx_regs <- names(rx_abo)[!(names(rx_abo) %in% demograph_df)] # susceptibility data
if (any(str_detect(rx_regs, fixed("+")))) { # if combinations have already been accounted for
rx_regs_ind <- which(!(names(rx_abo) %in% demograph_df))
rx_regs_comb <- rx_regs[str_detect(rx_regs, fixed("+"))]
rx_regs_comb_x_total <- data.frame()
for (jj in 1:length(rx_regs_comb)) {
rx_regs_comb_sep <- str_remove_all(unlist(str_split(rx_regs_comb[jj], fixed("+"))), "[[:punct:]]")
rx_regs_comb_pre <- data.frame(x = rep(0, times = 2), y = rep(0, times = 2))
for (ii in 1:length(rx_regs_comb_sep)) {
rx_regs_comb_name <- ab_name(rx_regs_comb_sep[ii], only_first = TRUE)
rx_regs_comb_ab <- as.ab(rx_regs_comb_name)
rx_regs_comb_antibiotic <- subset(antimicrobials, antimicrobials$ab %in% rx_regs_comb_ab)[, c("ab", "name")]
rx_regs_comb_pre[ii, ] <- rx_regs_comb_antibiotic
names(rx_regs_comb_pre) <- names(rx_regs_comb_antibiotic)
}
rx_regs_comb_x <- pivot_wider(rx_regs_comb_pre, names_from = name, values_from = ab)
rx_regs_comb_x_set <- rx_regs_comb_x %>%
mutate(namerx = str_flatten(str_c(names(rx_regs_comb_x), collapse = "+")))
rx_regs_new_ab <- sapply(X = as.character(1:(ncol(rx_regs_comb_x_set) - 1)), FUN = function(x) {
str_flatten(str_c("ab", x))
})
names(rx_regs_comb_x_set)[1:(ncol(rx_regs_comb_x_set) - 1)] <- rx_regs_new_ab
names(rx_regs_comb_x_set)[ncol(rx_regs_comb_x_set)] <- "namerx"
rx_regs_comb_x_total <- rbind.data.frame(rx_regs_comb_x_total, rx_regs_comb_x_set, row.names = NULL)
}
rx_regs_comb_rename <- select(rx_regs_comb_x_total, starts_with("ab"))
suppressWarnings({
for (k in 1:nrow(rx_regs_comb_x_total)) {
rx_regs_comb_rename_2 <- unlist(rx_regs_comb_rename[k, ])
rx_regs_comb_rename_vec <- str_flatten(str_c(rx_regs_comb_rename_2, collapse = "+"))
names(rx_abo)[names(rx_abo) == rx_regs_comb[k]] <- rx_regs_comb_rename_vec
}
})
names(rx_abo)[!(str_detect(names(rx_abo), fixed("+"))) & !(names(rx_abo) %in% demograph_df)] <-
as.ab(names(rx_abo)[!(str_detect(names(rx_abo), fixed("+"))) & !(names(rx_abo) %in% demograph_df)])
names(rx_abo)[which(duplicated(names(rx_abo)))] <- str_c(names(rx_abo)[which(duplicated(names(rx_abo)))], ".x")
rx_abo_upd <- left_join_microorganisms(x = rx_abo, by = "fullname")[, c(names(rx_abo), "mo")]
} else {
names(rx_abo)[!(names(rx_abo) %in% demograph_df)] <- as.ab(names(rx_abo)[!(names(rx_abo) %in% demograph_df)])
names(rx_abo)[which(duplicated(names(rx_abo)))] <- str_c(names(rx_abo)[which(duplicated(names(rx_abo)))], ".x")
rx_abo_upd <- left_join_microorganisms(x = rx_abo, by = "fullname")[, c(names(rx_abo), "mo")]
}
}
rx_abo_upd$fullname <- str_to_title(rx_abo_upd$fullname)
# generate new dataset with demographic information and only antimicrobials under investigation
rx_1 <- select(antibiotic_rx, starts_with("ab")) # all antimicrobials
rx_df <- test_isolates
var_date <- names(rx_df)[str_detect(names(rx_df), "date")]
rx_df[[var_date]] <- ymd(rx_df[[var_date]])
var_patient <- names(rx_df)[str_detect(names(rx_df), "patient")]
rx_df[[var_patient]] <- toupper(rx_df[[var_patient]])
names_vec <- vector()
suppressWarnings({
for (k in 1:nrow(rx_1)) {
rx_2 <- unlist(rx_1[k, ][!is.na(str_extract(rx_1[k, ], "[A-Z]+"))])
if ((length(rx_2) > 1) & !(str_flatten(str_c(rx_2, collapse = "+")) %in% names(rx_abo_upd))) { # if more than one antibiotic in regimen and susceptibility of the combination not considered
rx_df <- merge(x = rx_df, y = rx_abo_upd[, c("date", "patient", "mo", rx_2)], by = c("date", "patient", "mo")) # then bind separate antimicrobials in regimen
# if prior individual antibiotic was added, suffix will be generated
for (jj in 1:length(rx_2)) {
if (any(str_detect(names(rx_df), paste0(rx_2[jj], ".y")))) {
rx_df[[paste0(rx_2[jj], ".y")]] <- str_trim(rx_df[[paste0(rx_2[jj], ".y")]])
} else {
rx_df[[rx_2[jj]]] <- str_trim(rx_df[[rx_2[jj]]])
}
}
names(rx_df)[names(rx_df) %in% rx_2] <- str_flatten(str_c(rx_2, collapse = "+")) # rename both variables as the combination
names(rx_df)[names(rx_df) %in% paste0(rx_2, ".y")] <- str_flatten(str_c(rx_2, collapse = "+"))
names(rx_df)[names(rx_df) %in% paste0(rx_2, ".x")] <- rx_2
names_vec[k] <- str_flatten(str_c(rx_2, collapse = "+"))
} else if ((length(rx_2) > 1) & (str_flatten(str_c(rx_2, collapse = "+")) %in% names(rx_abo_upd))) { # if more than one antibiotic in regimen and susceptibility of the combination considered
rx_df <- merge(x = rx_df, y = rx_abo_upd[, c("date", "patient", "mo", str_flatten(str_c(rx_2, collapse = "+")))], by = c("date", "patient", "mo"))
rx_df[[str_flatten(str_c(rx_2, collapse = "+"))]] <- str_trim(rx_df[[str_flatten(str_c(rx_2, collapse = "+"))]])
names_vec[k] <- str_flatten(str_c(rx_2, collapse = "+"))
} else {
rx_df <- merge(x = rx_df, y = rx_abo_upd[, c("date", "patient", "mo", rx_2)], by = c("date", "patient", "mo")) # then bind separate antimicrobials in regimen
rx_df[[rx_2]] <- str_trim(rx_df[[rx_2]])
names(rx_df)[names(rx_df) %in% rx_2] <- str_flatten(str_c(rx_2, collapse = "+")) # rename both variables as the combination
names(rx_df)[names(rx_df) %in% paste0(rx_2, ".y")] <- str_flatten(str_c(rx_2, collapse = "+"))
names(rx_df)[names(rx_df) %in% paste0(rx_2, ".x")] <- rx_2
names_vec[k] <- rx_2
}
}
})
if (any(is.na(rx_df$mo))) {
missing1 <- which(is.na(rx_df$mo))
if (rx_df[missing1, "fullname"] != "NA") {
rx_df[missing1, "mo"] <- as.mo(rx_df[missing1, ]$fullname, keep_synonyms = TRUE)
} else {
rx_df <- rx_df[-missing1, ]
}
}
# Generate adapted first isolate dataset on antimicrobial level
if (isolate_first == "yes") { # if dataset has already been deduplicated for first isolates
test_isolates_first <- rx_df
} else {
test_isolates_first <- rx_df[first_isolate(
x = rx_df,
method = "e",
episode_days = 14,
col_date = var_date,
col_mo = "mo"
) == TRUE, ] # Only include isolates for microorganisms < 14 days apart
}
test_isolates_first_upd <<- test_isolates_first %>%
pivot_longer(
cols = names(test_isolates_first)[which(sapply(
test_isolates_first,
function(y) any(str_detect(y, "^S$")) | any(str_detect(y, "^R$")) | any(str_detect(y, "^SENSITIVE$")) | any(str_detect(y, "^RESISTANT$"))
))],
names_to = "keyantimicrobials", values_to = "antibiogram"
) %>% # combine all regimens and antibiograms into single variables
mutate(antibiogram = ifelse(antibiogram == "I" | antibiogram == "INTERMEDIATE", susceptible_I, antibiogram)) %>% # simplify susceptibility outputs - 90-60 rule
mutate(antibiogram = ifelse(antibiogram == "SENSITIVE", "S", antibiogram)) %>%
mutate(antibiogram = ifelse(antibiogram == "RESISTANT", "R", antibiogram)) %>%
mutate(
antibiogram = factor(antibiogram),
keyantimicrobials = as.factor(keyantimicrobials)
)
# recode susceptibility of regimes if not already in the dataset: if any S, then all S; if any NA and not S, then combination NA; if all R, then combination R.
print("-------------------")
print(test_isolates_first_upd)
print(names(test_isolates_first_upd))
print(duplicated(names(test_isolates_first_upd)))
print(any(duplicated(names(test_isolates_first_upd))))
print("-------------------")
if (any(duplicated(names(test_isolates_first_upd)))) {
test_isolates_temp3 <- test_isolates_upd %>%
mutate(antibiogram = as.character(antibiogram)) %>%
mutate(antibiogram = replace_na(antibiogram, "U")) %>%
mutate(combiantibiogram = "A")
test_isolates_temp4 <- test_isolates_temp3 %>%
group_by(date, patient, mo, keyantimicrobials) %>%
mutate(combiantibiogram = ifelse(any(str_detect(antibiogram, "S")), "S", combiantibiogram)) %>%
mutate(combiantibiogram = ifelse(any(str_detect(antibiogram, "U")) & !any(str_detect(antibiogram, "S")), "U", combiantibiogram)) %>%
mutate(combiantibiogram = ifelse(!any(str_detect(antibiogram, "U")) & !any(str_detect(antibiogram, "S")), "R", combiantibiogram)) %>%
distinct(date, patient, mo, .keep_all = TRUE) %>%
ungroup() %>%
select(-antibiogram) %>%
rename("antibiogram" = "combiantibiogram") %>%
mutate(antibiogram = ifelse(antibiogram == "U", NA_character_, antibiogram)) %>%
mutate(antibiogram = as.factor(antibiogram))
test_isolates_temp5 <- test_isolates_upd %>% # isolate the single regimens
group_by(date, patient, mo) %>%
filter(!str_detect(keyantimicrobials, fixed("+"))) %>%
ungroup()
test_isolates_final <- bind_rows(test_isolates_temp4, test_isolates_temp5)
test_isolates_final <- test_isolates_final %>%
arrange(patient)
} else {
test_isolates_final <- test_isolates_first_upd
}
test_isolates_final <<- test_isolates_final
test_isolates_final$fullname <- as.factor(test_isolates_final$fullname)
test_isolates_final$date <- ymd(test_isolates_final$date)
# Calculate pathogen incidence and susceptible by regimen
mo_name_total <- test_isolates_final %>% # deduplicate pathogen list
filter(!duplicated(mo)) %>%
select(mo, fullname)
organism_count_total <- test_isolates_final %>% # count the number of occurrences of pathogens within first isolates set
group_by(mo) %>%
distinct(date, patient, .keep_all = TRUE) %>%
count(mo) %>%
ungroup() %>%
arrange(desc(n))
organism_count_total_upd <- organism_count_total %>%
mutate(mo = as.character(mo)) %>%
mutate(mo = ifelse(str_detect(mo, "^B_KLBSL"), "B_KLBSL", mo)) %>%
mutate(mo = as.factor(mo))
klebsiella_combine <- organism_count_total_upd %>%
filter(mo == "B_KLBSL") %>%
summarise(n_kleb = sum(n))
organism_count_total_upd <- organism_count_total_upd %>%
mutate(n = ifelse(mo == "B_KLBSL", klebsiella_combine$n_kleb, n)) %>%
distinct(mo, .keep_all = TRUE)
organism_count_total_upd <- rename(organism_count_total_upd,
"n_inc" = "n"
)
regimen_organism <- slice_max(organism_count_total_upd, order_by = n_inc, n = pathogen_in) # choose only the top n number of occurring pathogens to consider in WISCA
total_pathogens <- sum(regimen_organism$n_inc) # total number of occurrences of pathogens
print(paste0("Total pathogens = ", sum(organism_count_total_upd$n_inc)))
print(paste0("Number of pathogens contributing to WISCA = ", total_pathogens))
print(paste0("Percentage of pathogens contributing to WISCA = ", round((total_pathogens / sum(organism_count_total_upd$n_inc)) * 100, 0), "%"))
test_isolates_final <- test_isolates_final %>%
mutate(mo = as.character(mo)) %>%
mutate(mo = ifelse(str_detect(mo, "^B_KLBSL"), "B_KLBSL", mo)) %>% # combine all Klebsiella species together
mutate(mo = as.factor(mo)) %>%
mutate(fullname = as.character(fullname)) %>%
mutate(fullname = ifelse(str_detect(fullname, "Klebsiella"), "Klebsiella sp.", fullname)) %>% # rename all Klebsiella species
mutate(fullname = as.factor(fullname))
# for each antimicrobial regimen calculate the pathogen incidence and sensitivity
params_set_out <- data.frame()
if (analysis %in% names(test_isolates_final)) {
# if there is a facility/ward/category level analysis required
for (j in 1:length(unique(test_isolates_final[[analysis]]))) { # for every facility/ward/category
infection_episodes <- length(unique(test_isolates_final[test_isolates_final[[analysis]] == unique(test_isolates_final[[analysis]])[j], ]$patient))
if (infection_episodes < 100) { # must have at least 100 infection episodes for WISCA
warning(paste0("Number of infection episodes = ", infection_episodes, ". Minimum sample size for WISCA is 100 infection episodes. Below minimum sample size will produce inaccurate coverage estimates."))
} else {
print(paste0("Number of infection episodes = ", infection_episodes, " > 100 minimum required. Sample size adequate for WISCA analysis."))
}
y_site <- subset(test_isolates_final, test_isolates_final[[analysis]] == unique(test_isolates_final[[analysis]])[j])
mo_name_sites <- y_site %>% # deduplicate
filter(!duplicated(mo)) %>%
select(mo, fullname, all_of(analysis))
organism_count_regimen <- y_site %>% # count the number of organisms per regimen
filter(mo %in% regimen_organism$mo) %>%
group_by(mo) %>%
distinct(date, patient, .keep_all = TRUE) %>%
count(mo) %>%
ungroup() %>%
arrange(desc(n))
organism_count_df <- left_join(x = organism_count_regimen, y = mo_name_sites, by = "mo")[, c("mo", analysis, "fullname", "n")]
total_sites_pathogens <- sum(organism_count_df$n)
print(paste0("Number of pathogens contributing to WISCA in ", unique(test_isolates_final[[analysis]])[j], " = ", total_sites_pathogens))
organism_count_df <- organism_count_df %>%
mutate(prop.n = n / total_sites_pathogens) # pathogen incidence = number of pathogen occurring/total pathogen occurrences included in WISCA
#
susceptible_regimen_tested <<- y_site %>% # determine sensitivity profile - assume those with NA not tested. Calculate susceptibility only on tested
filter(mo %in% regimen_organism$mo) %>%
filter(!is.na(antibiogram)) %>%
group_by(mo, keyantimicrobials) %>%
distinct(date, patient, .keep_all = TRUE) %>%
count(antibiogram) %>%
ungroup() %>%
arrange(desc(n))
susceptible_regimen_ntested <<- y_site %>% # determine sensitivity profile - assume those with NA not tested. Calculate susceptibility only on tested
filter(mo %in% regimen_organism$mo) %>%
filter(is.na(antibiogram)) %>% # Not tested
group_by(mo, keyantimicrobials) %>%
distinct(date, patient, .keep_all = TRUE) %>%
count(antibiogram) %>%
ungroup() %>%
arrange(desc(n))
organism_count_df_tested <<- susceptible_regimen_tested %>%
group_by(mo, keyantimicrobials) %>%
summarise(tested_n = sum(n), .groups = "keep") %>%
ungroup()
organism_count_df_ntested <<- susceptible_regimen_ntested %>%
group_by(mo, keyantimicrobials) %>%
summarise(ntested_n = sum(n), .groups = "keep") %>%
ungroup()
organism_count_df_combine_tested <- full_join(x = organism_count_df_tested, y = organism_count_df_ntested, by = c("mo", "keyantimicrobials")) # combine tested and nontested
organism_count_df_combine_tested <- organism_count_df_combine_tested %>%
mutate(
tested_n = ifelse(is.na(tested_n), 0, tested_n),
ntested_n = ifelse(is.na(ntested_n), 0, ntested_n)
)
organism_count_df_regimen_tested <- left_join(x = organism_count_df, y = organism_count_df_combine_tested[, c("mo", "keyantimicrobials", "tested_n")], by = "mo")
# minimum number of tested isolates per ABO should be 30 for accurate coverage estimates
condition_tested <- organism_count_df_regimen_tested %>%
filter(tested_n < 30)
if (nrow(condition_tested) > 0) {
warning("Number of tested isolates per regimen should exceed 30. Coverage estimates will be inaccurate for following regimens")
View(condition_tested[c("mo", "n", "keyantimicrobials", "tested_n")])
} else {
print("Number of tested isolates per regimen exceed minimum threshold of 30.")
}
organism_tested_sensitive <- susceptible_regimen_tested %>%
filter(antibiogram == "S")
if (nrow(susceptible_regimen_tested) > 0) {
sensitivity <- left_join(x = organism_count_df_regimen_tested, y = organism_tested_sensitive, by = c("mo", "keyantimicrobials"))
sensitivity <- sensitivity %>%
rename("S_n" = "n.y") %>%
mutate(S_n = ifelse(is.na(S_n), 0, S_n)) %>%
select(-antibiogram)
sensitivity <- sensitivity %>% # calculate proportion sensitive out of those tested and having results
mutate(prop.S = ifelse(tested_n != 0, S_n / tested_n, 0))
sensitivity$perc.S <- sensitivity$prop.S * 100
params_set_out <- rbind.data.frame(params_set_out, sensitivity, row.names = NULL)
} else {
next
}
}
params_set_out$fullname <- droplevels(params_set_out$fullname)
params_set_out[[names(test_isolates_final)[names(test_isolates_final) == analysis]]] <- as.factor(params_set_out[[names(test_isolates_final)[names(test_isolates_final) == analysis]]])
} else {
infection_episodes <- length(unique(test_isolates_final$patient))
if (infection_episodes < 100) { # must have at least 100 infection episodes for WISCA
warning(paste0("Number of infection episodes = ", infection_episodes, ". Minimum sample size for WISCA is 100 infection episodes. Below minimum sample size will produce inaccurate coverage estimates."))
} else {
print(paste0("Number of infection episodes = ", infection_episodes, " > 100 minimum required. Sample size adequate for WISCA analysis."))
}
mo_name_sites <- test_isolates_final %>% # deduplicate
filter(!duplicated(mo)) %>%
select(mo, fullname)
organism_count_path <- test_isolates_final %>% # count the number of organisms
filter(mo %in% regimen_organism$mo) %>%
group_by(mo) %>%
distinct(date, patient, .keep_all = TRUE) %>%
count(mo) %>%
ungroup() %>%
arrange(desc(n))
organism_count_df <- left_join(x = organism_count_path, y = mo_name_sites, by = "mo")[, c("mo", "fullname", "n")]
total_sites_pathogens <- sum(organism_count_df$n)
organism_count_df <- organism_count_df %>%
mutate(prop.n = n / total_pathogens) # pathogen incidence = number of pathogen occurring/total pathogen occurrences included in WISCA
susceptible_regimen_tested <<- test_isolates_final %>% # determine sensitivity profile - assume those with NA not tested. Calculate susceptibility only on tested
filter(mo %in% regimen_organism$mo) %>%
filter(!is.na(antibiogram)) %>% # Tested
group_by(mo, keyantimicrobials) %>%
distinct(date, patient, .keep_all = TRUE) %>%
count(antibiogram) %>%
ungroup() %>%
arrange(desc(n))
susceptible_regimen_ntested <<- test_isolates_final %>% # determine sensitivity profile - assume those with NA not tested. Calculate susceptibility only on tested
filter(mo %in% regimen_organism$mo) %>%
filter(is.na(antibiogram)) %>% # Not tested
group_by(mo, keyantimicrobials) %>%
distinct(date, patient, .keep_all = TRUE) %>%
count(antibiogram) %>%
ungroup() %>%
arrange(desc(n))
organism_count_df_tested <<- susceptible_regimen_tested %>%
group_by(mo, keyantimicrobials) %>%
summarise(tested_n = sum(n), .groups = "keep") %>%
ungroup()
organism_count_df_ntested <<- susceptible_regimen_ntested %>%
group_by(mo, keyantimicrobials) %>%
summarise(ntested_n = sum(n), .groups = "keep") %>%
ungroup()
organism_count_df_combine_tested <- full_join(x = organism_count_df_tested, y = organism_count_df_ntested, by = c("mo", "keyantimicrobials")) # combine tested and nontested
organism_count_df_combine_tested <- organism_count_df_combine_tested %>%
mutate(
tested_n = ifelse(is.na(tested_n), 0, tested_n),
ntested_n = ifelse(is.na(ntested_n), 0, ntested_n)
)
organism_count_df_regimen_tested <- left_join(x = organism_count_df, y = organism_count_df_combine_tested[, c("mo", "keyantimicrobials", "tested_n")], by = "mo")
# minimum number of tested isolates per ABO should be 30 for accurate coverage estimates
condition_tested <- organism_count_df_regimen_tested %>%
filter(tested_n < 30)
if (nrow(condition_tested) > 0) {
warning("Number of tested isolates per regimen should exceed 30. Coverage estimates will be inaccurate for these regimens.")
View(condition_tested[, c("mo", "n", "keyantimicrobials", "tested_n")])
} else {
print("Number of tested isolates per regimen exceed minimum threshold of 30.")
}
organism_tested_sensitive <- susceptible_regimen_tested %>%
filter(antibiogram == "S")
if (nrow(susceptible_regimen_tested) > 0) {
sensitivity <- left_join(x = organism_count_df_regimen_tested, y = organism_tested_sensitive, by = c("mo", "keyantimicrobials"))
sensitivity <- sensitivity %>%
rename("S_n" = "n.y") %>%
mutate(S_n = ifelse(is.na(S_n), 0, S_n)) %>%
select(-antibiogram)
sensitivity <- sensitivity %>% # calculate proportion sensitive out of those tested and having results
mutate(prop.S = ifelse(tested_n != 0, S_n / tested_n, 0))
sensitivity$perc.S <- sensitivity$prop.S * 100
params_set_out <- rbind.data.frame(params_set_out, sensitivity, row.names = NULL)
} else {
next
}
params_set_out$fullname <- droplevels(params_set_out$fullname)
}
# if (infection_in != ""){
# params_set_out <- params_set_out %>%
# mutate(infection_type = infection_in) %>%
# mutate(infection_type_full = infection_full) %>%
# rowwise() %>%
# mutate(regimens = ifelse(str_detect(keyantimicrobials, fixed("+")),
# str_c(sapply(unlist(unique(str_split(keyantimicrobials, fixed("+")))),ab_name,USE.NAMES = FALSE), collapse = " + "),
# ab_name(keyantimicrobials))) %>%
# select(mo:prop.n, regimens,keyantimicrobials,tested_n:infection_type, infection_type_full)
#
#
# } else {
# print("here??")
# params_set_out <- params_set_out %>%
# mutate(infection_type_full = infection_full) %>%
# rowwise() %>%
# mutate(regimens = ifelse(str_detect(keyantimicrobials, fixed("+")),
# str_c(sapply(unlist(unique(str_split(keyantimicrobials, fixed("+")))),ab_name,USE.NAMES = FALSE), collapse = " + "),
# ab_name(keyantimicrobials))) %>%
# select(mo:prop.n, regimens,keyantimicrobials,tested_n:infection_type, infection_type_full)
# }
return(params_set_out)
}
View Git Blame Copy Permalink