R/STOPP_F4.R

In agapiospanos/StartStopp: START and STOPP criteria evaluation for potentially inappropriate prescribing

#' Evaluates the imported patients' data for the STOPP F4 criterion.
#'
#' @param path (Character) the path that the excel file can be read from.
#' @param excel_out (Boolean) (optional) (default: TRUE) output excel file with the evaluated data.
#' @param export_data_path (Character) (optional) (default: working directory) the path for excel file output.
#' @param suppressNA (Boolean) (optional) (default: TRUE) set this to FALSE if you want to know for which patients have NAs and for which variable. By default all NAs will be ignored so that the algorithm can distinguish between patients who meet the criterion and those who do not.
#' @return list of lists of evaluated patient ids categorized in 1) the ids that fulfill the criterion, 2) the ids that do not fulfill the criterion and 3) the ids that has missing data
#'
#' @author
#' Agapios Panos <panosagapios@gmail.com>
#'
#' @importFrom writexl write_xlsx
#' @export


STOPP_F4 <- function(path, excel_out = TRUE, export_data_path = NULL, suppressNA = TRUE) {

  missing_data_patients <- list()

  # the variable to keep the final data frame of patients:
  # 0 marks the patient that does not fulfill the criterion,
  # 1 marks the patient that fulfills the criterion and
  # 2 marks the patient with missing data.
  evaluated_patients <- data.frame(patients = character(0), status = numeric(0), missing_variables = character(0))

  # Importing the data
  data <- import_excel_data(path = path, worksheet = 1, var_col = 'med_gen__decod', include_missing = suppressNA, ignore_na = suppressNA )
  data <- import_excel_data(current_data = data, path = path, worksheet = 1, var_col = 'med_strength', include_missing = TRUE, ignore_na = suppressNA )

  pdata <- data[[1]]
  missing_data_patients <- data[[2]]

  # iterration over all patients
  for ( i in 1: length(pdata)){
    # checking if the patient id is in the list of missing data
    pid <- names(sapply(pdata[i], names))

    if (is.na(match( pid, names(sapply(missing_data_patients, names))))){

      # get vectors of atc codes and medicine strength for the current patient
      patient_atc_codes <- unlist(pdata[[i]][1])
      med_strength <- unlist(pdata[[i]][2])

      cond1 <- cond2 <- cond3 <- FALSE

      index1 <- grep('B03AA02', patient_atc_codes, ignore.case = T)
      if (length(index1) > 0) { # we get length of index because the grep returns an empty integer vector if the B03AA02 is not found.
        med_strength1 <- as.numeric(unlist(med_strength[index1]))
        med_strength1 <- med_strength1[!is.na(med_strength1)]
        if (length(med_strength1) > 0) {
          cond1 <- any(med_strength1 > 600) # checking if med_strength for this atc code is greater than 600
        }
      }

      index2 <- grep('B03AA07', patient_atc_codes, ignore.case = T)
      if (length(index2) > 0) { # we get length of index because the grep returns an empty integer vector if the B03AA07 is not found.
        med_strength2 <- as.numeric(unlist(med_strength[index2]))
        med_strength2 <- med_strength2[!is.na(med_strength2)]
        if (length(med_strength2) > 0) {
          cond2 <- any(med_strength2 > 600) # checking if med_strength for this atc code is greater than 600
        }
      }

      index3 <- grep('B03AA03', patient_atc_codes, ignore.case = T)
      if (length(index3) > 0) { # we get length of index because the grep returns an empty integer vector if the B03AA03 is not found.
        med_strength3 <- as.numeric(unlist(med_strength[index3]))
        med_strength3 <- med_strength3[!is.na(med_strength3)]
        if (length(med_strength3) > 0) {
          cond3 <- any(med_strength3 > 1800) # checking if med_strength for this atc code is greater than 1800
        }
      }

      # checking if fulfills at least one set of primary condition AND secondary condition
      if ( cond1 | cond2 | cond3 ) {
        # inserting the record to the data.frame evaluated_patients
        evaluated_patients <- rbind(evaluated_patients, data.frame(patients = pid, status = 1, missing_variables = ''))
      } else {
        # inserting the record to the data.frame evaluated_patients
        evaluated_patients <- rbind(evaluated_patients, data.frame(patients = pid, status = 0, missing_variables = ''))
      }

    } else { # patient has missing data
      # inserting the record to the data.frame evaluated_patients
      evaluated_patients <- rbind(evaluated_patients, data.frame(patients = pid, status = 2, missing_variables = paste(missing_data_patients[[pid]], collapse = ', ')))
    }
  }

  # storing the results
  fulfill_count <- length(which(evaluated_patients$status == 1))
  total_count <- fulfill_count + length(which(evaluated_patients$status == 0))
  missing_count <- length(which(evaluated_patients$status == 2))

  # printing results to the console
  if (suppressNA) {
    cat('STOPP F4: ', fulfill_count, 'patients out of', total_count + missing_count, 'patients meet the criterion.\n')
  } else {
    cat('STOPP F4: ', fulfill_count, 'patients out of', total_count, 'patients meet the criterion.', missing_count, 'patients have missing data. \n')
  }

  if (excel_out) {
    # export the evaluated list of patients to excel file
    write_xlsx(evaluated_patients, path = paste0( export_data_path, '/STOPP-F4.xlsx'), col_names = TRUE)
  }

  invisible (list(evaluated_patients)) # instead of return as we do not want to be printed
}