R/seizure_02.R

In nemsqar: National Emergency Medical Service Quality Alliance Measure Calculations

#' @title Seizure-02 Calculation
#'
#' @description
#'
#' Calculates the NEMSQA Seizure-02 Measure.
#'
#' Calculates age-based seizure metrics for a dataset. This function filters
#' data for patients based on incident information, diagnoses, and administered
#' medications to assess adherence to Seizure-02 metrics.
#'
#' @param df A data frame where each row is an observation, containing all
#'   necessary columns for analysis.
#' @param patient_scene_table A data frame or tibble containing only epatient
#'   and escene fields as a fact table. Default is `NULL`.
#' @param response_table A data frame or tibble containing only the eresponse
#'   fields needed for this measure's calculations. Default is `NULL`.
#' @param situation_table A data.frame or tibble containing only the esituation
#'   fields needed for this measure's calculations. Default is `NULL`.
#' @param medications_table A data.frame or tibble containing only the
#'   emedications fields needed for this measure's calculations. Default is
#'   `NULL`.
#' @param erecord_01_col The column containing unique record identifiers for
#'   each encounter.
#' @param incident_date_col Column that contains the incident date. This
#'   defaults to `NULL` as it is optional in case not available due to PII
#'   restrictions.
#' @param patient_DOB_col Column that contains the patient's date of birth. This
#'   defaults to `NULL` as it is optional in case not available due to PII
#'   restrictions.
#' @param epatient_15_col Column name for patient age in numeric form.
#' @param epatient_16_col Column name for age unit (e.g., `"Years"` or
#'   `"Months"`).
#' @param eresponse_05_col Column name for response codes; "911" call codes are
#'   filtered.
#' @param esituation_11_col Column name for primary impressions.
#' @param esituation_12_col Column name for secondary impressions.
#' @param emedications_03_col Column name for medications administered; ideally
#'   a list column or string with comma-separated values.
#' @param confidence_interval `r lifecycle::badge("experimental")` Logical. If
#'   `TRUE`, the function calculates a confidence interval for the proportion
#'   estimate.
#' @param method `r lifecycle::badge("experimental")`Character. Specifies the
#'   method used to calculate confidence intervals. Options are `"wilson"`
#'   (Wilson score interval) and `"clopper-pearson"` (exact binomial interval).
#'   Partial matching is supported, so `"w"` and `"c"` can be used as shorthand.
#' @param conf.level `r lifecycle::badge("experimental")`Numeric. The confidence
#'   level for the interval, expressed as a proportion (e.g., 0.95 for a 95%
#'   confidence interval). Defaults to 0.95.
#' @param correct `r lifecycle::badge("experimental")`Logical. If `TRUE`,
#'   applies a continuity correction to the Wilson score interval when `method =
#'   "wilson"`. Defaults to `TRUE`.
#' @param ... optional additional arguments to pass onto `dplyr::summarize`.
#'
#' @return A data.frame summarizing results for two population groups (All,
#'   Adults and Peds) with the following columns:
#' - `pop`: Population type (All, Adults, and Peds).
#' - `numerator`: Count of incidents meeting the measure.
#' - `denominator`: Total count of included incidents.
#' - `prop`: Proportion of incidents meeting the measure.
#' - `prop_label`: Proportion formatted as a percentage with a specified number
#'    of decimal places.
#' - `lower_ci`: Lower bound of the confidence interval for `prop`
#'    (if `confidence_interval = TRUE`).
#' - `upper_ci`: Upper bound of the confidence interval for `prop`
#'    (if `confidence_interval = TRUE`).
#'
#' @examples
#'
#' # Synthetic test data
#'   test_data <- tibble::tibble(
#'     erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
#'     epatient_15 = c(34, 5, 45, 2, 60),  # Ages
#'     epatient_16 = c("Years", "Years", "Years", "Months", "Years"),
#'     eresponse_05 = rep(2205001, 5),
#'     esituation_11 = rep("G40", 5),
#'     esituation_12 = rep("r56", 5),
#'     emedications_03 = rep(3322, 5)
#'   )
#'
#' # Run the function
#' # Return 95% confidence intervals using the Wilson method
#'   seizure_02(
#'     df = test_data,
#'     erecord_01_col = erecord_01,
#'     epatient_15_col = epatient_15,
#'     epatient_16_col = epatient_16,
#'     eresponse_05_col = eresponse_05,
#'     esituation_11_col = esituation_11,
#'     esituation_12_col = esituation_12,
#'     emedications_03_col = emedications_03,
#'     confidence_interval = TRUE
#'   )
#'
#' @author Nicolas Foss, Ed.D., MS
#'
#' @export
#'
seizure_02 <- function(df = NULL,
                       patient_scene_table = NULL,
                       response_table = NULL,
                       situation_table = NULL,
                       medications_table = NULL,
                       erecord_01_col,
                       incident_date_col = NULL,
                       patient_DOB_col = NULL,
                       epatient_15_col,
                       epatient_16_col,
                       eresponse_05_col,
                       esituation_11_col,
                       esituation_12_col,
                       emedications_03_col,
                       confidence_interval = FALSE,
                       method = c("wilson", "clopper-pearson"),
                       conf.level = 0.95,
                       correct = TRUE,
                       ...) {

  # Set default method and adjustment method
  method <- match.arg(method, choices = c("wilson", "clopper-pearson"))

  # utilize applicable tables to analyze the data for the measure
  if (
    any(
      !is.null(patient_scene_table),
      !is.null(medications_table),
      !is.null(situation_table),
      !is.null(response_table)
    ) &&
    is.null(df)
  ) {

    # Start timing the function execution
    start_time <- Sys.time()

    # header
    cli::cli_h1("Seizure-02")

    # header
    cli::cli_h2("Gathering Records for Seizure-02")

    # gather the population of interest
    seizure_02_populations <- seizure_02_population(patient_scene_table = patient_scene_table,
                                                    response_table = response_table,
                                                    situation_table = situation_table,
                                                    medications_table = medications_table,
                                                    erecord_01_col = {{ erecord_01_col }},
                                                    incident_date_col = {{ incident_date_col }},
                                                    patient_DOB_col = {{ patient_DOB_col }},
                                                    epatient_15_col = {{ epatient_15_col }},
                                                    epatient_16_col = {{ epatient_16_col }},
                                                    eresponse_05_col = {{ eresponse_05_col }},
                                                    esituation_11_col = {{ esituation_11_col }},
                                                    esituation_12_col = {{ esituation_12_col }},
                                                    emedications_03_col = {{ emedications_03_col }}
                                                    )

  # create a separator
  cli::cli_text("\n")

  # header for calculations
  cli::cli_h2("Calculating Seizure-02")

  # summarize
  seizure.02 <- results_summarize(total_population = seizure_02_populations$initial_population,
                                  adult_population = seizure_02_populations$adults,
                                  peds_population = seizure_02_populations$peds,
                                  population_names = c("all", "adults", "peds"),
                                  measure_name = "Seizure-02",
                                  numerator_col = BENZO_MED,
                                  confidence_interval = confidence_interval,
                                  method = method,
                                  conf.level = conf.level,
                                  correct = correct,
                                  ...
                                  )

  # create a separator
  cli::cli_text("\n")

  # Calculate and display the runtime
  end_time <- Sys.time()
  run_time_secs <- difftime(end_time, start_time, units = "secs")
  run_time_secs <- as.numeric(run_time_secs)

  if (run_time_secs >= 60) {

    run_time <- round(run_time_secs / 60, 2)  # Convert to minutes and round
    cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 'm'))}.")

  } else {

    run_time <- round(run_time_secs, 2)  # Keep in seconds and round
    cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 's'))}.")

  }

  # create a separator
  cli::cli_text("\n")

  # when confidence interval is "wilson", check for n < 10
  # to warn about incorrect Chi-squared approximation
  if (any(seizure.02$denominator < 10) && method == "wilson" && confidence_interval) {

    cli::cli_warn("In {.fn prop.test}: Chi-squared approximation may be incorrect for any n < 10.")

  }

  return(seizure.02)

  } else if (
    any(
      is.null(patient_scene_table),
      is.null(medications_table),
      is.null(situation_table),
      is.null(response_table)
    ) &&
    !is.null(df)
  ) {

    # Start timing the function execution
    start_time <- Sys.time()

    # header
    cli::cli_h1("Seizure-02")

    # header
    cli::cli_h2("Gathering Records for Seizure-02")

    # gather the population of interest
    seizure_02_populations <- seizure_02_population(df = df,
                                                    erecord_01_col = {{ erecord_01_col }},
                                                    incident_date_col = {{ incident_date_col }},
                                                    patient_DOB_col = {{ patient_DOB_col }},
                                                    epatient_15_col = {{ epatient_15_col }},
                                                    epatient_16_col = {{ epatient_16_col }},
                                                    eresponse_05_col = {{ eresponse_05_col }},
                                                    esituation_11_col = {{ esituation_11_col }},
                                                    esituation_12_col = {{ esituation_12_col }},
                                                    emedications_03_col = {{ emedications_03_col }}
                                                    )

  # create a separator
  cli::cli_text("\n")

  # header for calculations
  cli::cli_h2("Calculating Seizure-02")

  # summarize
  seizure.02 <- results_summarize(total_population = seizure_02_populations$initial_population,
                                  adult_population = seizure_02_populations$adults,
                                  peds_population = seizure_02_populations$peds,
                                  population_names = c("all", "adults", "peds"),
                                  measure_name = "Seizure-02",
                                  numerator_col = BENZO_MED,
                                  confidence_interval = confidence_interval,
                                  method = method,
                                  conf.level = conf.level,
                                  correct = correct,
                                  ...
                                  )

  # create a separator
  cli::cli_text("\n")

  # Calculate and display the runtime
  end_time <- Sys.time()
  run_time_secs <- difftime(end_time, start_time, units = "secs")
  run_time_secs <- as.numeric(run_time_secs)

  if (run_time_secs >= 60) {

    run_time <- round(run_time_secs / 60, 2)  # Convert to minutes and round
    cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 'm'))}.")

  } else {

    run_time <- round(run_time_secs, 2)  # Keep in seconds and round
    cli::cli_alert_success("Function completed in {cli::col_green(paste0(run_time, 's'))}.")

  }

  # create a separator
  cli::cli_text("\n")

  # when confidence interval is "wilson", check for n < 10
  # to warn about incorrect Chi-squared approximation
  if (any(seizure.02$denominator < 10) && method == "wilson" && confidence_interval) {

    cli::cli_warn("In {.fn prop.test}: Chi-squared approximation may be incorrect for any n < 10.")

  }

  return(seizure.02)


  }

}