R/tbi_01.R

In nemsqar: National Emergency Medical Service Quality Alliance Measure Calculations

#' @title TBI-01 Calculation
#'
#' @description
#'
#' This function screens for potential traumatic brain injury (TBI) cases based
#' on specific criteria in a patient dataset. It produces a subset of the data
#' with calculated variables for TBI identification.
#'
#' @param df A data frame or tibble containing the patient data.
#' @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 disposition_table A data.frame or tibble containing only the
#'   edisposition fields needed for this measure's calculations. Default is
#'   `NULL`.
#' @param vitals_table A data.frame or tibble containing only the evitals fields
#'   needed for this measure's calculations. Default is `NULL`.
#' @param erecord_01_col Column name in df with the patient’s unique record ID.
#' @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 in df with the patient’s age value.
#' @param epatient_16_col Column name in df with the patient’s age unit (e.g.,
#'   years, months).
#' @param eresponse_05_col Column name in df with response codes for the type of
#'   EMS call.
#' @param esituation_11_col Column name in df with the primary provider
#'   impression.
#' @param esituation_12_col Column name in df with the secondary provider
#'   impression.
#' @param transport_disposition_col Column name in df with the transport
#'   disposition.
#' @param evitals_06_col Column name in df with systolic blood pressure (SBP).
#' @param evitals_12_col Column name in df with pulse oximetry values.
#' @param evitals_16_col Column name in df with ETCO2 values.
#'   values.
#' @param evitals_23_col Column name in df with Glasgow Coma Scale (GCS) scores.
#' @param evitals_26_col Column name in df with AVPU (alert, verbal, painful,
#'   unresponsive) 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 = c(rep("S02", 3), rep("S06", 2)),
#'     esituation_12 = c(rep("S09.90", 2), rep("S06.0X9", 3)),
#'     evitals_06 = c(85, 80, 100, 90, 82),
#'     evitals_12 = c(95, 96, 97, 98, 99),
#'     evitals_16 = c(35, 36, 37, 38, 39),
#'     evitals_23 = rep(8, 5),
#'     evitals_26 = c("Verbal", "Painful", "Unresponsive", "Verbal", "Painful"),
#'     edisposition_30 = c(4230001, 4230003, 4230001, 4230007, 4230007)
#'   )
#'
#' # Run the function
#' # Return 95% confidence intervals using the Wilson method
#'   tbi_01(
#'     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,
#'     evitals_06_col = evitals_06,
#'     evitals_12_col = evitals_12,
#'     evitals_16_col = evitals_16,
#'     evitals_23_col = evitals_23,
#'     evitals_26_col = evitals_26,
#'     transport_disposition_col = edisposition_30,
#'     confidence_interval = TRUE
#'   )
#'
#' @author Nicolas Foss, Ed.D., MS
#'
#' @export
#'
tbi_01 <- function(df = NULL,
                   patient_scene_table = NULL,
                   response_table = NULL,
                   situation_table = NULL,
                   disposition_table = NULL,
                   vitals_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,
                   transport_disposition_col,
                   evitals_06_col,
                   evitals_12_col,
                   evitals_16_col,
                   evitals_23_col,
                   evitals_26_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"))

  if (
    all(
      !is.null(patient_scene_table),
      !is.null(vitals_table),
      !is.null(disposition_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("TBI-01")

    # header
    cli::cli_h2("Gathering Records for TBI-01")

    # gather the population of interest
    tbi_01_populations <- tbi_01_population(patient_scene_table = patient_scene_table,
                                                    response_table = response_table,
                                                    situation_table = situation_table,
                                                    disposition_table = disposition_table,
                                                    vitals_table = vitals_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 }},
                                                    transport_disposition_col = {{ transport_disposition_col }},
                                                    evitals_06_col = {{ evitals_06_col }},
                                                    evitals_12_col = {{ evitals_12_col }},
                                                    evitals_16_col = {{ evitals_16_col }},
                                                    evitals_23_col = {{ evitals_23_col }},
                                                    evitals_26_col = {{ evitals_26_col }}
                                            )

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

    # header for calculations
    cli::cli_h2("Calculating TBI-01")

    # summarize
    tbi.01 <- results_summarize(
      total_population = NULL,
      adult_population = tbi_01_populations$adults,
      peds_population = tbi_01_populations$peds,
      population_names = c("adults", "peds"),
      measure_name = "TBI-01",
      numerator_col = VITALS_CHECK,
      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(tbi.01$denominator < 10) && method == "wilson" && confidence_interval) {

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

    }

    return(tbi.01)

  } else if (
    all(
      is.null(patient_scene_table),
      is.null(vitals_table),
      is.null(disposition_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("TBI-01")

    # header
    cli::cli_h2("Gathering Records for TBI-01")

    # gather the population of interest
    tbi_01_populations <- tbi_01_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 }},
                                            transport_disposition_col = {{ transport_disposition_col }},
                                            evitals_06_col = {{ evitals_06_col }},
                                            evitals_12_col = {{ evitals_12_col }},
                                            evitals_16_col = {{ evitals_16_col }},
                                            evitals_23_col = {{ evitals_23_col }},
                                            evitals_26_col = {{ evitals_26_col }}
                                            )

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

    # header for calculations
    cli::cli_h2("Calculating TBI-01")

    # summarize
    tbi.01 <- results_summarize(
      total_population = NULL,
      adult_population = tbi_01_populations$adults,
      peds_population = tbi_01_populations$peds,
      population_names = c("adults", "peds"),
      measure_name = "TBI-01",
      numerator_col = VITALS_CHECK,
      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(tbi.01$denominator < 10) && method == "wilson" && confidence_interval) {

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

    }

    return(tbi.01)

  }

}