trauma_03: Trauma-03 Calculation

Description

This function calculates the "Trauma-03" measure, which evaluates pain scale reassessment for trauma patients, using a comprehensive data frame with EMS records. The function processes input data to create both fact and dimension tables, identifies eligible patients, and summarizes results for adult and pediatric populations.

Usage

trauma_03(
  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,
  esituation_02_col,
  eresponse_05_col,
  edisposition_28_col,
  transport_disposition_col,
  evitals_01_col,
  evitals_27_col = NULL,
  evitals_27_initial_col = NULL,
  evitals_27_last_col = NULL,
  confidence_interval = FALSE,
  method = c("wilson", "clopper-pearson"),
  conf.level = 0.95,
  correct = TRUE,
  ...
)

Value

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).

Arguments

df: A data frame or tibble containing EMS data with all relevant columns. Default is NULL.
patient_scene_table: A data frame or tibble containing only epatient and escene fields as a fact table. Default is NULL.
response_table: A data frame or tibble containing only the eresponse fields needed for this measure's calculations. Default is NULL.
situation_table: A data frame or tibble containing only the esituation fields needed for this measure's calculations. Default is NULL.
disposition_table: A data frame or tibble containing only the edisposition fields needed for this measure's calculations. Default is NULL.
vitals_table: A data frame or tibble containing only the evitals fields needed for this measure's calculations. Default is NULL.
erecord_01_col: The column representing the EMS record unique identifier.
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.
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.
epatient_15_col: The column for patient age numeric value.
epatient_16_col: The column for patient age unit (e.g., "Years", "Months").
esituation_02_col: The column containing information on the presence of injury.
eresponse_05_col: The column representing the 911 response type.
edisposition_28_col: The column for patient care disposition details.
transport_disposition_col: The column for patient transport disposition.
evitals_01_col: The column for the time of pain scale measurement.
evitals_27_col: The column for the full set of pain scale scores.
evitals_27_initial_col: The column for the initial pain scale score.
evitals_27_last_col: The column for the last pain scale score.
confidence_interval: Logical. If TRUE, the function calculates a confidence interval for the proportion estimate.
method: 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.
conf.level: Numeric. The confidence level for the interval, expressed as a proportion (e.g., 0.95 for a 95% confidence interval). Defaults to 0.95.
correct: Logical. If TRUE, applies a continuity correction to the Wilson score interval when method = "wilson". Defaults to TRUE.
...: optional additional arguments to pass onto dplyr::summarize.

Author

Nicolas Foss, Ed.D., MS

Examples

Run this code

# Synthetic test data
# for testing a single pain scale column
  test_data2 <- 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_02 = rep("Yes", 5),
    evitals_01 = lubridate::as_datetime(c("2025-01-01 12:00:00", "2025-01-05
    18:00:00", "2025-02-01 06:00:00", "2025-01-01 01:00:00", "2025-06-01
    14:00:00")),
    edisposition_28 = rep(4228001, 5),
    edisposition_30 = c(4230001, 4230003, 4230001, 4230007, 4230007)
  )

  # Expand data so each erecord_01 has 2 rows (one for each pain score)
  test_data_expanded2 <- test_data2 |>
    tidyr::uncount(weights = 2) |>  # Duplicate each row twice
    # Assign pain scores
    dplyr::mutate(evitals_27 = c(0, 0, 2, 1, 4, 3, 6, 5, 8, 7)) |>
    dplyr::group_by(erecord_01) |>
    dplyr::mutate(
    # Lower score = later time
      time_offset = dplyr::if_else(dplyr::row_number() == 1, -5, 0),
      evitals_01 = evitals_01 + lubridate::dminutes(time_offset)
    ) |>
    dplyr::ungroup() |>
    dplyr::select(-time_offset)  # Remove temporary column

# Run function with the single pain score column
# Return 95% confidence intervals using the Wilson method
  trauma_03(
    df = test_data_expanded2,
    erecord_01_col = erecord_01,
    epatient_15_col = epatient_15,
    epatient_16_col = epatient_16,
    eresponse_05_col = eresponse_05,
    esituation_02_col = esituation_02,
    evitals_01_col = evitals_01,
    evitals_27_initial_col = NULL,
    evitals_27_last_col = NULL,
    evitals_27_col = evitals_27,
    edisposition_28_col = edisposition_28,
    transport_disposition_col = edisposition_30,
    confidence_interval = TRUE
  )

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