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Trauma-03 Calculation

Source: R/trauma_03.R
trauma_03.Rd

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,
  ...
)

Arguments

df

A dataframe or tibble contianing EMS data where each row represents an observation and columns represent features.

patient_scene_table

A data.frame or tibble containing at least ePatient, and eScene as a fact table.

response_table

A data.frame or tibble containing at least the eResponse fields needed for this measure's calculations.

situation_table

A data.frame or tibble containing at least 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.

vitals_table

A dataframe or tibble containing at least the eVitals fields needed.

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

Column representing the patient's numeric age agnostic of unit.

epatient_16_col

Column representing the patient's age unit ("Years", "Months", "Days", "Hours", or "Minutes").

esituation_02_col

Column indicating whether or not there was an injury.

eresponse_05_col

Column that contains eResponse.05 or the response type.

edisposition_28_col

Column name for patient care disposition details.

transport_disposition_col

One or more unquoted column names (such as edisposition.12, edisposition.30) containing transport disposition for an EMS event identifying whether a transport occurred and by which unit.

evitals_01_col

Date-time or POSIXct column containing vital signs date/time

evitals_27_col

Column giving the patient's indication of pain from a scale of 0-10.

evitals_27_initial_col

The column for the initial pain scale score. Default is NULL.

evitals_27_last_col

The column for the last pain scale score. Default is NULL.

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.

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).

Author

Nicolas Foss, Ed.D., MS

Examples

# 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,
    incident_date_col = NULL,
    patient_DOB_col = NULL,
    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
  )
#> 
#> ── Trauma-03 ───────────────────────────────────────────────────────────────────
#> 
#> ── Gathering Records for Trauma-03 ──
#> 
#> Running `trauma_03_population()`  [Working on 1 of 17 tasks] ●●●───────────────…
#> Running `trauma_03_population()`  [Working on 2 of 17 tasks] ●●●●●─────────────…
#> Running `trauma_03_population()`  [Working on 3 of 17 tasks] ●●●●●●────────────…
#> Running `trauma_03_population()`  [Working on 4 of 17 tasks] ●●●●●●●●──────────…
#> Running `trauma_03_population()`  [Working on 5 of 17 tasks] ●●●●●●●●●●────────…
#> Running `trauma_03_population()`  [Working on 6 of 17 tasks] ●●●●●●●●●●●●──────…
#> Running `trauma_03_population()`  [Working on 7 of 17 tasks] ●●●●●●●●●●●●●─────…
#> Running `trauma_03_population()`  [Working on 8 of 17 tasks] ●●●●●●●●●●●●●●●───…
#> Running `trauma_03_population()`  [Working on 9 of 17 tasks] ●●●●●●●●●●●●●●●●●─…
#> Running `trauma_03_population()`  [Working on 10 of 17 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `trauma_03_population()`  [Working on 11 of 17 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `trauma_03_population()`  [Working on 12 of 17 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `trauma_03_population()`  [Working on 13 of 17 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `trauma_03_population()`  [Working on 17 of 17 tasks] ●●●●●●●●●●●●●●●●●…
#> 
#> 
#> 
#> ── Calculating Trauma-03 ──
#> 
#> 
#> ✔ Function completed in 0.26s.
#> 
#> Warning: In `prop.test()`: Chi-squared approximation may be incorrect for any n < 10.
#> # A tibble: 3 × 8
#>   measure   pop    numerator denominator  prop prop_label lower_ci upper_ci
#>   <chr>     <chr>      <int>       <int> <dbl> <chr>         <dbl>    <dbl>
#> 1 Trauma-03 Adults         2           2     1 100%         0.198         1
#> 2 Trauma-03 Peds           1           1     1 100%         0.0546        1
#> 3 Trauma-03 All            4           4     1 100%         0.396         1