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Trauma-14 Calculation

Source: R/trauma_14.R
trauma_14.Rd

This function processes EMS data to generate a set of binary variables indicating whether specific trauma triage criteria are met. The output #' is a data frame enriched with these indicators for further analysis. The final outcome is whether or not the EMS record documents the use of #' a pre-hospital trauma activation.

Usage

trauma_14(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  situation_table = NULL,
  vitals_table = NULL,
  exam_table = NULL,
  procedures_table = NULL,
  injury_table = NULL,
  disposition_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,
  eresponse_10_col,
  transport_disposition_col,
  edisposition_24_col,
  evitals_06_col,
  evitals_10_col,
  evitals_12_col,
  evitals_14_col,
  evitals_15_col,
  evitals_21_col,
  eexam_16_col,
  eexam_20_col,
  eexam_23_col,
  eexam_25_col,
  eprocedures_03_col,
  einjury_01_col,
  einjury_03_col,
  einjury_04_col,
  einjury_09_col,
  confidence_interval = FALSE,
  method = c("wilson", "clopper-pearson"),
  conf.level = 0.95,
  correct = TRUE,
  ...
)

Arguments

df

A data frame or tibble containing EMS data with all relevant columns.

patient_scene_table

A data frame or tibble containing fields from epatient and escene needed for this measure's calculations.

response_table

A data frame or tibble containing fields from eresponse needed for this measure's calculations.

situation_table

A data frame or tibble containing fields from esituation needed for this measure's calculations.

vitals_table

A data frame or tibble containing fields from evitals needed for this measure's calculations.

exam_table

A data frame or tibble containing fields from eexam needed for this measure's calculations.

procedures_table

A data frame or tibble containing fields from eprocedures needed for this measure's calculations.

injury_table

A data frame or tibble containing fields from einjury needed for this measure's calculations.

disposition_table

A data frame or tibble containing fields from edisposition needed for this measure's calculations.

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.

eresponse_10_col

Column name containing scene delay information.

transport_disposition_col

The column for patient transport disposition.

edisposition_24_col

Column name containing pre-hospital trauma alert information.

evitals_06_col

Column name containing systolic blood pressure (SBP) values.

evitals_10_col

Column name containing heart rate values.

evitals_12_col

Column name containing pulse oximetry values.

evitals_14_col

Column name containing capillary refill information.

evitals_15_col

Column name containing respiratory effort values.

evitals_21_col

Column name containing Glasgow Coma Scale (GCS) Motor values.

eexam_16_col

Column name containing extremities assessment details.

eexam_20_col

Column name containing neurological assessment details.

eexam_23_col

Column name containing lung assessment details.

eexam_25_col

Column name containing chest assessment details.

eprocedures_03_col

Column name containing airway management or tourniquet usage details.

einjury_01_col

Column name containing injury cause details.

einjury_03_col

Column name containing trauma triage steps 1 and 2 information.

einjury_04_col

Column name containing trauma triage steps 3 and 4 information.

einjury_09_col

Column name containing fall height information.

confidence_interval

[Experimental] Logical. If TRUE, the function calculates a confidence interval for the proportion estimate.

method

[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.

conf.level

[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.

correct

[Experimental]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
  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),
    eresponse_10 = rep(2210011, 5),
    esituation_02 = rep("Yes", 5),
    evitals_06 = c(100, 90, 80, 70, 85),
    evitals_10 = c(110, 89, 88, 71, 85),
    evitals_12 = c(50, 60, 70, 80, 75),
    evitals_14 = c(30, 9, 8, 7, 31),
    evitals_15 = c("apneic", "labored", "rapid", "shallow", "weak/agonal"),
    evitals_21 = c(5, 4, 3, 2, 1),
    eexam_16 = c(3516043, 3516067, 3516043, 3516067, 3516067),
    eexam_20 = c(3520045, 3520043, 3520019, 3520017, 3520017),
    eexam_23 = c(3523011, 3523003, 3523001, 3523011, 3523003),
    eexam_25 = c(3525039, 3525023, 3525005, 3525039, 3525023),
    edisposition_24 = c(4224017, 4224003, 4224017, 4224003, 4224017),
    edisposition_30 = c(4230001, 4230003, 4230001, 4230007, 4230007),
    eprocedures_03 = c(424979004, 427753009, 429705000, 47545007, 243142003),
    einjury_01 = c("V20", "V36", "V86", "V39", "V32"),
    einjury_03 = c(2903011, 2903009, 2903005, 3903003, 2903001),
    einjury_04 = c(2904013, 2904011, 2904009, 2904007, 2904001),
    einjury_09 = c(11, 12, 13, 14, 15)
  )

  # Run function with the first and last pain score columns
  # Return 95% confidence intervals using the Wilson method
  trauma_14(
    df = test_data,
    erecord_01_col = erecord_01,
    epatient_15_col = epatient_15,
    epatient_16_col = epatient_16,
    eresponse_05_col = eresponse_05,
    eresponse_10_col = eresponse_10,
    esituation_02_col = esituation_02,
    evitals_06_col = evitals_06,
    evitals_10_col = evitals_10,
    evitals_12_col = evitals_12,
    evitals_14_col = evitals_14,
    evitals_15_col = evitals_15,
    evitals_21_col = evitals_21,
    eexam_16_col = eexam_16,
    eexam_20_col = eexam_20,
    eexam_23_col = eexam_23,
    eexam_25_col = eexam_25,
    edisposition_24_col = edisposition_24,
    transport_disposition_col = edisposition_30,
    eprocedures_03_col = eprocedures_03,
    einjury_01_col = einjury_01,
    einjury_03_col = einjury_03,
    einjury_04_col = einjury_04,
    einjury_09_col = einjury_09,
    confidence_interval = TRUE
  )
#> 
#> ── Trauma-14 ───────────────────────────────────────────────────────────────────
#> 
#> ── Gathering Records for Trauma-14 ──
#> 
#> Running `trauma_14_population()`  [Working on 1 of 33 tasks] ●●────────────────…
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#> 
#> 
#> 
#> ── Calculating Trauma-14 ──
#> 
#> 
#> ✔ Function completed in 0.47s.
#> 
#> 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-14 >= 65 yrs         0           0   NaN NA          NaN          NaN
#> 2 Trauma-14 10-64 yrs         3           3     1 100%          0.310        1
#> 3 Trauma-14 < 10 yrs          2           2     1 100%          0.198        1