trauma_04: Trauma-04 Calculations

Description

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 verified trauma center levels 1-5 in the hospital capability documentation.

Usage

trauma_04(
  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_23_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,
  ...
)

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.
patient_scene_table: A data.frame or tibble containing only epatient and escene fields as a fact table.
response_table: A data.frame or tibble containing only the eresponse fields needed for this measure's calculations.
situation_table: A data.frame or tibble containing only the esituation fields needed for this measure's calculations.
vitals_table: A data.frame or tibble containing only the evitals fields needed for this measure's calculations.
exam_table: A data.frame or tibble containing only the eexam fields needed for this measure's calculations.
procedures_table: A data.frame or tibble containing only the eprocedures fields needed for this measure's calculations.
injury_table: A data.frame or tibble containing only the einjury fields needed for this measure's calculations.
disposition_table: A data.frame or tibble containing only the edisposition fields 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_23_col: Column name containing trauma hospital verification 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: 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
  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_23 = c(9908029, 9908027, 9908025, 9908023, 9908021),
    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_04(
    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_23_col = edisposition_23,
    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
  )

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