nemsqar (version 1.1.0)

trauma_14_population: Trauma-14 Population

Description

This function processes EMS data to generate the population needed to calculate the Trauma-14 NEMSQA measure.

Usage

trauma_14_population(
  df = NULL,
  patient_scene_table = NULL,
  situation_table = NULL,
  response_table = NULL,
  disposition_table = NULL,
  vitals_table = NULL,
  exam_table = NULL,
  procedures_table = NULL,
  injury_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
)

Value

A list that contains the following:

• a tibble with counts for each filtering step,

• a tibble for each population of interest

• a tibble for the initial population

• a tibble for the total dataset with computations

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.

situation_table

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

response_table

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

disposition_table

A data frame or tibble containing fields from edisposition 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.

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.

Author

Nicolas Foss, Ed.D., MS

Examples

# create tables to test correct functioning

  # patient table
  patient_table <- tibble::tibble(

    erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
    incident_date = as.Date(c("2025-01-01", "2025-01-05", "2025-02-01",
    "2025-01-01", "2025-06-01")),
    patient_dob = as.Date(c("2000-01-01", "2020-01-01", "2023-02-01",
    "2023-01-01", "1970-06-01")),
    epatient_15 = c(25, 5, 2, 2, 55),  # Ages
    epatient_16 = c("Years", "Years", "Years", "Years", "Years")

  )

  # response table
  response_table <- tibble::tibble(

    erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
    eresponse_05 = rep(2205001, 5),
    eresponse_10 = rep(2210011, 5)
  )

  # situation table
  situation_table <- tibble::tibble(

    erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
    esituation_02 = rep("Yes", 5),
  )

  # vitals table
  vitals_table <- tibble::tibble(

    erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
    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)
  )

  # disposition table
  disposition_table <- tibble::tibble(
    erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
    edisposition_24 = c(4224017, 4224003, 4224017, 4224003, 4224017),
    edisposition_30 = c(4230001, 4230003, 4230001, 4230007, 4230007)
  )

  # injury table
  injury_table <- tibble::tibble(
    erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
    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)
  )

  # exam table
  exam_table <- tibble::tibble(
    erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
    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)
  )

  # procedures table
  procedures_table <- tibble::tibble(
    erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
    eprocedures_03 = c(424979004, 427753009, 429705000, 47545007, 243142003)
  )

  # test the success of the function
  result <- trauma_14_population(patient_scene_table = patient_table,
                      response_table = response_table,
                      situation_table = situation_table,
                      vitals_table = vitals_table,
                      disposition_table = disposition_table,
                      exam_table = exam_table,
                      injury_table = injury_table,
                      procedures_table = procedures_table,
                      erecord_01_col = erecord_01,
                      incident_date_col = incident_date,
                      patient_DOB_col = patient_dob,
                      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
  )

# show the results of filtering at each step
result$filter_process