nemsqar (version 1.2.1)

trauma_04_population: Trauma-04 Populations

Description

This function processes EMS data to generate the population needed to calculated the Trauma-04 NEMSQA measure.

Usage

trauma_04_population(
  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 = lifecycle::deprecated(),
  edisposition_02_col,
  trauma_center_facility_IDs,
  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

• a tibble with a summary of missingness for each column in each table

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.

vitals_table

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

exam_table

A data.frame or tibble containing only the eExam fields needed for this measure's calculations. Default is NULL.

procedures_table

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

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 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

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.

eresponse_10_col

Column name containing informatin about scene delays, if any, of the EMS unit associated with the EMS event.

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.

edisposition_23_col

Use edisposition_02_col instead. You must also pass a character vector of trauma center facility IDs to trauma_center_facility_IDs to ensure that destination facility IDs passed via edisposition_02_col are correctly identified as trauma centers as applicable.

edisposition_02_col

Column name containing the code of the destination the patient was delivered or transferred to.

trauma_center_facility_IDs

A character vector of trauma center facility IDs that will allow destination facilities documented in edisposition_02_col to be classified correctly as trauma centers when applicable.

evitals_06_col

Numeric column containing systolic blood pressure values.

evitals_10_col

Column name containing the patient's heart rate expressed as a number per minute.

evitals_12_col

Numeric column containing pulse oximetry values.

evitals_14_col

Column name containing the patient's respiratory rate expressed as a number per minute.

evitals_15_col

Column name containing the patient's respiratory effort.

evitals_21_col

Column name containing the patient's Glasgow Coma Score Motor response.

eexam_16_col

Column name containing the assessment findings associated with the patient's extremities.

eexam_20_col

Column name containing the assessment findings of the patient's neurological examination.

eexam_23_col

Column name containing the assessment findings associated with the patient's lungs.

eexam_25_col

Column name containing the assessment findings associated with the patient's chest.

eprocedures_03_col

Column containing procedure codes with or without procedure names.

einjury_01_col

Column name containing the category of the reported/suspected external cause of the injury.

einjury_03_col

Column describing Trauma triage criteria for the red boxes (Injury Patterns and Mental Status and Vital Signs) in the 2021 ACS National Guideline for the Field Triage of Injured Patients.

einjury_04_col

Column name containing Trauma triage criteria for the yellow boxes (Mechanism of Injury and EMS Judgment) in the current ACS National Guideline for the Field Triage of Injured Patients.

einjury_09_col

Column name containing the distance in feet the patient fell, measured from the lowest point of the patient to the ground.

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_02 = as.character(c(
    9908029,
    9908027,
    9908025,
    9908023,
    9876543
  )),
  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_04_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_02_col = edisposition_02,
  trauma_center_facility_IDs = as.character(c(
    9908029,
    9908027,
    9908025,
    9908023,
    9908021
  )),
  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