nemsqar (version 1.1.0)

tbi_01_population: TBI-01 Populations

Description

This function screens for potential traumatic brain injury (TBI) cases based on specific criteria in a patient dataset. It produces a subset of the data with calculated variables for TBI identification.

Usage

tbi_01_population(
  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,
  eresponse_05_col,
  esituation_11_col,
  esituation_12_col,
  transport_disposition_col,
  evitals_06_col,
  evitals_12_col,
  evitals_16_col,
  evitals_23_col,
  evitals_26_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 the patient data.

patient_scene_table

A data frame or tibble containing only epatient and escene fields as a fact table. Default is NULL.

response_table

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

situation_table

A data.frame or tibble containing only 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. Default is NULL.

vitals_table

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

erecord_01_col

Column name in df with the patient’s unique record ID.

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 name in df with the patient’s age value.

epatient_16_col

Column name in df with the patient’s age unit (e.g., years, months).

eresponse_05_col

Column name in df with response codes for the type of EMS call.

esituation_11_col

Column name in df with the primary provider impression.

esituation_12_col

Column name in df with the secondary provider impression.

transport_disposition_col

Column name in df with the transport disposition.

evitals_06_col

Column name in df with systolic blood pressure (SBP).

evitals_12_col

Column name in df with pulse oximetry values.

evitals_16_col

Column name in df with ETC02 values. values.

evitals_23_col

Column name in df with Glasgow Coma Scale (GCS) scores.

evitals_26_col

Column name in df with AVPU (alert, verbal, painful, unresponsive) values.

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)

  )

  # situation table
  situation_table <- tibble::tibble(

    erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
    esituation_11 = c(rep("S02", 3), rep("S06", 2)),
    esituation_12 = c(rep("S09.90", 2), rep("S06.0X9", 3)),
  )


  # vitals table
  vitals_table <- tibble::tibble(

    erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
    evitals_06 = c(85, 80, 100, 90, 82),
    evitals_12 = c(95, 96, 97, 98, 99),
    evitals_16 = c(35, 36, 37, 38, 39),
    evitals_23 = rep(8, 5),
    evitals_26 = c("Verbal", "Painful", "Unresponsive", "Verbal", "Painful")
  )

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

# test the success of the function
  result <- tbi_01_population(patient_scene_table = patient_table,
                         response_table = response_table,
                         situation_table = situation_table,
                         vitals_table = vitals_table,
                         disposition_table = disposition_table,
                         erecord_01_col = erecord_01,
                         epatient_15_col = epatient_15,
                         epatient_16_col = epatient_16,
                         eresponse_05_col = eresponse_05,
                         esituation_11_col = esituation_11,
                         esituation_12_col = esituation_12,
                         evitals_06_col = evitals_06,
                         evitals_12_col = evitals_12,
                         evitals_16_col = evitals_16,
                         evitals_23_col = evitals_23,
                         evitals_26_col = evitals_26,
                         transport_disposition_col = edisposition_30
                     )

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