ttr_01_population: TTR-01 Populations

Description

Filters data down to the target populations for TTR-01, and categorizes records to identify needed information for the calculations.

Identifies key categories to records that are 911 requests for patients not transported by EMS during which a basic set of vital signs is documented based on specific criteria and calculates related ECG measures. This function segments the data by age into adult and pediatric populations.

Usage

ttr_01_population(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  disposition_table = NULL,
  vitals_table = NULL,
  arrest_table = NULL,
  erecord_01_col,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col,
  epatient_16_col,
  eresponse_05_col,
  transport_disposition_col,
  earrest_01_col,
  evitals_06_col,
  evitals_07_col,
  evitals_10_col,
  evitals_12_col,
  evitals_14_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 dataset to analyze. Default is NULL.
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.
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.
arrest_table: A data frame or tibble containing only the earrest fields needed for this measure's calculations. Default is NULL.
erecord_01_col: A column specifying unique patient records.
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: A column indicating the patient’s age in numeric form.
epatient_16_col: A column specifying the unit of patient age (e.g., "Years", "Days").
eresponse_05_col: A column specifying the type of response (e.g., 911 codes).
transport_disposition_col: A column specifying transport disposition for the patient.
earrest_01_col: A column containing cardiac arrest data.
evitals_06_col: A column containing systolic blood pressure (SBP) data from initial vital signs.
evitals_07_col: A column containing diastolic blood pressure (DBP) data from initial vital signs.
evitals_10_col: A column containing heart rate data from initial vital signs.
evitals_12_col: A column containing spO2 data from the initial vital signs.
evitals_14_col: A column containing respiratory rate data from initial vital signs.
evitals_23_col: A column containing total Glasgow Coma Scale (GCS) scores from initial vital signs.
evitals_26_col: A column containing alert, verbal, painful, unresponsive (AVPU) vital signs.

Author

Nicolas Foss, Ed.D., MS

Examples

Run this code

# 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),
  )

  # arrest table
  arrest_table <- tibble::tibble(

    erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
    earrest_01 = rep("No", 5)
  )

  # vitals table
  vitals_table <- tibble::tibble(

    erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
    evitals_06 = c(100, 90, 80, 70, 85),
    evitals_07 = c(80, 90, 50, 60, 87),
    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_23 = c(6, 7, 8, 9, 10),
    evitals_26 = c(3326007, 3326005, 3326003, 3326001, 3326007),
  )

  # disposition table
  disposition_table <- tibble::tibble(
    erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
    edisposition_30 = c(4230013, 4230009, 4230013, 4230009, 4230013)
  )

  # test the success of the function
  result <- ttr_01_population(patient_scene_table = patient_table,
                        response_table = response_table,
                        arrest_table = arrest_table,
                        vitals_table = vitals_table,
                        disposition_table = disposition_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,
                        earrest_01_col = earrest_01,
                        evitals_06_col = evitals_06,
                        evitals_07_col = evitals_07,
                        evitals_10_col = evitals_10,
                        evitals_12_col = evitals_12,
                        evitals_14_col = evitals_14,
                        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