nemsqar (version 1.0.0)

airway_05: Airway-05 Calculation

Description

Calculates the NEMSQA Airway-05 measure.

Calculates the proportion of endotracheal intubation attempts with adequate oxygenation.

Usage

airway_05(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  arrest_table = NULL,
  procedures_table = NULL,
  vitals_table = NULL,
  erecord_01_col,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col,
  epatient_16_col,
  earrest_01_col,
  eresponse_05_col,
  evitals_01_col,
  evitals_12_col,
  eprocedures_01_col,
  eprocedures_02_col,
  eprocedures_03_col,
  ...
)

Value

A data.frame summarizing results for three population groups (All, Adults, and Peds) with the following columns:

measure: The name of the measure being calculated. pop: Population type (All, Adults, or Peds). numerator: Count of incidents where beta-agonist medications were administered. denominator: Total count of incidents. prop: Proportion of incidents involving beta-agonist medications. prop_label: Proportion formatted as a percentage with a specified number of decimal places.

Arguments

df

A dataframe or tibble containing EMS data where each row represents an observation and columns represent features. Default is NULL.

patient_scene_table

A data.frame or tibble containing at least epatient, escene, and earrest.01 fields as a fact table. Default is NULL.

response_table

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

arrest_table

A data.frame or tibble containing at least the earrest fields needed for this measure's calculations. Default is NULL.

procedures_table

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

vitals_table

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

erecord_01_col

The column representing the EMS record unique identifier.

incident_date_col

Column that contains the incident date. Default is NULL.

patient_DOB_col

Column that contains the patient's date of birth. Default is NULL.

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").

earrest_01_col

Column representing whether or not the patient is in arrest.

eresponse_05_col

Column that contains eResponse.05.

evitals_01_col

Date-time or POSIXct column containing vital signs date/time

evitals_12_col

Numeric column containing pulse oximetry values.

eprocedures_01_col

Date-time or POSIXct column for procedures

eprocedures_02_col

Column name for whether or not the procedure was performed prior to EMS care being provided.

eprocedures_03_col

Column containing procedure codes with or without procedure names.

...

optional additional arguments to pass onto dplyr::summarize.

Author

Samuel Kordik, BBA, BS, Nicolas Foss Ed.D., MS

Examples

# If you are sourcing your data from a SQL database connection
# or if you have your data in several different tables,
# you can pass table inputs versus a single data.frame or tibble

# create tables to test correct functioning

  # patient table
  patient_table <- tibble::tibble(

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

  )

  # response table
  response_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    eresponse_05 = rep(2205001, 10)

  )

  # vitals table
  vitals_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    evitals_01 = lubridate::as_datetime(c("2025-01-01 22:59:00",
    "2025-01-05 11:58:00", "2025-02-01 18:57:00", "2025-01-01 04:58:00",
    "2025-06-01 12:57:00", "2025-01-01 23:05:00", "2025-01-05 12:04:00",
    "2025-02-01 19:03:00", "2025-01-01 05:02:00", "2025-06-01 13:01:00")),
    evitals_12 = rep(c(94, 95, 96, 97, 98), 2)

  )

# arrest table
  arrest_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    earrest_01 = rep("No", 10)
  )

  # procedures table
  procedures_table <- tibble::tibble(

    erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
    eprocedures_01 = rep(lubridate::as_datetime(c("2025-01-01 23:00:00",
    "2025-01-05 12:00:00", "2025-02-01 19:00:00", "2025-01-01 05:00:00",
    "2025-06-01 13:00:00")), 2),
    eprocedures_02 = rep("No", 10),
    eprocedures_03 = rep(c(16883004, 112798008, 78121007, 49077009,
                           673005), 2)

  )

# Run the function
airway_05(df = NULL,
         patient_scene_table = patient_table,
         procedures_table = procedures_table,
         vitals_table = vitals_table,
         arrest_table = arrest_table,
         response_table = response_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,
         eprocedures_01_col = eprocedures_01,
         eprocedures_02_col = eprocedures_02,
         eprocedures_03_col = eprocedures_03,
         earrest_01_col = earrest_01,
         evitals_01_col = evitals_01,
         evitals_12_col = evitals_12
         )