nemsqar (version 1.1.0)

airway_18: Airway-18 Calculation

Description

This function processes and analyzes the dataset to calculate the "Airway-18" NEMSQA metric. It includes cleaning and transforming several columns related to patient data, airway procedures, and vital signs, and it returns a cleaned dataset with the relevant calculations. The final calculation is an assessment of the successful last invasive airway procedures performed during an EMS response originating from a 911 request in which waveform capnography is used for tube placement confirmation.

Usage

airway_18(
  df = NULL,
  patient_scene_table = NULL,
  procedures_table = NULL,
  vitals_table = NULL,
  airway_table = NULL,
  response_table = NULL,
  erecord_01_col,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col,
  epatient_16_col,
  eresponse_05_col,
  eprocedures_01_col,
  eprocedures_02_col,
  eprocedures_03_col,
  eprocedures_06_col,
  eairway_02_col = NULL,
  eairway_04_col = NULL,
  evitals_01_col,
  evitals_16_col,
  confidence_interval = FALSE,
  method = c("wilson", "clopper-pearson"),
  conf.level = 0.95,
  correct = TRUE,
  ...
)

Value

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

• pop: Population type (Adults and Peds).

• numerator: Count of incidents meeting the measure.

• denominator: Total count of included incidents.

• prop: Proportion of incidents meeting the measure.

• prop_label: Proportion formatted as a percentage with a specified number of decimal places.

• lower_ci: Lower bound of the confidence interval for prop (if confidence_interval = TRUE).

• upper_ci: Upper bound of the confidence interval for prop (if confidence_interval = TRUE).

Arguments

df

A data frame or tibble containing the dataset to be processed. Default is NULL.

patient_scene_table

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

procedures_table

A data frame or tibble containing only the eProcedures 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.

airway_table

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

response_table

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

erecord_01_col

Column name containing the unique patient record identifier.

incident_date_col

Column name containing the incident date. Default is NULL.

patient_DOB_col

Column name containing the patient's date of birth. Default is NULL.

epatient_15_col

Column name for patient information (exact purpose unclear).

epatient_16_col

Column name for patient information (exact purpose unclear).

eresponse_05_col

Column name for emergency response codes.

eprocedures_01_col

Column name for procedure times or other related data.

eprocedures_02_col

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

eprocedures_03_col

Column name for procedure codes.

eprocedures_06_col

Column name for procedure success codes.

eairway_02_col

Column name for airway procedure data (datetime). Default is NULL.

eairway_04_col

Column name for airway procedure data. Default is NULL.

evitals_01_col

Column name for vital signs data (datetime).

evitals_16_col

Column name for additional vital signs data.

confidence_interval

Logical. If TRUE, the function calculates a confidence interval for the proportion estimate.

method

Character. Specifies the method used to calculate confidence intervals. Options are "wilson" (Wilson score interval) and "clopper-pearson" (exact binomial interval). Partial matching is supported, so "w" and "c" can be used as shorthand.

conf.level

Numeric. The confidence level for the interval, expressed as a proportion (e.g., 0.95 for a 95% confidence interval). Defaults to 0.95.

correct

Logical. If TRUE, applies a continuity correction to the Wilson score interval when method = "wilson". Defaults to TRUE.

...

optional additional arguments to pass onto dplyr::summarize.

Author

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

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 23:02:00",
    "2025-01-05 12:03:00", "2025-02-01 19:04:00", "2025-01-01 05:05:00",
    "2025-06-01 13:01:00", "2025-01-01 23:02:00",
    "2025-01-05 12:03:00", "2025-02-01 19:04:00", "2025-01-01 05:05:00",
    "2025-06-01 13:06:00")),
    evitals_16 = rep(c(5, 6, 7, 8, 9), 2)

  )

  # airway table
  airway_table <- tibble::tibble(
  erecord_01 = rep(c("R1", "R2", "R3", "R4", "R5"), 2),
  eairway_02 = rep(lubridate::as_datetime(c("2025-01-01 23:05:00",
    "2025-01-05 12:02:00", "2025-02-01 19:03:00", "2025-01-01 05:04:00",
    "2025-06-01 13:06:00")), 2),
  eairway_04 = rep(4004019, 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),
    eprocedures_06 = rep(9923003, 10)

  )

# Run the function
# Return 95% confidence intervals using the Wilson method
airway_18(df = NULL,
         patient_scene_table = patient_table,
         procedures_table = procedures_table,
         vitals_table = vitals_table,
         response_table = response_table,
         airway_table = airway_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,
         eprocedures_06_col = eprocedures_06,
         evitals_01_col = evitals_01,
         evitals_16_col = evitals_16,
         eairway_02_col = eairway_02,
         eairway_04_col = eairway_04,
         confidence_interval = TRUE
         )