Respiratory-02 Populations — respiratory_02

The respiratory_02_population function calculates metrics for pediatric and adult respiratory populations based on pre-defined criteria, such as low oxygen saturation and specific medication or procedure codes. It returns a summary table of the overall, pediatric, and adult populations, showing counts and proportions.

Usage

respiratory_02_population(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  vitals_table = NULL,
  medications_table = NULL,
  procedures_table = NULL,
  erecord_01_col,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col,
  epatient_16_col,
  eresponse_05_col,
  evitals_12_col,
  emedications_03_col,
  eprocedures_03_col
)

Arguments

df: A data frame containing incident data with each row representing an observation.
patient_scene_table: A data.frame or tibble containing at least epatient and escene fields as a fact table.
response_table: A data.frame or tibble containing at least the eresponse fields needed for this measure's calculations.
vitals_table: A data.frame or tibble containing at least the evitals fields needed for this measure's calculations.
medications_table: A data.frame or tibble containing only the emedications fields needed for this measure's calculations.
procedures_table: A data.frame or tibble containing only the eprocedures fields needed for this measure's calculations.
erecord_01_col: Column name for eRecord.01, used to form a unique patient 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: integer Column giving the calculated age value.
epatient_16_col: Column giving the provided age unit value.
eresponse_05_col: Column name for response codes (e.g., incident type).
evitals_12_col: Column name for oxygen saturation (SpO2) values.
emedications_03_col: Column name for medication codes.
eprocedures_03_col: Column name for procedure codes.

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

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)

  )

  # medications table
  medications_table <- tibble::tibble(

    erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
    emedications_03 = c("Oxygen", "Oxygen", "Oxygen", "Oxygen", "Oxygen")

  )

  # vitals table
  vitals_table <- tibble::tibble(

    erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
    evitals_12 = c(60, 59, 58, 57, 56),

  )

  # procedures table
  procedures_table <- tibble::tibble(

    erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
    eprocedures_03 = rep("applicable thing", 5)

  )

# Run the function
result <- respiratory_02_population(patient_scene_table = patient_table,
                              response_table = response_table,
                              medications_table = medications_table,
                              vitals_table = vitals_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,
                              emedications_03_col = emedications_03,
                              evitals_12_col = evitals_12,
                              eprocedures_03_col = eprocedures_03
                             )
#> Running `respiratory_02_population()`  [Completed 1 of 11 tasks] ●●●●──────────…
#> Running `respiratory_02_population()`  [Completed 2 of 11 tasks] ●●●●●●────────…
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#> Running `respiratory_02_population()`  [Completed 5 of 11 tasks] ●●●●●●●●●●●●●●…
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#> Running `respiratory_02_population()`  [Completed 11 of 11 tasks] ●●●●●●●●●●●●●…
#> 

# show the results of filtering at each step
result$filter_process
#> # A tibble: 8 × 2
#>   filter                   count
#>   <chr>                    <int>
#> 1 Oxygen given as med          5
#> 2 Oxygen therapy procedure     0
#> 3 Pulse oximetry < 90          5
#> 4 911 calls                    5
#> 5 Adults denominator           2
#> 6 Peds denominator             3
#> 7 Initial population           5
#> 8 Total dataset                5