Syncope-01 Populations

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

Identifies key categories to identify potential syncope (fainting) cases based on specific criteria and calculates related ECG measures. This function segments the data by age into adult and pediatric populations.

Usage

syncope_01_population(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  situation_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_09_col,
  esituation_10_col,
  esituation_11_col,
  esituation_12_col,
  evitals_04_col
)

Arguments

df

Main data frame containing EMS records.

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.

vitals_table

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

erecord_01_col

The column containing unique record identifiers for each encounter.

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 representing the patient age (numeric).

epatient_16_col

Column for the patient age units (e.g., "Years", "Months").

eresponse_05_col

Column containing response type codes, specifically 911 codes.

esituation_09_col

Column with primary symptoms associated with the patient encounter.

esituation_10_col

Column with other associated symptoms.

esituation_11_col

Column for primary impression code.

esituation_12_col

Column for secondary impression codes.

evitals_04_col

Column with ECG information if available.

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)

  )

  # situation table
  situation_table <- tibble::tibble(

    erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
    esituation_09 = c(rep("R55", 3), rep("R40.4", 2)),
    esituation_10 = c(rep("R40.4", 2), rep("R55", 3)),
    esituation_11 = c(rep("R55", 3), rep("R40.4", 2)),
    esituation_12 = c(rep("R40.4", 2), rep("R55", 3)),
  )

  # vitals table
  vitals_table <- tibble::tibble(

    erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
    evitals_04 = rep("15 Lead", 5)

  )

  # test the success of the function
  result <- syncope_01_population(patient_scene_table = patient_table,
                         response_table = response_table,
                         situation_table = situation_table,
                         vitals_table = vitals_table,
                         erecord_01_col = erecord_01,
                         epatient_15_col = epatient_15,
                         epatient_16_col = epatient_16,
                         eresponse_05_col = eresponse_05,
                         esituation_09_col = esituation_09,
                         esituation_10_col = esituation_10,
                         esituation_11_col = esituation_11,
                         esituation_12_col = esituation_12,
                         evitals_04_col = evitals_04
                         )
#> Running `syncope_01_population()`  [Working on 1 of 10 tasks] ●●●●─────────────…
#> Running `syncope_01_population()`  [Working on 2 of 10 tasks] ●●●●●●●──────────…
#> Running `syncope_01_population()`  [Working on 3 of 10 tasks] ●●●●●●●●●●───────…
#> Running `syncope_01_population()`  [Working on 4 of 10 tasks] ●●●●●●●●●●●●●────…
#> Running `syncope_01_population()`  [Working on 5 of 10 tasks] ●●●●●●●●●●●●●●●●─…
#> Running `syncope_01_population()`  [Working on 6 of 10 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `syncope_01_population()`  [Working on 7 of 10 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `syncope_01_population()`  [Working on 8 of 10 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `syncope_01_population()`  [Working on 9 of 10 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `syncope_01_population()`  [Working on 10 of 10 tasks] ●●●●●●●●●●●●●●●●…
#> 

# show the results of filtering at each step
result$filter_process
#> # A tibble: 7 × 2
#>   filter             count
#>   <chr>              <int>
#> 1 911 calls              5
#> 2 Syncope cases          5
#> 3 ECG performed          5
#> 4 Adults denominator     2
#> 5 Peds denominator       3
#> 6 Initial population     5
#> 7 Total dataset          5