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Syncope-01 Calculation

Source: R/syncope_01.R
syncope_01.Rd

The syncope_01 function processes EMS dataset to identify potential syncope (fainting) cases based on specific criteria and calculates related ECG measures. This function dplyr::filters data for 911 response calls, assesses primary and associated symptoms for syncope, determines age-based populations (adult and pediatric), and aggregates results by unique patient encounters.

Usage

syncope_01(
  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,
  confidence_interval = FALSE,
  method = c("wilson", "clopper-pearson"),
  conf.level = 0.95,
  correct = TRUE,
  ...
)

Arguments

df

A dataframe or tibble contianing EMS data where each row represents an observation and columns represent features.

patient_scene_table

A data.frame or tibble containing at least ePatient, and eScene as a fact table.

response_table

A data.frame or tibble containing at least the eResponse fields needed for this measure's calculations.

situation_table

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

vitals_table

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

erecord_01_col

The column representing the EMS record unique identifier.

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's numeric age agnostic of unit.

epatient_16_col

Column representing the patient's age unit ("Years", "Months", "Days", "Hours", or "Minutes").

eresponse_05_col

Column that contains eResponse.05 or the response type.

esituation_09_col

Column with primary sign and symptom present in the patient or observed by EMS personnel.

esituation_10_col

Column with other symptoms identified by the patient or observed by EMS personnel.

esituation_11_col

Column that contains eSituation.11 provider primary impression data.

esituation_12_col

Column that contains all eSituation.12 values as (possible a single comma-separated list), provider secondary impression data.

evitals_04_col

Column with type of ECG associated with the cardiac rhythm if available.

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.

Value

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

  • pop: Population type (All, 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).

Author

Nicolas Foss, Ed.D., MS

Examples


# Synthetic test data
  test_data <- tibble::tibble(
    erecord_01 = c("R1", "R2", "R3", "R4", "R5"),
    epatient_15 = c(34, 5, 45, 2, 60),  # Ages
    epatient_16 = c("Years", "Years", "Years", "Months", "Years"),
    eresponse_05 = rep(2205001, 5),
    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)),
    evitals_04 = rep("15 Lead", 5)
  )

# Run the function
# Return 95% confidence intervals using the Wilson method
  syncope_01(
    df = test_data,
    erecord_01_col = erecord_01,
    incident_date_col = NULL,
    patient_DOB_col = NULL,
    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,
    confidence_interval = TRUE
  )
#> 
#> ── Syncope-01 ──────────────────────────────────────────────────────────────────
#> 
#> ── Gathering Records for Syncope-01 ──
#> 
#> 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] ●●●●●●●●●●●●●●●●…
#> 
#> 
#> 
#> ── Calculating Syncope-01 ──
#> 
#> 
#> ✔ Function completed in 0.18s.
#> 
#> Warning: In `prop.test()`: Chi-squared approximation may be incorrect for any n < 10.
#> # A tibble: 2 × 8
#>   measure    pop    numerator denominator  prop prop_label lower_ci upper_ci
#>   <chr>      <chr>      <int>       <int> <dbl> <chr>         <dbl>    <dbl>
#> 1 Syncope-01 Adults         3           3     1 100%          0.310        1
#> 2 Syncope-01 Peds           2           2     1 100%          0.198        1