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Seizure-02 Calculation

Source: R/seizure_02.R
seizure_02.Rd

Calculates the NEMSQA Seizure-02 Measure.

Calculates age-based seizure metrics for a dataset. This function filters data for patients based on incident information, diagnoses, and administered medications to assess adherence to Seizure-02 metrics.

Usage

seizure_02(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  situation_table = NULL,
  medications_table = NULL,
  erecord_01_col,
  incident_date_col = NULL,
  patient_DOB_col = NULL,
  epatient_15_col,
  epatient_16_col,
  eresponse_05_col,
  esituation_11_col,
  esituation_12_col,
  emedications_03_col,
  confidence_interval = FALSE,
  method = c("wilson", "clopper-pearson"),
  conf.level = 0.95,
  correct = TRUE,
  ...
)

Arguments

df

A data frame where each row is an observation, containing all necessary columns for analysis.

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.

medications_table

A data.frame or tibble containing only the emedications 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 name for patient age in numeric form.

epatient_16_col

Column name for age unit (e.g., "Years" or "Months").

eresponse_05_col

Column name for response codes; "911" call codes are filtered.

esituation_11_col

Column name for primary impressions.

esituation_12_col

Column name for secondary impressions.

emedications_03_col

Column name for medications administered; ideally a list column or string with comma-separated values.

confidence_interval

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

method

[Experimental]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

[Experimental]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

[Experimental]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_11 = rep("G40", 5),
    esituation_12 = rep("r56", 5),
    emedications_03 = rep(3322, 5)
  )

# Run the function
# Return 95% confidence intervals using the Wilson method
  seizure_02(
    df = test_data,
    erecord_01_col = erecord_01,
    epatient_15_col = epatient_15,
    epatient_16_col = epatient_16,
    eresponse_05_col = eresponse_05,
    esituation_11_col = esituation_11,
    esituation_12_col = esituation_12,
    emedications_03_col = emedications_03,
    confidence_interval = TRUE
  )
#> 
#> ── Seizure-02 ──────────────────────────────────────────────────────────────────
#> 
#> ── Gathering Records for Seizure-02 ──
#> 
#> Running `seizure_02_population()`  [Working on 1 of 10 tasks] ●●●●─────────────…
#> Running `seizure_02_population()`  [Working on 2 of 10 tasks] ●●●●●●●──────────…
#> Running `seizure_02_population()`  [Working on 3 of 10 tasks] ●●●●●●●●●●───────…
#> Running `seizure_02_population()`  [Working on 4 of 10 tasks] ●●●●●●●●●●●●●────…
#> Running `seizure_02_population()`  [Working on 5 of 10 tasks] ●●●●●●●●●●●●●●●●─…
#> Running `seizure_02_population()`  [Working on 6 of 10 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `seizure_02_population()`  [Working on 7 of 10 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `seizure_02_population()`  [Working on 8 of 10 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `seizure_02_population()`  [Working on 9 of 10 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `seizure_02_population()`  [Working on 10 of 10 tasks] ●●●●●●●●●●●●●●●●…
#> 
#> 
#> 
#> ── Calculating Seizure-02 ──
#> 
#> 
#> ✔ Function completed in 0.18s.
#> 
#> Warning: In `prop.test()`: Chi-squared approximation may be incorrect for any n < 10.
#> # A tibble: 3 × 8
#>   measure    pop    numerator denominator  prop prop_label lower_ci upper_ci
#>   <chr>      <chr>      <int>       <int> <dbl> <chr>         <dbl>    <dbl>
#> 1 Seizure-02 Adults         3           3     1 100%         0.310         1
#> 2 Seizure-02 Peds           1           1     1 100%         0.0546        1
#> 3 Seizure-02 All            5           5     1 100%         0.463         1