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Stroke-01 Calculation

Source: R/stroke_01.R
stroke_01.Rd

The stroke_01 function processes EMS dataset to identify potential stroke cases based on specific criteria and calculates the stroke scale measures. It filters the data for 911 response calls, identifies stroke-related impressions and scales, and aggregates results by unique patient encounters.

Usage

stroke_01(
  df = NULL,
  patient_scene_table = NULL,
  response_table = NULL,
  situation_table = NULL,
  vitals_table = NULL,
  erecord_01_col,
  eresponse_05_col,
  esituation_11_col,
  esituation_12_col,
  evitals_23_col,
  evitals_26_col,
  evitals_29_col,
  evitals_30_col,
  confidence_interval = FALSE,
  method = c("wilson", "clopper-pearson"),
  conf.level = 0.95,
  correct = TRUE,
  ...
)

Arguments

df

A data frame or tibble containing the dataset. Each row should represent a unique patient encounter.

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.

eresponse_05_col

The column containing EMS response codes, which should include 911 response codes.

esituation_11_col

The column containing the primary impression codes or descriptions related to the situation.

esituation_12_col

The column containing secondary impression codes or descriptions related to the situation.

evitals_23_col

The column containing the Glasgow Coma Scale (GCS) score.

evitals_26_col

The column containing the AVPU (alert, verbal, pain, unresponsive) scale value.

evitals_29_col

The column containing the stroke scale score achieved during assessment.

evitals_30_col

The column containing stroke scale type descriptors (e.g., FAST, NIH, etc.).

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 = c(rep("I60", 3), rep("I61", 2)),
    esituation_12 = c(rep("I63", 2), rep("I64", 3)),
    evitals_23 = c(16, 15, 14, 13, 12),
    evitals_26 = c("Alert", "Painful", "Verbal", "Unresponsive", "Alert"),
    evitals_29 = rep("positive", 5),
    evitals_30 = rep("a pain scale", 5)
  )

# Run the function
# Return 95% confidence intervals using the Wilson method
  stroke_01(
    df = test_data,
    erecord_01_col = erecord_01,
    eresponse_05_col = eresponse_05,
    esituation_11_col = esituation_11,
    esituation_12_col = esituation_12,
    evitals_23_col = evitals_23,
    evitals_26_col = evitals_26,
    evitals_29_col = evitals_29,
    evitals_30_col = evitals_30,
    confidence_interval = TRUE
  )
#> 
#> ── Stroke-01 ───────────────────────────────────────────────────────────────────
#> 
#> ── Gathering Records for Stroke-01 ──
#> 
#> Running `stroke_01_population()`  [Working on 1 of 11 tasks] ●●●●──────────────…
#> Running `stroke_01_population()`  [Working on 2 of 11 tasks] ●●●●●●────────────…
#> Running `stroke_01_population()`  [Working on 3 of 11 tasks] ●●●●●●●●●─────────…
#> Running `stroke_01_population()`  [Working on 4 of 11 tasks] ●●●●●●●●●●●●──────…
#> Running `stroke_01_population()`  [Working on 5 of 11 tasks] ●●●●●●●●●●●●●●●───…
#> Running `stroke_01_population()`  [Working on 6 of 11 tasks] ●●●●●●●●●●●●●●●●●─…
#> Running `stroke_01_population()`  [Working on 7 of 11 tasks] ●●●●●●●●●●●●●●●●●●…
#> Running `stroke_01_population()`  [Working on 8 of 11 tasks] ●●●●●●●●●●●●●●●●●●…
#> Running `stroke_01_population()`  [Working on 9 of 11 tasks] ●●●●●●●●●●●●●●●●●●…
#> Running `stroke_01_population()`  [Working on 10 of 11 tasks] ●●●●●●●●●●●●●●●●●…
#> Running `stroke_01_population()`  [Working on 11 of 11 tasks] ●●●●●●●●●●●●●●●●●…
#> 
#> 
#> 
#> ── Calculating Stroke-01 ──
#> 
#> 
#> ✔ Function completed in 0.17s.
#> 
#> Warning: In `prop.test()`: Chi-squared approximation may be incorrect for any n < 10.
#> # A tibble: 1 × 8
#>   measure   pop   numerator denominator  prop prop_label lower_ci upper_ci
#>   <chr>     <chr>     <int>       <int> <dbl> <chr>         <dbl>    <dbl>
#> 1 Stroke-01 All           5           5     1 100%          0.463        1