nemsqar (version 1.1.0)

stroke_01: Stroke-01 Calculation

Description

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,
  ...
)

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).

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

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.

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
  )