traumar (version 1.2.2)

seqic_indicator_4: SEQIC Indicator 4 - Autopsy and Long LOS Without Autopsy

Description

Computes SEQIC Indicator 4a and 4b for trauma center performance. Indicator 4a captures the proportion of deceased trauma patients at trauma level I–IV facilities who had an autopsy performed. Indicator 4b identifies deceased trauma patients with a prolonged length of stay (LOS > 3 days) but without an autopsy.

Usage

seqic_indicator_4(
  data,
  level,
  included_levels = c("I", "II", "III", "IV"),
  ed_disposition,
  ed_LOS,
  hospital_disposition,
  hospital_LOS,
  unique_incident_id,
  autopsy,
  groups = NULL,
  calculate_ci = NULL,
  ...
)

Value

A tibble summarizing SEQIC Indicator 4a and 4b results. Includes numerator, denominator, and performance rate for the indicator. 95% confidence intervals are provided optionally.

Arguments

data

A data frame containing trauma incident records.

level

Column indicating the trauma center designation level (e.g., I, II, III, IV).

included_levels

Character vector indicating what facility levels to include in the analysis. Defaults to c("I", "II", "III", "IV").

ed_disposition

Column representing the emergency department disposition. For a record to be picked up in this function, the ED dispostion must be documented as "Deceased/Expired".

ed_LOS

Column for the calculated ED length of stay, measured in minutes.

hospital_disposition

Column representing the hospital disposition. For a record to be picked up in this function, the hospital dispostion must be documented as "Deceased/Expired".

hospital_LOS

Column for the calculated hospital length of stay, measured in minutes.

unique_incident_id

Unique identifier for each record.

autopsy

Unquoted column name indicating whether an autopsy was performed. Expected values: "Yes" or NA.

groups

Additional columns passed as a vector of strings to dplyr::summarize() via the .by argument for grouped summaries. Defaults to NULL.

calculate_ci

If NULL, 95% confidence intervals will not be calculated for the performance estimates. Otherwise, options of "wilson" or "clopper-pearson" can be supplied to utilize the corresponding methods to calculate the confidence intervals for the proportions. Defaults to NULL.

...

Arguments passed on to nemsqar::nemsqa_binomial_confint

conf.level

Numeric value between 0 and 1 indicating the confidence level. Defaults to 0.95 (95% confidence interval).

correct

Logical, indicating whether to apply continuity correction for Wilson intervals. Defaults to TRUE.

Author

Nicolas Foss, Ed.D., MS

Details

This function:

• Filters trauma records to those with a trauma center level of I–IV.

• Identifies records where the patient died, based on ED or hospital disposition.

• Deduplicates by unique_incident_id to ensure one record per incident.

• For Indicator 4a, calculates the proportion of deceased patients who received an autopsy.

• For Indicator 4b, calculates the proportion of deceased patients with a hospital or ED length of stay greater than 72 hours (4320 minutes) and no autopsy performed.

Examples

# Packages
library(dplyr)
library(traumar)

# Create a synthetic test dataset
test_data <- tibble::tibble(
  id = as.character(1:8),
  trauma_level = c("I", "II", "III", "IV", "I", "II", "III", "IV"),
  ed_disp = c(
    "Operating Room",
    "Admitted",
    "Deceased/Expired",
    "Transferred",
    "Deceased/Expired",
    "Deceased/Expired",
    "Admitted",
    "Deceased/Expired"
  ),
  ed_los = c(120, 200, 5000, 180, 3000, 4321, 60, 4000),
  hosp_disp = c(
    "Deceased/Expired",
    "Deceased/Expired",
    "Deceased/Expired",
    "Discharged",
    "Deceased/Expired",
    "Deceased/Expired",
    "Discharged",
    "Deceased/Expired"
  ),
  hosp_los = c(3000, 4500, 1000, 200, 5000, 4400, 150, 3000),
  autopsy_done = c("Yes", "No", "No", NA, "Yes", "No", NA, "Yes")
)

# Run the indicator function
traumar::seqic_indicator_4(
  data = test_data,
  level = trauma_level,
  ed_disposition = ed_disp,
  ed_LOS = ed_los,
  hospital_disposition = hosp_disp,
  hospital_LOS = hosp_los,
  unique_incident_id = id,
  autopsy = autopsy_done
)