traumar (version 1.2.2)

seqic_indicator_11: SEQIC Indicator 11 – Overtriage for Minor Trauma Patients

Description

Calculates SEQIC Indicator 11, which estimates the proportion of minor trauma patients who were transferred into a trauma center and remained in the Emergency Department for less than 24 hours. This indicator is designed to identify potential overtriage events within the trauma system. Minor trauma patients are identified using the Injury Severity Score (ISS < 9). Patients must not have been transferred out and must have been received at a trauma center level included in included_levels.

Usage

seqic_indicator_11(
  data,
  level,
  included_levels = c("I", "II", "III", "IV"),
  transfer_out_indicator,
  receiving_indicator,
  unique_incident_id,
  iss,
  ed_LOS,
  groups = NULL,
  calculate_ci = NULL,
  ...
)

Value

A tibble summarizing the numerator, denominator, and proportion of overtriaged patients (Indicator 11), with optional 95% confidence intervals.

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").

transfer_out_indicator

Column name indicating whether the patient was transferred out of the initial trauma center to definitive care. Logical, character, or factor type. Values representing "No" (e.g., FALSE, "No") indicate no transfer out.

receiving_indicator

Column name indicating whether the patient was transferred into the trauma center. Logical, character, or factor type. Values representing "Yes" (e.g., TRUE, "Yes") indicate transfer in.

unique_incident_id

Unique identifier for each record.

iss

Optional numeric column representing the Injury Severity Score.

ed_LOS

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

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 the dataset to include only patients treated at trauma centers designated Level I through IV.

• Excludes patients transferred out and retains only those received by the trauma center.

• Deduplicates incident-level records using unique_incident_id.

• Classifies patients as low-risk based on the Injury Severity Score (ISS < 9).

• Flags low-risk patients who were discharged from the ED in under 24 hours.

• Stratifies results by one or more user-defined grouping variables.

• Returns a summarized tibble with the number of eligible low-risk short-stay discharges (numerator), all received patients meeting inclusion criteria (denominator), and the resulting proportion.

• Optionally includes 95% confidence intervals if calculate_ci is specified.

Users must ensure appropriate column names are passed and data is pre-processed to include the necessary fields without missing critical identifiers or timestamps.

References

Roden-Foreman JW, Rapier NR, Yelverton L, Foreman ML. Asking a Better Question: Development and Evaluation of the Need For Trauma Intervention (NFTI) Metric as a Novel Indicator of Major Trauma. J Trauma Nurs. 2017 May/Jun;24(3):150-157. doi: 10.1097/JTN.0000000000000283. PMID: 28486318.

Examples

# Packages
library(dplyr)
library(traumar)

# Simulated data for SEQIC Indicator 11
test_data <- tibble::tibble(
  id = as.character(1:10),
  trauma_level = c("I", "II", "III", "IV", "II", "I", "IV", "III", "II",
  "I"),
  transferred_out = c(FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, FALSE,
  FALSE, FALSE),
  received = c(TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE),
  iss = c(4, 8, 10, 6, 5, 7, 6, 15, 3, 2),
  ed_LOS = c(6, 20, 30, 18, 8, 5, 22, 40, 2, 4),
  region = rep(c("East", "West"), each = 5)
)

# Run the function
traumar::seqic_indicator_11(
  data = test_data,
  level = trauma_level,
  included_levels = c("I", "II", "III", "IV"),
  transfer_out_indicator = transferred_out,
  receiving_indicator = received,
  unique_incident_id = id,
  iss = iss,
  ed_LOS = ed_LOS,
  groups = "region",
  calculate_ci = "clopper-pearson"
)