SEQIC Indicator 8 - Survival by Risk Group — seqic_indicator

Calculates the proportion of patients who survived based on risk groups existing in the data among trauma patients transported to Level I–IV trauma centers.

Usage

seqic_indicator_8(
  data,
  level,
  included_levels = c("I", "II", "III", "IV"),
  unique_incident_id,
  mortality_indicator,
  risk_group,
  groups = NULL,
  calculate_ci = NULL,
  ...
)

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").

unique_incident_id

Unique identifier for each record.

mortality_indicator

A logical, character, or factor variable indicating whether the patient died at the trauma center. Accepts values like TRUE/FALSE or "Yes"/"No".

risk_group

A character or factor column indicating the patient's risk group (e.g., "High", "Moderate", "Low"). See risk definitions below.

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.

Value

A named list with two tibbles:

overall: A tibble summarizing overall mortality among trauma patients, grouped by the variables specified in groups. Columns include:

numerator_8_all (number of survivors),
denominator_8_all (total number of unique trauma incidents),
seqic_8_all (survival proportion), and optionally
lower_ci_8,
upper_ci_8 (confidence interval bounds if calculate_ci is specified).

risk_group: A tibble summarizing mortality stratified by risk group and any additional grouping variables. Columns include:

risk_group (used for stratification),
numerator_8_risk (survivors per group),
denominator_8_risk (total incidents per group),
seqic_8_risk (survival proportion per group), and optionally
lower_ci_8_risk,
upper_ci_8_risk (confidence interval bounds if calculate_ci is specified).

Details

Filters the dataset to include only trauma center levels I through IV.
Deduplicates the dataset using unique_incident_id to ensure one record per incident.
Accepts a mortality indicator that may be logical, character, or factor, and identifies survivors as those with values of FALSE or "No".
Requires a predefined risk_group variable representing categories such as "Low", "Moderate", or "High" risk.
Calculates overall survival proportions and survival proportions stratified by risk group.
Optionally includes 95% confidence intervals using binomial methods if calculate_ci is specified.

Note

This function calculates survival outcomes for patients transported to trauma centers, stratified by risk of mortality. Risk groups—low, moderate, and high— are defined by the Iowa System Evaluation and Quality Improvement Committee (SEQIC) as described below. Users may also apply alternative risk stratification methods if preferred.

Abnormal Physiology Criteria: GCS 3–5; Respirations <5 or >30 per minute; Systolic BP <60 mm Hg
High Risk: Probability of Survival < 0.2; ISS > 41; ISS > 24 with abnormal physiology
Moderate Risk: Probability of Survival 0.2–0.5; ISS 16–41
Low Risk: Probability of Survival > 0.5; ISS < 16; Normal physiology

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

Author

Nicolas Foss, Ed.D., MS

Examples

# Packages
library(dplyr)
library(traumar)

# Simulated dataset for SEQIC Indicator 8
test_data <- tibble::tibble(
  id = as.character(1:12),
  trauma_level = c("I", "II", "III", "IV", "V", "II", "I", "III", "IV", "II",
  "I", "III"),
  mortality = c(FALSE, "No", TRUE, "Yes", FALSE, TRUE, "No", FALSE, "Yes",
  FALSE, TRUE, "No"),
  risk = c("High", "High", "Moderate", "Moderate", "Low", "Low", "High",
  "Moderate", "Low", "Moderate", "High", "Low")
)

# Run indicator 8 function
traumar::seqic_indicator_8(
  data = test_data,
  level = trauma_level,
  unique_incident_id = id,
  mortality_indicator = mortality,
  risk_group = risk
)
#> $overall
#> # A tibble: 1 × 4
#>   data              numerator_8_all denominator_8_all seqic_8_all
#>   <chr>                       <int>             <int>       <dbl>
#> 1 population/sample               6                11       0.545
#> 
#> $risk_group
#> # A tibble: 3 × 5
#>   data                    risk  numerator_8_risk denominator_8_risk seqic_8_risk
#>   <chr>                   <chr>            <int>              <int>        <dbl>
#> 1 population/sample risk… High                 3                  4        0.75 
#> 2 population/sample risk… Low                  1                  3        0.333
#> 3 population/sample risk… Mode…                2                  4        0.5  
#>