Related Experiment Video
Updated: Oct 18, 2025

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Comparison of Outcome Tools Used to Test Mass-Casualty Algorithms in the Pediatric Population
J Joelle Donofrio1,2, Alaa Shaban3, Amy H Kaji4
1University of California San Diego Departments of Pediatrics and Emergency Medicine, San Diego, CaliforniaUSA.
Insights
Comparing pediatric mass-casualty incident (MCI) triage tools reveals significant variability. The Criteria Outcomes Tool (COT) showed inconsistent agreement with other measures, highlighting the need for a standardized pediatric MCI assessment.
Area of Science:
- Emergency Medicine
- Pediatric Triage
- Mass Casualty Incident (MCI) Management
Background:
- Mass-casualty incident (MCI) algorithms are crucial for rapid patient categorization based on severity.
- Current pediatric MCI triage lacks a consensus on standardized accuracy testing and inter-tool agreement.
- Existing outcome measures show variability when applied to pediatric MCI scenarios.
Purpose of the Study:
- To compare the agreement of the Criteria Outcomes Tool (COT) with established outcome measures.
- To assess the performance of various tools in categorizing simulated pediatric MCI patients.
- To evaluate inter-tool agreement for pediatric mass-casualty incident triage.
Main Methods:
- A retrospective cohort of 247 pediatric trauma patients (<14 years) was analyzed.
- Triage categories (black, red, yellow, green) were assigned using COT and other outcome measures (modified Baxt, mortality, LOS, ISS).
- Descriptive statistics were used to quantify agreement between the Criteria Outcomes Tool and other measures.
Main Results:
- Mortality showed 100% agreement with the COT black category.
- The 'modified Baxt positive and alive' outcome had the highest agreement with COT red (65%).
- Agreement for COT yellow ranged from 47%-53%, and 'modified Baxt negative and <24 hours LOS' showed 89% agreement with COT green.
Conclusions:
- Assessing pediatric MCI triage algorithms is challenging due to the absence of a gold standard outcome tool.
- Significant variability exists between different outcome measures used for pediatric MCI triage.
- Further research is needed to establish standardized and reliable methods for evaluating pediatric MCI triage tools.
Introduction:
Mass-casualty incident (MCI) algorithms are used to sort large numbers of patients rapidly into four basic categories based on severity. To date, there is no consensus on the best method to test the accuracy of an MCI algorithm in the pediatric population, nor on the agreement between different tools designed for this purpose.
Study Objective:
This study is to compare agreement between the Criteria Outcomes Tool (COT) to previously published outcomes tools in assessing the triage category applied to a simulated set of pediatric MCI patients.
Methods:
An MCI triage category (black, red, yellow, and green) was applied to patients from a pre-collected retrospective cohort of pediatric patients under 14 years of age brought in as a trauma activation to a Level I trauma center from July 2010 through November 2013 using each of the following outcome measures: COT, modified Baxt score, modified Baxt combined with mortality and/or length-of-stay (LOS), ambulatory status, mortality alone, and Injury Severity Score (ISS). Descriptive statistics were applied to determine agreement between tools.
Results:
A total of 247 patients were included, ranging from 25 days to 13 years of age. The outcome of mortality had 100% agreement with the COT black. The "modified Baxt positive and alive" outcome had the highest agreement with COT red (65%). All yellow outcomes had 47%-53% agreement with COT yellow. "Modified Baxt negative and <24 hours LOS" had the highest agreement with the COT green at 89%.
Conclusions:
Assessment of algorithms for triaging pediatric MCI patients is complicated by the lack of a gold standard outcome tool and variability between existing measures.
More Related Videos
Related Concept Videos
Comparing the Survival Analysis of Two or More Groups
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Kaplan-Meier Approach
Statistical Software for Data Analysis and Clinical Trials
Survival Curves
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
Hazard Ratio
For example, in a clinical trial...

