Redefining Trauma Triage for Elderly Adults: Development of Age-Specific Guidelines for Improved Patient Outcomes Based on a Machine-Learning Algorithm

TL;DR

This study develops machine-learning-based triage guidelines for elderly trauma patients to improve ICU admissions and mortality predictions.

Contribution

The novelty lies in creating age-specific triage guidelines using machine learning to better predict ICU needs and mortality in elderly trauma patients.

Findings

The new guidelines showed higher sensitivity for ICU admissions (0.728 vs. 0.541) compared to current guidelines.

The guidelines also improved sensitivity for in-hospital mortality predictions (0.815 vs. 0.599).

Abstract

Background and Objectives: Elderly trauma patients face unique physiological challenges that often lead to undertriage under the current guidelines. The present study aimed to develop machine-learning (ML)-based, age-specific triage guidelines to improve predictions for intensive care unit (ICU) admissions and in-hospital mortality. Materials and Methods: A total of 274,347 trauma cases transported via Emergency Medical System (EMS)-119 in Seoul (2020–2022) were analyzed. Physiological indicators (e.g., systolic blood pressure; saturation of partial pressure oxygen; and alert, verbal, pain, unresponsiveness scale) were incorporated. Bayesian optimization was used to fine-tuned models for sensitivity and specificity, emphasizing the F2 score to minimize undertriage. Results: Compared with the current guidelines, the alternative guidelines achieved superior sensitivity for ICU admissions (0.728 vs. 0.541) and in-hospital mortality (0.815 vs. 0.599). Subgroup analyses across injury severities, including traumatic brain and chest injuries, confirmed the enhanced performance of the alternative guidelines. Conclusions: ML-based, age-specific triage guidelines improve the sensitivity of triage decisions, reduce undertriage, and optimize elderly trauma care. Implementing these guidelines can significantly enhance patient outcomes and resource allocation in emergency settings.

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