Development of an anticipatory triage ranking algorithm by dynamic simulation of the expected time course of trauma patients (Preprint)

Abstract

Background: In cases of terrorism, disasters or mass casualty incidents, far-reaching life-and-death decisions about prioritizing patients are currently made using triage algorithms that focus solely on the patient's current health status rather than their prognosis, thus leaving a fatal gap of patients who are under- or overtriaged. In this proof-of-concept study, we demonstrate a novel approach for triage that no longer classifies patients into triage categories but ranks their urgency according to the anticipated survival time without intervention. Using this approach, we aimed to improve prioritization of casualties by respecting individual injury patterns and vital signs, survival likelihoods and availability of rescue resources.

Objective: The objective of this proof-of-concept study was to demonstrate a novel approach for triage that no longer classifies patients into triage categories but ranks their urgency according to the anticipated survival time without intervention. Using this approach, we aimed to improve prioritization of casualties by respecting individual injury patterns and vital signs, survival likelihoods and availability of rescue resources.

Methods: A mathematical model was designed that allows dynamic simulation of the time course of a patient’s vital parameters depending on individual baseline vital signs and injury severity. The two variables are integrated by the well-established Revised Trauma Score (RTS) and New Injury Severity Score (NISS). An artificial patient database of unique trauma patients (n = 82,277) was generated and used for analysis of the time course modeling and triage classification. In addition, we applied a sophisticated, state-of-the-art clustering method using Gower Distance to visualize patient cohorts at risk for mistriage.

Results: The proposed triage model ranks patients according to their anticipated temporal course. Regarding the identification of patients at risk for mistriage, the model outperforms Simple Triage And Rapid Treatment (START)’s triage algorithm, but also exclusive stratification by RTS or NISS. Multidimensional analysis separated patients with similar patterns of injuries and vital parameters into clusters with different triage classifications. In this large-scale analysis, our algorithm confirmed the previously mentioned conclusions during simulation and descriptive analysis, and underlined the significance of this novel approach to triage.

Conclusions: The findings in this study suggest the feasibility and relevance of our model, which is unique in terms of its ranking system, prognosis outline and time course anticipation. The proposed triage ranking algorithm could offer an innovative triage method with a wide range of applications in prehospital, disaster and emergency medicine, as well as simulation and research.

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