A Monte Carlo method for the quantitative analysis of triage algorithms in mass casualty events

Abstract Objective. In mass casualty scenarios, efficient triage algorithms are used to prioritize medical care when resources are outnumbered by victims. This research proposes a computational approach to quantitatively analyze and optimize triage algorithms by developing a Monte Carlo code which is subsequently validated against the few quantitative data. Approach. The developed Monte Carlo code is used to simulate several mass casualty events, namely car accidents, burns, shootings, sinking ships and a human stampede. Four triage algorithms— modified simple triage and rapid treatment, primäres Ranking zur initialen Orientierung im Rettungsdienst, CareFlight, and field triage score (FTS)—are evaluated using metrics like mortality, overtriage, undertriage, sensitivity, and specificity. Main results. Results indicate that, on average, the analyzed algorithms achieve about 35% accuracy in classifying critical casualties when compared to a perfect algorithm, with FTS being the less accurate. However, when all casualties are considered, algorithm performance improves to around 63% of a perfect algorithm, except for FTS. The study identifies an increased probability of false positives for red categorization due to comorbidities and a higher tendency for false negatives in casualties with burns or internal trunk injuries. Significance. Despite variations in vital sign measurements, triage classification results do not depend on the measurement uncertainties of the paramedics. The ethically challenging decision, of withholding medical care from low-survival probability victims, leads to a 63% reduction in mortality among critical casualties. This research establishes a quantitative method for triage algorithm studies, highlighting their robustness to measurement uncertainties.