Abstract Background Crisis Standards of Care (CSC) may require rationing of life-sustaining resources, such as mechanical ventilation, during public health emergencies. Simulation modeling offers a scalable, transparent method to evaluate triage frameworks before implementation. While ventilator triage frameworks vary in their use of exclusion criteria, comorbidity adjustments, and reassessment frequency, few have been rigorously compared using real-world data under realistic surge conditions. Robust platforms that simulate both front-end (initiation) and back-end (reassessment or reallocation) triage are critical for evaluating clinical, operational, and ethical performance. Methods We developed a computational simulation platform using retrospective real-world data from intubated adults during the Spring 2020 COVID-19 surge across a large New York City health system. The simulated surge cohort included all patients mechanically ventilated between March 1 and June 30, 2020. A crisis cohort was defined as those patients receiving ventilation once 95% of the health system’s pre-pandemic ventilator supply was in use. Eight CSC strategies were evaluated, including policies from New York, Pennsylvania, Maryland, Canada, two academic frameworks, a lottery-based system, and first-come first-served. Strategies varied in their use of exclusion criteria, comorbidity modifiers, reassessment intervals, and prioritization for special populations. Daily ICU census and ventilator availability were used to simulate resource strain and drive triage decision-making. Patients simulated for ventilator rationing were simulated to expire. Manual abstraction of comorbidities and structured rules for imputing missing SOFA subscores were applied uniformly. Results The platform simulated 10,000 iterations per strategy and included 2,365 intubated patients. Final analyses will be published separately. Conclusion This platform provides a scalable, reproducible framework for evaluating ventilator triage strategies under pandemic-like conditions. By integrating both initial triage and serial reassessment (front- and back-end) logic, operational constraints, and clinical trajectories, it enables detailed comparisons of survival, resource utilization, and prognostic accuracy. The simulation also supports ethical evaluation by testing the practical impact of exclusion and comorbidity-based criteria. Such models can assist governments, health systems, and public health agencies in designing triage protocols that are evidence-informed, ethically defensible, and operationally feasible. This work demonstrates how computational modeling can strengthen health system preparedness and support public trust.
Walsh et al. (2026) studied this question.