20: Improving Prehospital Stroke Destination Decisions with a Bayesian Predictive Algorithm

Thomas M. Menino Convention & Exhibition Center 

Stroke remains a leading cause of death and disability in the USA, with timely treatment being crucial for optimal recovery. Endovascular therapy (EVT) is associated with a 67% reduction in stroke-related disability for eligible patients; however, its benefit is highly time sensitive. Conventional prehospital stroke protocols often direct patients to the nearest hospital, which may not be the optimal destination when specialized care is required.
MAP-STROKE is a prehospital destination-selection algorithm that predicts stroke type and treatment outcomes under alternative triage strategies. It uses a joint Bayesian model for diagnosis and outcomes, along with model-based imputation to harmonize data across clinical trials, and recommends the optimal initial transport destination to maximize recovery. Using a large-scale geospatial posterior-predictive simulation, representing over 400 million stroke alerts across the USA, MAP-STROKE improved outcomes for patients with large vessel occlusion (LVO), with particularly notable benefits in rural settings. These gains were accompanied by modest trade-offs for some non–LVO patients due to time-sensitive competing therapies.

Bayesian Models

Machine Learning

Simulation

Stroke

Emergency medical services

Geospatial 

Section on Bayesian Statistical Science