Optimization of Prehospital Triage of Patients With Suspected Ischemic Stroke

Background and Purpose- Prehospital routing algorithms for patients with suspected stroke because of large vessel occlusions should account for likelihood of benefit from endovascular therapy (EVT), risk of alteplase delays, and transport times. We built a mathematical model to give a real-time, location-based optimal emergency medical service routing location based on local resources, transport times, and patient characteristics. Methods- Using location, onset time, age, sex, and prehospital stroke severity, we calculated odds of a favorable outcome for a patient with suspected large vessel occlusions under 2 scenarios: direct to EVT-capable hospital versus transport to the nearest alteplase-capable hospital with transfer to EVT-capable hospital if appropriate. We project lifetime outcomes incorporating disability, quality of life utility, and cost. Multiple parameter sets of center-specific times (eg, door to alteplase) were randomly selected within a clinically plausible range to account for the model sensitivity to these estimates; for each iteration, the optimal strategy was defined as the most cost-effective outcome (threshold, $100 000 per quality-adjusted life-years gained). After 1000 simulations, the most frequently occurring optimal strategy was the final recommendation, with its strength measured as the proportion of runs for which it was optimal. Results- Routing recommendations were highly sensitive to small changes in model input parameters. Under many scenarios, the recommendations for direct transfer to the EVT site increased with increasing stroke severity and geographic proximity but did not vary substantially with respect to sex, age, or onset time. Conclusions- We present a mathematical decision model that determines ideal prehospital routing recommendations for patients with suspected stroke because of large vessel occlusions, with consideration of patient characteristics and location at onset. This model may be further refined by incorporating real-time data on traffic patterns and actual EVT and alteplase timeliness performance. Further studies are needed to verify model predictions.