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Background: Endovascular clot retrieval (ECR) is the standard of care for acute ischemic stroke caused by large vessel occlusion. Reducing stroke symptom onset to reperfusion time is associated with improved functional outcomes. This study aims to develop a computational model to predict and identify time-related outcomes of community stroke calls within a geographic area based on variable parameters to support planning and coordination of ECR services., Methods: A discrete event simulation (DES) model to simulate and predict ECR service was designed using SimPy, a process-based DES framework written in Python. Geolocation data defined by the user, as well as that used by the model, were sourced using the Google Maps application programming interface. Variables were customized by the user on the basis of their local environment to provide more accurate prediction., Results: A DES model can estimate the delay between the time that emergency services are notified of a potential stroke and potential cerebral reperfusion using ECR at a capable hospital. Variables can be adjusted to observe the effect of modifying each parameter input. By varying the percentage of stroke patients receiving ECR, we were able to define the levels at which our existing service begins to fail in service delivery and assess the effect of adding centers., Conclusions: This novel computational DES model can aid the optimization of delivery of a stroke service within a city, state, or country. By varying geographic, population, and other user-defined inputs, the model can be applied to any location worldwide., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Background and Purpose- Mobile stroke units (MSUs) are increasingly used worldwide to provide prehospital triage and treatment. The benefits of MSUs in giving earlier thrombolysis have been well established, but the impacts of MSUs on endovascular thrombectomy (EVT) and effect on disability avoidance are largely unknown. We aimed to determine the clinical impact and disability reduction for reperfusion therapies in the first operational year of the Melbourne MSU. Methods- Treatment time metrics for MSU patients receiving reperfusion therapy were compared with control patients presenting to metropolitan Melbourne stroke units via standard ambulance within MSU operating hours. The primary outcome was median time difference in first ambulance dispatch to treatment modeled using quantile regression analysis. Time savings were subsequently converted to disability-adjusted life years avoided using published estimates. Results- In the first 365-day operation of the Melbourne MSU, prehospital thrombolysis was administered to 100 patients (mean age, 73.8 years; 62% men). The median time savings per MSU patient, compared with the control cohort, was 26 minutes ( P <0.001) for dispatch to hospital arrival and 15 minutes ( P <0.001) for hospital arrival to thrombolysis. The calculated overall time saving from dispatch to thrombolysis was 42.5 minutes (95% CI, 36.0-49.0). In the same period, 41 MSU patients received EVT (mean age, 76 years; 61% men) with median dispatch-to-treatment time saving of 51 minutes ([95% CI, 30.1-71.9], P <0.001). This included a median time saving of 17 minutes ([95% CI, 7.6-26.4], P =0.001) for EVT hospital arrival to arterial puncture for MSU patients. Estimated median disability-adjusted life years saved through earlier provision of reperfusion therapies were 20.9 for thrombolysis and 24.6 for EVT. Conclusions- The Melbourne MSU substantially reduced time to reperfusion therapies, with the greatest estimated disability avoidance driven by the more powerful impact of earlier EVT. These findings highlight the benefits of prehospital notification and direct triage to EVT centers with facilitated workflow on arrival by the MSU.