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.
Keywords: Acute ischemic stroke; Discrete event simulation; Endovascular treatment; Google.
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