Background Endovascular thrombectomy is the standard of care for acute ischaemic stroke caused by large vessel occlusion. Reducing stroke symptom onset to reperfusion time is associated with improved functional outcomes. In this study we aimed to develop a computational model to predict and identify time-related outcomes of community stroke calls within an area based on variable parameters.
Method A model to simulate and predict EVT service delivery at capable hospital within a geographic area, city or state was designed using SimPy, a Python based discrete event simulation framework. Geolocation data defined by the user as well as that used by the model is sourced using the Google Maps Application Programming Interface (API) and GeoPy. Variables are customized by the user based on their local environment to provide more acute prediction.
Result This algorithm can predict the delay between the time that emergency services are notified of a potential stroke and cerebral reperfusion using EVT at capable hospital. Factors including area size and population, number of EVT capable hospitals, number of available angiography machines, availability of ambulances and time of patient can be adjusted to observe the effect of modifying each parameter input.
Conclusion This model, made available as an open source web-based application, provides a mean of predicting resource utilisation and wait times for stroke service delivery planning. By modifying parameter inputs, the delivery and coordination of a stroke service within an area, city or state may be optimised and aid the development of new EVT service protocols.
Disclosures Y. Ren: None. M. Phan: None. J. Maingard: None. C. Seah: None. J. Wu: None. P. Luong: None. D. Shell: None. M. Burney: None. K. Zhou: None. A. lamanna: None. H. Kok: None. R. Chandra: None. A. Jhamb: None. V. Thijs: None. D. Brooks: None. H. Asadi: None.
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