Googling Boundaries for Operating Mobile Stroke Unit for Stroke Codes

Thanh G Phan; Richard Beare; Mark Parsons; Henry Zhao; Stephen Davis; Geoffrey A Donnan; Velandai Srikanth; Henry Ma

doi:10.3389/fneur.2019.00331

Googling Boundaries for Operating Mobile Stroke Unit for Stroke Codes

Front Neurol. 2019 Apr 4:10:331. doi: 10.3389/fneur.2019.00331. eCollection 2019.

Authors

Thanh G Phan¹, Richard Beare², Mark Parsons³, Henry Zhao³, Stephen Davis³, Geoffrey A Donnan⁴, Velandai Srikanth², Henry Ma¹

Affiliations

¹ Stroke Unit, Clinical Trials Imaging and Informatics Division of Stroke and Aging Research Group, Monash Medical Centre, Monash University, Clayton, VIC, Australia.
² Department of Medicine, Peninsula Health, Melbourne University, Frankston, VIC, Australia.
³ Melbourne Brain Centre, Melbourne University, Parkville, VIC, Australia.
⁴ Florey Neuroscience Institute, Melbourne University, Parkville, VIC, Australia.

Abstract

Background: Mobile stroke units (MSU) have been proposed to expedite delivery of recombinant tissue plasminogen activator (tPA) and expedite endovascular clot retrieval (ECR). Unexplored questions in the use of MSU include: maximal distance from base, time limit with regards to the use CT imaging, CT Angiography, CT Perfusion, and Telemedicine. We developed a computational model as an app (https://gntem3.shinyapps.io/ambmc/), taking into account traveling time to explore this issue. The aim of this study was to define the operating parameters for an MSU in a large metropolitan city, based on the geography of Melbourne. Methods: There are 2 hospitals (Royal Melbourne Hospital/RMH, Monash Medical Center/MMC) designated to provide state-wide ECR services. In these spatial simulations, the MSU is based at RMH and delivers tPA at the patient's pick-up address and then takes the patient to the nearest ECR center. We extracted the geocode of suburbs in Melbourne and travel time to each hospital using ggmap, an interface to Google Map API. The app contains widgets for varying the processing time at the patient location (default = 30 min), performing CT angiography (default = 10 min), performing telemedicine consultation (default = 15 min). The data were compared against those for usual ambulance metrics (default traveling time = 15 min, processing time at patient's location = 20 min, door to tPA = 60 min, door to groin = 90 min). Varying the widgets allow the viewer to explore the trade-off between the variable of interest and time to therapy at a suburb level. Results: The MSU was superior for delivering tPA to all Melbourne suburbs (up to 76 min from RMH). If the CTA times or processing time at location increased by 20 min then it was superior for providing ECR to only 74.9% of suburbs if the return base was RMH. Addition of CT Perfusion or telemedicine consultation affect the ability of a single hospital to provide ECR but not tPA if these additions can be limited to 20 min. Conclusion: The app can help to define how best to deploy the MSU across Melbourne. This app can be modified and used to optimize operating characteristics of MSU in other centers around the world.

Keywords: Google Map; modeling; optimization; stroke; transport.