Geospatial modeling for stroke care

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PROJECT SUMMARY Acute Ischemic stroke (AIS) remains the leading cause of disability in the US. Large vessel occlusion (LVO) represents up to 20% of all ischemic strokes, but causes 90% of stroke-related death and severe disability. Both intravenous thrombolysis (IVT) and endovascular therapy (EVT) are effective time-sensitive treatments to prevent stroke-related morbidity and mortality. EVT is highly effective for LVOs, does not provide any benefit in non-LVO strokes and is available in less than 20% of US stroke centers. IVT is readily available, has a modest effect for LVOs and is the only therapeutic alternative for non-LVO strokes. The challenge for paramedics is to expedite EVT for eligible patients without harming a large proportion of non-qualified patients in need of IVT, in the context of initial diagnostic uncertainty. The current system triage criteria have lagged behind emerging therapies available to the sickest subset, and the disparity in stroke outcomes is exacerbated in rural areas and for ethnic minorities. Herein, we propose a study to foster the development of an innovative geospatial triage algorithm of stroke care in the U.S. health system through a multidisciplinary collaboration to maximize neurological recovery to all stroke patients. The model will be constructed to provide optimal predicted outcomes for individual patients, using a Bayesian framework to inform each link of the treatment decision tree, building on prior studies while overcoming their limitations and closing the implementation gap. First, the patient outcome model will be built using individual and hospital level data randomized trials, which will enable a context sensitive triage decision algorithm without reliance on overbroad assumptions about the treatment pathway. We will uniquely incorporate uncertainty through modelling of individual level data in a Bayesian framework, rather than relying on point estimates at an aggregate level. Additionally, our model will be adaptable; we will be able to incorporate emerging LVO diagnostic tools with improved diagnostic accuracy, as well as new therapeutic strategies as the stroke field evolves. Furthermore, the conditional structure will allow the modification of facility capabilities, including the introduction of new EVT-capable stroke centers. The clinical and cost-benefit algorithm impact will be assessed by comparing with the current real-world triage by incorporating local stroke center and EVT-capable center data on stroke flow metrics from Get-With-The Guidelines-Stroke registry to better estimate the probability of good outcomes and improve triage capabilities. Finally, the triage algorithm will be integrated into a point-of-care decision tool support readily available for all EMS to recommend the optimal destination for all the entire stroke population after their initial assessment. After appropriate refinement and adequate implementation in subsequent studies, this tool will not only have the potential to optimize stroke outcomes, but also reduce the actual geographic and racial disparities in the U.S.