Quantitative model-based ESUS reclassification using cardiac and cerebral vessel wall MRI

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Quantitative model-based ESUS reclassification using cardiac and cerebral vessel wall MRI Stroke is a major cause of death and the leading cause of permanent disability worldwide. Ischemic stroke is the dominant stroke variety, representing approximately 80+% of strokes in the United States. Defining the specific underlying pathophysiology of ischemic strokes is critical for personalized secondary prevention treatments with the goal of minimizing the risk of recurrent events. However, even with extensive diagnostic workup in current clinical practice, a large portion of ischemic strokes are classified as embolic stroke of undetermined source (ESUS), leaving these patients without optimal treatment tailored to their specific pathophysiology. Recent literature has demonstrated that among subjects diagnosed with ESUS, there may be under-detected lesions of atherosclerosis in intra/extracranial arteries or cardiac pathology on a path towards atrial fibrillation, a so called “atrial cardiopathy”. This implies that there are opportunities to improve the sensitivity and accuracy of etiologic diagnosis to reduce ischemic strokes classified into the ESUS category, allowing for more targeted, personalized secondary prevention measures. New developments in magnetic resonance imaging (MRI) of intra/extracranial atherosclerosis and atrial cardiopathy may provide new opportunities to detect these currently under-detected lesions and allow reclassification of ESUS patients into large-artery atherosclerosis or cardioembolic categories leading to focused treatment strategies. However, there are still significant challenges to using these imaging methods in practice: 1) Specialized vessel wall and cardiac MRI (ESUS-imaging) and image analysis algorithms need to be integrated into the standard of care workflow of stroke patients; 2) A model-based analysis will be needed that combines new findings from ESUS-imaging and findings from existing clinical workup so that new “risk features (RFs)” can be defined for reclassification; and 3) The impact of using these RFs on stroke subtype reclassification needs to be studied prospectively. In this proposal, we plan to develop a model-based analysis focused on ESUS-imaging and test the hypothesis that among acute ischemic stroke subjects diagnosed as ESUS under current clinical workup, a new set of RFs drawn from ESUS-imaging will allow reclassification of a subset of ESUS into large-artery atherosclerosis or cardioembolic categories. The specific aims will: 1) establish new vessel wall and cardiac MRI (ESUS-imaging) and image analysis techniques; 2) develop a multiparametric statistical model that combines information from the standard stroke workup and new ESUS-imaging to identify a set of RFs that can reclassify ischemic stroke etiology; and 3) evaluate the impact of the model on ischemic stroke subtype re-classification. If successful, this proposal will help to establish a clinical workflow that includes ESUS-imaging in ischemic stroke workup and provide a model-based algorithm to assist in future stroke subtype classification.