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Toward noninvasive assessment of stroke risk in pediatric cerebrovascular disease

Background and Purpose: Moyamoya disease (MMD) is a progressive steno-occlusive cerebrovascular disease leading to recurrent stroke. There is a lack of reliable biomarkers to identify unilateral stroke MMD patients who are likely to progress to bilateral disease and experience subsequent contralateral stroke(s). We hypothesized that local hemodynamics are predictive of future stroke and set out to noninvasively assess this stroke risk in pediatric MMD patients. Methods: MR and X-ray angiography imaging were utilized to reconstruct patient-specific models of the circle of Willis of 6 pediatric MMD patients who had previous strokes, along with a control subject. Blood flow simulations were performed by using a Navier-Stokes solver within an isogeometric analysis framework. Vascular regions with a wall shear rate (WSR) above the coagulation limit (> 5000 s-1) were identified to have a higher probability of thrombus formation, potentially leading to ischemic stroke(s). Two metrics, namely, ''critical WSR coverage'' and ''WSR score'', were derived to assess contralateral stroke risk and compared with clinical follow-up data. Results: In two patients that suffered a contralateral stroke within two months of the primary stroke, critical WSR coverages exceeding 50% of vessel surface and WSR scores greater than 6x the control were present in multiple contralateral vessels. These metrics were not as conclusive in two additional patients with 3-to-5-year gaps between primary and contralateral strokes. However, a longitudinal study of one of these two cases, where a subsequent timepoint was analyzed, accurately predicted disease stabilization on the primary stroke side and an elevated contralateral stroke risk, thus indicating that post-stroke follow-up at regular intervals might be warranted for secondary stroke prevention. Conclusions: WSR-based metrics could be predictive of future stroke risk after an initial stroke in MMD patients. In addition, more accurate predictions may be possible by performing patient-specific hemodynamic analysis at multiple timepoints during patient follow-up to monitor changes in the WSR-based metrics.