Numerical flow experiment for assessing predictors for cerebrovascular accidents in patients with PHACES syndrome

Abstract

Abstract There is an increased risk of cerebrovascular accidents (CVA) in individuals with PHACES, yet the precise causes are not well understood. In this analysis, we aimed to examine the role of arteriopathy in PHACES syndrome as a potential contributor to CVA. We analyzed clinical and radiological data from 282 patients with suspected PHACES syndrome. We analyzed clinical features, including the presence of infantile hemangioma and radiological features based on magnetic resonance angiography or computed tomography angiography, in individuals with PHACES syndrome according to the Garzon criteria. To analyze intravascular blood flow, we conducted a simulation based on the Fluid–Structure Interaction (FSI) method, utilizing radiological data. The collected data underwent statistical analysis. Twenty patients with PHACES syndrome were included. CVAs were noted in 6 cases. Hypoplasia (p = 0.03), severe tortuosity (p < 0.01), absence of at least one main cerebral artery (p < 0.01), and presence of persistent arteries (p = 0.01) were associated with CVAs, with severe tortuosity being the strongest predictor. The in-silico analysis showed that the combination of hypoplasia and severe tortuosity resulted in a strongly thrombogenic environment. Severe tortuosity, combined with hypoplasia, is sufficient to create a hemodynamic environment conducive to thrombus formation and should be considered high-risk for cerebrovascular accidents (CVAs) in PHACES patients.

Keywords

• PHACES syndrome
• Cerebrovascular accident
• Predictors
• Computational fluid dynamics
• Thrombogenic environment