Geometric determinants of local hemodynamics in severe carotid artery stenosis. (November 2019)

• Record Type: Journal Article
• Authors: Azar, Dara; Torres, William M.; Davis, Lindsey A.; Shaw, Taylor; Eberth, John F.; Kolachalama, Vijaya B.; Lessner, Susan M.; Shazly, Tarek
• Is Part Of: Computers in biology and medicine. Volume 114(2019)
• Publication Date: 2019-11
• Subjects: Carotid endarterectomy -- Carotid stenosis -- Computational fluid dynamics -- Plaque geometry -- Prediction models; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2019.103436
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: In cases of severe carotid artery stenosis (CAS), carotid endarterectomy (CEA) is performed to recover lumen patency and alleviate stroke risk. Under current guidelines, the decision to surgically intervene relies primarily on the percent loss of native arterial lumen diameter within the stenotic region (i.e. the degree of stenosis). An underlying premise is that the degree of stenosis modulates flow-induced wall shear stress elevations at the lesion site, and thus indicates plaque rupture potential and stroke risk. Here, we conduct a retrospective study on pre-CEA computed tomography angiography (CTA) images from 50 patients with severe internal CAS (>60% stenosis) to better understand the influence of plaque and local vessel geometry on local hemodynamics, with geometrical descriptors that extend beyond the degree of stenosis. We first processed CTA images to define a set of multipoint geometric metrics characterizing the stenosed region, and next performed computational fluid dynamics simulations to quantify local wall shear stress and associated hemodynamic metrics. Correlation and regression analyses were used to relate obtained geometric and hemodynamic metrics, with inclusion of patient sub-classification based on the degree of stenosis. Our results suggest that in the context of severe CAS, prediction of shear stress-based metrics can be enhanced by consideration of readily available, multipoint geometric metrics in addition to the degree of stenosis.