Journal article
Geometric determinants of local hemodynamics in severe carotid artery stenosis
Computers in biology and medicine, v 114, pp 103436-103436
01 Nov 2019
PMID: 31521900
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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 preCEA 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.
Metrics
Details
- Title
- Geometric determinants of local hemodynamics in severe carotid artery stenosis
- Creators
- Dara Azar - University of South CarolinaWilliam M. Torres - University of South CarolinaLindsey A. Davis - University of South CarolinaTaylor Shaw - University of South CarolinaJohn F. Eberth - University of South CarolinaVijaya B. Kolachalama - Boston UniversitySusan M. Lessner - University of South CarolinaTarek Shazly - University of South Carolina
- Publication Details
- Computers in biology and medicine, v 114, pp 103436-103436
- Publisher
- Elsevier
- Number of pages
- 9
- Grant note
- 1R03 EB019663 / NIH/NIBIB; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of Biomedical Imaging & Bioengineering (NIBIB) R03EB019663 / NATIONAL INSTITUTE OF BIOMEDICAL IMAGING AND BIOENGINEERING; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of Biomedical Imaging & Bioengineering (NIBIB) CMMI-1760906 / NSF; National Science Foundation (NSF) P20GM103499 / NIH INBRE Grant for South Carolina P20GM103499 / NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of General Medical Sciences (NIGMS)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000495520100009
- Scopus ID
- 2-s2.0-85072050858
- Other Identifier
- 991021902502804721
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- Collaboration types
- Industry collaboration
- Domestic collaboration
- Web of Science research areas
- Biology
- Computer Science, Interdisciplinary Applications
- Engineering, Biomedical
- Mathematical & Computational Biology