Geometric determinants of local hemodynamics in severe carotid artery stenosis.
Carotid endarterectomy
Carotid stenosis
Computational fluid dynamics
Plaque geometry
Prediction models
Journal
Computers in biology and medicine
ISSN: 1879-0534
Titre abrégé: Comput Biol Med
Pays: United States
ID NLM: 1250250
Informations de publication
Date de publication:
11 2019
11 2019
Historique:
received:
05
07
2019
revised:
03
09
2019
accepted:
04
09
2019
pubmed:
16
9
2019
medline:
29
9
2020
entrez:
16
9
2019
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 31521900
pii: S0010-4825(19)30313-0
doi: 10.1016/j.compbiomed.2019.103436
pmc: PMC6817414
mid: NIHMS1539671
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
103436Subventions
Organisme : NIGMS NIH HHS
ID : P20 GM103499
Pays : United States
Organisme : NIBIB NIH HHS
ID : R03 EB019663
Pays : United States
Informations de copyright
Copyright © 2019 Elsevier Ltd. All rights reserved.
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