Plaque contact surface area and lumen volume predict stroke risk in extracranial carotid artery stenosis.
3D modeling
Carotid artery stenosis
Contact surface area
Flow dynamics
Lumen volume
NASCET
Stroke
Vascular surgery
Journal
Journal of vascular surgery
ISSN: 1097-6809
Titre abrégé: J Vasc Surg
Pays: United States
ID NLM: 8407742
Informations de publication
Date de publication:
08 2022
08 2022
Historique:
received:
02
10
2021
accepted:
11
03
2022
pubmed:
31
3
2022
medline:
27
7
2022
entrez:
30
3
2022
Statut:
ppublish
Résumé
The standard indication for intervention in asymptomatic disease is currently percent stenosis in the internal carotid artery as measured by the North American Symptomatic Carotid Endarterectomy Trial (NASCET) method, which remains limited in discriminating power. Computed tomography angiography (CTA) is widely used to calculate NASCET stenosis, but also offers the opportunity to analyze carotid artery plaques from a morphological perspective that has not been widely used. We aim to improve stroke risk stratification of patients with carotid artery stenosis using plaque three-dimensional (3D) modeling and image analysis. Patients with computed tomography angiographies appropriate for 3D reconstruction were identified from a National Institutes of Health-designated stroke center database, and carotid arteries were segmented and analyzed using software algorithms to calculate contact surface area (CSA) between the plaque and blood flow, and volume of the flow lumen within the region of the plaque (lumen volume [LV]). These novel parameters factor in the 3D morphometry inherent to each carotid plaque and were compared between stroke and nonstroke groups. A total of 134 carotid arteries were analyzed, 33 of which were associated with an ipsilateral stroke. Plaques associated with stroke demonstrated statistically significant increases in average CSA (541.52 mm The data presented here demonstrate morphological features of carotid plaques that are independent of NASCET criteria stratification and may present an improved method in assessing stroke risk in patients with carotid artery stenosis.
Sections du résumé
BACKGROUND
The standard indication for intervention in asymptomatic disease is currently percent stenosis in the internal carotid artery as measured by the North American Symptomatic Carotid Endarterectomy Trial (NASCET) method, which remains limited in discriminating power. Computed tomography angiography (CTA) is widely used to calculate NASCET stenosis, but also offers the opportunity to analyze carotid artery plaques from a morphological perspective that has not been widely used. We aim to improve stroke risk stratification of patients with carotid artery stenosis using plaque three-dimensional (3D) modeling and image analysis.
METHODS
Patients with computed tomography angiographies appropriate for 3D reconstruction were identified from a National Institutes of Health-designated stroke center database, and carotid arteries were segmented and analyzed using software algorithms to calculate contact surface area (CSA) between the plaque and blood flow, and volume of the flow lumen within the region of the plaque (lumen volume [LV]). These novel parameters factor in the 3D morphometry inherent to each carotid plaque and were compared between stroke and nonstroke groups.
RESULTS
A total of 134 carotid arteries were analyzed, 33 of which were associated with an ipsilateral stroke. Plaques associated with stroke demonstrated statistically significant increases in average CSA (541.52 mm
CONCLUSIONS
The data presented here demonstrate morphological features of carotid plaques that are independent of NASCET criteria stratification and may present an improved method in assessing stroke risk in patients with carotid artery stenosis.
Identifiants
pubmed: 35351605
pii: S0741-5214(22)00448-7
doi: 10.1016/j.jvs.2022.03.008
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
482-488Informations de copyright
Copyright © 2022 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.