Atherosclerosis in stroke-related vascular beds and stroke risk: A 3-D MR vessel wall imaging study

Ann Clin Transl Neurol. 2018 Oct 22;5(12):1599-1610. doi: 10.1002/acn3.673. eCollection 2018 Dec.

Abstract

Objectives: To investigate the characteristics of atherosclerotic plaques in stroke-related vascular beds and their relationship with stroke using three-dimensional magnetic resonance (MR) vessel wall imaging.

Methods: Fifty-two symptomatic patients (mean age: 56.3 ± 13.4 years; 38 males) were enrolled and underwent MR vessel wall imaging for stroke-related vascular beds including intracranial and extracranial carotid arteries and aortic arch and routine MR imaging for brain. The maximum wall thickness (Max WT) and luminal stenosis of each plaque were measured. The presence/absence of atherosclerotic plaque, intraplaque hemorrhage (IPH), and severe stenosis (stenosis >50%) at each vascular bed and acute ischemic lesion (AIL) were determined. The correlation between Max WT of each vascular bed and AIL was analyzed.

Results: Of 52 patients, 24 (46.2%) had AILs, and 30 (57.7%), 34 (65.4%), and 11 (21.2%) had plaques in intracranial artery, extracranial carotid artery, and aortic arch, respectively. The prevalence of IPH and severe stenosis was 25% and 26.9% for intracranial arteries, 13.5% and 9.6% for extracranial carotid artery, and 3.8% and 0% for aortic arch, respectively. In discriminating AIL, Max WT of intracranial artery had the highest area-under-the-curve (AUC = 0.84), followed by extracranial carotid artery (AUC = 0.83) and aortic arch (AUC = 0.78) after adjusted for confounding factors. The AUC of Max WT combined three stroked-related vascular beds reached 0.87.

Conclusion: Extracranial carotid arteries have the highest prevalence of plaques and intraplaque hemorrhage and severe stenosis are most frequently seen in intracranial arteries in Asian symptomatic patients. The Max WT combined three stroke-related vascular beds show stronger predictive value for AIL than each vascular bed alone.

Grants and funding

This work was funded by Ministry of Science and Technology of People's Republic of China grant 2017YFC1307904; Grants of National Natural Science Foundation of China grant 81771825; Beijing Municipal Science and Technology Project grants D131100002313002 and D171100003017003.