AUTHOR=Park Moo-Seok , Kwon Soonwook , Lee Mi Ji , Kim Keon Ha , Jeon Pyoung , Park Yang-Jin , Kim Dong-Ik , Kim Young-Wook , Bang Oh Young , Chung Chin-Sang , Lee Kwang Ho , Kim Gyeong-Moon
TITLE=Identification of High Risk Carotid Artery Stenosis: A Multimodal Vascular and Perfusion Imaging Study
JOURNAL=Frontiers in Neurology
VOLUME=10
YEAR=2019
URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2019.00765
DOI=10.3389/fneur.2019.00765
ISSN=1664-2295
ABSTRACT=
Background: Risk stratification of asymptomatic carotid artery stenosis (ACAS) is still an issue for carotid revascularization. We sought to identify factors associated with symptomatic carotid artery stenosis (SCAS) using multimodal imaging techniques.
Methods: We retrospectively collected data on patients who underwent carotid artery revascularization. Results from duplex sonography, computerized tomography angiography, brain magnetic resonance imaging (MRI), magnetic resonance angiography (MRA), perfusion-weighted imaging, and demographic profiles were compared between ACAS and SCAS patients. Differences in baseline characteristics between the two groups were balanced by the propensity matching score method. Multivariable regression analysis was performed to identify factors associated with symptomaticity of carotid artery stenosis. We compared the strength of associations between significant imaging factors and symptomatic carotid stenosis using C statistics.
Results: A total of 259 patients (asymptomatic 57.1%, symptomatic 42.9%) with carotid stenosis were included. After 1:1 propensity score matching, the multivariable regression analysis revealed that the absence of plaque calcification [Odds ratio 0.41, 95% confidence interval (CI) 0.182–0.870, p = 0.023], deep white matter hyperintensity (DWMH; Odds ratio 3.46, 95% CI 1.842–6.682, p < 0.001), susceptibility vessel sign seen on gradient-echo MRI (Odds ratio 2.35, 95% CI 1.113–5.107, p = 0.027), and increased cerebral blood volume (CBV) seen on perfusion-weighted MRI (CBV; Odds ratio 2.17, 95% CI 1.075–4.454, p = 0.032) were associated with SCAS. The combination of these variables had a fair accuracy to classify SCAS (Area under the curve 0.733, 95% CI 0.662–0.803).
Conclusions: We identified several multimodal imaging markers independently associated with SCAS. These markers may provide information to identify ACAS patients with high risk of ischemic stroke. Future studies are needed to predict SCAS using our findings in other independent cohorts.