Image_1_Identification of High Risk Carotid Artery Stenosis: A Multimodal Vascular and Perfusion Imaging Study.pdf (134.35 kB)

Image_1_Identification of High Risk Carotid Artery Stenosis: A Multimodal Vascular and Perfusion Imaging Study.pdf

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posted on 16.07.2019 by Moo-Seok Park, Soonwook Kwon, Mi Ji Lee, Keon Ha Kim, Pyoung Jeon, Yang-Jin Park, Dong-Ik Kim, Young-Wook Kim, Oh Young Bang, Chin-Sang Chung, Kwang Ho Lee, Gyeong-Moon Kim

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.