Zhongguo cuzhong zazhi (Sep 2022)
颈动脉几何形态——60岁及以下无高危因素人群颅内动脉粥样硬化的潜在预测指标 Carotid Artery Geometry: A Potential Predictor for Intracranial Atherosclerosis in People at Age 60 or Less than 60 Years without Traditional Vascular Risk Factors
Abstract
目的 基于头颈联合HR-MRI技术，探讨在60岁及以下无传统心脑血管高危因素的人群中，颈动脉几何形态与大脑中动脉（middle cerebral artery，MCA）粥样硬化的相关性，寻找MCA粥样硬化的潜在预测指标。 方法 回顾性分析来自60岁及以下无传统心脑血管高危因素患者的150个同侧前循环动脉单位，根据MCA是否存在动脉粥样硬化斑块将其分为MCA粥样硬化（+）组和MCA粥样硬化（-）组。分析两组间颈动脉分叉夹角、颈动脉管腔面积比值和颈内动脉颅外段及颅内段的形态分型，并采用多因素logistic回归探索MCA粥样硬化的独立相关因素，绘制ROC曲线分析颈动脉几何形态对MCA粥样硬化的预测效能。 结果 与MCA粥样硬化（-）组（113个）相比，MCA粥样硬化（+）组（37个）颈动脉分叉夹角更大[41.2°（28.8°～56.5°）vs. 32.6°（24.7°～46.2°），P=0.026]，而流出道与流入道管腔面积比值更大、颈内动脉颅外段形态平滑型更少、迂曲型和扭折型更多、颅内段形态钝角型更少、直角型和锐角型更多，但差异无统计学意义。多因素logistic回归分析结果显示，颈动脉分叉夹角与MCA粥样硬化独立相关（每增加10°，OR 1.232，95%CI 1.007～1.507，P=0.042），逐步退后logistic回归分析结果相同（每增加10°，OR 1.276，95%CI 1.050～1.550，P=0.014）；ROC曲线显示，颈动脉分叉夹角预测MCA粥样硬化的AUC为0.622（95%CI 0.515～0.730），最佳截断值为37.0°，敏感度为57.5%，特异度为64.9%。 结论 对于60岁及以下无传统心脑血管高危因素人群，颈动脉分叉夹角与MCA粥样硬化独立相关，有望成为颅内动脉粥样硬化的有效影像学标志物。 Abstract: Objective To evaluate the association between carotid artery geometry and middle cerebral artery (MCA) atherosclerosis in patients at age 60 or less than 60 years old without traditional vascular risk factors, using head-neck HR-MRI, to seek the potential predictive factors for MCA atherosclerosis. Methods The patients at age 60 or less than 60 years old without traditional vascular risk factors from Peking Union Medical College Hospital between January 2018 and February 2019 and Guangdong Provincial People’s Hospital between August 2019 and January 2020 were included in this retrospective study, and a total of 150 artery units of ipsilateral anterior circulation of all the patients were analyzed. According to having MCA atherosclerotic plaque or not, artery units were categorized into two groups: MCA atherosclerosis (+) vs. MCA atherosclerosis (-). Carotid bifurcation angle, carotid artery lumen-area ratio and configuration of extracranial and intracranial internal carotid artery (ICA) were compared between two groups. Logistic regression analysis was used to evaluate the independent risk factors of MCA atherosclerosis. ROC curve was drawn to analyze the predictive value of carotid artery geometry for MCA atherosclerosis. Results Compared with MCA atherosclerosis (-) group (n=113), MCA atherosclerosis (+) group (n=37) had a larger carotid bifurcation angle [41.2° (28.8°-56.5°) vs. 32.6° (24.7°-46.2°), P=0.026], larger outflow/inflow lumen-area ratio, less smooth type, more tortuous and kinked type of extracranial ICA, and less obtuse angle, more right angle and acute angle shape of intracranial ICA, all without statistical differences. Multivariate logistic regression analysis showed that carotid bifurcation angle was independently associated with MCA atherosclerosis (per 10° increase, OR 1.232, 95%CI 1.007-1.507, P=0.042), and also in backward stepwise logistic regression analysis (per 10° increase, OR 1.276, 95%CI 1.050-1.550, P=0.014). The area under the ROC curve of carotid bifurcation angle for MCA atherosclerosis was 0.622 (95%CI 0.515-0.730), the cutoff value was 37.0°, the sensitivity was 57.5%, and the specificity was 64.9%. Conclusions For people at age 60 or less than 60 years old without traditional vascular risk factors, carotid bifurcation angle is independently associated with MCA atherosclerosis, which may be a potential imaging marker for intracranial atherosclerosis.
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