基于多模态超声的颈动脉斑块致前循环卒中的风险分层研究

目的 探讨颈动脉斑块患者发生前循环缺血性卒中的临床及多模态超声(multimodal ultrasound, MMU)影响因素，构建基于超微血流成像技术(superb microvascular imaging, SMI)的风险分层预测模型。方法 回顾性分析颈动脉斑块患者683例，根据临床表现和多层螺旋电子计算机断层扫描(computed tomography, CT)/核磁共振扫描(magnetic resonance imaging, MRI),分为前循环卒中组(n=301)和非前循环卒中组(n=382)。收集患者颈动脉斑块的MMU特征、临床和实验室检查数据，采用多因素二元Logistic回归分析筛选出前循环缺血性卒中的影响因素。构建列线图风险预测模型，进行模型验证与风险分层。结果 前循环卒中组和非前循环卒中组年龄、体重指数(body mass index, BMI)、饮酒史、吸烟史、脑梗死既往史、高血压、糖尿病、低密度脂蛋白(low-density lipoprotein, LDL)、高密度脂蛋白(high-density lipoprotein, HDL)、同型半胱氨酸(hom...

Risk stratification of anterior circulation stroke caused by carotid plaque based on multimodal ultrasound

Objective To explore influencing factors of anterior circulation ischemic stroke in patients with carotid plaque by the clinical and multimodal ultrasound (MMU), and to construct a risk stratification prediction model based on superb microvascular imaging (SMI). Methods A total of 683 patients with carotid plaque were retrospectively analyzed. They were assigned to anterior circulation stroke group(n=301) and non-anterior circulation stroke group(n=382) based on clinical presentation and computed tomography (CT)/magnetic resonance imaging (MRI). MMU characteristics of carotid plaques, and clinical and laboratory examination data were collected. Multivariate binary Logistic regression analysis was used to screen the influencing factors of anterior circulation stroke. The nomogram prediction model was constructed, to carry out model verification and risk stratification.Results There were statistically significant differencesin age, body mass index（BMI）, drinking history, smoking history, previous history of cerebral infarction, hypertension, diabetes, low-density lipoprotein （LDL）, high-density lipoprotein （HDL）, homocysteine （HCY）, carotid stenosis, plaque surface morphology and intraplaque neovascularization between anterior circulation stroke group and non-anterior circulation stroke group (P＜0.05).Multivariate binary Logistic regression analysis found that age, BMI, smoking history, drinking history, previous history of cerebral infarction, diabetes, LDL, HCY and MMU (carotid stenosis, plaque surface morphology, intraplaque neovascularization) were independent risk factors for anterior circulation stroke in patients with carotid plaque(P＜0.05). Based on the above 11 indicators, the individualized nomogram prediction model of anterior circulation stroke was constructed. The total score of the obtained nomogram could more effectively predict the risk of anterior circulation stroke in patients with carotid plaque (AUC: 0.781,95%CI: 0.747-0.816， Hosmer-lemeshow P=0.637). The optimal cutoff value of the model was determined to be 0.465(the nomogram score:138), which divided patients with carotid plaque into low-risk and high-risk subgroups. The clinical performance of the model was verified by Bootstrap.Conclusion The prediction model based on MMU can readily, quickly and accurately predict the occurrence and risk stratification of anterior circulation stroke in patients with carotid plaque.