临床荟萃 ›› 2025, Vol. 40 ›› Issue (9): 790-795.doi: 10.3969/j.issn.1004-583X.2025.09.003

• 论著 • 上一篇    下一篇

颈动脉CTA影像学指标及血脂指标与缺血性脑卒中的关系

王辉a, 夏新建a(), 刘赛a, 魏西福a, 张树忠b   

  1. 寿光市人民医院 a.影像中心;b.神经内科,山东 潍坊 262700
  • 收稿日期:2025-07-02 出版日期:2025-09-20 发布日期:2025-09-26
  • 通讯作者: 夏新建 E-mail:xxjxhm@163.com
  • 基金资助:
    潍坊市科技发展计划项目——颈动脉粥样硬化相关影像学指标联合血脂指标与缺血性脑血管病相关性研究(2024YX127)

The association of imaging indicators on computed tomography angiography of the carotid arteries and blood lipid indicators with ischemic stroke

Wang Huia, Xia Xinjiana(), Liu Saia, Wei Xifua, Zhang Shuzhongb   

  1. a.Imaging Center; b.Department of Neurology,Shouguang People's Hospital,Weifang 262700,China
  • Received:2025-07-02 Online:2025-09-20 Published:2025-09-26
  • Contact: Xia Xinjian E-mail:xxjxhm@163.com

摘要:

目的 探讨颈部血管计算机断层血管造影(computed tomography angiography,CTA)影像学指标与血脂指标联合应用在预测缺血性脑卒中方面的价值。方法 选取颈部动脉硬化患者122例,根据是否发生缺血性脑卒中分为两组(卒中组、非卒中组),分别检测其血脂指标(总胆固醇、甘油三酯、高密度脂蛋白胆固醇、低密度脂蛋白胆固醇),并进行颈部血管CTA检查,分析相关影像学指标(血管狭窄程度、斑块性质、血管周围脂肪密度),运用logistic回归分析筛选出与缺血性脑卒中相关的独立危险因素,构建联合预测模型,并通过绘制受试者工作特征(receiver operating characteristic,ROC)曲线评估模型的预测效能。根据斑块性质分为无斑块组、非钙化斑块组、钙化斑块组、混合斑块组,运用无序分类logistic回归分析,探究血管周围脂肪密度联合血脂指标与血管斑块性质的关系;根据血管狭窄程度分为无狭窄组、轻度狭窄组、中度狭窄组、重度狭窄组,运用有序分类logistic回归分析,探究血管周围脂肪密度联合血脂指标与血管狭窄程度的关系。结果 卒中组、非卒中组的颈内动脉斑块性质、颈内动脉狭窄程度、周围脂肪平均密度、周围脂肪谷值、低密度脂蛋白胆固醇差异有统计学意义(P<0.05)。通过logistic回归分析,筛选出血管狭窄程度、血管周围脂肪密度、低密度脂蛋白胆固醇作为缺血性脑卒中的独立危险因素(P值分别为<0.001、0.029、<0.001),以此构建联合预测模型。绘制 ROC 曲线结果显示,该联合预测模型的曲线下面积为0.827(95%置信区间:0.756~0.899),联合指标构建的预测模型具有良好的预测准确性。血管周围脂肪平均密度对血管斑块性质的影响差异有统计学意义(χ2=20.84, P<0.001)。结论 颈部血管CTA影像学指标联合血脂指标对于缺血性脑卒中有重要的预测价值,有助于早期识别高危人群,为临床干预提供依据。

关键词: 卒中, 颈部血管CTA, 血脂指标, 预测价值

Abstract:

Objective To explore the correlation of a combination of imaging indicators on computed tomography angiography(CTA) of the carotid arteries and blood lipid indicators with ischemic stroke, and the predictive value. Methods A total of 122 patients with cervical arteriosclerosis were selected and divided into the stroke group and non-stroke group based on whether ischemic stroke occurred. Their blood lipid indicators, including total cholesterol(TC), triglycerides(TG), high-density lipoprotein cholesterol(HDL-C), and low-density lipoprotein cholesterol(LDL-C) were measured. CTA of the carotid arteries was performed to analyze relevant imaging indicators, including the degree of vascular stenosis, plaque properties, and perivascular fat density. Logistic regression analysis was used to screen for independent risk factors for ischemic stroke. A joint prediction model was constructed. The predictive performance of the model was evaluated by drawing receiver operating characteristic(ROC) curves. According to the nature of plaques, they were divided into the non-plaque group, non-calcified plaque group, calcified plaque group, and mixed plaque group. Using unordered classification logistic regression analysis, the correlation of a combination of perivascular fat density and blood lipid indicators with vascular plaque properties was explored. According to the degree of vascular stenosis, patients were divided into non-stenosis group, mild stenosis group, moderate stenosis group, and severe stenosis group. Ordered classification logistic regression analysis was used to explore the correlation of a combination of perivascular fat density and blood lipid indicators with the degree of vascular stenosis. Results There were differences in the nature of internal carotid artery plaques, degree of internal carotid artery stenosis, average peripheral fat density, peripheral fat trough value, and LDL-C between the stroke group and the non-stroke group(P<0.05). Through logistic regression analysis, the degree of vascular stenosis, perivascular fat density, and LDL-C were identified as independent risk factors for ischemic stroke(P<0.001, 0.029, and <0.001, respectively), and a joint prediction model was constructed. The ROC curve results showed that the area under the curve(AUC) of the joint prediction model was 0.827(95% confidence interval [CI]: 0.756-0.899). The prediction model constructed by the joint indicators had good prediction accuracy. The average density of perivascular fat had a significant impact on the properties of vascular plaques(χ2=20.84, P<0.001). Conclusion The combination of cervical vascular CTA imaging indicators and blood lipid indicators has important predictive value for ischemic stroke, helping to identify high-risk populations early and providing a basis for clinical intervention.

Key words: stroke, neck vascular CTA, blood lipid indicators, predictive value

中图分类号: