作者：徐金辉，张保朝

单位：

1.453000河南省新乡市，新乡医学院研究生院 2.473000河南省南阳市中心医院神经内科 通信作者：张保朝，E-mail：zbc177777@163.com

Units：

1.Graduate School of Xinxiang Medical College, Xinxiang 453000, China 2.Department of Neurology, Nanyang Central Hospital, Nanyang 473000, China Corresponding author: ZHANG Baochao, E-mail: zbc177777@163.com

关键词：

脑缺血发作,短暂性; 前循环短暂性脑缺血发作; 磁共振成像; 大脑中动脉; 斑块,动脉粥样硬;

Keywords：

Ischemic attack, transient; Anterior circulation transient ischemic attack; Magnetic resonance imaging;Middle cerebral artery; Plaque, atherosclerotic; Forcasting

CLC：

DOI：

10.12114/j.issn.1008-5971.2022.00.100

Funds：

摘要：

目的 探讨高分辨率(HR)-磁共振成像(MRI)增强扫描下大脑中动脉(MCA)粥样硬化斑块的强化特点对频发前循环短暂性脑缺血发作(TIA)的预测价值。方法 选取2019年12月至2021年3月南阳市中心医院神经内科收治的前循环TIA患者118例为研究对象。收集患者的基线资料,根据患者24 h内TIA发作频率,将其分为频发组(≥2次,56例)和非频发组(<2次,62例)。记录患者斑块位置及血管狭窄程度、斑块强化特点(包括强化等级、强化模式)。采用多因素Logistic回归分析探讨频发前循环TIA的影响因素,并构建斑块强化特点预测频发前循环TIA的模型;采用ROC曲线评估斑块强化特点预测频发前循环TIA的价值。结果 频发组高血压、糖尿病发生率高于非频发组,血管狭窄程度重于非频发组(P<0.05)。多因素Logistic回归分析结果显示,强化等级〔OR=3.607,95%CI(1.226,10.612)〕、强化模式〔OR=0.251,95%CI(0.065,0.969)〕是频发前循环TIA的影响因素(P<0.05)。根据多因素Logistic回归分析结果,构建斑块强化特点预测频发前循环TIA风险的模型,为Y=-1.697+1.283X1-1.382X2,其中Y为频发前循环TIA风险、X1为强化等级、X2为强化模式。ROC曲线分析结果显示,该模型预测频发前循环TIA的AUC为0.761〔95%CI(0.649,0.852)〕,最佳截断值为0.44,灵敏度为79.55%,特异度为64.52%。结论 HR-MRI增强扫描下MCA粥样硬化斑块的强化特点(强化等级及强化模式)是频发前循环TIA的影响因素,且其对频发前循环TIA有一定预测价值。

Abstract：

【Abstract】　Objective To investigate the predictive value of the enhancement features of middle cerebral artery(MCA) atherosclerotic plaque on high resolution (HR) -magnetic resonance imaging (MRI) enhanced scan for the frequencyof anterior circulation transient ischemic attacks (TIA) . Methods A total of 118 patients with anterior circulation TIA whowere admitted to the Department of Neurology, Nanyang Central Hospital from December 2019 to March 2021 were selected assubjects. The baseline data of patients were collected and patients were divided into frequent group ( ≥ 2 times, 56 cases) andnon-frequent group ( < 2 times, 62 cases) based on the frequency of TIA within 24 h. The location of plaque, degree of vascularstenosis and plaque enhancement features (including enhancement grade and enhancement mode) were recorded. MultivariateLogistic regression analysis was used to investigate the influencing factors of the frequency of anterior circulation TIA, and amodel was established to predict the frequency of anterior circulation TIA by the of plaque enhancement features. ROC curvewas used to evaluate the value of plaque enhancement features in predicting the frequency of anterior circulation TIA. ResultsThe incidence of hypertension and diabetes in the frequent group was higher than that in the non-frequent group, and thedegree of vascular stenosis was heavier than that in the non-frequent group (P <0.05) . Multivariate Logistic regression analysisresults showed that enhancement grade [OR=3.607, 95%CI (1.226, 10.612) ] and enhancement mode [OR=0.251, 95%CI(0.065, 0.969) ] were the influencing factors for the frequency of anterior circulation TIA (P <0.05) . According to the resultsof multivariate Logistic regression analysis, a model was established to predict the frequency risk of anterior circulation TIA byplaque enhancement features, which was Y=-1.697+1.283X1-1.382X2, where Y was the frequency risk of anterior circulationTIA, X1 was the enhancement grade, and X2 was the enhancement mode. ROC curve analysis results showed that the AUC of themodel in predicting the frequency of anterior circulation TIA was 0.761 [95%CI (0.649, 0.852) ] , the best cut-off value was 0.44,the sensitivity was 79.55%, and the specificity was 64.52%. Conclusion The enhancement features (enhancement grade andenhancement mode) of MCA atherosclerotic plaque on HR-MRI enhanced scan were the influencing factors for the frequency ofanterior circulation TIA, and they have certain predictive value for the frequency of anterior circulation TIA.

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