Saba Luca, Raz Eytan, di Martino Michele, Suri Jasjit S, Montisci Roberto, Sanfilippo Roberto, Piga Mario
1Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari -Polo di Monserrato, Monserrato (Cagliari), Italy.
Int J Neurosci. 2015 Jun;125(6):456-63. doi: 10.3109/00207454.2014.948116. Epub 2014 Aug 21.
Previous publications demonstrated that multi-detector-row computed tomography Angiography (MDCTA) can evaluate the carotid artery wall thickness (CAWT). The purpose of this work was to compare the asymmetry of CAWT between carotids in symptomatic and asymptomatic patients.
Sixty consecutive symptomatic (males 44; median age 64) and 60 asymptomatic sex- and age-matched patients were analysed by using a 40-detector-row CT system. CAWT was calculated for both carotids in each patient and the ratio between the thicker CAWT and the contra-lateral was calculated to obtain the ACAWT index. Bland-Altman, logistic regression and receiver operating characteristic (ROC) curve analysis were calculated.
The Bland-Altman plot demonstrates a very good agreement between measurements with a mean difference value of 3.4% and 95% CI from -8% to 14.8%. The ACAWT was significantly different between symptomatic and asymptomatic patients (with a p value of 0.0001). The ROC area under the curve was 0.742 (p = 0.001). Logistic regression model indicated that ACAWT, CAWT, stenosis degree, and fatty plaques were independent variables associated with cerebrovascular symptoms (p value, respectively, 0.0108, 0.0231, 0.0002, and 0.013).
Results of our study indicated that the index of asymmetry in the CAWT might be used as a further parameter to stratify the risk of symptoms related to carotid artery.
以往的研究表明，多排螺旋计算机断层血管造影（MDCTA）可用于评估颈动脉壁厚度（CAWT）。本研究旨在比较有症状和无症状患者两侧颈动脉CAWT的不对称性。
使用40排CT系统对60例连续的有症状患者（男性44例；中位年龄64岁）和60例年龄及性别匹配的无症状患者进行分析。计算每位患者两侧颈动脉的CAWT，并计算较厚侧CAWT与对侧CAWT的比值，以获得不对称颈动脉壁厚度（ACAWT）指数。进行Bland-Altman分析、逻辑回归分析和受试者工作特征（ROC）曲线分析。
Bland-Altman图显示测量结果之间具有很好的一致性，平均差值为3.4%，95%可信区间为-8%至14.8%。有症状和无症状患者的ACAWT存在显著差异（p值为0.0001）。ROC曲线下面积为0.742（p = 0.001）。逻辑回归模型表明，ACAWT、CAWT、狭窄程度和脂肪斑块是与脑血管症状相关的独立变量（p值分别为0.0108、0.0231、0.0002和0.013）。
我们的研究结果表明，CAWT的不对称指数可能作为进一步评估颈动脉相关症状风险的参数。
