Yang Xia, Gai Lu-yue, Li Ping, Chen Yun-dai, Li Tao, Yang Li
Department of Cardiology, Chinese People’s Liberation Army General Hospital, Beijing, China.
Vasc Health Risk Manag. 2010 Oct 21;6:935-41. doi: 10.2147/VHRM.S13879.
The aim of this study was to evaluate the diagnostic accuracy of dual-source computed tomography (DSCT) in coronary artery disease, and to test the possibility of using this technique for coronary risk stratification.
With the advent of DSCT, it is possible to image coronary plaque noninvasively. However, the accuracy of this method in terms of sensitivity and specificity has not been determined. Furthermore, noninvasive determination of plaque composition and plaque burden may be important for improving coronary risk stratification.
Forty-six patients with known coronary artery disease underwent DSCT quantitative coronary angiography (QCA), and intravascular ultrasound (IVUS) were included in the study. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of DSCT was calculated against QCA and IVUS. Plaque analysis software in a DSCT workstation was used to detect plaque characteristics associated with the Hounsfield unit (Hu) value compared with IVUS. Coronary artery plaques were classified into three types of lesions based on DSCT, and the relationship between different coronary lesions and clinical diagnosis was determined.
DSCT angiography was performed in 46 patients, and a diagnostic-quality CT image was obtained in 44 patients. Coronary angiography was performed in 138 vessels and IVUS in 102 vessels of all 46 patients. Sensitivity, specificity, PPV, and NPV of DSCT compared with QCA was 100%, 98%, 92%, and 100%, respectively. The same corresponding index of DSCT compared with IVUS was 100%, 99%, 95%, and 100%, respectively. Quantitative coronary stenosis analysis revealed a good correlation between DSCT and QCA (r = 0.85, P < 0.05, 95% confidence interval [CI] 0.60-0.87). There was also a good correlation between DSCT and IVUS (r = 0.81, P < 0.05, 95% CI 0.56-0.82). In comparison with IVUS, DSCT predicted plaque characteristics more accurately. The coefficient correlation (r) of luminal cross-sectional area and external elastic membrane cross-sectional area between DSCT and IVUS was 0.82 (P < 0.01, CI 0.67-0.89) and 0.78 (P < 0.01, CI 0.67-0.86), respectively. Three different types of plaque were identified on IVUS. Fatty plaque had a 45 ± 14 Hu value, fibrous plaque 90 ± 20, and calcified plaque 530 ± 185, respectively, on DSCT. The relationship between clinical diagnosis and coronary plaque on DSCT indicated that lesions in patients with unstable angina pectoris or ST elevation myocardial infarction were mainly discrete soft plaques, but there was no significant difference in the distributive characteristics of the lesions in patients with non-ST elevation myocardial infarction and stable angina pectoris patients.
DSCT is a noninvasive tool that allows accurate evaluation of plaque characteristics, diagnosis of coronary artery disease, and stratification of coronary risk according to different coronary plaque type.
本研究旨在评估双源计算机断层扫描(DSCT)在冠状动脉疾病中的诊断准确性,并测试使用该技术进行冠状动脉风险分层的可能性。
随着DSCT的出现,无创成像冠状动脉斑块成为可能。然而,该方法在敏感性和特异性方面的准确性尚未确定。此外,无创确定斑块成分和斑块负荷对于改善冠状动脉风险分层可能很重要。
46例已知冠状动脉疾病患者接受了DSCT定量冠状动脉造影(QCA),并纳入血管内超声(IVUS)研究。计算DSCT相对于QCA和IVUS的敏感性、特异性、阳性预测值(PPV)和阴性预测值(NPV)。使用DSCT工作站中的斑块分析软件检测与Hounsfield单位(Hu)值相关的斑块特征,并与IVUS进行比较。基于DSCT将冠状动脉斑块分为三种病变类型,并确定不同冠状动脉病变与临床诊断之间的关系。
46例患者进行了DSCT血管造影,44例患者获得了诊断质量的CT图像。所有46例患者中,138支血管进行了冠状动脉造影,102支血管进行了IVUS检查。DSCT与QCA相比的敏感性、特异性、PPV和NPV分别为100%、98%、92%和100%。DSCT与IVUS相比的相同相应指标分别为100%、99%、95%和100%。定量冠状动脉狭窄分析显示DSCT与QCA之间具有良好的相关性(r = 0.85,P < 0.05,95%置信区间[CI] 0.60 - 0.87)。DSCT与IVUS之间也具有良好的相关性(r = 0.81,P < 0.05,95% CI 0.56 - 0.82)。与IVUS相比,DSCT能更准确地预测斑块特征。DSCT与IVUS之间管腔横截面积和外弹力膜横截面积的系数相关性(r)分别为0.82(P < 0.01,CI 0.67 - 0.89)和0.78(P < 0.01,CI 0.67 - 0.86)。IVUS上识别出三种不同类型的斑块。在DSCT上,脂肪斑块的Hu值为45±14,纤维斑块为90±20,钙化斑块为530±185。DSCT上临床诊断与冠状动脉斑块之间的关系表明,不稳定型心绞痛或ST段抬高型心肌梗死患者的病变主要是散在的软斑块,但非ST段抬高型心肌梗死患者和稳定型心绞痛患者病变的分布特征无显著差异。
DSCT是一种无创工具,能够准确评估斑块特征、诊断冠状动脉疾病,并根据不同冠状动脉斑块类型进行冠状动脉风险分层。