Prognostic value of computed tomographic coronary angiography and exercise electrocardiography for cardiovascular events

计算机断层扫描冠状动脉造影和运动心电图对心血管事件的预后价值

阅读:1

Abstract

BACKGROUND/AIMS: This study is a head-to-head comparison of predictive values for long-term cardiovascular outcomes between exercise electrocardiography (ex-ECG) and computed tomography coronary angiography (CTCA) in patients with chest pain. METHODS: Four hundred and forty-two patients (mean age, 56.1 years; men, 61.3%) who underwent both ex-ECG and CTCA for evaluation of chest pain were included. For ex-ECG parameters, the patients were classified according to negative or positive results, and Duke treadmill score (DTS). Coronary artery calcium score (CACS), presence of plaque, and coronary artery stenosis were evaluated as CTCA parameters. Cardiovascular events for prognostic evaluation were defined as unstable angina, acute myocardial infarction, revascularization, heart failure, and cardiac death. RESULTS: The mean follow-up duration was 2.8 ± 1.1 years. Fifteen patients experienced cardiovascular events. Based on pretest probability, the low- and intermediate-risks of coronary artery disease were 94.6%. Odds ratio of CACS > 40, presence of plaque, coronary stenosis ≥ 50% and DTS ≤ 4 were significant (3.79, p = 0.012; 9.54, p = 0.030; 6.99, p < 0.001; and 4.58, p = 0.008, respectively). In the Cox regression model, coronary stenosis ≥ 50% (hazard ratio, 7.426; 95% confidence interval, 2.685 to 20.525) was only significant. After adding DTS ≤ 4 to coronary stenosis ≥ 50%, the integrated discrimination improvement and net reclassification improvement analyses did not show significant. CONCLUSIONS: CTCA was better than ex-ECG in terms of predicting long-term outcomes in low- to intermediate-risk populations. The predictive value of the combination of CTCA and ex-ECG was not superior to that of CTCA alone.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。