Diagnostic accuracy of coronary angiography using 64-slice computed tomography in coronary artery disease

Fu-Bin Yang, Wan-Liang Guo, Mao Sheng, Ling Sun, Yue-Yue Ding, Qiu-Qin Xu, Ming-Guo Xu, Hai-Tao Lv, Fu-Bin Yang, Wan-Liang Guo, Mao Sheng, Ling Sun, Yue-Yue Ding, Qiu-Qin Xu, Ming-Guo Xu, Hai-Tao Lv

Abstract

Objectives: To conduct a meta-analysis and investigate the diagnostic value of 64-slice computed tomography (CT) angiography for diagnosing coronary artery disease (CAD) in patients.

Methods: A comprehensive literature search from March 2005 to August 2014 was performed on the following databases: Cochrane Library; Medline; EmBase; PubMed; and BioMed Central database. As a reference standard, studies that assessed 64-slice CT angiography in detecting coronary artery stenosis (CAS) with invasive coronary angiography were included. Coronary artery stenosis was defined as ≥50% diameter stenosis. Diagnostic value was determined by pooling sensitivity, specificity, positive likelihood ratio (PLR) and negative likelihood ratio (NLR) values at segment-level analysis. Diagnostic accuracy was undertaken using area under the curve (AUC) value and summary receiver operating characteristic (SROC) curves. Publication bias was examined by Deek's funnel plot asymmetry test.

Results: Eight studies were included in the analysis, enrolling a total of 579 patients (7,407 segment coronary vessels). At segment-level, pooled sensitivity value was 90% (95% confidence interval [CI]: 83-95%), specificity was 91% (95% CI: 61-98%), PLR value was 9.7 (95% CI: 1.8-53.3), and NLR value was 0.11 (95% CI: 0.05-0.22) for CAS. Optimal cut-off point of sensitivity was 90%, and specificity under the SROC curve was 91%. The AUC value was 0.94.

Conclusion: The 64-slice CT angiography is a reliable tool for detection of CAD when using a cut-off of more than or equal to 50% diameter stenosis in elderly population.

Figures

Figure 1
Figure 1
Flow chart of study selection process.
Figure 2
Figure 2
Forest plots for sensitivity (A) and specificity (B) of 64-slice computed tomography angiography in determining coronary artery stenosis.
Figure 3
Figure 3
Forest plots for negative likelihood ratio and positive likelihood ratio of 64-slice computed tomography angiography in determining coronary artery stenosis.
Figure 4
Figure 4
An image showing that in 64-slice CCTA, the maximum intensity projection technique demonstrates normal coronary artery (A), and severe narrowing of the right coronary artery (B).
Figure 5
Figure 5
Summary receiver operating characteristic (SROC) curve with confidence and prediction regions around mean operating sensitivity and specificity points for determining coronary artery stenosis is shown. Red diamond represents the best diagnostic cut-off point. Peripheral relative (densely dotted) represents prediction confidence, while inner oval dashed lines indicate the confidence region.
Figure 6
Figure 6
An image showing Deek’s funnel plot with superimposed regression line for identifying publication bias.

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Source: PubMed

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