Dataset Information

Diagnostic performance of coronary computed tomography angiography stenosis score for coronary stenosis.

ABSTRACT:

Background

Coronary computed tomography angiography stenosis score (CCTA-SS) is a proposed diagnosis score that considers the plaque characteristics, myocardial function, and the diameter reduction rate of the lesions. This study aimed to evaluate the diagnostic performance of the CCTA-SS in seeking coronary artery disease (CAD).

Methods

The 228 patients with suspected CAD who underwent CCTA and invasive coronary angiography (ICA) procedures were under examination. The diagnostic performance was evaluated with the receiver operating curve (ROC) for CCTA-SS in detecting CAD (defined as a diameter reduction of ≥ 50%) and severe CAD (defined as a diameter reduction of ≥ 70%).

Results

The area under ROC (AUC) of CCTA-SS was 0.909 (95% CI: 0.864-0.943), which was significantly higher than that of CCTA (AUC: 0.826; 95% CI: 0.771-0.873; P = 0.0352) in diagnosing of CAD with a threshold of 50%. The optimal cutoff point of CCTA-SS was 51% with a sensitivity of 90.66%, specificity of 95.65%, positive predictive value of 98.80%, negative predictive value of 72.13%, and accuracy of 91.67%, whereas the optimal cutoff point of CCTA was 55%, and the corresponding values were 87.36%, 93.48%, 98.15%, 65.15%, and 88.60%, respectively. With a threshold of 70%, the performance of CCTA-SS with an AUC of 0.927 (95% CI: 0.885-0.957) was significantly higher than that of CCTA with an AUC of 0.521 (95% CI: 0.454-0.587) (P < 0.0001).

Conclusions

CCTA-SS significantly improved the diagnostic accuracy of coronary stenosis, including CAD and severe CAD, compared with CCTA.

SUBMITTER: Xiong QF

PROVIDER: S-EPMC10854174 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Diagnostic performance of coronary computed tomography angiography stenosis score for coronary stenosis.

Xiong Qing-Feng QF Fu Xiao-Rong XR Ku Lei-Zhi LZ Zhou Di D Guo Sheng-Peng SP Zhang Wen-Sheng WS

BMC medical imaging 20240209 1

<h4>Background</h4>Coronary computed tomography angiography stenosis score (CCTA-SS) is a proposed diagnosis score that considers the plaque characteristics, myocardial function, and the diameter reduction rate of the lesions. This study aimed to evaluate the diagnostic performance of the CCTA-SS in seeking coronary artery disease (CAD).<h4>Methods</h4>The 228 patients with suspected CAD who underwent CCTA and invasive coronary angiography (ICA) procedures were under examination. The diagnostic ...[more]

PMID: 38336622

Similar Datasets

Project description:BackgroundCoronary pressure-derived fractional flow reverse (FFR) is the standard of the functional assessment of lesion severity. In spite of its strengths in determining ischemia-related coronary stenosis, the invasive operation involved still limits its clinical application. Coronary computed tomography angiography-derived FFR (CCTA-FFR) or computed tomography-derived FFR (CT-FFR) has been indicated as an effective and non-invasive index to evaluate lesion-specific ischemia. However, its diagnostic performance, especially in patients with different severity of coronary stenosis, remains unknown. The current research attempted to demonstrate this problem and provided the foundation for extensive clinical application of CCTA-FFR.MethodsThe design of this study was a diagnostic test. A total of 97 vessels from 91 patients who performed CCTA and coronary angiography (CAG) during a hospitalization collected from two research centers were included in this study. CCTA-FFR and FFR were obtained by CCTA and CAG separately. The Gensini score was calculated according to the CAG in each patient. FFR was indicated as the golden diagnosis of lesion-specific ischemia with a cut-off value of 0.80, which was consistent with most contemporary studies. A receiver-operating characteristic (ROC) curve, simple linear analysis, and Bland-Altman plot were performed to determine the diagnostic performance of CCTA-FFR.ResultsCCTA-FFR was well correlated with invasive FFR (R2=0.745, P<0.001) and the area under the curve (AUC) was 0.976. The sensitivity was 94.6% and the specificity was 95.1%. The mean difference between FFR and CT-FFR was 0.011, and the 95% confidence interval was -0.173 to 0.196. The AUCs were 0.989 and 0.928 in the low and high Gensini groups, respectively, and there was no significant difference in the diagnostic accuracies between these two groups (Z=0.003, P>0.500). CT-FFR still exhibited a good correlation with FFR (R2=0.713, P<0.001 in the low Gensini group and R2=0.743, P<0.001 in the high Gensini group). The systematic differences were calculated, and the mean difference between FFR and CT-FFR was -0.005 and 0.025, respectively, in these two groups.ConclusionsCCTA-FFR exhibited good diagnostic performance in patients with different Gensini score levels. Our results indicate that CCTA-FFR could be an effective tool to screen lesion-specific ischemia in patients with coronary artery disease.

Project description:BackgroundAlthough coronary computed tomography angiography (CCTA) is currently utilized as the frontline test to accurately diagnose coronary artery disease (CAD) in clinical practice, there are still debates regarding its use as a screening tool for the asymptomatic population. Using deep learning (DL), we sought to develop a prediction model for significant coronary artery stenosis on CCTA and identify the individuals who would benefit from undergoing CCTA among apparently healthy asymptomatic adults.MethodsWe retrospectively reviewed 11,180 individuals who underwent CCTA as part of routine health check-ups between 2012 and 2019. The main outcome was the presence of coronary artery stenosis of ≥70% on CCTA. We developed a prediction model using machine learning (ML), including DL. Its performance was compared with pretest probabilities, including the pooled cohort equation (PCE), CAD consortium, and updated Diamond-Forrester (UDF) scores.ResultsIn the cohort of 11,180 apparently healthy asymptomatic individuals (mean age 56.1 years; men 69.8%), 516 (4.6%) presented with significant coronary artery stenosis on CCTA. Among the ML methods employed, a neural network with multi-task learning (19 selected features), one of the DL methods, was selected due to its superior performance, with an area under the curve (AUC) of 0.782 and a high diagnostic accuracy of 71.6%. Our DL-based model demonstrated a better prediction than the PCE (AUC, 0.719), CAD consortium score (AUC, 0.696), and UDF score (AUC, 0.705). Age, sex, HbA1c, and HDL cholesterol were highly ranked features. Personal education and monthly income levels were also included as important features of the model.ConclusionWe successfully developed the neural network with multi-task learning for the detection of CCTA-derived stenosis of ≥70% in asymptomatic populations. Our findings suggest that this model may provide more precise indications for the use of CCTA as a screening tool to identify individuals at a higher risk, even in asymptomatic populations, in clinical practice.

Project description:PurposeAn important aspect in the prevention and treatment of coronary artery disease is the functional evaluation of narrowed blood vessels. Medical image-based Computational Fluid Dynamic methods are currently increasingly being used in the clinical setting for flow studies of cardio vascular system. The aim of our study was to confirm the feasibility and functionality of a non-invasive computational method providing information about hemodynamic significance of coronary stenosis.MethodsA comparative method was used to simulate the flow energy losses in real (stenotic) and reconstructed models without (reference) stenosis of the coronary arteries under stress test conditions, i.e. for maximum blood flow and minimal, constant vascular resistance. In addition to the absolute pressure drop in the stenotic arteries (FFRsten) and in the reconstructed arteries (FFRrec), a new energy flow reference index (EFR) was also defined, which expresses the total pressure changes caused by stenosis in relation to the pressure changes in normal coronary arteries, which also allows a separate assessment of the haemodynamic significance of the atherosclerotic lesion itself. The article presents the results obtained from flow simulations in coronary arteries, reconstructed on the basis of 3D segmentation of cardiac CT images of 25 patients from retrospective data collection, with different degrees of stenoses and different areas of their occurrence.ResultsThe greater the degree of narrowing of the vessel, the greater drop of flow energy. Each parameter introduces an additional diagnostic value. In contrast to FFRsten, the EFR indices that are calculated on the basis of a comparison of stenosed and reconstructed models, are associated directly with localization, shape and geometry of stenosis only. Both FFRsten and EFR showed very significant positive correlation (P < 0.0001) with coronary CT angiography-derived FFR, with a correlation coefficient of 0.8805 and 0.9011 respectively.ConclusionThe study presented promising results of non-invasive, comparative test to support of prevention of coronary disease and functional evaluation of stenosed vessels.

Dataset Information

Diagnostic performance of coronary computed tomography angiography stenosis score for coronary stenosis.

Background

Methods

Results

Conclusions

Publications

Diagnostic performance of coronary computed tomography angiography stenosis score for coronary stenosis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets