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Background: Ruling out obstructive coronary artery disease (CAD) using coronary computed tomography angiography (CCTA) is time‐consuming and challenging. This study developed a deep learning (DL) model to assist in detecting obstructive CAD on CCTA to streamline workflows. Methods: In total, 2929 DICOM files and 7945 labels were extracted from curved planar reformatted CCTA images. A modified Inception V3 model was adopted. To validate the artificial intelligence (AI) model, two cardiologists labelled and adjudicated the classification of coronary stenosis on CCTA. The model was trained to differentiate the coronary artery into binary stenosis classifications <50% and ≥50% stenosis. Using the quantitative coronary angiography (QCA) consensus results as a reference standard, the performance of the AI model and CCTA radiology readers was compared by calculating Cohen's kappa coefficients at patient and vessel levels. The net reclassification index was used to evaluate the net benefit of the DL model. Results: The diagnostic accuracy of the AI model was 92.3% and 88.4% at the patient and vessel levels, respectively. Compared with CCTA radiology readers, the AI model had a better agreement for binary stenosis classification at both patient and vessel levels (Cohen kappa coefficient:.79 vs..39 and.77 vs..40, p <.0001). The AI model also exhibited significantly improved model discrimination and reclassification (Net reclassification index =.350; Z = 4.194; p <.001). Conclusions: The developed AI model identified obstructive CAD, and the model results correlated well with QCA results. Incorporating the model into the reporting system of CCTA may improve workflows. [ABSTRACT FROM AUTHOR]

Background: Obesity is associated with metabolic syndrome which increases further risk of coronary artery disease and adverse cardiovascular events. Impact of body mass index (BMI) on long‐term outcome in patients with coronary chronic total occlusion (CTO) is less clear. Method and results: From January 2005 to November 2020, a total of 1301 patients with coronary angiographic confirmed CTO were enrolled in our study. Patients were divided into two groups: low BMI group: 18–24.99 kg/m2 and high BMI group ≥25 kg/m2. Clinical outcomes were 3‐year all‐cause mortality, 3‐year cardiovascular mortality and 3‐year non‐fatal myocardial infarct. During the 3‐year follow‐up period, all‐cause mortality was significantly higher in patients with low BMI group compared to those in high BMI groups (14% vs. 6%, p =.0001). Kaplan–Meier analysis showed patients with high BMI groups had significant better survival compared with those in low BMI group (p =.0001). In multivariate analysis, higher BMI was independently associated with decreased risk of 3‐year all‐cause mortality (Hazard ratio [HR]: 0.534; 95% confidence interval [CI]: 0.349–0.819, p =.004) after controlling for age, renal function, prior history of stroke, coronary artery bypass graft, co‐morbidities with peripheral arterial disease, heart failure and revascularization status for CTO. In propensity‐matched multivariate analysis, high BMI remained a significant predictor of 3‐year all‐cause mortality (HR, 0.525; 95% CI, 0.346–0.795, p =.002). Conclusion: Higher BMI was associated with better long‐term outcome in patients with coronary CTO. [ABSTRACT FROM AUTHOR]