| Institution: | 1. Acute Multidisciplinary Imaging and Interventional Centre, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom;2. Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom;3. Caristo Diagnostics Pty Ltd, Oxford, United Kingdom;4. Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, Scotland, United Kingdom;5. Northwestern University, Evanston, Illinois, USA;6. Royal Brompton and Harefield National Health Service (NHS) Foundation Trust, London, United Kingdom;7. Translational Cardiovascular Research Group, Department of Cardiology, Milton Keynes University Hospital, Milton Keynes, United Kingdom;8. Faculty of Medicine and Health Sciences, University of Buckingham, Buckingham, United Kingdom;9. Department of Cardiovascular Sciences and National Institute for Health Research Leicester Biomedical Research Centre, University of Leicester, Leicester, United Kingdom;10. Royal United Hospitals Bath NHS Foundation Trust, Bath, United Kingdom;11. Department of Health, University of Bath, Bath, United Kingdom;12. Department of Cardiology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom;13. University College London, London, United Kingdom;14. National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA;15. The Cleveland Clinic, Cleveland, Ohio, USA;p. School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom |
| Abstract: | BackgroundEpicardial adipose tissue (EAT) volume is a marker of visceral obesity that can be measured in coronary computed tomography angiograms (CCTA). The clinical value of integrating this measurement in routine CCTA interpretation has not been documented.ObjectivesThis study sought to develop a deep-learning network for automated quantification of EAT volume from CCTA, test it in patients who are technically challenging, and validate its prognostic value in routine clinical care.MethodsThe deep-learning network was trained and validated to autosegment EAT volume in 3,720 CCTA scans from the ORFAN (Oxford Risk Factors and Noninvasive Imaging Study) cohort. The model was tested in patients with challenging anatomy and scan artifacts and applied to a longitudinal cohort of 253 patients post-cardiac surgery and 1,558 patients from the SCOT-HEART (Scottish Computed Tomography of the Heart) Trial, to investigate its prognostic value.ResultsExternal validation of the deep-learning network yielded a concordance correlation coefficient of 0.970 for machine vs human. EAT volume was associated with coronary artery disease (odds ratio OR] per SD increase in EAT volume: 1.13 95% CI: 1.04-1.30]; P = 0.01), and atrial fibrillation (OR: 1.25 95% CI: 1.08-1.40]; P = 0.03), after correction for risk factors (including body mass index). EAT volume predicted all-cause mortality (HR per SD: 1.28 95% CI: 1.10-1.37]; P = 0.02), myocardial infarction (HR: 1.26 95% CI:1.09-1.38]; P = 0.001), and stroke (HR: 1.20 95% CI: 1.09-1.38]; P = 0.02) independently of risk factors in SCOT-HEART (5-year follow-up). It also predicted in-hospital (HR: 2.67 95% CI: 1.26-3.73]; P ≤ 0.01) and long-term post–cardiac surgery atrial fibrillation (7-year follow-up; HR: 2.14 95% CI: 1.19-2.97]; P ≤ 0.01).ConclusionsAutomated assessment of EAT volume is possible in CCTA, including in patients who are technically challenging; it forms a powerful marker of metabolically unhealthy visceral obesity, which could be used for cardiovascular risk stratification. |
