| Autor: |
West, H, Antoniades, C, Siddique, M, Channon, K, Williams, M, Neubauer, S, Shirodaria, C, Thomas, S, Lyasheva, M, Dangas, K, Volpe, L, Kotanidis, C |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Popis: |
BACKGROUND: Epicardial adipose tissue volume (EAT) is a marker of visceral obesity that can be measured in coronary CT angiograms (CCTA). The clinical value of integrating this measurement in routine CCTA interpretation has not been documented. OBJECTIVE: To develop a deep-learning network (DLN) for automated quantification of EAT from CCTA, test it in technically challenging patients, and validate its prognostic value in routine clinical care. METHODS: The DLN was trained and validated to auto-segment EAT in 3720 CCTA scans from the Oxford Risk Factors And Non-invasive imaging cohort. The model was tested in patients with challenging anatomy and scan artefacts and applied to a longitudinal cohort of 253 post-cardiac surgery patients and 1558 patients from the SCOT-HEART trial, to investigate its prognostic value. RESULTS: External validation of the DLN yielded a concordance correlation coefficient of 0.970 for machine vs human. EAT was associated with coronary artery disease (OR[95%CI] per SD increase in EAT 1.13[1.04-1.30] p=0.01), and atrial fibrillation (AF) (1.25[1.08-1.40], p=0.03), after correction for risk factors (including BMI). EAT predicted all-cause mortality (HR[95%CI] per SD = 1.28[1.10-1.37], p=0.02), myocardial infarction (1.26[1.09-1.38] p=0.001) and stroke (1.20[1.09-1.38] p=0.02) independently of risk factors in SCOT-HEART (5y follow-up). It also predicted in-hospital (HR[95%CI] = 2.67[1.26-3.73], p=< 0.01) and long-term post-cardiac surgery AF (7y follow-up; HR[95%CI] = 2.14[1.19-2.97], p=< 0.01). CONCLUSIONS: Automated assessment of EAT is possible in CCTA, including in challenging patients; it forms a powerful marker of metabolically unhealthy visceral obesity, which could be used for cardiovascular risk stratification. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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