ArticleMayo Clinic proceedings. Digital health2026
Deep Learning-Derived Body Composition Analysis Predicts Long-Term Mortality After Transcatheter Aortic Valve Replacement.
Article in Mayo Clinic proceedings. Digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: To examine the association between body composition metrics derived from preprocedural computed tomography (CT) angiography and all-cause mortality after transcatheter aortic valve replacement (TAVR). Patients and Methods: We included patients who underwent TAVR between September 1, 2011 and November 30, 2023 at a single academic center. Skeletal muscle (SM), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and intermuscular adipose tissue areas (cm Results: Among 2642 patients (median age, 80.0 years [interquartile range, 74.0-85.0 years]; 1572 were men [59.5%]), median follow-up was 2.8 years, and 74.8% survived to 3 years. Lower SM, SAT, VAT, and SMI (analyzed as continuous variables) were independently associated with higher 3-year all-cause mortality (SM: aHR, 0.831; 95% CI, 0.762-0.906; SAT: aHR, 0.847; 95% CI, 0.775-0.926; VAT: aHR, 0.826; 95% CI, 0.762-0.896; SMI: aHR, 0.832; 95% CI, 0.763-0.907; all Conclusion: Reduced SM and adipose tissue reserves are independently associated with increased mortality after TAVR. Automated CT-derived body composition assessment may improve preoperative risk stratification and guide clinical decision making in TAVR candidates.
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