Deep Learning-Derived Body Composition Analysis Predicts Long-Term Mortality After Transcatheter Aortic Valve Replacement

基于深度学习的身体成分分析可预测经导管主动脉瓣置换术后的长期死亡率

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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(2)), as well as SM index (SMI; cm(2)/m(2)), were quantified from CT angiography using a validated U-Net-based deep learning model. Associations between each parameter and 3-year all-cause mortality were assessed using multivariable Cox proportional hazards models adjusted for clinical covariates, with adjusted hazard ratios (aHRs) expressed per 1-SD increase. 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 P≤.001). Restricted cubic spline analysis showed increased mortality risk below threshold values of the following-SM<128 cm(2), SAT<161 cm(2), VAT<104 cm(2), and SMI<41 cm(2)/m(2); sex-specific thresholds were also derived. 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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