Do deep learning-derived body composition metrics from preprocedural CT angiography predict 3-year all-cause mortality in patients undergoing TAVR?
Automated CT-derived body composition assessment of skeletal muscle and adipose tissue reserves may improve preoperative risk stratification in TAVR candidates.
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: ), 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: ; 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.
Liu et al. (Thu,) studied this question.