Circulation Cardiovascular Imaging
Deep Learning-Based 3D CT Body Composition Is Associated With Mortality in Patients Undergoing Transcatheter Aortic Valve Replacement
By Ibrahim Hadžić, Jan Michael Brendel, Thomas Mayrhofer and 19 others.
Abstract
Background
Recent advances in artificial intelligence enable fully automated 3-dimensional computed tomography (CT)-based body composition quantification. We investigated whether automated CT-derived thoraco-abdominopelvic body composition measures are associated with mortality after transcatheter aortic valve replacement.
Methods
In this retrospective multicenter study, consecutive patients undergoing transcatheter aortic valve replacement with preprocedural CT imaging were included between 2014 and 2023. A deep learning-based pipeline performed automated 3-dimensional segmentation of thoraco-abdominopelvic noncontrast CT scans to quantify volumes (L/m 2 ) and attenuations (Hounsfield units) of subcutaneous adipose tissue, visceral adipose tissue, muscular adipose tissue, and lean skeletal muscle, indexed to body surface area. Associations with mortality were evaluated using Cox models and Harrell C statistics, adjusting for demographics (age, sex, body mass index) and cardiometabolic risk factors (hypertension, diabetes, chronic renal failure).