Predicting mortality after transcatheter aortic valve replacement using preprocedural CT.

ABSTRACT: Transcatheter aortic valve replacement (TAVR) is a widely used intervention for patients with severe aortic stenosis. Identifying high-risk patients is crucial due to potential postprocedural complications. Currently, this involves manual clinical assessment and time-consuming radiological assessment of preprocedural computed tomography (CT) images by an expert radiologist. In this study, we introduce a probabilistic model that predicts post-TAVR mortality automatically using unprocessed, preprocedural CT and 25 baseline patient characteristics. The model utilizes CT volumes by automatically localizing and extracting a region of interest around the aortic root and ascending aorta. It then extracts task-specific features with a 3D deep neural network and integrates them with patient charact

SUBMITTER: Bruggemann D 

PROVIDER: S-EPMC11143216 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature