Using CT Imaging to Predict Success in Transcatheter Tricuspid Valve Treatments for Adults with Tricuspid Regurgitation

What this Trial Is About

Researchers are investigating how to better predict the success of transcatheter tricuspid valve intervention TTVI for patients with severe tricuspid regurgitation TR. Severe TR often leads to poor outcomes, and traditional treatments like medication become less effective in advanced stages, while surgery carries high risks. This observational study focuses on using artificial intelligence AI to analyze pre-interventional computed tomography CT scans to improve patient selection and treatment planning. The study uses AI-powered software to automatically measure heart structures from CT imaging, enabling detailed three-dimensional analysis of the right heart anatomy. This approach aims to identify which patients might benefit most from different catheter-based treatment options, such as annuloplasty, leaflet-based repair, or valve replacement. The research is conducted through a multi-center collaboration analyzing outcomes after TTVI procedures. Participants will have undergone full-cycle cardiac CT scans and received TTVI. Researchers will monitor outcomes including mortality at one year, residual tricuspid regurgitation at discharge and follow-ups, rehospitalization, reintervention rates, symptom improvement measured by NYHA class, and intraprocedural success. The study spans assessments at discharge, 30 days, and one year, aiming to refine treatment strategies and improve quality of life in severe TR patients.

CT Prediction for Transcatheter Tricuspid Interventions