CT Prediction for Transcatheter Tricuspid Interventions

The aim of this study is to enhance the predictability of therapeutic success in transcatheter tricuspid valve intervention (TTVI) for patients with severe tricuspid regurgitation (TR). This will be achieved through automated analyses of pre-interventional computed tomography (CT) scans. Severe tricuspid regurgitation is associated with poor patient outcomes. In advanced stages, pharmacological therapy becomes ineffective, and surgical intervention carries a high mortality risk. Given this clinical challenge, catheter-based treatment of the tricuspid valve has become a focal point of research. One well-established treatment strategy is percutaneous tricuspid valve intervention, which aims to reduce regurgitation either through annuloplasty, leaflet-based edge-to-edge repair or valve replacement. This approach has been shown to significantly decrease the severity of regurgitation, leading to a dramatic reduction in symptom burden and a marked improvement in quality of life. However, predicting which patients will benefit most from TTVI and determining the optimal technique for each individual remain largely unresolved challenges. Artificial intelligence (AI)-powered software, such as heart.ai by LARALAB (Munich), enables automated measurement of anatomical structures captured via CT imaging. This technology already allows for rapid and precise assessment of cardiac chambers and the tricuspid annulus throughout the entire cardiac cycle, facilitating a comprehensive three-dimensional evaluation of right heart anatomy. To refine patient selection and optimize procedural strategies for TR treatment, the researcher work a multi-center collaboration to analyze treatment outcomes and patient response to specific therapeutic approaches.

NCT Number	NCT06951126
Lead Sponsor	Heart and Diabetes Center North-Rhine Westfalia Collaborators: Ludwig-Maximilians - University of Munich, University Hospital Schleswig-Holstein, University of Cologne, Heart Center Leipzig - University Hospital, Charite University, Berlin, Germany, University of Bern, University Medical Center Mainz
Conditions	Tricuspid Regurgitation, Computed Tomography
Enrollment	500 participants
Start Date	2023-07-19
Primary Completion	2026-06-01 (estimated)
Study Completion	2030-06-01 (estimated)
Updated on ClinicalTrials.gov	2025-04-30