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Recruiting NCT06951126

CT Prediction for Transcatheter Tricuspid Interventions

— Observational Tricuspid Regurgitation Computed Tomography

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
This is an observational study: the protocol does not assign a study treatment.
Who it may be relevant to
Registry conditions: Tricuspid Regurgitation, Computed Tomography. Basic parameters: from 18 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
Germany
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

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Overview

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.

Primary outcome measures

  • Mortality [Time frame: 1 year]
Secondary outcome measures (5)
  • Residual Tricuspid Regurgitation [Time frame: At discharge, 30 days and 1 year]
  • rehospitalization rate [Time frame: 1 year]
  • reintervention rate [Time frame: 1 year]
  • NYHA Class [Time frame: 30 days and 1 year]
  • Intraprocedural success [Time frame: 30 days]

Eligibility criteria

Inclusion criteria

  • the patient underwent full cycle cardiac computed tomography for analysis of valvular heart disease
  • a transcatheter tricuspid valve intervention is performed
  • the patient is 18 years or older

Exclusion criteria

  • none

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model
Cohort

Study locations

Germany · 1 center
  • Herz- und Diabeteszentrum — Bad Oeynhausen

Identifiers

NCT: NCT06951126 · HDZ-KA CESAR-TR

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗