Clinical trial NCT06951126: CT Prediction for Transcatheter Tricuspid Interventions
Official title: CT Prediction for Transcatheter Tricuspid Interventions
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Tricuspid Regurgitation
Computed Tomography
Keywords: transcatheter tricuspid valve intervention
TTVI
TR
MSCT
Tricuspid regurgitation
Lead sponsor: Heart and Diabetes Center North-Rhine Westfalia
Sponsor class: OTHER
Collaborators: name: Ludwig-Maximilians - University of Munich
name: University Hospital Schleswig-Holstein
name: University of Cologne
name: Heart Center Leipzig - University Hospital
name: Charite University, Berlin, Germany
name: University of Bern
name: University Medical Center Mainz
First posted: 2025-04-30
Enrollment: 500
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: The study includes patients with \>moderate tricuspid regurgitation who underwent cardiac computed tomography and transcatheter tricupid valve intervention.

Brief summary: 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.


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

Primary outcomes:
measure: Mortality
timeFrame: 1 year
Secondary outcomes:
measure: Residual Tricuspid Regurgitation
timeFrame: At discharge, 30 days and 1 year
measure: rehospitalization rate
timeFrame: 1 year
measure: reintervention rate
timeFrame: 1 year
measure: NYHA Class
timeFrame: 30 days and 1 year
measure: Intraprocedural success
timeFrame: 30 days
Other outcomes:
Locations:
city: Bad Oeynhausen
country: Germany
facility: Herz- und Diabeteszentrum
state: NRW
Study officials:
References: