CT Prediction for Transcatheter Tricuspid Interventions

NCT ID: NCT06951126

Last Updated: 2025-04-30

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

500 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-07-19

Study Completion Date

2030-06-01

Brief Summary

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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.

Detailed Description

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Conditions

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Tricuspid Regurgitation Computed Tomography

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Eligibility Criteria

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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
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Ludwig-Maximilians - University of Munich

OTHER

Sponsor Role collaborator

University Hospital Schleswig-Holstein

OTHER

Sponsor Role collaborator

University of Cologne

OTHER

Sponsor Role collaborator

Heart Center Leipzig - University Hospital

OTHER

Sponsor Role collaborator

Charite University, Berlin, Germany

OTHER

Sponsor Role collaborator

University of Bern

OTHER

Sponsor Role collaborator

University Medical Center Mainz

OTHER

Sponsor Role collaborator

Heart and Diabetes Center North-Rhine Westfalia

OTHER

Sponsor Role lead

Responsible Party

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Johannes Kirchner

Dr. med.

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Herz- und Diabeteszentrum

Bad Oeynhausen, North Rhine-Westphalia, Germany

Site Status RECRUITING

Countries

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Germany

Central Contacts

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Johannes Kirchner, Dr. med.

Role: CONTACT

0405731971258

Facility Contacts

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Johannes Kirchner, Dr. med.

Role: primary

Other Identifiers

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HDZ-KA CESAR-TR

Identifier Type: -

Identifier Source: org_study_id

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