Advances in Imaging for Tricuspid Transcatheter Edge-to-Edge Repair: Lessons Learned and Future Perspectives

Author:

Prandi Francesca Romana12ORCID,Lerakis Stamatios2,Belli Martina13ORCID,Illuminato Federica1,Margonato Davide3,Barone Lucy1ORCID,Muscoli Saverio1ORCID,Chiocchi Marcello4,Laudazi Mario4ORCID,Marchei Massimo1,Di Luozzo Marco1,Kini Annapoorna2,Romeo Francesco5,Barillà Francesco1ORCID

Affiliation:

1. Division of Cardiology, Department of Systems Medicine, Tor Vergata University, 00133 Rome, Italy

2. Department of Cardiology, Mount Sinai Hospital, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA

3. Cardiovascular Imaging Unit, San Raffaele Scientific Institute, 20132 Milan, Italy

4. Department of Diagnostic Imaging and Interventional Radiology, Tor Vergata University, 00133 Rome, Italy

5. Department of Departmental Faculty of Medicine, Unicamillus-Saint Camillus International University of Health and Medical Sciences, 00131 Rome, Italy

Abstract

Severe tricuspid valve (TV) regurgitation (TR) has been associated with adverse long-term outcomes in several natural history studies, but isolated TV surgery presents high mortality and morbidity rates. Transcatheter tricuspid valve interventions (TTVI) therefore represent a promising field and may currently be considered in patients with severe secondary TR that have a prohibitive surgical risk. Tricuspid transcatheter edge-to-edge repair (T-TEER) represents one of the most frequently used TTVI options. Accurate imaging of the tricuspid valve (TV) apparatus is crucial for T-TEER preprocedural planning, in order to select the right candidates, and is also fundamental for intraprocedural guidance and post-procedural follow-up. Although transesophageal echocardiography represents the main imaging modality, we describe the utility and additional value of other imaging modalities such as cardiac CT and MRI, intracardiac echocardiography, fluoroscopy, and fusion imaging to assist T-TEER. Developments in the field of 3D printing, computational models, and artificial intelligence hold great promise in improving the assessment and management of patients with valvular heart disease.

Publisher

MDPI AG

Subject

General Medicine

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