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| http://dx.doi.org/10.21037/atm-23-1521 | DOI Listing |
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Reply: Characteristics of the Population Undergoing Tricuspid Transcatheter Edge-to-Edge Repair in a Real-World Study.
J Am Coll Cardiol
January 2025
University Medical Center Mainz, Mainz, Germany. Electronic address:
Characteristics of the Population Undergoing Tricuspid Transcatheter Edge-to-Edge Repair in a Real-World Study.
J Am Coll Cardiol
January 2025
Zhongshan Hospital, Fudan University, Shanghai, China. Electronic address:
Transcatheter management of residual mitral regurgitation after transcatheter edge-to-edge repair: a systematic review.
Cardiovasc Interv Ther
January 2025
Heart Valve Center, IRCCS San Raffaele, Milan, Italy.
Background: Treatment of residual mitral regurgitation (MR) with different percutaneous devices after transcatheter edge-to-edge repair (TEER) has been reported as an alternative option to reclipping or surgery. This review aims at describing the different transcatheter strategies available and their results when managing residual MR after TEER.
Methods: A literature search was undertaken across Pubmed, ScienceDirect, SciELO, DOAJ, and Cochrane library databases, to identify article reporting patients with post-TEER residual MR managed by a transcatheter approach that did not involve only the implantation of new clips.
Comparative Outcomes of Transcatheter Edge-to-Edge Repair and Tricuspid Valve Surgery for Isolated Tricuspid Valve Regurgitation.
Circ Cardiovasc Interv
January 2025
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN.
Deep Learning-Enabled Assessment of Right Ventricular Function Improves Prognostication After Transcatheter Edge-to-Edge Repair for Mitral Regurgitation.
Circ Cardiovasc Imaging
January 2025
DZHK (German Center for Cardiovascular Research), partner site Munich Heart Alliance (M.L., A.H., E.R., M.v.S., A. Kastrati, H.S., M.J., E.X., K.-L.L., J.H., T.T.).
Background: Right ventricular (RV) function has a well-established prognostic role in patients with severe mitral regurgitation (MR) undergoing transcatheter edge-to-edge repair (TEER) and is typically assessed using echocardiography-measured tricuspid annular plane systolic excursion. Recently, a deep learning model has been proposed that accurately predicts RV ejection fraction (RVEF) from 2-dimensional echocardiographic videos, with similar diagnostic accuracy as 3-dimensional imaging. This study aimed to evaluate the prognostic value of the deep learning-predicted RVEF values in patients with severe MR undergoing TEER.
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