Key result
Transcatheter tricuspid valve interventions promote RV reverse remodeling linked to enhanced survival.
Why the study?
Tricuspid regurgitation has historically had suboptimal management due to high surgical risks, making transcatheter tricuspid valve interventions and understanding associated right ventricular reverse remodeling important.
Advanced imaging and AI/ML integration are crucial for assessing right ventricular reverse remodeling and optimizing prognostication in patients undergoing transcatheter tricuspid valve interventions.
AI integration may refine post-intervention prognostication; leaves open effects on clinical outcomes.
Tricuspid regurgitation (TR) represents a significant, often silently progressing, valvular heart disease with historically suboptimal management due to perceived high surgical risks. Transcatheter tricuspid valve interventions (TTVI) offer a promising, less invasive therapeutic avenue. Central to the success of TTVI is Right Ventricular Reverse Remodelling (RVRR), defined as an improvement in RV structure and function, which strongly correlates with enhanced patient survival. The right ventricle (RV) undergoes complex multi-scale biomechanical maladaptations, progressing from adaptive concentric to maladaptive eccentric hypertrophy, coupled with increased stiffness and fibrosis. Molecular drivers of this pathology include early failure of antioxidant defenses, metabolic shifts towards glycolysis, and dysregulation of microRNAs. Accurate RV function assessment necessitates advanced imaging modalities like 3D echocardiography, Cardiac Magnetic Resonance Imaging (CMR), and Computed Tomography (CT), along with strain analysis. Following TTVI, RVRR typically manifests as a biphasic reduction in RV volume overload, improved myocardial strain, and enhanced RV-pulmonary arterial coupling. Emerging molecular biomarkers alongside advanced imaging-derived biomechanical markers like CT-based 3D-TAPSE and RV longitudinal strain, are proving valuable. Artificial intelligence (AI) and machine learning (ML) are transforming prognostication by integrating diverse clinical, laboratory, and multi-modal imaging data, enabling unprecedented precision in risk stratification and optimizing TTVI strategies.
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Doldi et al. (2025) conducted a review in Tricuspid regurgitation. Transcatheter tricuspid valve interventions (TTVI) was evaluated. Transcatheter tricuspid valve interventions promote right ventricular reverse remodeling, which correlates with enhanced survival and can be assessed using advanced imaging and AI.
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