# Right Ventricular Dynamics in Tricuspid Regurgitation: Insights into Reverse Remodeling and Outcome Prediction Post Transcatheter Valve Intervention

## TL;DR

This paper explores how the right ventricle responds to tricuspid valve treatment, focusing on recovery and predicting outcomes using advanced imaging and AI.

## Contribution

The paper introduces AI and ML as tools to improve outcome prediction and optimize treatment strategies for tricuspid regurgitation.

## Key findings

- RV reverse remodeling after TTVI shows biphasic reduction in volume overload and improved strain.
- AI and ML enhance risk stratification by integrating clinical and imaging data.
- Molecular biomarkers and biomechanical imaging markers improve outcome prediction.

## Abstract

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.