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Decoding the Complexity of Tricuspid Regurgitation Prognostic Drivers Using Explainable Artificial Intelligence
Gal Tsaban1, Giovanni Benfari1,2, Benjamin A Essayagh1,3
1Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
European Heart Journal. Cardiovascular Imaging
|June 27, 2026
Summary
Explainable AI identified key predictors of mortality in tricuspid regurgitation (TR), revealing hemodynamics and right-ventricular dysfunction as crucial factors. This improves risk stratification for better patient management.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Prognostics
Background:
- Tricuspid regurgitation (TR) prognosis is complex due to intricate clinical and hemodynamic interactions, challenging accurate risk stratification.
- Current methods may not fully capture the multifactorial nature of TR outcomes.
Purpose of the Study:
- To employ explainable artificial intelligence (EAI) to model TR prognosis.
- To identify and rank key predictors of mortality in patients with TR across the full severity spectrum.
Main Methods:
- Analysis of a large prospective registry of 9,761 patients with native TR.
- Application of supervised EAI models to identify non-linear associations and interactions among baseline characteristics.
- Quantitative Doppler-echocardiography and detailed clinical evaluation.
Main Results:
- EAI identified 20 prognostic determinants of mortality, with hemodynamic measures (pulmonary-artery systolic and right-atrial pressures) being most significant.
- Right-ventricular dysfunction and effective regurgitant orfice area (EROA) were stronger TR-specific predictors than integrated scores.
- Non-linear effects and interactions (e.g., EROA with pulmonary pressures) influenced survival.
Conclusions:
- EAI effectively ranked mortality predictors in TR, emphasizing hemodynamics, patient factors, and TR-specific measures.
- Effective regurgitant orfice area (EROA) emerged as a strong TR severity marker, with increased mortality observed at 40mm² and 60mm².
- These findings can refine risk stratification, guide intervention timing, and inform long-term management strategies for TR.
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