4.6 Editorial Material

Can Artificial Intelligence Solve Pulmonary Hypertension Paradox in Severe Tricuspid Regurgitation?

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Solving the Pulmonary Hypertension Paradox in Patients With Severe Tricuspid Regurgitation by Employing Artificial Intelligence

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Summary: This study aimed to improve the assessment of pulmonary hypertension (PH) in patients with severe tricuspid regurgitation (TR) using echocardiography. An extreme gradient boosting (XGB) algorithm was trained to predict mean pulmonary artery pressure (mPAP) based on echocardiographic parameters. The results showed that echocardiography consistently underestimated systolic pulmonary artery pressure compared to right heart catheterization. The XGB algorithm using 9 echocardiographic parameters could reliably predict mPAP levels. Patients with elevated predicted mPAP levels showed significantly reduced 2-year survival after transcatheter tricuspid valve intervention (TTVI). This study highlights the importance of accurate assessment of PH in patients with severe TR.

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