Article
Machine learning identifies pathophysiologically and prognostically informative phenotypes among patients with mitral regurgitation undergoing transcatheter edge-to-edge repair.
European heart journal. Cardiovascular Imaging - 24 Apr 2023
Trenkwalder Teresa, Lachmann Mark, Stolz Lukas, Fortmeier Vera, Covarrubias Héctor Alfonso Alvarez, Rippen Elena, Schürmann Friederike, Presch Antonia, von Scheidt Moritz, Ruff Celine, Hesse Amelie, Gerçek Muhammed, Mayr N Patrick, Ott Ilka, Schuster Tibor, Harmsen Gerhard, Yuasa Shinsuke, Kufner Sebastian, Hoppmann Petra, Kupatt Christian, Schunkert Heribert, Kastrati Adnan, Laugwitz Karl-Ludwig, Rudolph Volker, Joner Michael, Hausleiter Jörg, Xhepa Erion
Abstract excerpt
AIMS: Patients with mitral regurgitation (MR) present with considerable heterogeneity in cardiac damage depending on underlying aetiology, disease progression, and comorbidities. This study aims to capture their cardiopulmonary complexity by employing a machine-learning (ML)-based phenotyping approach. METHODS AND RESULTS: Data were obtained from 1426 patients undergoing mitral valve transcatheter edge-to-edge...
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