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Title:

Artificial intelligence-enabled phenotyping of patients with severe aortic stenosis: on the recovery of extra-aortic valve cardiac damage after transcatheter aortic valve replacement.

Document type:
Journal Article; Research Support, Non-U.S. Gov't
Author(s):
Lachmann, Mark; Rippen, Elena; Schuster, Tibor; Xhepa, Erion; von Scheidt, Moritz; Trenkwalder, Teresa; Pellegrini, Costanza; Rheude, Tobias; Hesse, Amelie; Stundl, Anja; Harmsen, Gerhard; Yuasa, Shinsuke; Schunkert, Heribert; Kastrati, Adnan; Laugwitz, Karl-Ludwig; Joner, Michael; Kupatt, Christian
Abstract:
OBJECTIVE: A novel artificial intelligence-based phenotyping approach to stratify patients with severe aortic stenosis (AS) prior to transcatheter aortic valve replacement (TAVR) has been proposed, based on echocardiographic and haemodynamic data. This study aimed to analyse the recovery of extra-aortic valve cardiac damage in accordance with this novel stratification system following TAVR. METHODS: The proposed phenotyping approach was previously established employing data from 366 patients wit...     »
Journal title abbreviation:
Open Heart
Year:
2022
Journal volume:
9
Journal issue:
2
Fulltext / DOI:
doi:10.1136/openhrt-2022-002068
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/36261218
TUM Institution:
Klinik für Herz- und Kreislauferkrankungen im Erwachsenenalter (Prof. Schunkert); Klinik und Poliklinik für Innere Medizin I, Kardiologie
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