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Titel:

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

Dokumenttyp:
Journal Article; Research Support, Non-U.S. Gov't
Autor(en):
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...     »
Zeitschriftentitel:
Open Heart
Jahr:
2022
Band / Volume:
9
Heft / Issue:
2
Volltext / DOI:
doi:10.1136/openhrt-2022-002068
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/36261218
TUM Einrichtung:
Klinik für Herz- und Kreislauferkrankungen im Erwachsenenalter (Prof. Schunkert); Klinik und Poliklinik für Innere Medizin I, Kardiologie
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