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Machine learning-based risk prediction of intrahospital clinical outcomes in patients undergoing TAVI

Currently, patient selection in TAVI is based upon a multidisciplinary heart team assessment of patient comorbidities and surgical risk stratification. In an era of increasing need for precision medicine and quickly expanding TAVI indications, machine learning has shown promise in making accurate pr...

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Bibliographische Detailangaben
Hauptverfasser: Gomes Botelho Quintas, Bruna (VerfasserIn) , Pilz, Maximilian (VerfasserIn) , Reich, Christoph (VerfasserIn) , Leuschner, Florian (VerfasserIn) , Konstandin, Mathias (VerfasserIn) , Katus, Hugo (VerfasserIn) , Meder, Benjamin (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 2021
In: Clinical research in cardiology
Year: 2021, Jahrgang: 110, Heft: 3, Pages: 343-356
ISSN:1861-0692
DOI:10.1007/s00392-020-01691-0
Online-Zugang:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1007/s00392-020-01691-0
Volltext
Verfasserangaben:Bruna Gomes, Maximilian Pilz, Christoph Reich, Florian Leuschner, Mathias Konstandin, Hugo A. Katus, Benjamin Meder
Beschreibung
Zusammenfassung:Currently, patient selection in TAVI is based upon a multidisciplinary heart team assessment of patient comorbidities and surgical risk stratification. In an era of increasing need for precision medicine and quickly expanding TAVI indications, machine learning has shown promise in making accurate predictions of clinical outcomes. This study aims to predict different intrahospital clinical outcomes in patients undergoing TAVI using a machine learning-based approach. The main clinical outcomes include all-cause mortality, stroke, major vascular complications, paravalvular leakage, and new pacemaker implantations.
Beschreibung: Published online: 24 June 2020
Gesehen am 08.09.2021
Beschreibung:Online Resource
ISSN:1861-0692
DOI:10.1007/s00392-020-01691-0

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