Prognostic Value of Artificial Intelligence–Enabled Electrocardiography–Derived Diastolic Dysfunction Grading and Trajectory in Patients Undergoing Transcatheter Aortic Valve Replacement
AI-enabled electrocardiography-derived DD independently predicts mortality after transcatheter aortic valve replacement, adds value beyond echocardiographic grading, and enables dynamic risk stratification through longitudinal tracking.
Authors
- Sorin V. Pislaru (ORCID: https://orcid.org/0000-0002-5256-3150)
- Jeremy J. Thaden (ORCID: https://orcid.org/0000-0003-4489-2349)
- Jwan A. Naser (ORCID: https://orcid.org/0000-0002-6210-7669)
- Vuyisile T. Nkomo (ORCID: https://orcid.org/0000-0001-6095-5579)
- Eunjung Lee (ORCID: https://orcid.org/0000-0002-6476-9527)
- Gal Tsaban
- Mackram F. Eleid
- Allen S. Luis
- Jae K. Oh
- Garvan C. Kane
- Chieh‐Mei Tsai
- Julia Wood (ORCID: https://orcid.org/0000-0003-3370-4461)
- Trevor J. Simard
Institutions
- WinnMed (US)
Publication Details
- Journal
- Journal of the American Heart Association
- Published
- 2026-01-30
- DOI
- https://doi.org/10.1161/jaha.125.046558
- Primary Topic
- Cardiac Valve Diseases and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00