Prognostic Value of Artificial Intelligence–Enabled Electrocardiography–Derived Diastolic Dysfunction Grading and Trajectory in Patients Undergoing Transcatheter Aortic Valve Replacement

Journal of the American Heart Association 2026 Open access

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Authors: Chieh-Mei Tsai, Jwan A. Naser, Gal Tsaban, Eunjung Lee, Julia Wood, Allen S. Luis, Jeremy J. Thaden, Garvan C. Kane, Sorin V. Pislaru, Trevor J. Simard, Kevin L. Greason, Mackram F. Eleid
+2 more Vuyisile T. Nkomo, Jae K. Oh
DOI: 10.1161/jaha.125.046558
Cited by: 4
References: 42

Abstract

Background Artificial intelligence (AI)–enabled electrocardiography has emerged as a tool for detecting cardiac dysfunction. The prognostic relevance of AI‐enabled electrocardiography–derived diastolic dysfunction (DD) in patients undergoing transcatheter aortic valve replacement had not been assessed. Methods We analyzed 3197 patients undergoing transcatheter aortic valve replacement for severe aortic stenosis between 2010 and 2023 with baseline 12‐lead ECGs processed by a validated AI model to classify diastolic function (grades 0–3). Multivariable Cox models and nested model comparisons assessed associations with all‐cause mortality, including a prespecified analysis in patients with indeterminate echocardiographic grading. Trajectories were defined by change in AI‐enabled electrocardiography DD grade (

Fields of Study & Subjects

Cardiac Valve Diseases and Treatments Cardiovascular Function and Risk Factors Atrial Fibrillation Management and Outcomes

Publication details

Journal / source
Journal of the American Heart Association
Publisher
Ovid Technologies (Wolters Kluwer Health)
Volume
15
Issue
3
Pages
e046558–e046558
Publication date
30 January 2026
Language
EN