ArticleJournal of the American Heart Association2026
Prognostic Value of Artificial Intelligence-Enabled Electrocardiography-Derived Diastolic Dysfunction Grading and Trajectory in Patients Undergoing Transcatheter Aortic Valve Replacement.
Article in Journal of the American Heart Association, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions.International journal of general medicine · 2026Review
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14 authors.
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Abstract
backgroundArtificial 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.
methodsWe 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 (<2 versus ≥2) from baseline to 30-day or 1-year follow-up.
resultsAt baseline, 21% had grade 3, 57% had grade 2, 8% had grade 1, and 14% had grade 0 AI-enabled electrocardiography DD. Higher grades were associated with adverse cardiac remodeling and comorbidities. Over a median follow-up of 3.4 years, grade 3 AI-enabled electrocardiography DD independently predicted mortality (hazard ratio [HR], 1.80 [95% CI, 1.47-2.20];
conclusionsAI-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.
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