ArticleMayo Clinic proceedings. Digital health2026
Prediction of Heart Failure With Reduced Ejection Fraction With Artificial Intelligence Electrocardiography in Patients With Atrial Fibrillation.
Article in Mayo Clinic proceedings. Digital health, 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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Who cites it
1 citing paper in PubMed.
- Artificial intelligence-enabled electrocardiography for assessment of left ventricular systolic dysfunction in the era of foundation models.Frontiers in cardiovascular medicine · 2026Review
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Authors and funding
9 authors.
Funding
Abstract
Objective: To determine whether a low ejection fraction artificial intelligence electrocardiogram (AI-ECG) algorithm predicts incident heart failure with reduced ejection fraction (HFrEF) in patients with atrial fibrillation (AF) independently of known HF risk factors. Patients and Methods: A validated AI-ECG algorithm for low ejection fraction (EF) detection was applied to a cohort of patients aged 18 years or older from Olmsted County, Minnesota, with incident AF between January 1, 2000, and December 31, 2014. Risk of HFrEF by tertiles of AI-ECG score were calculated, and model performance was assessed using Results: Among 2569 patients with AF, 248 (9.7%) developed incident HFrEF (EF<50%) over an average follow-up of 7.0 years. Patients in the highest risk AI-ECG tertile had an increased risk of developing HFrEF in comparison with those in the lowest risk tertile after adjusting for common HF risk factors (hazard ratio [HR], 3.50; 95% CI, 2.51-4.87). The Conclusion: Incorporation of the AI-ECG algorithm into routine clinical care may provide enhanced ability to identify patients with AF at risk of developing HFrEF with predictive performance that is superior to current clinical models.
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