| Title: | Prediction of Atrial Fibrillation From the ECG in the Community Using Deep Learning: A Multinational Study |
| Journal: | Circulation Arrhythmia and Electrophysiology |
| Published: | 30 Sep 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41025252/ |
| DOI: | https://doi.org/10.1161/circep.125.013734 |
| URL: | https://pmc.ncbi.nlm.nih.gov/articles/PMC12569998/ |
Publication 19364
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Abstract
BACKGROUND: We aimed to refine and validate a deep neural network model from the ECG to predict atrial fibrillation (AF) risk, using samples from diverse backgrounds: the Framingham Heart Study (FHS), UK Biobank, and Estudo Longitudinal da Saúde do Adulto (ELSA-Brasil). We compared the model's performance to the clinical Cohorts for Heart and Aging Research in Genomic Epidemiology consortium (CHARGE-AF) risk score and evaluated the association with other cardiovascular outcomes.</p>
METHODS: The ECG-derived deep-learning prediction of AF (ECG-AF) model was refined using 60% of FHS samples free of AF. Its performance was then tested in the remaining FHS samples, UK Biobank, and ELSA-Brasil, with discrimination assessed by the area under the receiver operating characteristic curve. The association of ECG-AF with cardiovascular outcomes was assessed using Cox proportional hazards models.</p>
RESULTS: The study sample included 10 097 FHS participants (mean age 53±12 years; 54.9% women), 49 280 participants from the UK Biobank (mean age 64±8 years, 47.9% women), and 12 284 participants from ELSA-Brasil (mean age 53±8 years, 54.7% women). The ECG-AF model showed moderate discrimination for incident AF (area under the curve, 0.82 [95% CI, 0.80-0.84]) in the FHS, comparable to the CHARGE-AF score (area under the curve, 0.83 [95% CI, 0.81-0.85]), and incremental when combined (area under the curve, 0.85 [95% CI, 0.83-0.87]). In UK Biobank and ELSA-Brasil, combining ECG-AF and CHARGE also improved prediction. Higher ECG-AF scores were associated with increased risks of heart failure, myocardial infarction, stroke, and all-cause mortality in all 3 cohorts.</p>
CONCLUSIONS: In multinational cohort studies, the single-input ECG-AF deep neural network model demonstrated good performance in predicting AF and other cardiovascular outcomes, comparable to a multivariable clinical risk score, with improved performance when combined.</p>
17 Keywords
- Action Potentials
- Adult
- Aged
- Atrial Fibrillation
- Deep Learning
- Electrocardiography
- Female
- Heart Rate
- Humans
- Male
- Middle Aged
- Predictive Value of Tests
- Prognosis
- Reproducibility of Results
- Risk Assessment
- Risk Factors
- United Kingdom
10 Authors
- Luisa C.C. Brant
- Antônio H. Ribeiro
- Oseiwe B. Eromosele
- Marcelo M. Pinto-Filho
- Sandhi M. Barreto
- Bruce B. Duncan
- Martin G. Larson
- Emelia J. Benjamin
- Antonio L.P. Ribeiro
- Honghuang Lin
Enabling scientific discoveries that improve human health