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Prediction of incident atrial fibrillation: A comprehensive evaluation of conventional and artificial intelligence-enhanced approaches

Year: 2025
Type: article
Study design: Observational Study
Abstract: BACKGROUND: Multiple risk scores and biomarkers have been proposed for the prediction of atrial fibrillation (AF), but it is unknown how these compare with each other and whether they could be combined. OBJECTIVE: This study aimed to evaluate and compare approaches for incident AF prediction. METHODS: The artificial intelligence-enhanced electrocardiogram risk estimator-AF (AIRE-AF), a convolutional neural network with a discrete-time survival loss function, was developed to predict incident AF. It was trained using a dataset of 1,163,401 electrocardiograms from 189,539 patients from the Beth Israel Deaconess Medical Center and externally validated in the UK Biobank (n = 38,892). AIRE-AF was compared with other risk prediction approaches including the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE)-AF, a clinical risk score. RESULTS: In the Beth Israel Deaconess Medical Center cohort, AIRE-AF predicted incident AF with a C-index of 0.750 (0.743-0.758). AIRE-AF was superior to CHARGE-AF, left atrial size, and N-terminal pro-B-type natriuretic peptide. The addition of CHARGE-AF and left atrial size provided a minor improvement in performance (C-index improvement 0.017). There was no additive value of N-terminal pro-B-type natriuretic peptide in combination with AIRE-AF. The single best-performing single predictor in the volunteer population (UK Biobank) was CHARGE-AF (C-index 0.750 [0.734-0.769]). The best-performing combination of 2 predictors was AIRE-AF and CHARGE-AF (C-index 0.768 [0.743-0.792]). The addition of a polygenic risk score to AIRE-AF and CHARGE-AF provided a further significant improvement in performance (C-index 0.791 [0.766-0.816]). CONCLUSION: We present the first comprehensive evaluation of methodologies for predicting incident AF. Risk prediction with a model including AIRE-AF and CHARGE-AF resulted in similar performance to more complex models.
Source: Heart Rhythm
DOI: 10.1016/j.hrthm.2025.08.024
Authors: Arunashis Sau, Ewa Sieliwonczyk, Joseph Barker, Boroumand Zeidaabadi, Libor Pastika, Konstantinos Patlatzoglou, Gul Rukh Khattak, Kathryn A. McGurk, Nicholas S. Peters, Daniel B. Kramer, Jonathan W. Waks, James S. Ware, Fu Siong Ng
Institutions: Imperial College Healthcare NHS Trust, Imperial College London, University of Antwerp, Antwerp University Hospital, Beth Israel Deaconess Medical Center, Harvard University, Guy's and St Thomas' NHS Foundation Trust, Harefield Hospital, Chelsea and Westminster Hospital NHS Foundation Trust
Language: English
Field: Medicine
Open Access status: hybrid
Estimated APC paid: $4,790
Funders: Pfizer, NIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research, National Institute for Health and Care Research, British Heart Foundation, Sir Jules Thorn Charitable Trust, Medical Research Council, NIHR Imperial Biomedical Research Centre
Awards: G1138586538, Research Excellence (round 3), Identification of specific electrophenotypes in human persistent atrial fibrillation, G2759487863, G3226961849, Rare variant and phenotype analyses of adult cardiac trabecular morphology in the UK Biobank, G402563391, Understanding and Reducing Racial Bias in Cardiovascular Risk Prediction Using Novel AI Methods, G7227888905, G7562238089, Artificial intelligence to guide mechanism- specific electrophenotype-directed treatments for complex arrhythmias
OpenAlex ID: W4413478482
FWCI: 6.299
Cites: 29
Cited by: 10
Related to: 10