ArticleJournal of arrhythmia2026
Prediction of Atrial Fibrillation Risk Through the Integration of Genetic Information and Artificial Intelligence-Based Electrocardiogram Data.
Article in Journal of arrhythmia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Accurate prediction of atrial fibrillation (AF) is essential for prevention. The CHARGE-AF score, based on routinely available clinical factors, provides a practical tool for estimating AF risk but has limited predictive accuracy. This study aimed to improve AF risk prediction by integrating artificial intelligence (AI)-based electrocardiogram (ECG) analysis of age and sex with genetic information in addition to established clinical risk factors. Methods: We analyzed 39 478 UK Biobank participants without prior AF. A polygenic risk score for AF (AF-PRS), the AI-ECG age gap (AI-ECG-predicted age minus chronological age), and AI-ECG-predicted sex mismatch were evaluated on top of the CHARGE-AF model. Model performance was compared across sequential prediction models. Results: Over a median follow-up of 2.7 years (interquartile range, 1.7-4.2; maximum, 6.7), 533 participants (1.3%) developed AF. AF-PRS (HR 1.61, 95% CI: 1.48-1.76) and the AI-ECG age gap (HR 1.37, 95% CI: 1.24-1.51) were independently associated with incident AF. Compared with CHARGE-AF alone (C-index 0.708, 95% CI: 0.686-0.730), adding AF-PRS improved discrimination (C-index 0.743; Δ0.035, 95% CI: 0.021-0.049; Conclusions: Integrating genetic risk and AI-ECG-derived features enhances AF prediction beyond an established clinical model, supporting the use of combined digital and genetic biomarkers for risk stratification.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.