Trial reportCirculation. Genomic and precision medicine2021
Predictive Accuracy of a Clinical and Genetic Risk Model for Atrial Fibrillation.
Trial report in Circulation. Genomic and precision medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
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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
27 citing papers in PubMed, 32 citations in OpenAlex.
- Prediction of Atrial Fibrillation Risk Through the Integration of Genetic Information and Artificial Intelligence-Based Electrocardiogram Data.Journal of arrhythmia · 2026Article
- Development and Validation of a Clinical Polygenic Risk Report in U.S.-Based Health Systems for 8 Cardiovascular Conditions.Journal of the American College of Cardiology · 2026Article
- Interaction between clinical and genetic risk of atrial fibrillation in the danish diet, cancer and health cohort.Open heart · 2026Article
- Predicting Recurrence and Outcomes After Stressor-Associated Atrial Fibrillation Using ECG-Based Deep Learning.Journal of the American Heart Association · 2026Article
- Artificial intelligence 12-lead electrocardiography to determine atrial fibrillation risk among UK Biobank participants with predisposing conditions.European heart journal. Digital health · 2026Article
- Genetic variants and polygenic risk scores associated with paroxysmal atrial fibrillation in the Japanese population.PloS one · 2026Article
- Contributions of Common, Rare, and Somatic Genetic Variants to Incidence of Atrial Fibrillation.JAMA cardiology · 2025Article
- Biomarkers for Personalised Primary or Secondary Prevention in Cardiovascular Diseases: A Rapid Scoping Review.International journal of molecular sciences · 2025Article
- Machine Learning-Based Plasma Protein Risk Score Improves Atrial Fibrillation Prediction Over Clinical and Genomic Models.Circulation. Genomic and precision medicine · 2025Article
- Proteomic Signatures for Risk Prediction of Atrial Fibrillation.Circulation · 2025Article
- Characteristics Associated with Prevalent Atrial Fibrillation and Risk Profile for Incident Atrial Fibrillation an Elderly Population from ELSA-Brasil.Arquivos brasileiros de cardiologia · 2025Article
- Another piece in the puzzle of atrial fibrillation risk: clinical, genetic, and electrocardiogram-based artificial intelligence.European heart journal · 2024Article
- Prediction of incident atrial fibrillation using deep learning, clinical models, and polygenic scores.European heart journal · 2024Article
- Validation, bias assessment, and optimization of the UNAFIED 2-year risk prediction model for undiagnosed atrial fibrillation using national electronic health data.Heart rhythm O2 · 2024Article
- Integrating Clinical, Genetic, and Electrocardiogram-Based Artificial Intelligence to Estimate Risk of Incident Atrial Fibrillation.medRxiv : the preprint server for health sciences · 2024Article
- Innovative approaches to atrial fibrillation prediction: should polygenic scores and machine learning be implemented in clinical practice?Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2024Review
- Interaction of genetic risk and lifestyle on the incidence of atrial fibrillation.Heart (British Cardiac Society) · 2024Article
- Atrial fibrillation: pathophysiology, genetic and epigenetic mechanisms.The Lancet regional health. Europe · 2024Review
- Prevention of atrial fibrillation: a call to action.European heart journal · 2023Article
- Advances in basic and translational research in atrial fibrillation.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Review
Corrections and comments
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Authors and funding
17 authors at 7 institutions in 3 countries.
Funding
Abstract
backgroundAtrial fibrillation (AF) risk estimation using clinical factors with or without genetic information may identify AF screening candidates more accurately than the guideline-based age threshold of ≥65 years.
methodsWe analyzed 4 samples across the United States and Europe (derivation: UK Biobank; validation: FINRISK, Geisinger MyCode Initiative, and Framingham Heart Study). We estimated AF risk using the CHARGE-AF (Cohorts for Heart and Aging Research in Genomic Epidemiology AF) score and a combination of CHARGE-AF and a 1168-variant polygenic score (Predict-AF). We compared the utility of age, CHARGE-AF, and Predict-AF for predicting 5-year AF by quantifying discrimination and calibration.
resultsAmong 543 093 individuals, 8940 developed AF within 5 years. In the validation sets, CHARGE-AF (C index range, 0.720-0.824) and Predict-AF (0.749-0.831) had largely comparable discrimination, both favorable to continuous age (0.675-0.801). Calibration was similar using CHARGE-AF (slope range, 0.67-0.87) and Predict-AF (0.65-0.83). Net reclassification improvement using Predict-AF versus CHARGE-AF was modest (net reclassification improvement range, 0.024-0.057) but more favorable among individuals aged <65 years (0.062-0.11). Using Predict-AF among 99 530 individuals aged ≥65 years across each sample, 70 849 had AF risk <5%, of whom 69 067 (97.5%) did not develop AF, whereas 28 681 had AF risk ≥5%, of whom 2264 (7.9%) developed AF. Of 11 379 individuals aged <65 years with AF risk ≥5%, 435 (3.8%) developed AF before age 65 years, with roughly half (46.9%) meeting anticoagulation criteria.
conclusionsAF risk estimation using clinical factors may prioritize individuals for AF screening more precisely than the age threshold endorsed in current guidelines. The additional value of genetic predisposition is modest but greatest among younger individuals.
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