ArticleCirculation. Arrhythmia and electrophysiology2020
Artificial Intelligence-Electrocardiography to Predict Incident Atrial Fibrillation: A Population-Based Study.
Article in Circulation. Arrhythmia and electrophysiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 84 papers, 2 of them syntheses that pooled it.
What it found
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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.
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Who cites it
84 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review.BMC medicine · 2024Pooled it
- Electrocardiographic Predictors of Atrial Fibrillation.Medical sciences (Basel, Switzerland) · 2023Pooled it
- AI-Driven Atrial Fibrillation Management: From Signal to Strategy.Balkan medical journal · 2026Review
- From Automated ECG Interpretation to Multimodal Cardiovascular Intelligence: The Evolution of Artificial Intelligence in Cardiovascular Medicine.Medical sciences (Basel, Switzerland) · 2026Review
- ECG Signatures and Long-Term Ischemic Stroke Risk: A Deep Learning Analysis of 200,000 Patients.Journal of the American College of Cardiology · 2026Article
- Artificial Intelligence for Prediction and Detection of Atrial Fibrillation from Sinus-Rhythm Electrocardiograms and Ambulatory Monitoring.Biomedicines · 2026Review
- Artificial intelligence 12-lead electrocardiography to determine atrial fibrillation risk among UK Biobank participants with predisposing conditions.European heart journal. Digital health · 2026Article
- Inadequate Preventive Dental Care Is Associated With Higher Cardiovascular Risk Identified by ECG-Based Artificial Intelligence Algorithms.Journal of the American Heart Association · 2026Article
- Artificial Intelligence-Enhanced Electrocardiography for Predicting Paroxysmal Atrial Fibrillation From Sinus Rhythm: Impact of Data Integration Across Institutions and Devices.Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc · 2026Article
- Artificial intelligence for atrial fibrillation prediction while in sinus rhythm: a narrative review.European heart journal. Digital health · 2026Review
- Artificial Intelligence and the Expanding Universe of Cardio-Oncology: Beyond Detection Toward Prediction and Prevention of Therapy-Related Cardiotoxicity-A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2026Review
- Prognostic Value of Artificial Intelligence-Enabled Electrocardiography-Derived Diastolic Dysfunction Grading and Trajectory in Patients Undergoing Transcatheter Aortic Valve Replacement.Journal of the American Heart Association · 2026Article
- Artificial Intelligence Applied to Electrocardiograms Recorded in Sinus Rhythm for Detection and Prediction of Atrial Fibrillation: A Scoping Review.Medicina (Kaunas, Lithuania) · 2026Review
- Application of Artificial Intelligence in the Early Detection and Management of Atrial Fibrillation: State-of-the-art Review.European cardiology · 2026Review
- Artificial Intelligence-Enabled Electrocardiography for Preoperatively Detecting Atrial Fibrillation and Mortality Risk in Patients with Sinus Rhythm.International journal of medical sciences · 2026Article
- Predicting Incident Atrial Fibrillation after Stroke: A Scoping Review of Clinical Scores, Biomarkers, and AI-Enhanced Strategies.Cerebrovascular diseases extra · 2026Article
- ECG-based deep learning for chronic kidney disease detection and cardiovascular risk prediction.BMC medical informatics and decision making · 2025Article
- Artificial intelligence-enabled analysis of handheld single-lead electrocardiograms to predict incident atrial fibrillation: an analysis of the VITAL-AF randomized trial.NPJ digital medicine · 2025Article
- Atrial fibrillation in continuous flow-left ventricular assist device (CF-LVAD) patients: impact on pathophysiology.Journal of artificial organs : the official journal of the Japanese Society for Artificial Organs · 2025Review
- Detection of atrial fibrillation via artificial intelligence-assisted auscultation.BMC cardiovascular disorders · 2025Article
24 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
backgroundAn artificial intelligence (AI) algorithm applied to electrocardiography during sinus rhythm has recently been shown to detect concurrent episodic atrial fibrillation (AF). We sought to characterize the value of AI-enabled electrocardiography (AI-ECG) as a predictor of future AF and assess its performance compared with the CHARGE-AF score (Cohorts for Aging and Research in Genomic Epidemiology-AF) in a population-based sample.
methodsWe calculated the probability of AF using AI-ECG, among participants in the population-based Mayo Clinic Study of Aging who had no history of AF at the time of the baseline study visit. Cox proportional hazards models were fit to assess the independent prognostic value and interaction between AI-ECG AF model output and CHARGE-AF score. C statistics were calculated for AI-ECG AF model output, CHARGE-AF score, and combined AI-ECG and CHARGE-AF score.
resultsA total of 1936 participants with median age 75.8 (interquartile range, 70.4-81.8) years and median CHARGE-AF score 14.0 (IQR, 13.2-14.7) were included in the analysis. Participants with AI-ECG AF model output of >0.5 at the baseline visit had cumulative incidence of AF 21.5% at 2 years and 52.2% at 10 years. When included in the same model, both AI-ECG AF model output (hazard ratio, 1.76 per SD after logit transformation [95% CI, 1.51-2.04]) and CHARGE-AF score (hazard ratio, 1.90 per SD [95% CI, 1.58-2.28]) independently predicted future AF without significant interaction (
conclusionsIn the present study, both the AI-ECG AF model output and CHARGE-AF score independently predicted incident AF. The AI-ECG may offer a means to assess risk with a single test and without requiring manual or automated clinical data abstraction.
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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 OpenQuestion graph.