ArticleAmerican heart journal2016
A comparison of the CHARGE-AF and the CHA2DS2-VASc risk scores for prediction of atrial fibrillation in the Framingham Heart Study.
Article in American heart journal, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05442203 (Electrocardiogram-based Artificial Intelligence-assisted Detection of Heart Disease), which is not on this map. Cited by 59 papers, 3 of them syntheses that pooled it.
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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.
Electrocardiogram-based Artificial Intelligence-assisted Detection of Heart Disease
Who cites it
59 citing papers in PubMed, 3 syntheses or guidelines pooled it, 100 citations in OpenAlex.
- Racial and ethnic disparities in arrhythmia care: A call for action.Heart rhythm · 2022Guideline
- Utility of risk prediction models to detect atrial fibrillation in screened participants.European journal of preventive cardiology · 2021Pooled it
- Prediction models for atrial fibrillation applicable in the community: a systematic review and meta-analysis.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 · 2020Pooled it
- Validation of a genetic risk score for atrial fibrillation: A prospective multicenter cohort study.PLoS medicine · 2018Trial
- Real-time prediction of atrial fibrillation in intensive care unit: a meta-learning approach.JAMIA open · 2026Article
- ECG Signatures and Long-Term Ischemic Stroke Risk: A Deep Learning Analysis of 200,000 Patients.Journal of the American College of Cardiology · 2026Article
- Repeated CHARGE-AF Score Assessments for Predicting Incident Atrial Fibrillation: A Longitudinal Analysis from the ARIC Cohort.medRxiv : the preprint server for health sciences · 2026Article
- 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
- A new score with superior stroke risk prediction in atrial fibrillation: entropy-based information gain approaches in a large nationwide cohort.Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing · 2025Review
- Enhancing atrial fibrillation risk prediction in an observational cohort of tobacco-exposed individuals: the role of pulmonary function tests, symptom scores, and imaging.Respiratory research · 2025Observational
- Utility of biological aging acceleration in capturing transitions of atrial fibrillation and dementia: a population-based study.npj aging · 2025Article
- The incidence and risk factor of atrial fibrillation after percutaneous coronary intervention.BMC cardiovascular disorders · 2025Observational
- Machine Learning-Based Plasma Protein Risk Score Improves Atrial Fibrillation Prediction Over Clinical and Genomic Models.Circulation. Genomic and precision medicine · 2025Article
- Influence and role of polygenic risk score in the development of 32 complex diseases.Journal of global health · 2025Article
- Autonomic Nervous System Activity before Atrial Fibrillation Onset as Assessed by Heart Rate Variability.Reviews in cardiovascular medicine · 2025Article
- Prediction of incident atrial fibrillation using deep learning, clinical models, and polygenic scores.European heart journal · 2024Article
- Another piece in the puzzle of atrial fibrillation risk: clinical, genetic, and electrocardiogram-based artificial intelligence.European heart journal · 2024Article
- Risk Analysis of Atrial Fibrillation Based on ECG Phenotypes: The RAF-ECP Study Protocol.Phenomics (Cham, Switzerland) · 2024Article
- Cardiac mechanics and the risk of atrial fibrillation in a community-based cohort of older adults.European heart journal. Cardiovascular Imaging · 2024Article
- Integrating Clinical, Genetic, and Electrocardiogram-Based Artificial Intelligence to Estimate Risk of Incident Atrial Fibrillation.medRxiv : the preprint server for health sciences · 2024Article
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Authors and funding
8 authors at 5 institutions in 2 countries.
Funding
Abstract
backgroundAtrial fibrillation (AF) affects more than 33 million individuals worldwide and increases risks of stroke, heart failure, and death. The CHARGE-AF risk score was developed to predict incident AF in three American cohorts and it was validated in two European cohorts. The CHA2DS2-VASc risk score was derived to predict risk of stroke, peripheral embolism, and pulmonary embolism in individuals with AF, but it has been increasingly used for AF risk prediction. We compared CHARGE-AF risk score versus CHA2DS2-VASc risk score for incident AF risk in a community-based cohort. METHODS AND
resultsWe studied Framingham Heart Study participants aged 46 to 94 years without prevalent AF and with complete covariates. We predicted AF risk using Fine-Gray proportional sub-distribution hazards regression. We used the Wald χ(2) statistic for model fit, C-statistic for discrimination, and Hosmer-Lemeshow (HL) χ(2) statistic for calibration. We included 9722 observations (mean age 63.9 ± 10.6 years, 56% women) from 4548 unique individuals: 752 (16.5%) developed incident AF and 793 (17.4%) died. The mean CHARGE-AF score was 12.0 ± 1.2 and the sub-distribution hazard ratio (sHR) for AF per unit increment was 2.15 (95% CI, 99-131%; P < .0001). The mean CHA2DS2-VASc score was 2.0 ± 1.5 and the sHR for AF per unit increment was 1.43 (95% CI, 37%-51%; P < .0001). The CHARGE-AF model had better fit than CHA2DS2-VASc (Wald χ(2) = 403 vs 209, both with 1 df), improved discrimination (C-statistic = 0.75, 95% CI, 0.73-0.76 vs C-statistic = 0.71, 95% CI, 0.69-0.73), and better calibration (HL χ(2) = 5.6, P = .69 vs HL χ(2) = 28.5, P < .0001).
conclusionThe CHARGE-AF risk score performed better than the CHA2DS2-VASc risk score at predicting AF in a community-based cohort.
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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.