ArticleJournal of the American Heart Association2021
Re-CHARGE-AF: Recalibration of the CHARGE-AF Model for Atrial Fibrillation Risk Prediction in Patients With Acute Stroke.
Article in Journal of the American Heart Association, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 18 citations in OpenAlex.
- Impact of a clinical atrial fibrillation risk estimation tool on cardiac rhythm monitor utilization following acute ischemic stroke: A prepost clinical trial.American heart journal · 2025Trial
- Dynamic Increase of the CJournal of the American Heart Association · 2025Article
- Artificial intelligence for prediction of atrial fibrillation in the stroke unit: a retrospective derivation validation cohort study.EBioMedicine · 2025Article
- Predicting Atrial Fibrillation After Stroke by Combining Polygenic Risk Scores and Clinical Features.Stroke · 2025Article
- Against reflexive recalibration: towards a causal framework for addressing miscalibration.Diagnostic and prognostic research · 2025Article
- The mCJACC. Advances · 2025Article
- Predicting post-stroke cognitive impairment using electronic health record data.International journal of stroke : official journal of the International Stroke Society · 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
- Natural Language Processing to Improve Prediction of Incident Atrial Fibrillation Using Electronic Health Records.Journal of the American Heart Association · 2022Article
- Development and Validation of a Novel Score for Predicting Paroxysmal Atrial Fibrillation in Acute Ischemic Stroke.International journal of environmental research and public health · 2022Article
- Cohort design and natural language processing to reduce bias in electronic health records research.NPJ digital medicine · 2022Article
- Automated risk assessment of newly detected atrial fibrillation poststroke from electronic health record data using machine learning and natural language processing.Frontiers in cardiovascular medicine · 2022Article
- Validation of Risk Scores for Predicting Atrial Fibrillation Detected After Stroke Based on an Electronic Medical Record Algorithm: A Registry-Claims-Electronic Medical Record Linked Data Study.Frontiers in cardiovascular medicine · 2022Article
- Natural courses of atrial fibrillation following cryptogenic stroke: A systematic review and meta-analysis.Indian pacing and electrophysiology journalArticle
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Authors and funding
17 authors at 5 institutions in 1 country.
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Abstract
Background Performance of existing atrial fibrillation (AF) risk prediction models in poststroke populations is unclear. We evaluated predictive utility of an AF risk model in patients with acute stroke and assessed performance of a fully refitted model. Methods and Results Within an academic hospital, we included patients aged 46 to 94 years discharged for acute ischemic stroke between 2003 and 2018. We estimated 5-year predicted probabilities of AF using the Cohorts for Heart and Aging Research in Genomic Epidemiology for Atrial Fibrillation (CHARGE-AF) model, by recalibrating CHARGE-AF to the baseline risk of the sample, and by fully refitting a Cox proportional hazards model to the stroke sample (Re-CHARGE-AF) model. We compared discrimination and calibration between models and used 200 bootstrap samples for optimism-adjusted measures. Among 551 patients with acute stroke, there were 70 incident AF events over 5 years (cumulative incidence, 15.2%; 95% CI, 10.6%-19.5%). Median predicted 5-year risk from CHARGE-AF was 4.8% (quartile 1-quartile 3, 2.0-12.6) and from Re-CHARGE-AF was 16.1% (quartile 1-quartile 3, 8.0-26.2). For CHARGE-AF, discrimination was moderate (C statistic, 0.64; 95% CI, 0.57-0.70) and calibration was poor, underestimating AF risk (Greenwood-Nam D'Agostino chi-square,
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