ArticleEuropean journal of clinical investigation2026
Machine learning-based prediction of atrial fibrillation in patients with atrial high-rate episodes.
Article in European journal of clinical investigation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A bibliometric analysis of artificial intelligence applications in heart failure.Journal of thoracic disease · 2026Article
- Comparative study of 11 machine learning algorithms for predicting recurrence risk after atrial fibrillation catheter ablation based on a real-world cohort: a retrospective study.BMC cardiovascular disorders · 2026Article
- Development and validation of an interpretable machine learning model for predicting atrial fibrillation risk in middle-aged and older patients with coronary heart disease.Frontiers in cardiovascular medicine · 2026Article
- Machine learning-based prediction of atrial fibrillation in patients with atrial high-rate episodes.European journal of clinical investigation · 2026Article
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Authors and funding
10 authors.
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Abstract
backgroundGiven the modest performance of available predictive models in estimating the risk of atrial fibrillation (AF) in patients with atrial high-rate episodes (AHREs) detected by cardiac implantable electronic devices (CIEDs), this study explores the potential use of machine learning (ML) algorithms in this context. PURPOSE: To assess the ability of ML techniques in identifying patients with AHRE at high risk of AF.
methodsIn this prospective study, we enrolled patients without a prior history of AF who experienced at least one AHRE episode detected by CIEDs. ML techniques were applied to predict the 1-year risk of developing new-onset AF based on the following variables: age, BMI, sex, smoking, hypertension, diabetes, coronary artery disease, chronic kidney disease, dyslipidaema, history of stroke or transient ischaemic attack, vascular heart disease, left atrial enlargement (LAE) and congestive heart failure.
resultsStudy population consists of 100 patients (48% male, mean age 66.0 ± 18.0 years), of whom 24 developed AF (24%) after 1-year follow-up. The CatBoost ML model achieved the highest AUC (.857, 95% CI .671-.999) when compared to other ML models and all clinical risk scores. The top four most influential predictors of AF in the CatBoost model were LAE, hypertension, diabetes and age.
conclusionsML techniques are robust in predicting AF in patients with AHREs. Further validation in larger, independent cohorts is warranted.
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