ArticleEuropean journal of clinical investigation2025
Residual risk prediction in anticoagulated patients with atrial fibrillation using machine learning: A report from the GLORIA-AF registry phase II/III.
Article in European journal of clinical investigation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Observational
- Article
- Left Ventricular Thrombus in Ischemic Heart Failure: Machine-learning-based Prediction of Six-month Persistence and One-year Outcomes.Journal of cardiovascular translational research · 2026Article
- The COOL-AF 3-year prediction models for death, ischemic stroke, and major bleeding: a report from the COOL-AF registry.Journal of thrombosis and thrombolysis · 2026Article
- Atherogenic Index of Plasma and Residual Risk in Anticoagulated Patients With Atrial Fibrillation: The Prospective Murcia Atrial Fibrillation Project III Cohort.Journal of the American Heart Association · 2026Article
- Relation of the stress hyperglycaemia ratio to residual risk in anticoagulated patients with atrial fibrillation: A report from the prospective Murcia AF Project III cohort.Diabetes, obesity & metabolism · 2026Article
- Machine learning prediction of moderate-to-severe acute kidney injury after ICU admission and cardiac surgery with urine trace elements.European journal of clinical investigation · 2026Article
- Application of Artificial Intelligence in the Early Detection and Management of Atrial Fibrillation: State-of-the-art Review.European cardiology · 2026Review
- Sex differences in the association of social determinants of health and adverse cardiovascular outcomes in patients with atrial fibrillation.Open heart · 2025Article
- Relation of the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio to residual risk in anticoagulated patients with atrial fibrillation: a report from the prospective Murcia AF Project III cohort.Cardiovascular diabetology · 2025Observational
- How Far Can Time-Series Models Go in Capturing Dynamic Stroke Risk Post Atrial Fibrillation?JACC. Asia · 2025Article
- Remnant-to-LDL cholesterol discordance as a predictor of thromboembolic events in anticoagulated patients with atrial fibrillation: a report from the prospective Murcia AF project III (MAFP-III) cohort study.Cardiovascular diabetology · 2025Observational
- Does hospitalisation improve oral anticoagulant optimisation in patients with atrial fibrillation?European journal of clinical investigation · 2025Observational
- Residual risk prediction in anticoagulated patients with atrial fibrillation using machine learning: A report from the GLORIA-AF registry phase II/III.European journal of clinical investigation · 2025Article
- Machine learning to identify phenotypic clusters of patients with atrial fibrillation.Heart rhythm O2 · 2025Article
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11 authors.
Funding
Abstract
backgroundAlthough oral anticoagulation decreases the risk of thromboembolism in patients with atrial fibrillation (AF), a residual risk of thrombotic events still exists. This study aimed to construct machine learning (ML) models to predict the residual risk in these patients.
methodsPatients with newly diagnosed non-valvular AF were collected from the Global Registry on Long-Term Oral Anti-Thrombotic Treatment in Patients with Atrial Fibrillation (GLORIA-AF) registry. To predict the residual risk of the composite outcome of thrombotic events (defined as ischemic stroke, systemic embolism, transient ischemic attack and myocardial infarction), we constructed four prediction models using the logistic regression (LR), random forest, light gradient boosting machine and extreme gradient boosting machine ML algorithms. Performance was mainly evaluated by area under the receiver-operating characteristic curve (AUC), g-means and F1 scores. Feature importance was evaluated by SHapley Additive exPlanations.
results15,829 AF patients (70.33 ± 9.94 years old, 55% male) taking oral anticoagulation were included in our study, and 641 (4.0%) had residual risk, sustaining thrombotic events. In the test set, LR had the best performance with higher AUC trend of 0.712. RF has highest g-means of 0.295 and F1 score of 0.249. This was superior when compared with the CHA
conclusionML algorithms can improve the prediction of residual risk of anticoagulated AF patients compared to clinical risk factor-based scores.
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