ArticleEClinicalMedicine2025
Performance of an AI prediction tool for new-onset atrial fibrillation after coronary artery bypass grafting.
Article in EClinicalMedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-analysis and external validation of a risk model for gastrointestinal bleeding after percutaneous coronary intervention.BMC medicine · 2025Pooled it
- Clinical subtypes and prognosis of new-onset atrial fibrillation in critically ill patients.International journal of cardiology. Cardiovascular risk and prevention · 2026Article
- Development and Validation of an Interpretable Machine Learning Model for Predicting Early Pulmonary Metastasis Risk in Osteosarcoma.Cancer medicine · 2026Article
- Artificial intelligence and biomarker-driven prediction of post-coronary artery bypass grafting atrial fibrillation: Integrating clinical, genomic, and metabolic insights.Heart rhythm O2 · 2026Review
- Machine learning and Regression-Based models for prediction of postoperative atrial fibrillation following coronary artery bypass grafting: A systematic review and meta-analysis.International journal of cardiology. Cardiovascular risk and prevention · 2026Review
- Development and validation of a nomogram for assessing the risk of periodontitis in youth: a cross-sectional study.BMC oral health · 2026Article
- Adherence to guideline recommendations for postoperative atrial fibrillation: a nationwide survey reveals major prevention gaps.International journal of surgery (London, England) · 2026Article
- Development of a machine learning-based early prediction model for disease severity in acute pancreatitis.European journal of medical research · 2026Article
- Postoperative Atrial Fibrillation Impacted by Completeness of Coronary Revascularization and Antiplatelet Regimen.Cardiovascular therapeutics · 2026Article
- Development and validation of a machine learning-based predictive model for new-onset atrial fibrillation after CABG.Frontiers in cardiovascular medicine · 2026Article
- Neural network-based prediction of atrial fibrillation at discharge following cardiac surgery.Frontiers in cardiovascular medicine · 2026Article
- Predicting plaque-gingivitis risk in schoolchildren using an interpretable machine learning model: a cross-sectional study.BMC oral health · 2025Article
- Relative hyperglycemia and new-onset atrial fibrillation after coronary artery bypass grafting: a focus on stress hyperglycemia ratio.BMC cardiovascular disorders · 2025Article
- Risk factor identification and prediction of pleural effusion following coronary artery bypass grafting.American journal of translational research · 2025Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: There is lack of tools to predict new-onset postoperative atrial fibrillation (NOAF) after coronary artery bypass grafting (CABG). We aimed to develop and validate a novel AI-based bedside tool that accurately predicts predict NOAF after CABG. Methods: Data from 2994 patients who underwent CABG between March 2015 and July 2024 at two tertiary hospitals in China were retrospectively analyzed. 2486 patients from one hospital formed the derivation cohort, split 7:3 into training and test sets, while the 508 patients from a separate hospital formed the external validation cohort. A stacking model integrating 11 base learners was developed and evaluated using Accuracy, Precision, Recall, F1 score, and Area Under Curve (AUC). SHapley Additive exPlanations (SHAP) values were calculated and plotted to interpret the contributions of individual characteristics to the model's predictions. Findings: Seventy-seven predictive characteristics were analyzed. The stacking model achieved superior performance with AUCs 0·931 and F1 scores 0·797 in the independent external validation, outperforming CHA2DS2-VASc, HATCH, and POAF scores (AUC 0·931 vs. 0·713, 0·708, and 0·667; Interpretation: The AI-based tool offers superior prediction of NOAF, outperforming three existing predictive tools. Future studies should further explore how various patient characteristics influence the timing of NOAF onset, whether early or late. Funding: This work was funded by Lingnan Nightingale Nursing Research Institute of Guangdong Province, and Guangdong Nursing Society (GDHLYJYZ202401).
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