ArticleJournal of cardiovascular development and disease2023
Machine Learning to Identify Patients at Risk of Developing New-Onset Atrial Fibrillation after Coronary Artery Bypass.
Article in Journal of cardiovascular development and disease, 2023. 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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Who cites it
14 citing papers in PubMed.
- Artificial intelligence and biomarker-driven prediction of post-coronary artery bypass grafting atrial fibrillation: Integrating clinical, genomic, and metabolic insights.Heart rhythm O2 · 2026Review
- Prediction Models for Postoperative Atrial Fibrillation After Cardiac Surgery: A Systematic Review and Critical Appraisal.Journal of clinical medicine · 2026Review
- Machine learning-based prediction of postoperative atrial fibrillation risk in coronary artery bypass grafting patients.Journal of cardiothoracic surgery · 2026Article
- 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
- Synthetic artificial intelligence in cardiology: from generative models to clinical applications.European heart journal open · 2026Review
- Development and validation of a machine learning-based predictive model for new-onset atrial fibrillation after CABG.Frontiers in cardiovascular medicine · 2026Article
- Multilevel predictors categorization for post-CABG atrial fibrillation prediction.Biology methods & protocols · 2026Article
- Prediction Model for POstoperative atriaL fibrillAtion in caRdIac Surgery: The POLARIS Score.Journal of clinical medicine · 2025Article
- Optimizing clinical prediction model for new-onset atrial fibrillation in critically ill patient: Based on machine learning.PloS one · 2025Article
- Machine learning prediction of post-CABG atrial fibrillation using clinical and pharmacogenomic biomarkers.Frontiers in medicine · 2025Article
- Risk factors and prediction model for acute ischemic stroke after off-pump coronary artery bypass grafting based on Bayesian network.BMC medical informatics and decision making · 2024Article
- Postoperative Atrial Fibrillation: A Review.Biomedicines · 2024Review
- Machine learning in risk prediction of continuous renal replacement therapy after coronary artery bypass grafting surgery in patients.Clinical and experimental nephrology · 2024Article
- Postoperative atrial fibrillation (POAF) after cardiac surgery: clinical practice review.Journal of thoracic disease · 2024Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aims to get an effective machine learning (ML) prediction model of new-onset postoperative atrial fibrillation (POAF) following coronary artery bypass grafting (CABG) and to highlight the most relevant clinical factors.
methodsFour ML algorithms were employed to analyze 394 patients undergoing CABG, and their performances were compared: Multivariate Adaptive Regression Spline, Neural Network, Random Forest, and Support Vector Machine. Each algorithm was applied to the training data set to choose the most important features and to build a predictive model. The better performance for each model was obtained by a hyperparameters search, and the Receiver Operating Characteristic Area Under the Curve metric was selected to choose the best model. The best instances of each model were fed with the test data set, and some metrics were generated to assess the performance of the models on the unseen data set. A traditional logistic regression was also performed to be compared with the machine learning models.
resultsRandom Forest model showed the best performance, and the top five predictive features included age, preoperative creatinine values, time of aortic cross-clamping, body surface area, and Logistic Euro-Score.
conclusionsThe use of ML for clinical predictions requires an accurate evaluation of the models and their hyperparameters. Random Forest outperformed all other models in the clinical prediction of POAF following CABG.
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