ArticleFrontiers in cardiovascular medicine2022
Machine learning algorithms for predicting mortality after coronary artery bypass grafting.
Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The Role of the Cardiothoracic Surgeon in the Age of AI-Are the Robots Going to Take Our Jobs?Medical sciences (Basel, Switzerland) · 2026Pooled it
- Evaluation of machine learning models for stroke risk prediction in atrial fibrillation: a systematic review and meta-analysis.BMC cardiovascular disorders · 2026Pooled it
- Evolution of coronary artery bypass grafting: conventional, minimally invasive, robotic, and hybrid revascularisation techniques.Journal of robotic surgery · 2026Review
- Cardiac Rehabilitation in Patients After Coronary Artery Bypass Grafting: Core Components and Long-Term Follow-Up.Journal of clinical medicine · 2026Review
- Multimodality Cardiovascular Imaging in Patients After Coronary Artery Bypass Grafting: Diagnosis and Risk Stratification.Diagnostics (Basel, Switzerland) · 2025Review
- Development and validation of interpretable machine learning models to predict intensive care unit outcomes in patients on hemodialysis: a multicenter study.BMC medical informatics and decision making · 2025Article
- Machine Learning-Based Prediction of Three-Year Heart Failure and Mortality After Premature Ventricular Contraction Ablation.Diagnostics (Basel, Switzerland) · 2025Article
- Machine learning in predicting preoperative intra-aortic balloon pump use in patients undergoing coronary artery bypass grafting.Journal of cardiothoracic surgery · 2025Article
- Predicting Major Adverse Cardiovascular Events After Cardiac Surgery Using Combined Clinical, Laboratory, and Echocardiographic Parameters: A Machine Learning Approach.Medicina (Kaunas, Lithuania) · 2025Article
- Artificial Intelligence in Risk Stratification and Outcome Prediction for Transcatheter Aortic Valve Replacement: A Systematic Review and Meta-Analysis.Journal of personalized medicine · 2025Review
- Machine learning model for the prediction of postoperative acute kidney injury (AKI) in neonates undergoing digestive surgery.Pediatric surgery international · 2025Article
- Machine learning-based hybrid risk estimation system (ERES) in cardiac surgery: Supplementary insights from the ASA score analysis.PLOS digital health · 2025Article
- Review
- Lactate-related gene signatures as prognostic predictors and comprehensive analysis of immune profiles in nasopharyngeal carcinoma.Journal of translational medicine · 2024Article
- Predictive Value of Preoperative Morphology Parameters in Patients Undergoing On-Pump and Off-Pump Coronary Artery Bypass Surgery.Journal of cardiovascular development and disease · 2024Article
- Coronary artery bypass graftingJournal of thoracic disease · 2024Article
- Heart surgery over two decades: what we have learned about results and changing risks.BMC cardiovascular disorders · 2024Article
- Prognostic factors of surgical management for heart failure patients with reduced left ventricular ejection fraction.Heliyon · 2024Article
- Predictive modeling for acute kidney injury after percutaneous coronary intervention in patients with acute coronary syndrome: a machine learning approach.European journal of medical research · 2024Article
- Article
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: As the era of big data analytics unfolds, machine learning (ML) might be a promising tool for predicting clinical outcomes. This study aimed to evaluate the predictive ability of ML models for estimating mortality after coronary artery bypass grafting (CABG). Materials and methods: Various baseline and follow-up features were obtained from the CABG data registry, established in 2005 at Tehran Heart Center. After selecting key variables using the random forest method, prediction models were developed using: Logistic Regression (LR), Support Vector Machine (SVM), Naïve Bayes (NB), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) algorithms. Area Under the Curve (AUC) and other indices were used to assess the performance. Results: A total of 16,850 patients with isolated CABG (mean age: 67.34 ± 9.67 years) were included. Among them, 16,620 had one-year follow-up, from which 468 died. Eleven features were chosen to train the models. Total ventilation hours and left ventricular ejection fraction were by far the most predictive factors of mortality. All the models had AUC > 0.7 (acceptable performance) for 1-year mortality. Nonetheless, LR (AUC = 0.811) and XGBoost (AUC = 0.792) outperformed NB (AUC = 0.783), RF (AUC = 0.783), SVM (AUC = 0.738), and KNN (AUC = 0.715). The trend was similar for two-to-five-year mortality, with LR demonstrating the highest predictive ability. Conclusion: Various ML models showed acceptable performance for estimating CABG mortality, with LR illustrating the highest prediction performance. These models can help clinicians make decisions according to the risk of mortality in patients undergoing CABG.
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