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Machine Learning Prediction of One-Year Mortality After Percutaneous Coronary Intervention in Acute Coronary Syndrome Patients Publisher Pubmed



Hosseini K1, 2 ; Behnoush AH1, 2, 3, 4 ; Khalaji A1, 2, 3, 4 ; Etemadi A1, 2, 5 ; Soleimani H1, 2, 4 ; Pasebani Y1, 2, 6 ; Jenab Y1, 2 ; Masoudkabir F1, 2 ; Tajdini M1, 2 ; Mehrani M1, 2 ; Nanna MG7
Authors
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Authors Affiliations
  1. 1. Cardiac Primary Prevention Research Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
  2. 2. Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran
  3. 3. School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
  4. 4. Non–Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
  5. 5. Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  6. 6. Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
  7. 7. Section of Cardiovascular Medicine, Yale School of Medicine, New Haven, CT, United States

Source: International Journal of Cardiology Published:2024


Abstract

Background: Machine learning (ML) models have the potential to accurately predict outcomes and offer novel insights into inter-variable correlations. In this study, we aimed to design ML models for the prediction of 1-year mortality after percutaneous coronary intervention (PCI) in patients with acute coronary syndrome. Methods: This study was performed on 13,682 patients at Tehran Heart Center from 2015 to 2021. Patients were split into 70:30 for testing and training. Four ML models were designed: a traditional Logistic Regression (LR) model, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Ada Boost models. The importance of features was calculated using the RF feature selector and SHAP based on the XGBoost model. The Area Under the Receiver Operating Characteristic Curve (AUC-ROC) for the prediction on the testing dataset was the main measure of the model's performance. Results: From a total of 9,073 patients with >1-year follow-up, 340 participants died. Higher age and higher rates of comorbidities were observed in these patients. Body mass index and lipid profile demonstrated a U-shaped correlation with the outcome. Among the models, RF had the best discrimination (AUC 0.866), while the highest sensitivity (80.9%) and specificity (88.3%) were for LR and XGBoost models, respectively. All models had AUCs of >0.8. Conclusion: ML models can predict 1-year mortality after PCI with high performance. A classic LR statistical approach showed comparable results with other ML models. The individual-level assessment of inter-variable correlations provided new insights into the non-linear contribution of risk factors to post-PCI mortality. © 2024 Elsevier B.V.
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