Machine learning for prediction of bleeding in acute myocardial infarction patients after percutaneous coronary intervention

Background: Prediction of bleeding is critical for acute myocardial infarction (AMI) patients after percutaneous coronary intervention (PCI). Machine learning methods can automatically select the combination of the important features and learn their underlying relationship with the outcome. Objectives: We aimed to evaluate the predictive value of machine learning methods to predict in-hospital bleeding for AMI patients. Design: We used data from the multicenter China Acute Myocardial Infarction (CAMI) registry. The cohort was randomly partitioned into derivation set (50%) and validation set (50%). We applied a state-of-art machine learning algorithm, eXtreme Gradient Boosting (XGBoost), to automatically select features from 98 candidate variables and developed a risk prediction model to predict in-hospital bleeding (Bleeding Academic Research Consortium [BARC] 3 or 5 definition). Results: A total of 16,736 AMI patients who underwent PCI were finally enrolled. 45 features were automatically selected and were used to construct the prediction model. The developed XGBoost model showed ideal prediction results. The area under the receiver-operating characteristic curve (AUROC) on the derivation data set was 0.941 (95% CI = 0.909–0.973, p < 0.001); the AUROC on the validation set was 0.837 (95% CI = 0.772–0.903, p < 0.001), which was better than the CRUSADE score (AUROC: 0.741; 95% CI = 0.654–0.828, p < 0.001) and ACUITY-HORIZONS score (AUROC: 0.731; 95% CI = 0.641–0.820, p < 0.001). We also developed an online calculator with 12 most important variables (http://101.89.95.81:8260/), and AUROC still reached 0.809 on the validation set. Conclusion: For the first time, we developed the CAMI bleeding model using machine learning methods for AMI patients after PCI. Trial registration: NCT01874691. Registered 11 Jun 2013.