P4622Prediction of in-hospital bleeding for AMI patients undergoing PCI using machine learning method

Abstract Background Prediction of in-hospital bleeding is critical for clinical decision making for acute myocardial infarction (AMI) patients undergoing percutaneous coronary intervention (PCI). Machine learning methods can automatically select the combination of the important features and learn their underlying relationship with the outcome. Objective We aim to evaluate the predictive value of machine learning methods to predict in-hospital bleeding for AMI patients. Methods We used data from the multicenter China Acute Myocardial Infarction (CAMI) registry. We randomly partitioned the cohort into derivation set (75%) and validation set (25%). Using data from the derivation set, we applied a state-of-art machine learning algorithm, XGBoost, to automatically select features from 106 candidate variables and train a risk prediction model to predict in-hospital bleeding (BARC 3, 5 definition). Results 16736 AMI patients who underwent PCI were consecutively included in the analysis, while 70 (0.42%) patients had in-hospital bleeding followed the BARC 3,5 definition of bleeding. Fifty-nine features were automatically selected from the candidate features and were used to construct the prediction model. The area under the curve (AUC) of the XGBoost model was 0.816 (95% CI: 0.745–0.887) on the validation set, while AUC of the CRUSADE risk score was 0.723 (95% CI: 0.619–0.828). Relative contribution of the 12 most important features Feature Relative Importance Direct bilirubin 0.078 Heart rate 0.077 CKMB 0.076 Creatinine 0.064 GPT 0.052 Age 0.048 SBP 0.036 TG 0.035 Glucose 0.035 HCT 0.031 Total bilirubin 0.030 Neutrophil 0.030 ROC of the XGBoost model and CRUSADE Conclusion The XGBoost model derived from the CAMI cohort accurately predicts in-hospital bleeding among Chinese AMI patients undergoing PCI. Acknowledgement/Funding the CAMS innovation Fund for Medical Sciences (CIFMS) (2016-12M-1-009); the Twelfth Five-year Planning Project of China (2011BAI11B02)