Comparison of Machine Learning Methods With National Cardiovascular Data Registry Models for Prediction of Risk of Bleeding After Percutaneous Coronary Intervention

Abstract

Key Points Question Can machine learning techniques, bolstered by better selection of variables, improve prediction of major bleeding after percutaneous coronary intervention (PCI)? Findings In this comparative effectiveness study that modeled more than 3 million PCI procedures, machine learning techniques improved the prediction of post-PCI major bleeding to a C statistic of 0.82 compared with a C statistic of 0.78 from the existing model. Machine learning techniques improved the identification of an additional 3.7% of bleeding cases and 1.0% of nonbleeding cases. Meaning By leveraging more complex, raw variables, machine learning techniques are better able to identify patients at risk for major bleeding and who can benefit from bleeding avoidance therapies.