Machine-learning models to predict serious adverse hospitalization events after ACS

Machine-learning-based approaches for identifying predictors of SAHE after an ACS were feasible and practical. Based on this, we developed two online risk prediction websites for clinicians' decision-making. The CCC-ACS-MSAE score showed accurate discriminative capabilities for predicting severe adverse hospitalization events and might help guide clinical decision-making. Key messages: Three research questions and three bullet points What is already known on this topic? Observational studies have identified risk factors for in-hospital death in patients with acute coronary syndromes (ACS). However, the real-world results of a large sample in China still need to be further explored. What does this study add? Machine-learning-based approaches for identifying predictors of SAHE after an ACS were feasible and practical. Based on these findings, we developed two CCC-ACS In-hospital Major Adverse Events Risk Scores and its online calculator. One is based on machine-learning model (https://ccc-acs-sae-3-xcnjsvoccusjwkfhfthh44.streamlit.app/), and another is based on logistic regression model (https://ccc-acs-sae-logistic-9te57ylnq3kazkeuyc7dub.streamlit.app/), offering a validated tool to predict survival for patients with ACS during hospitalization. How this study might affect research, practice, or policy? Early identification of high-risk ACS patients will help reduce in-hospital deaths and improve the prognosis of ACS patients.