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

We developed a risk stratification model to predict serious adverse hospitalization events (mortality, cardiac shock, cardiac arrest) (SAHE) after acute coronary syndrome (ACS) based on machine-learning models and logistic regression model.

This cohort study is based on the CCC-ACS project. The primary efficacy outcomes were SAHE. Clinical prediction models were established based on five machine-learning (XGBoost, RF, MLP, KNN, and stacking model) and logistic regression models.

Among the 112 363 patients in the study, age (55-65 years: OR: 1.392; 95%CI: 1.212-1.600; 65-75 years: OR: 1.878; 95%CI: 1.647-2.144; ≥75 year: OR: 2.976; 95%CI: 2.615-3.393), history of diabetes mellitus (OR: 1.188; 95%CI: 1.083-1.302), history of renal failure (OR: 1.645; 95%CI: 1.311-2.044), heart rate (60-100 beats/min: OR: 0.468; 95%CI: 0.409-0.536; ≥100 beats/min: OR: 0.540; 95%CI: 0.454-0.643), shock index (0.4-0.8: OR: 1.796; 95%CI: 1.440-2.264; ≥0.8: OR: 5.883; 95%CI: 4.619-7.561), KILLIP (II: OR: 1.171; 95%CI: 1.048-1.306; III: OR: 1.696; 95%CI: 1.469-1.952; IV: OR: 7.811; 95%CI: 7.023-8.684), and cardiac arrest at admission (OR: 12.507; 95%CI: 10.757-14.530) were independent predictors of severe adverse hospitalization events for ACS patients. In several machine-learning models, RF (AUC: 0.817; 95%CI: 0.808-0.826) and XGBoost (AUC: 0.816; 95%CI: 0.807-0.825) also showed good discrimination in the training set, which ranked the first two positions. They also presented good accuracy and the best clinical benefits in the decision curve analysis. In addition, logistic regression was able to discriminate the SAHE (AUC: 0.816; 95%CI: 0.807-0.825) and performed the best prediction accuracy (0.822; 95%CI: 0.822-0.822) compared to several machine-learning models. Model calibration and decision curve analysis showed these prediction models have similar predictive performance. 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.

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.