Predicting major adverse cardiovascular and cerebrovascular events in chronic heart failure: a machine learning study - PubMed
2 hours ago
- #Cardiopulmonary Exercise Testing
- #Machine Learning Prediction
- #Chronic Heart Failure
- Machine learning models predict major adverse cardiovascular and cerebrovascular events (MACCE) in chronic heart failure.
- The Categorical Boosting (CatBoost) model performed best among seven ML models.
- Lower heart rate recovery at 1 minute (HRR1) and higher VE/VCO2 slope are key predictors.
- A retrospective cohort study analyzed 271 patients using cardiopulmonary exercise test data.
- SHAP analysis confirmed HRR1 and VE/VCO2 slope as significant independent predictors.