Machine learning predicts major adverse kidney and cardiovascular events in heart failure with reduced ejection fraction

Background: Heart failure with reduced ejection fraction (HFrEF) links to adverse kidney and cardiovascular events. However, current machine learning (ML)-based algorithms do not include comprehensive echocardiographic features or predict adverse kidney outcomes. Methods: We built ML models to predict 3-year all-cause mortality, major adverse cardiovascular events (MACE), and major adverse kidney events (MAKE) in retrospective HFrEF cohorts. Models were developed from 1,486 patients at Taipei Veterans General Hospital during 2011-2018 and externally validated in 246 patients from the heart-failure-post-acute-care program at Taichung Veterans General Hospital during 2020-2023. Model performance was compared to the traditional Meta-Analysis Global Group in Chronic Heart Failure score (MAGGIC), Logistic Regression (LR), and Kidney-Failure-Risk-Equation (KFRE). Results: For mortality, Ensemble had the highest area under receiver-operating-characteristic curve (AUROC, 0.863; 95% confidence interval [CI], 0.814-0.913; p < 0.001), outperforming MAGGIC (AUROC, 0.741; 95% CI, 0.673-0.810) with net reclassification index (NRI) of 0.294 (95% CI, 0.147-0.440; p = 0.001). For MACE, Ensemble (AUROC, 0.816; 95% CI, 0.769-0.864; p < 0.001) surpassed LR (AUROC, 0.734; 95% CI, 0.677-0.791). For MAKE, Ensemble (AUROC, 0.858; 95% CI, 0.806-0.910; p < 0.001) exceeded KFRE (AUROC, 0.647; 95% CI, 0.560-0.735) with NRI of 0.267 (95% CI, 0.133-0.396; p < 0.001). External validation confirmed good Ensemble discrimination (AUROCs of 0.803, 0.808, and 0.859 for mortality, MACE, and MAKE, respectively). An online tool (https://vghhfrefai.com/) was created for application. Conclusion: Ensemble ML models with detailed clinical and echocardiographic features surpassed conventional risk scores, enabling early detection of major adverse cardiorenal events among HFrEF.