Machine learning predicts major adverse kidney and cardiovascular events in heart failure with reduced ejection fraction.
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.
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).
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.
Ensemble ML models with detailed clinical and echocardiographic features surpassed conventional risk scores, enabling early detection of major adverse cardiorenal events among HFrEF.
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).
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.
Ensemble ML models with detailed clinical and echocardiographic features surpassed conventional risk scores, enabling early detection of major adverse cardiorenal events among HFrEF.
Authors
Lai Lai, Huang Huang, Lu Lu, Wu Wu, Wu Wu, Wu Wu, Huang Huang, Lin Lin, Tseng Tseng
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