Predicting 28-Day Mortality in First-Time ICU Patients with Heart Failure and Hypertension Using LightGBM: A MIMIC-IV Study
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Background
Heart Failure (HF) and Hypertension (HTN) are common yet severe cardiovascular conditions, both of which significantly increase the risk of adverse outcomes. Patients with comorbid HF and HTN face an elevated risk of mortality. Despite the importance of early risk assessment, current ICU management strategies struggle to accurately predict mortality, limiting effective clinical interventions. To address this gap, we developed a machine learning model to predict 28-day mortality in ICU patients with HF and HTN.
Methods
We extracted data from the MIMIC-IV database, identifying a cohort of 10,010 patients with both HF and HTN, among whom the 28-day mortality rate was 9.82%. The dataset was randomly split into training (70%) and testing (30%) cohorts. Feature selection was performed using a combination of the SelectKBest and Recursive Feature Elimination (RFE) wrapper methods. To address the class imbalance, we employed the Synthetic Minority Over-sampling Technique (SMOTE). Six machine learning models were developed and evaluated: Random Forest (RF), XGBoost, LightGBM, AdaBoost, Logistic Regression (LR), and a Neural Network (NN).
Results
A total of 18 features were selected. The best-performing model was LightGBM, achieving an AUROC of 0.8921 (95% CI: 0.8694 - 0.9118) with a sensitivity of 0.7941 and a specificity of 0.8391 at an optimal threshold of 0.2067. External validation further demonstrated strong performance with an AUROC of 0.7404 (95% CI: 0.7130 - 0.7664).
Conclusion
Our proposed model achieved an AUROC improvement of 16.8% compared to the best existing study on the same topic, while reducing the number of predictive features by 18.2%. This enhanced model underscores the potential of leveraging these selected features and our LightGBM model as a valuable tool in enhancing resource allocation and providing more personalized interventions for HF patients with HTN in ICU settings.
