Novel Glucose Metrics and Machine Learning Predict Mortality in ICU Heart Failure

Introduction: Heart failure (HF) in critical care settings exhibits complex interactions with glycemic dysregulation. Traditional metrics fail to capture dynamic glucose fluctuations linked to poor outcomes. This study defines ICU-specific glycemic targets and establishes a machine learning (ML) framework integrating novel glucose indicators to predict 28-day mortality.


Methods: Cohort: 18,211 ICU HF patients (MIMIC-IV, 2008-2019).
Analysis:
1. Glycemic Metrics: Calculated time-weighted average glucose (TWABG), time-in-range (TIR), time-below-range (TBR), time-above-range (TAR)
2. Association Studies: Restricted cubic splines (RCS) to identify optimal glycemic ranges. Multivariable Cox regression with adjustment and subgroup analysis with interaction testing.
3. ML Modeling:
Dual-stage feature selection: Lasso regression and Boruta algorithm.
Model development: Developed models using 14 algorithms; SHAP interpretation applied to best-performing model (highest ROC-AUC).
Validation: SHAP analysis of an exemplar case demonstrated individualized mortality risk interpretation without formal model validation.


Results: 1. Optimal Glycemic Ranges: Patients within the optimal TWABG range (85–133 mg/dL) demonstrated the lowest mortality (15.09%，P < 0.001), with improved survival associated with both higher time-in-range (TIR) proportion and absence of glycemic excursions (time-below-range [TBR] or time-above-range [TAR] exposure)
2. ML Prediction: Feature selection via Lasso regression identified 30 predictors. Boruta algorithm confirmed 29 significant features. The optimized XGBoost model predicted 28-day mortality with AUC 0.779 (0.762-0.796)


Conclusions: Maintaining blood glucose within TWABG 85–133 mg/dL and maximizing TIR significantly reduces 28-day mortality in ICU HF patients, while avoiding excursions independently improves survival. Our ML framework, integrating dynamic glucose metrics with clinically selected features, accurately predicted mortality risk (AUC 0.779). Findings advocate ICU-specific glucose management and demonstrate ML's potential for risk stratification and personalized interventions.