Abstract
BACKGROUND AND OBJECTIVE: Despite advances in intensive care, sepsis remains a leading cause of mortality in intensive care unit (ICU) patients, especially middle-aged and elderly individuals. Given the limitations of conventional scoring systems and the interpretability challenges of machine learning models, this study aims to develop and temporally validate a nomogram for predicting 28-day ICU mortality in middle-aged and elderly sepsis patients via the eICU database (2014--2015), providing a clinically practical prediction tool. METHODS: This retrospective study included 13,717 sepsis patients aged ≥45 years. The cohort was temporally divided into training (n = 6,397, 2014) and validation (n = 7,320, 2015) sets. Variable selection was performed via random forest importance ranking and LASSO regression. A nomogram was developed on the basis of multivariable logistic regression analysis. RESULTS: The 28-day ICU mortality rates were 9.08% and 9.49% in the training and validation cohorts, respectively. The final nomogram incorporated 11 independent predictors: red cell distribution width (RDW), SOFA score, lactate, pH, 24-hour urine output, platelet count, total protein, temperature, heart rate, GCS score, and white blood cell (WBC) count. The model showed good discrimination in both the training (AUC: 0.805) and validation (AUC: 0.756) cohorts. The calibration curves demonstrated good agreement between the predicted and observed probabilities. CONCLUSIONS: We developed and temporally validated a nomogram with good predictive performance for 28-day ICU mortality in middle-aged and elderly sepsis patients, providing a practical tool for risk stratification and clinical decision-making.