eICU critical care database: Patient clinical parameters and outcomes for ICU risk prediction model development

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. 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. 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. 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.