Sex Differences in ICU Mortality and Prediction of Prolonged ICU Stay: A Study Using the MIMIC-III Critical Care Database

Abstract

Background: Sex differences in critical illness outcomes remain contested after illness severity adjustment. Separately, early prediction of prolonged ICU stay has direct clinical utility for resource planning and discharge decision-making.

Objectives: This study aimed to determine whether biological sex is independently associated with in-hospital mortality after adjusting for SOFA-based illness severity, comorbidity, and ICU case-mix, and to develop predictive models for prolonged ICU stay of five days or more using first-24-hour clinical features.

Methods: The study used the MIMIC-III critical care database. Sequential multivariable logistic regression with multiple imputation by chained equations was applied to 31,000 adult first ICU admissions. For prediction of prolonged ICU stay, logistic regression, LASSO, Random Forest, and XGBoost models were evaluated on 26,729 patients using a stratified 70/30 train-test split.

Results: Female sex was not independently associated with in-hospital mortality in the fully adjusted model (OR 1.11, 95% CI 0.99–1.25, p = 0.064). For prolonged ICU stay prediction, Random Forest achieved the highest AUROC (0.829) and the lowest Brier score (0.126), while XGBoost achieved the highest AUPRC (0.591). Mean Glasgow Coma Scale was the dominant predictor across models.

Conclusion: Biological sex was not independently associated with in-hospital ICU mortality after adjustment for illness severity, comorbidity, and ICU case-mix. First-24-hour neurological status and oxygenation parameters were the strongest early predictors of prolonged ICU stay.