Abstract
Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder, and critically ill patients with T2DM in intensive care unit (ICU) have an increased risk of mortality. In this study, we investigated the relationship between nine inflammatory indicators and prognosis in critically ill patients with T2DM to provide a clinical reference for assessing the prognosis of patients admitted to the ICU. Critically ill patients with T2DM were extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and divided into training and testing sets (7:3 ratio). An external validation cohort was collected from a single center in China using identical criteria. Logistic and Cox regression analyses were used to evaluate the relationship between nine inflammatory indicators and ICU, 30-day, and 90-day mortality rates. Significant predictive variables were chosen using least absolute shrinkage selection operator (LASSO) regression from logistic regression results, and a prognostic prediction model was built with multivariate logistic regression. The model was validated in both test and external validation sets. A total of 4,783 patients were included for model development and testing; an additional 204 served as the external validation set. The levels of eight inflammatory indicators were significantly correlated with short-term prognosis in critically ill patients with T2DM (P < 0.05 for all). The prediction model showed excellent discrimination performance, with AUC values of 0.825 (95% CI, 0.785-0.864) in the test set and 0.741 (95% CI, 0.630-0.851) in the external validation set. Calibration curves demonstrated strong consistency in both sets. In addition, decision curve analysis showed a net clinical benefit within 1-60% threshold probability in the test set and 10-41% threshold probability in the external validation set. Eight inflammatory indicators were identified as independent risk factors for prognosis in critically ill patients with T2DM. The prediction model showed promising performance in both internal and external validation cohorts, highlighting its potential as a valuable tool for early risk stratification and prediction of the outcomes of personalized treatment strategies in ICU settings.