Glycemic Variability as a Predictor of Mortality in Sepsis Patients With Concurrent Persistent Inflammation, Immunosuppression, and Catabolism Syndrome.

Key Points

• To evaluate the relationship between glycemic variability and mortality in sepsis patients with persistent inflammation, immunosuppression, and catabolism syndrome.
• Analyzed correlations between glycemic variability and 28-day/180-day mortality in sepsis patients.
• Utilized deep learning models to assess prediction capabilities of glycemic variability.
• Focused on septic patients complicated with persistent inflammation, immunosuppression, and catabolism syndrome.
• Glycemic variability correlated significantly with both 28-day and 180-day mortality rates.
• Deep learning models confirmed the predictive accuracy of glycemic variability as a tool for assessing mortality.
• Findings offer important references for clinical decision-making regarding sepsis management.