Machine learning-driven sedation-analgesia optimization in mechanically ventilated sepsis patients: a retrospective MIMIC-IV analysis - Hu, Qinxue; Xu, Tao; Gao, Xiaolan; Lei, Xianying; Hu, Lirong (Frontiers in Pharmacology, 2026)
BackgroundIn the intensive care unit (ICU), septic patients frequently require endotracheal intubation followed by invasive mechanical ventilation. Nonetheless, the optimal sedation-analgesia regimen for these critically ill patients remains undetermined.MethodsThis retrospective observational study analyzed data from the Medical Information Mart for Intensive Care IV (MIMIC-IV version 3.0) database to examine septic patients who underwent endotracheal intubation and subsequent invasive mechanical ventilation in the intensive care unit. Initially, Kaplan–Meier survival analysis and Cox proportional hazards models were employed to evaluate the prognostic impact of different sedation-analgesia regimens. Subsequently, the least absolute shrinkage and selection operator (LASSO) regression was utilized to identify key prognostic factors. Multiple machine learning algorithms were then implemented to develop predictive models, and the SHapley Additive exPlanations (SHAP) method was used to interpret the model outputs and determine the most influential predictors.ResultsFollowing the initial screening process, seven distinct sedation-analgesia regimens with sample sizes greater than 100 were incorporated into the final analysis. Utilizing Kaplan–Meier estimates and Cox regression models, the combination of fentanyl and midazolam was identified as the most advantageous regimen. This association remained statistically significant after adjusting for confounding variables, demonstrating a reduction in the length of stay in the intensive care unit (length of stay in ICU, HR [95% CI]: 0.66 [0.52–0.85]) and a decrease in ICU mortality (OR [95% CI]: 0.62 [0.46–0.85]). Subsequently, LASSO regression analysis identified seven key prognostic factors associated with outcomes in this patient subgroup. Among the machine learning models developed for outcome prediction, the LightGBM model exhibited superior performance (AUC = 0.838). SHAP analysis indicated that the top three predictors of 28-day mortality were the Acute Physiology Score III (APS III), patient age, and the presence of acute renal failure.ConclusionThe concurrent administration of fentanyl and midazolam was associated with lower ICU mortality and shorter length of ICU stay among septic patients necessitating endotracheal intubation and invasive mechanical ventilation, suggesting potential clinical benefit. Furthermore, the LightGBM algorithm exhibited superior predictive accuracy for ICU mortality within this cohort, suggesting its potential utility as a tool for supporting data-driven clinical decision-making.