The monocyte-to-lymphocyte ratio (MLR) has been associated with the prognosis of various diseases. However, evidence delineating its relationship with adverse outcomes in sepsis-associated delirium (SAD) remains sparse. The present study seeks to elucidate the association between MLR and 28-day all-cause mortality in patients with SAD. Patients diagnosed with SAD were identified from the MIMIC-IV and eICU-CRD databases according to the Sepsis-3 criteria and Confusion Assessment Method for the Intensive Care Unit (CAM-ICU). Patients were grouped by MLR quartiles. The primary outcome was 28-day all-cause mortality. Nonlinearity was assessed with restricted cubic splines (RCS). Survival was compared with Kaplan-Meier (K-M) curves and the log rank test. Hazard ratios (HR) and 95% confidence interval (CI) were estimated with stratified and adjusted Cox models. We also built survival machine learning models that included MLR and clinical features selected with the Random Forest to predict 28-day all-cause mortality. Model performance was quantified using the concordance index (C-index), time-dependent area under the curve (td-AUC), and median survival time ROC curves. External validation in the eICU-CRD database was used to assess generalizability. SHapley Additive exPlanations (SHAP) analyses were performed using a reduced background dataset derived through K-means clustering. A total of 3463 SAD patients were included in the study, of whom 748 (21.60%) died within 28 days. Cox regression analysis demonstrated that higher MLR levels were significantly associated with an increased risk of 28-day all-cause mortality (HR: 1.08; 95% CI: 1.02–1.15; p = 0.013). RCS suggested a nonlinear association (P overall = 0.0038, P nonlinearity = 0.0026). K-M curves showed that the Q4 group exhibited a significantly higher risk of 28-day all-cause mortality (HR: 1.44; 95% CI: 1.15–1.80; p = 0.001). Subgroup analyses indicated the robustness of the observed associations, with no interaction effects between MLR and any subgroup (p > 0.05). The best machine learning model, the Random Survival Forest model, achieved an AUC of 0.76 in internal validation and 0.67 in external validation. SHAP analysis confirmed that elevated MLR was linked to a high predicted risk of death. Elevated MLR is independently associated with 28-day all-cause mortality in patients with SAD. Survival machine learning models established based on MLR can effectively predict 28-day all-cause mortality in SAD patients. Not applicable.
Zhao et al. (Sat,) studied this question.