Abstract

Background: The association between the glucose-to-lymphocyte ratio (GLR) and adverse outcomes in intensive care unit patients receiving mechanical ventilation (MV) has not been clearly established.

Aims: To examine the link between GLR and 28-day mortality in MV patients and to develop an interpretable machine learning model to predict mortality risk.

Study Design: A retrospective study.

Methods: Data were obtained from the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 3.1) database. Receiver operating characteristic (ROC) and restricted cubic spline (RCS) curves were employed to assess the relationship between GLR and mortality. Patients were categorized into high and low GLR groups for Kaplan-Meier survival analysis. Subgroup analyses were performed to evaluate the association across different patient populations. Selected variables were used to construct eXtreme Gradient Boosting (XGBoost), support vector machine, Naive Bayes, and k-nearest neighbors models. Model interpretability was assessed using SHapley Additive exPlanations (SHAP) values.

Results: A total of 5,738 patients met the inclusion criteria. RCS analysis indicated a nonlinear relationship between GLR and 28-day mortality. Patients with elevated GLR had significantly higher 28-day mortality rates (hazard ratio > 1, p < 0.05). Among the models, XGBoost demonstrated the best performance, achieving an area under the ROC curve of 0.969 and an F1-score of 0.963. SHAP analysis identified Acute Physiology Score III, GLR, and lactate as the three most important predictors.

Conclusion: GLR is nonlinearly associated with 28-day mortality in patients undergoing MV and may serve as a valuable prognostic marker. The interpretable XGBoost model confirmed the significant association between GLR and short-term mortality.