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Association Between the Glucose-to-Lymphocyte Ratio and 28-Day Mortality in Patients with Mechanical Ventilation
Mengqi Zhang1,2,3, Daoxin Wang1,2,3, Jing He1,2,3
1Second Affiliated Hospital of Chongqing Medical University, Department of Respiratory and Critical Care Medicine, Chongqing, China.
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.
Insights
The glucose-to-lymphocyte ratio (GLR) is linked to 28-day mortality in mechanically ventilated (MV) patients. An interpretable machine learning model highlights GLR as a key predictor of mortality risk.
Area of Science:
- Critical Care Medicine
- Biomarkers
- Machine Learning in Healthcare
Background:
- The prognostic value of the glucose-to-lymphocyte ratio (GLR) in intensive care unit (ICU) patients requiring mechanical ventilation (MV) is not well-defined.
- Adverse outcomes and mortality in MV patients remain a significant clinical challenge.
Purpose of the Study:
- To investigate the association between GLR and 28-day mortality in patients receiving MV.
- To develop and validate an interpretable machine learning model for predicting mortality risk in this population.
Main Methods:
- Retrospective analysis of data from the MIMIC-IV database (version 3.1).
- Utilized Receiver Operating Characteristic (ROC) and restricted cubic spline (RCS) curves to assess GLR-mortality relationship.
- Employed Kaplan-Meier survival analysis and subgroup analyses.
- Developed and compared XGBoost, SVM, Naive Bayes, and k-NN models, with SHAP values for interpretability.
Main Results:
- A total of 5,738 patients were included in the study.
- RCS analysis revealed a nonlinear association between GLR and 28-day mortality.
- Elevated GLR was significantly associated with higher 28-day mortality (HR > 1, p < 0.05).
- The XGBoost model achieved superior performance (AUC 0.969, F1-score 0.963), with Acute Physiology Score III, GLR, and lactate identified as key predictors via SHAP analysis.
Conclusions:
- GLR demonstrates a nonlinear association with 28-day mortality in MV patients, suggesting its utility as a prognostic marker.
- An interpretable XGBoost model confirms the significant predictive value of GLR for short-term mortality in critically ill patients.
