Stress-hyperglycemia ratio and glycemic variability predict severity and mortality in sepsis-associated acute respiratory distress syndrome
10.5847/wjem.j.1920-8642.2026.055
- Author:
Hui Chen
1
Author Information
1. Department of Critical Care Medicine, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, China
- Publication Type:Journal Article
- Keywords:
Sepsis;
Acute respiratory distress syndrome;
Stress-hyperglycemia ratio;
Glycemic variability;
Machine learning
- From:
World Journal of Emergency Medicine
2026;17(3):250-257
- CountryChina
- Language:English
-
Abstract:
BACKGROUND: Sepsis-associated acute respiratory distress syndrome (SA-ARDS) is frequently accompanied by dysregulated glucose metabolism. The stress-hyperglycemia ratio (SHR) and glycemic variability (GV) have emerged as valuable tools for assessing acute dysglycemia. However, their ability to predict disease severity and mortality in patients with SA-ARDS remains unclear.
METHODS: This retrospective study included 3,243 SA-ARDS patients from the MIMIC-IV database. Patients were stratified into four phenotypes based on the cohort median SHR (1.09) and GV (58.42%). Ordinal logistic regression was used to evaluate ARDS severity, while binary logistic and Cox proportional hazards models were used to assess mortality. Predictive performance was assessed using the area under the receiver operating characteristic curve (AUC). In addition, five machine learning models with SHapley Additive exPlanations (SHAP) analysis were used to identify clinically relevant risk thresholds. External validation using the eICU database was limited to GV-related findings because of the unavailability of hemoglobin A1c (HbA1c) data.
RESULTS: Compared with the low-GV/low-SHR phenotype, the high-GV/high-SHR phenotype had the strongest association with increased ARDS severity (common odds ratio [cOR] 4.37, 95% confidence intervals [95% CI]: 3.60-5.30, P<0.001) and markedly elevated 28-day mortality (hazard ratio [HR] 12.4, 95% CI: 7.95-19.30, P<0.001). However, compared with conventional clinical scores, the combined assessment of the SHR and GV had superior performance in mortality prediction (all AUC=0.748). Furthermore, machine learning and SHAP analyses revealed a non-linear increase in mortality risk when GV exceeded approximately 39.1%.
CONCLUSION: Combined assessment of the SHR and GV may provide prognostic information for patients with SA-ARDS. A high-GV/high-SHR phenotype may indicate a high risk of poor prognosis.
