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World Journal of Emergency Medicine ›› 2026, Vol. 17 ›› Issue (3): 250-257.doi: 10.5847/wjem.j.1920-8642.2026.055

• Original Articles • Previous Articles     Next Articles

Stress-hyperglycemia ratio and glycemic variability predict severity and mortality in sepsis-associated acute respiratory distress syndrome

Hui Chen1, Yuanhua Lu1, Songjie Bai2, Yang Li3, Tao Wang4, Long Cai5, Xinyi Yang3, Yang Fang1, Jianguo Wan6, Yaqun Tang6, Wenqiang Tao1, Meiling Huang1, Wei Zhong1, Fen Liu1,3(), Kejian Qian1()   

  1. 1 Department of Critical Care Medicine, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, China
    2 Department of Cardiovascular Surgery ICU, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330006, China
    3 Jiangxi Provincial Key Laboratory of Prevention and Treatment of Infectious Diseases, Jiangxi Medical Center for Critical Public Health Events, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang 330052, China
    4 Department of Critical Care Medicine, Guixi People's Hospital, Guixi 335400, China
    5 Department of Critical Care Medicine, Yichun People's Hospital, Yichun 336000, China
    6 Department of Critical Care Medicine, Nanchang First Hospital, Nanchang 330008, China

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.

Key words: Sepsis, Acute respiratory distress syndrome, Stress-hyperglycemia ratio, Glycemic variability, Machine learning