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Association between stress hyperglycemia ratio and poor outcomes in Trauma surgery ICU patients
Heshan Cao1, Junying Wei2, Wuhua Ma3
1Department of Biobank and Bioinformatics, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Background:
Patients in the Trauma Surgery Intensive Care Unit (TSICU) often experience severe stress responses, which may lead to the occurrence of stress hyperglycemia. The stress hyperglycemia ratio (SHR), a biomarker quantifying the relative severity of stress hyperglycemia, has garnered increasing attention. This study aims to investigate the association between SHR and poor outcomes in TSICU patients.
Methods:
A retrospective cohort study was conducted based on the Medical Information Mart for Intensive Care IV database. Patients in the TSICU were stratified into tertiles based on SHR values. The primary outcomes were 30-day and 365-day all-cause mortality, and the secondary outcome was hospital mortality. Kaplan-Meier survival analysis, logistic regression, Cox proportional hazards models, and restricted cubic spline analysis were employed to examine the relationship between SHR and poor outcomes. The potential incremental value of incorporating SHR into traditional disease severity scoring systems was also explored.
Results:
A total of 569 eligible TSICU patients were included. The 30-day and 365-day all-cause mortality rates were 20.7% (118 patients) and 32.5% (185 patients), respectively. Higher SHR was associated with significantly increased risks of 30-day, 365-day, and hospital mortality (HR/OR > 1, P < 0.05). Restricted cubic spline analysis demonstrated no significant non-linear relationship between SHR and mortality risk (P > 0.05). Furthermore, SHR provided incremental prognostic value when integrated into traditional disease severity scoring systems.
Conclusion:
High SHR is significantly associated with increased all-cause mortality in TSICU patients, particularly among non-diabetic individuals. As a prognostic marker, SHR shows potential clinical utility for early risk stratification and management optimization.
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