Impact of Diabetes on Clinical Characteristics and Prognosis in Sepsis: A Retrospective Study

糖尿病对脓毒症临床特征和预后的影响:一项回顾性研究

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Abstract

BACKGROUND: Diabetes mellitus (DM) may alter the clinical trajectory of sepsis by modulating immune responses, infection patterns, and outcomes. This study aimed to assess the impact of diabetes on the clinical characteristics and prognosis of sepsis patients. METHODS: This retrospective included 256 adult sepsis patients admitted between January 2021 and December 2024. Based on diabetes status, patients were categorized into a diabetic group (n = 151) and a non-diabetic group (n = 105). Clinical features, laboratory parameters, infection types, and outcomes were compared. Prognostic factors in diabetic sepsis were assessed using Spearman correlation and logistic regression. RESULTS: Compared to non-diabetic patients, diabetic sepsis patients had higher rates of Escherichia coli infection (25.5% vs 10.6%, χ² = 8.450, p = 0.004), fungal co-infection (23.84% vs 5.71%, p = 0.004), and urinary tract infections (45.03% vs 30.48%). Diabetic patients also had elevated Acute Physiology and Chronic Health Evaluation II (APACHE II) scores (13.4 ± 6.5 vs 10.7 ± 4.4, t = 3.706, p < 0.001), C-reactive protein (CRP) levels (median 0.45 vs 0.37 mg/dL, Z = 4.506, p < 0.01), and procalcitonin (PCT) levels (median 7.9 vs 3.7 ng/mL, Z = 3.118, p < 0.05), along with increased mortality (25.83% vs 15.24%, χ² = 4.117, p < 0.05). Among diabetic patients, APACHE II score correlated with 28-day mortality (r = 0.463, p < 0.001) and was an independent predictor (OR = 1.177, 95% CI: 1.019-1.361, p = 0.029), whereas CRP and PCT were not independently associated with prognosis (p > 0.05). CONCLUSION: Diabetic sepsis patients showed distinct microbiological profiles, more urinary and fungal infections, and poorer outcomes. While the APACHE II score was independently associated with 28-day mortality, its moderate correlation suggests a multifactorial interplay. These results support the potential utility of integrated prognostic models combining clinical scores and biomarkers.

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