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"Marco van Londen"

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Critical Care

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Renal dysfunction as a marker of adverse outcomes in early sepsis in the emergency department
Clin Exp Emerg Med. 2026;13(2):178-187.   Published online January 28, 2026
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Renal dysfunction as a marker of adverse outcomes in early sepsis in the emergency department
Clin Exp Emerg Med. 2026;13(2):178-187.   Published online January 28, 2026
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Objective
Sepsis is a leading cause of acute kidney injury (sepsis-associated acute kidney injury, SA-AKI) and is associated with multiorgan failure, cardiovascular events, and increased mortality. While research has generally focused on critically ill patients in intensive care units (ICUs), most sepsis cases are managed outside the ICU. This study investigated renal dysfunction as an early risk marker in patients presenting to the emergency department (ED) with severe infection and at risk for early sepsis. Methods This post hoc analysis of patients presenting to the ED used data from the Acutelines cohort (2020–2023). Kaplan-Meier curves and univariable and multivariable Cox regression analyses were performed to assess the association between AKI and all-cause mortality, as well as in-hospital mortality and cardiovascular death, adjusting for potential confounders. Results In total, 2,045 patients presented with sepsis at the ED, of whom 246 (12%) had AKI. The overall mortality rate was 25% over a median follow-up of 346 days. AKI was associated with higher all-cause mortality (38% vs. 26%; P<0.001). After adjustment for sex, age, comorbidities, and sepsis severity, AKI remained independently associated with all-cause mortality (hazard ratio [HR], 1.44; 95% confidence interval [CI], 1.14–1.82; P=0.003), in-hospital mortality (HR, 1.65; 95% CI, 1.16–2.34; P=0.006), and cardiovascular death (HR, 2.50; 95% CI, 1.39–4.48; P=0.001). Similar outcomes were observed in a subanalysis excluding ICU patients. Conclusion SA-AKI at ED presentation is independently associated with increased all-cause, in-hospital, and cardiovascular mortality. These findings underscore the need for earlier recognition across care settings and the implementation of structured follow-up to improve patient outcomes.

Citations

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  • Machine Learning-Based Subtype Classification of Sepsis-Associated Acute Kidney Injury and Differential Responses to Renal Replacement Therapy
    Hongkun Zhang, Shengzhi Wang, Tao Zhang, Baisen Wang, Ziqi Jiang, Zhenqi Guo
    Journal of Intensive Care Medicine.2026;[Epub]     CrossRef
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