Cystatin C for predicting acute kidney injury in critically ill children with bacterial infections: a retrospective cohort study

胱抑素C用于预测细菌感染危重儿童急性肾损伤:一项回顾性队列研究

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Abstract

BACKGROUND: Acute kidney injury (AKI) is a common and severe complication in paediatric intensive care units (PICUs), particularly among children with bacterial sepsis. Although the epidemiology of sepsis-associated AKI is well described, reliable biomarkers to predict both its onset and associated mortality are still lacking, limiting their integration into routine clinical practice. OBJECTIVE: To characterise the incidence, clinical phenotype and modifiable risk factors of bacterial sepsis-related AKI in a single-centre PICU cohort and to evaluate the utility of novel biomarkers in refining early risk-stratification. METHODS: In this single-centre retrospective cohort study, data from 475 children admitted to the PICU with severe bacterial infections were analysed. Least absolute shrinkage and selection operator regression and logistic regression analyses were employed to identify biomarkers associated with the occurrence and prognosis of AKI. A nomogram model was developed to facilitate clinical application. The predictive utility of these biomarkers was further validated using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). RESULTS: Among the 475 children with bacterial infections, 50 (10.53%) developed AKI. Of these, 20 children died, resulting in a mortality rate of 40%. Regression analysis, model construction and validation through ROC and DCA revealed that elevated cystatin C levels were significantly associated with both AKI occurrence and AKI-related mortality in children with bacterial infections. CONCLUSIONS: This study confirms that elevated cystatin C is a robust predictor of both AKI onset and AKI-related mortality in critically ill children with severe bacterial infections. The nomogram incorporating cystatin C enables early identification of high-risk patients and may support clinical decision-making to improve outcomes.

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