ArticleBioMed research international2020
The Predictive Value of Serum Uric Acid on Acute Kidney Injury following Traumatic Brain Injury.
Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 12 citations in OpenAlex.
- Brain-derived extracellular vesicles circUsp32 polarized macrophages causing acute kidney injury after traumatic brain injury.Science advances · 2025Article
- Effect of Creatinine on Various Clinical Outcomes in Patients with Severe Traumatic Brain Injury (TBI).Metabolites · 2025Article
- A dynamic model to predict early occurrence of acute kidney injury in ICU hospitalized cirrhotic patients: a MIMIC database analysis.BMC gastroenterology · 2024Article
- Classification and Regression Tree Predictive Model for Acute Kidney Injury in Traumatic Brain Injury Patients.Therapeutics and clinical risk management · 2024Article
- Association of hyperchloremia and acute kidney injury in pediatric patients with moderate and severe traumatic brain injury.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2023Article
- A Machine Learning Approach for the Prediction of Severe Acute Kidney Injury Following Traumatic Brain Injury.Neurocritical care · 2023Article
- Article
- The establishment and validation of a prediction model for traumatic intracranial injury patients: a reliable nomogram.Frontiers in neurology · 2023Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundsAcute kidney injury (AKI) is a prevalent nonneurological complication in patients with traumatic brain injury (TBI). We designed this study to explore the association between serum uric acid (SUA) level and the occurrence of AKI following TBI.
methodsThis is a retrospective single-center study. A total of 479 patients admitted with TBI were included in this study. We utilized SUA and other risk factors for AKI to construct a predictive model by performing multivariate logistic regression. 374 patients and 105 patients were, respectively, divided into a training set and validation set. The predictive value of the single SUA and constructed model was evaluated by drawing a receiver operating characteristic (ROC) curve. AKI was diagnosed according to the KIDGO criteria.
results79 (21.12%) patients were diagnosed with AKI in the training cohort. The patients in the AKI group are older than those in the non-AKI group (
conclusionsSUA is an effective risk factor for AKI following TBI. Combining SUA with serum creatinine could more accurately identify TBI patients with high risk of developing AKI.
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