Background Red blood cell distribution width (RDW) has been validated valuable in predicting outcome and acute kidney injury (AKI) in several clinical settings. The aim of this study was to explore whether RDW is associated with outcome and AKI in patients with traumatic brain injury (TBI). Methods Patients admitted to our hospital for TBI from January 2015 to August 2018 were included in this study. Multivariate logistic regression analysis was performed to identify risk factors of AKI and outcome in patients with TBI. The value of RDW in predicting AKI and outcome was evaluated by receiver operating characteristic (ROC) curve. Results Three hundred and eighteen patients were included in this study. The median of RDW was 14.25%. We divided subjects into two groups based on the median and compared difference of variables between two groups. The incidence of AKI and mortality was higher in high RDW (RDW > 14.25) group (31.45% vs 9.43%, P < .001; 69.81% vs 29.56%, P < .001). Spearman's method showed RDW was moderately associated with 90‐day Glasgow Outcome Scale (GOS) (P < .001). In multivariate logistic regression analysis, RDW, lymphocyte, chlorine, and serum creatinine were risk factors of AKI. And Glasgow Coma Scale (GCS), glucose, chlorine, AKI, and RDW were risk factors of mortality. The area under the ROC curve (AUC) of RDW for predicting AKI and mortality was 0.724 (0.662‐0.786) and 0.754 (0.701‐0.807), respectively. Patients with higher RDW were likely to have shorter median survival time (58 vs 70, P < .001). Conclusions Red blood cell distribution width is an independent risk factor of AKI and mortality in patients with TBI.
Backgrounds. Acute 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. Methods. This 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. Results. 79 (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 (p=0.01). And the Glasgow Coma Scale (GCS) of the AKI group was lower than that of the non-AKI group (p<0.001). In a multivariate logistic regression analysis, we found that heart rate (p=0.041), shock (p=0.018), serum creatinine (p<0.001), and serum uric acid (SUA) (p<0.001) were significant risk factors for AKI. Bivariate correlation analyses showed that serum creatinine was moderately positively correlated with SUA (r=0.523, p<0.001). Finally, the area under the receiver operating characteristic curve (AUC) of SUA for predicting AKI in the training set and validation set was 0.850 (0.805-0.895) and 0.869 (0.801-0.938), respectively. Conclusions. SUA 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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