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Background: Evaluating risk of poor outcome for Traumatic Brain Injury (TBI) in early stage is necessary to make treatment strategies and decide the need for intensive care. This study is designed to verify the prognostic value of serum cystatin C in TBI patients., Methods: 415 TBI patients admitted to West China hospital were included. Logistic regression was performed to explore risk factors of mortality and testify the correlation between cystatin C and mortality. Mediation analysis was conducted to test whether Acute Kidney Injury (AKI) and brain injury severity mediate the relationship between cystatin C level and mortality. Area under the receiver operating characteristic curve (AUC) was used to evaluate the prognostic value of cystatin C and the constructed model incorporating cystatin C., Results: The mortality rate of 415 TBI patients was 48.9%. Non-survivors had lower GCS (5 vs 8, p < 0.001) and higher cystatin C (0.92 vs 0.71, p < 0.001) than survivors. After adjusting confounding effects, multivariate logistic regression indicated GCS (p < 0.001), glucose (p < 0.001), albumin (p = 0.009), cystatin C (p < 0.001) and subdural hematoma (p = 0.042) were independent risk factors of mortality. Mediation analysis showed both AKI and brain injury severity exerted mediating effects on relationship between cystatin C and mortality of included TBI patients. The AUC of combining GCS with cystatin C was 0.862, which was higher than that of GCS alone (Z = 1.7354, p < 0.05)., Conclusion: Both AKI and brain injury severity are mediating variables influencing the relationship between cystatin C and mortality of TBI patients. Serum cystatin C is an effective prognostic marker for TBI patients., (© 2023. The Author(s) under exclusive licence to Belgian Neurological Society.)

Background: A common non-neurologic complication after traumatic brain injury (TBI), acute kidney injury (AKI) is a risk factor of mortality. Some studies confirmed the predictive value of procalcitonin (PCT) on AKI in several clinical settings. We designed this study to explore the predictive value of PCT on AKI after TBI., Methods: We retrospectively enrolled patients with TBI admitted to our hospital from February 2015 to June 2019. Multivariate logistic regression analysis was performed to find the risk factors of AKI and construct a predictive model for AKI. Receiver operating characteristics curves were drawn to compare the predictive value of PCT and the constructed model., Results: A total of 214 patients were included in this study. The incidence of AKI after TBI was 25.70% in this study. Compared with the non-AKI group, the AKI group had higher age (P = 0.031), lower Glasgow Coma Scale (P < 0.001), and higher incidence of coagulopathy (P < 0.001) and shock (P < 0.001). Moreover, patients complicated with AKI had higher in-hospital mortality (P < 0.001) and worse 90-day outcome (P < 0.001). Multivariate logistic regression analysis indicated that age (P = 0.033), PCT (P = 0.002), serum chlorine (P = 0.011), and creatinine (P < 0.001) were independent risk factors of AKI. We constructed a predictive model using these 4 risk factors. The area under receiver operating characteristics curves of the predictive model was 0.928, which was significantly higher than that of a single PCT value (area under receiver operating characteristics curves = 0.833) (Z = 2.395, P < 0.05)., Conclusions: PCT is valuable in predicting AKI after TBI. To avoid AKI after TBI, physicians can adjust treatment strategies according to the level of PCT., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Background: Acute kidney injury (AKI) is prevalent in hospitalized patients with traumatic brain injury (TBI), and increases the risk of poor outcomes. We designed this study to develop a visual and convenient decision-tree-based model for predicting AKI in TBI patients. Methods: A total of 376 patients admitted to the emergency department of the West China Hospital for TBI between January 2015 and June 2019 were included. Demographic information, vital signs on admission, laboratory test results, radiological signs, surgical options, and medications were recorded as variables. AKI was confirmed since the second day after admission, based on the Kidney Disease Improving Global Outcomes criteria. We constructed two predictive models for AKI using least absolute shrinkage and selection operator (LASSO) regression and classification and regression tree (CART), respectively. Receiver operating characteristic (ROC) curves of these two predictive models were drawn, and the area under the ROC curve (AUC) was calculated to compare their predictive accuracy. Results: The incidence of AKI on the second day after admission was 10.4% among patients with TBI. Lasso regression identified five potent predictive factors for AKI: glucose, serum creatinine, cystatin C, serum uric acid, and fresh frozen plasma transfusions. The CART analysis showed that glucose, serum uric acid, and cystatin C ranked among the top three in terms of the feature importance of the decision tree model. The AUC value of the decision-tree predictive model was 0.892, which was higher than the 0.854 of the LASSO regression model, although the difference was not statistically significant. Conclusion: The decision tree model is valuable for predicting AKI among patients with TBI. This tree-based flowchart is convenient for physicians to identify patients with TBI who are at high risk of AKI and prompts them to develop suitable therapeutic strategies. [ABSTRACT FROM AUTHOR]

Background: Acute kidney injury (AKI) commonly develops among traumatic brain injury (TBI) patients and causes poorer outcomes. We perform this study to explore the relationship between serum magnesium and the risk of AKI among TBI. Methods: TBI patients recorded in the Medical Information Mart for Intensive Care-III database were eligible for this research. The restricted cubic spline (RCS) was utilized to fit the correlation between serum magnesium level and the AKI. Univariate and subsequent multivariate logistic regression analysis were utilized to explore risk factors of AKI and confirmed the correlation between serum magnesium and AKI. Results: The incidence of AKI in included TBI was 21.0%. The RCS showed that the correlation between magnesium level and risk of AKI was U-shaped. Compared with patients whose magnesium level was between 1.5 and 2.0 mg/dL, those with a magnesium level of <1.5 mg/dL or >2.0 mg/dL had a higher incidence of AKI. Multivariate logistic regression confirmed age, chronic renal disease, ISS, serum creatinine, vasopressor, mechanical ventilation, and serum magnesium <1.5 mg/dL were independently related with the AKI in TBI. Conclusion: Abnormal low serum magnesium level is correlated with AKI development in TBI patients. Physicians should pay attention on renal function of TBI patients especially those with hypomagnesemia. [ABSTRACT FROM AUTHOR]