A Model for Predicting Acute Kidney Injury in Patients With Traumatic Brain Injury

OBJECTIVE: The early detection of acute kidney injury (AKI) in patients with traumatic brain injury (TBI) may enable the prompt initiation of preventive strategies and the development of personalized treatment plans, thereby enhancing prognostic outcomes. This study seeks to develop a predictive model for AKI in TBI patients. METHODS: An analysis was conducted on data from 409 adult TBI patients treated at our hospital between January 2021 and December 2021, with the outcome being the incidence of AKI. A logistic regression model was constructed, using the least absolute shrinkage and selection operator (LASSO) regression to select optimal variables. The model's performance was assessed using several metrics, including the Brier score, the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA). RESULTS: AKI was identified in 9.0% of the patients. The final model included 5 variables: estimated glomerular filtration rate, a Glasgow Coma Scale score of