Predicting acute kidney injury in trauma using an extreme gradient boosting model.

Background: Acute kidney injury (AKI) is a significant complication in patients with trauma. The early identification of AKI in these patients poses challenges. This study aimed to predict AKI in trauma patients 24 or 48 hours in advance using an extreme gradient boosting (XGBoost) model. Methods: We analyzed 17 859 trauma patients admitted to a regional trauma center between January 2015 and July 2023. Demographic, clinical, and laboratory parameters were collected. The model was developed using data until July 2021 and validated using data from August 2021. We developed models to predict AKI stages 1-3 and AKI stages 2 and 3 occurring 48 and 24 hours later and measured predictive performance in the validation group. The models' performance was evaluated using the area under the receiver operating characteristic curve (AUROC), and feature importance was assessed through SHapley Additive exPlanations values. Results: The study population exhibited an incidence of AKI of 6.6% in the development group and 5.4% in the validation group. The models demonstrated predictive performance with AUROCs of 0.864 and 0.886 for 48-hour predictions of AKI stages 1-3 and stages 2 and 3, and 0.904 and 0.903 for 24-hour predictions of AKI stages 1-3 and stages 2 and 3, respectively. Key features influencing model predictions included baseline and in-hospital serum creatinine values, injury severity score, age, lactate dehydrogenase, D-dimer, platelets, albumin, and C-reactive protein levels. Conclusions: The XGBoost models effectively predicted AKI in trauma patients up to 48 hours in advance using clinical data.