Development and Validation of a Machine Learning Model for Early Prediction of Acute Kidney Injury in Neurocritical Care: A Comparative Analysis of XGBoost, GBM, and Random Forest Algorithms

Abstract

Background: Acute Kidney Injury (AKI) is a pivotal concern in neurocritical care, impacting patient survival and quality of life. This study harnesses machine learning (ML) techniques to predict the occurrence of AKI in patients receiving hyperosmolar therapy, aiming to optimize patient outcomes in neurocritical settings. Methods: We conducted a retrospective cohort study of 4886 patients who underwent hyperosmolar therapy in the neurosurgical intensive care unit (ICU). Comparative predictive analyses were carried out using advanced ML algorithms—eXtreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Random Forest (RF)—against standard multivariate logistic regression. Predictive performance was assessed using an 8:2 training-testing data split, with model fine-tuning through cross-validation. Results: The RF with KNN imputation showed slightly better performance than other approaches in predicting AKI. When applied to an independent test set, it achieved a sensitivity of 79% (95% CI: 70–87%) and specificity of 85% (95% CI: 82–88%), with an overall accuracy of 84% (95% CI: 81–87%) and AUROC of 0.86 (95% CI: 0.82–0.91). The multivariate logistic regression analysis, while informative, showed less predictive strength compared to the ML models. Delta chloride levels and serum osmolality proved to be the most influential predictors, with additional significant variables including pH, age, bicarbonate, and the osmolar gap. Conclusions: The prominence of delta chloride and serum osmolality among the predictive variables underscores its potential as a biomarker for AKI risk in this patient population.