Machine Learning for the Prediction of Progression in Patients with Acute Kidney Injury in Critical Care

Abstract

Background: Acute kidney injury (AKI) is a serve and harmful syndrome in the intensive care unit. The purpose of this study is to develop a prediction model that predict whether patients with AKI stage 1/2 will progress to AKI stage 3. Methods: Patients with AKI stage 1/2, when they were first diagnosed with AKI in the Medical Information Mart for Intensive Care (MIMIC-III), were included. We excluded patients who had underwent RRT or progressed to AKI stage 3 within 72 hours of the first AKI diagnosis. We also excluded patients with chronic kidney disease (CKD). We used the Logistic regression and machine learning extreme gradient boosting (XGBoost) to build two models which can predict patients who will progress to AKI stage 3. Established models were evaluated by cross-validation, receiver operating characteristic curve (ROC), and precision-recall curves (PRC). Results: We included 25711 patients, of whom 2130 (8.3%) progressed to AKI stage 3. Creatinine, multiple organ failure syndromes (MODS), blood urea nitrogen (BUN), sepsis, and respiratory failure were the most important in AKI progression prediction. The XGBoost model has a better performance than the Logistic regression model on predicting AKI stage 3 progression (AU-ROC, 0.926; 95%CI, 0.917 to 0.931 vs. 0.784; 95%CI, 0.771 to 0.796, respectively). Conclusions: The XGboost model can better identify patients with AKI progression than Logistic regression model. Machine learning techniques may improve predictive modeling in medical research. Keywords: Acute kidney injury; Critical care; Logistic Models; Extreme gradient boosting