Predicting the risk factors of diabetic ketoacidosis-associated acute kidney injury: A machine learning approach using XGBoost - Fan, Tingting; Wang, Jiaxin; Li, Luyao; Kang, Jing; Wang, Wenrui; Zhang, Chuan (Frontiers in Public Health, 2023)
Abstract Objective: The purpose of this study was to develop and validate a predictive model based on a machine learning (ML) approach to identify patients with DKA at increased risk of AKI within one week of hospitalization in the intensive care unit (ICU). Methods: Patients diagnosed with DKA from the Medical Information Mart for Intensive Care Ⅳ (MIMIC-Ⅳ) database according to the International Classification of Diseases (ICD) -9/10 code were included. The patient's medical history is extracted, along with data on their demographics, vital signs, clinical characteristics, laboratory results, and therapeutic measures. The best-performing model is chosen by contrasting the 8 ML models. The area under the receiver operating characteristic curve (AUC), sensitivity, accuracy, and specificity were calculated to select the best performing ML model. Results: The final study enrolled 1322 patients with DKA in total, randomly split into training (1124, 85%) and validation sets (198, 15%). 497 (37.5%) of them experienced AKI within a week of being admitted to the ICU. The eXtreme Gradient Boosting (XGBoost) model performed best of the 8 ML models, and the AUC of the training and validation sets were 0.835 and 0.800, respectively. According to the result of feature importance, the top 5 main features contributing to the XGBoost model were blood urea nitrogen (BUN), urine output, weight, age, and platelet count (PLT). Conclusion: An ML-based individual prediction model for DKA-associated AKI (DKA-AKI) was developed and validated. The model performs robustly, identifies high-risk patients early, can assist in clinical decision making, and can improve the prognosis of DKA patients to some extent.