Machine Learning-Assisted Ensemble Analysis for the Prediction of Acute Pancreatitis with Acute Kidney Injury

Abstract

Yi Yang,1 Wei Xiao,2 Xingtai Liu,1 Yan Zhang,1 Xin Jin,1 Xiao Li1 1Department of Clinical Laboratory, The Third Clinical Medical College of the Three Gorges University, Gezhouba Central Hospital of Sinopharm, Yichang, People’s Republic of China; 2Department of Gastroenterology, Xianning central Hospital, The First Affiliated Hospital of Hubby University of Science and Technology, Xianning, People’s Republic of ChinaCorrespondence: Xiao Li, Department of Clinical Laboratory, The Third Clinical Medical College of the Three Gorges University, Gezhouba Central Hospital of Sinopharm, Yichang, 443002, People’s Republic of China, Tel [phone], Email [email protected]: Acute kidney injury (AKI) is a frequent complication of severe acute pancreatitis (AP) and carries a very poor prognosis. The present study aimed to construct a model capable of accurately identifying those patients at high risk of harboring occult acute kidney injury (AKI) characteristics.Patients and Methods: We retrospectively recruited a total of 424 consecutive patients at the Gezhouba central hospital of Sinopharm and Xianning central hospital between January 1, 2016, and October 30, 2021. ML-assisted models were developed from candidate clinical features using two-step estimation methods. The receiver operating characteristic curve (ROC), decision curve analysis (DCA), and clinical impact curve (CIC) were performed to evaluate the robustness and clinical practicability of each model.Results: Finally, a total of 30 candidate variables were included, and the AKI prediction model was established by an ML-based algorithm. The areas under the ROC curve (AUCs) of the random forest classifier (RFC) model, support vector machine (SVM), eXtreme gradient boosting (XGBoost), artificial neural network (ANN), and decision tree (DT) ranged from 0.725 (95% CI 0.223– 1.227) to 0.902 (95% CI 0.400– 1.403). Among them, RFC obtained the optimal prediction efficiency via adding inflammatory factors, which are serum creatinine (Scr), C-reactive protein (CRP), platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), neutrophil-to-albumin ratio (NAR), and CysC, respectively.Conclusion: We successfully developed ML-based prediction models for AKI, particularly the RFC, which can improve the prediction of AKI in patients with AP. The practicality of prediction and early detection may be greatly beneficial to risk stratification and management decisions.Keywords: acute pancreatitis, acute kidney injury, serum cytokines, cystatin-C, machine learning algorithms, prediction

Keywords

• acute pancreatitis
• acute kidney injury
• serum cytokines
• cystatin-c
• machine learning algorithms
• prediction