Comparison of Prediction Models for Acute Kidney Injury Among Patients with Hepatobiliary Malignancies Based on XGBoost and LASSO-Logistic Algorithms

Abstract

Yunlu Zhang,1– 3,* Yimei Wang,1– 3,* Jiarui Xu,1– 3 Bowen Zhu,1– 3 Xiaohong Chen,1– 3 Xiaoqiang Ding,1– 3 Yang Li1– 3 1Department of Nephrology, Zhongshan Hospital, Fudan University, Shanghai, People’s Republic of China; 2Shanghai Medical Center of Kidney, Shanghai, People’s Republic of China; 3Shanghai Key Laboratory of Kidney and Blood Purification, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiaoqiang Ding; Yang Li Email [email protected]; [email protected]: Based on the admission data, we applied the XGBoost algorithm to create a prediction model to estimate the AKI risk in patients with hepatobiliary malignancies and then compare its prediction capacity with the logistic model.Methods: We reviewed clinical data of 7968 and 589 liver/gallbladder cancer patients admitted to Zhongshan Hospital during 2014 and 2015. They were randomly divided into the training set and test set. Data were collected from the electronic medical record system. XGBoost and LASSO-logistic were used to develop prediction models, respectively. The performance measures included the classification matrix, the area under the receiver operating characteristic curve (AUC), lift chart and learning curve.Results: Of 6846 participants in the training set, 792 (11.6%) cases developed AKI. In XGBoost model, the top 3 most important variables for AKI were serum creatinine (SCr), glomerular filtration rate (eGFR) and antitumor treatment in liver cancer patients. Similarly, SCr and eGFR also ranked second and third most important variables in the gallbladder cancer-related AKI model just after phosphorus. In the classification matrix, XGBoost model possessed a comparably better agreement between the actual observations and the predictions than LASSO-logistic model. The Youden’s index of XGBoost model was 47.5% and 59.3%, respectively, which was significantly higher than that of LASSO-logistic model (41.6% and 32.7%). The AUCs of XGBoost model were 0.822 in liver cancer and 0.850 in gallbladder cancer. By comparison, the AUC values of Logistic models were significantly lower as 0.793 and 0.740 (p=0.024 and 0.018). With the accumulation of training samples, XGBoost model maintained greater robustness in the learning curve.Conclusion: XGBoost model based on admission data has higher accuracy and stronger robustness in predicting AKI. It will benefit AKI risk classification management in clinical practice and take an advanced intervention among patients with hepatobiliary malignancies.Keywords: hepatobiliary malignancy, acute kidney injury, extreme gradient boosting, LASSO-logistic regression, disease prediction, machine learning

Keywords

• hepatobiliary malignancy
• acute kidney injury
• extreme gradient boosting
• lasso-logistic regression
• disease prediction
• machine learning