Qu Cheng, Gao Lin, Yu Xian-Qiang, Wei Mei, Fang Guo-Quan, He Jianing, Cao Long-Xiang, Ke Lu, Tong Zhi-Hui, Li Wei-Qin
Surgical Intensive Care Unit (SICU), Department of General Surgery, Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
Surgical Intensive Care Unit (SICU), Department of General Surgery, Jinling Clinical Medical College of Southeast University, Nanjing, China.
Gastroenterol Res Pract. 2020 Sep 29;2020:3431290. doi: 10.1155/2020/3431290. eCollection 2020.
. Acute kidney injury (AKI) has long been recognized as a common and important complication of acute pancreatitis (AP). In the study, machine learning (ML) techniques were used to establish predictive models for AKI in AP patients during hospitalization. This is a retrospective review of prospectively collected data of AP patients admitted within one week after the onset of abdominal pain to our department from January 2014 to January 2019. Eighty patients developed AKI after admission (AKI group) and 254 patients did not (non-AKI group) in the hospital. With the provision of additional information such as demographic characteristics or laboratory data, support vector machine (SVM), random forest (RF), classification and regression tree (CART), and extreme gradient boosting (XGBoost) were used to build models of AKI prediction and compared to the predictive performance of the classic model using logistic regression (LR). XGBoost performed best in predicting AKI with an AUC of 91.93% among the machine learning models. The AUC of logistic regression analysis was 87.28%. Present findings suggest that compared to the classical logistic regression model, machine learning models using features that can be easily obtained at admission had a better performance in predicting AKI in the AP patients.
急性肾损伤（AKI）长期以来一直被认为是急性胰腺炎（AP）常见且重要的并发症。在本研究中，机器学习（ML）技术被用于建立AP患者住院期间发生AKI的预测模型。这是一项对2014年1月至2019年1月期间因腹痛发作后一周内入院的AP患者前瞻性收集数据的回顾性研究。80例患者入院后发生AKI（AKI组），254例患者未发生（非AKI组）。在提供人口统计学特征或实验室数据等额外信息后，使用支持向量机（SVM）、随机森林（RF）、分类与回归树（CART）以及极端梯度提升（XGBoost）构建AKI预测模型，并与使用逻辑回归（LR）的经典模型的预测性能进行比较。在机器学习模型中，XGBoost在预测AKI方面表现最佳，AUC为91.93%。逻辑回归分析的AUC为87.28%。目前的研究结果表明，与经典逻辑回归模型相比，使用入院时易于获取特征的机器学习模型在预测AP患者发生AKI方面具有更好的性能。
