Zhang Xu, Yue Ping, Zhang Jinduo, Yang Man, Chen Jinhua, Zhang Bowen, Luo Wei, Wang Mingyuan, Da Zijian, Lin Yanyan, Zhou Wence, Zhang Lei, Zhu Kexiang, Ren Yu, Yang Liping, Li Shuyan, Yuan Jinqiu, Meng Wenbo, Leung Joseph W, Li Xun
The First School of Clinical Medicne, Lanzhou University, Lanzhou,730030, Gansu, China.
Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou, 730030,Gansu, China.
EClinicalMedicine. 2022 May 13;48:101431. doi: 10.1016/j.eclinm.2022.101431. eCollection 2022 Jun.
Endoscopic retrograde cholangiopancreatography (ERCP) is an established treatment for common bile duct (CBD) stones. Post- ERCP cholecystitis (PEC) is a known complication of such procedure and there are no effective models and clinical applicable tools for PEC prediction.
A random forest (RF) machine learning model was developed to predict PEC. Eligible patients at The First Hospital of Lanzhou University in China with common bile duct (CBD) stones and gallbladders in-situ were enrolled from 2010 to 2019. Logistic regression analysis was used to compare the predictive discrimination and accuracy values based on receiver operation characteristics (ROC) curve and decision and clinical impact curve. The RF model was further validated by another 117 patients. This study was registered with ClinicalTrials.gov, NCT04234126.
A total of 1117 patients were enrolled (90 PEC, 8.06%) to build the predictive model for PEC. The RF method identified white blood cell (WBC) count, endoscopic papillary balloon dilatation (EPBD), increase in WBC, residual CBD stones after ERCP, serum amylase levels, and mechanical lithotripsy as the top six predictive factors and has a sensitivity of 0.822, specificity of 0.853 and accuracy of 0.855, with the area under curve (AUC) value of 0.890. A separate logistic regression prediction model was built with sensitivity, specificity, and AUC of 0.811, 0.791, and 0.864, respectively. An additional 117 patients (11 PEC, 9.40%) were used to validate the RF model, with an AUC of 0.889 compared to an AUC of 0.884 with the logistic regression model.
The results suggest that the proposed RF model based on the top six PEC risk factors could be a promising tool to predict the occurrence of PEC.
内镜逆行胰胆管造影术（ERCP）是治疗胆总管（CBD）结石的既定方法。ERCP术后胆囊炎（PEC）是该手术已知的并发症，目前尚无有效的模型和临床适用工具来预测PEC。
开发了一种随机森林（RF）机器学习模型来预测PEC。2010年至2019年，纳入了中国兰州大学第一医院患有胆总管结石且胆囊原位的符合条件的患者。采用逻辑回归分析，基于受试者操作特征（ROC）曲线、决策曲线和临床影响曲线比较预测辨别力和准确性值。RF模型通过另外117名患者进一步验证。本研究已在ClinicalTrials.gov注册，注册号为NCT04234126。
共纳入1117例患者（90例发生PEC，8.06%）构建PEC预测模型。RF方法确定白细胞（WBC）计数、内镜乳头球囊扩张术（EPBD）、白细胞升高、ERCP术后胆总管残余结石、血清淀粉酶水平和机械碎石术为前六个预测因素，其灵敏度为0.822，特异度为0.853，准确度为0.855，曲线下面积（AUC）值为0.890。建立了一个单独的逻辑回归预测模型，其灵敏度、特异度和AUC分别为0.811、0.791和0.864。另外117例患者（11例发生PEC，9.40%）用于验证RF模型，其AUC为0.889，而逻辑回归模型的AUC为0.884。
结果表明，基于前六个PEC危险因素提出的RF模型可能是预测PEC发生的有前景的工具。
