基于机器学习算法构建食道闭锁术后吻合口漏概率在线交互式网页计算工具及相应风险分层系统  

Construction of an online interactive calculation tool and corresponding risk stratification system for the probability of postoperative anastomotic leak in esophageal atresia based on machine learning algorithms

作　　者：魏晓钦 项明[1] 申玉洁[1] 邱宏翔 廖福清 潘征夏[1] 吴春[1] 习林云 Wei Xiaoqin;Xiang Ming;Shen Yujie;Qiu Hongxiang;Liao Fuqing;Pan Zhengxia;Wu Chun;Xi Linyun(Department of Cardiothoracic Surgery,Children’s Hospital of Chongqing Medical University,National Clinical Research Center for Child Health and Disorders,Ministry of Education Key Laboratory of Child Development and Disorders,China International Science and TechnologyCooperation Base of Child Development and Critical Disorders,Chongqing Key Laboratory of Structural Birth Defect and Reconstruction)

机构地区：[1]重庆医科大学附属儿童医院胸心外科,国家儿童健康与疾病临床医学研究中心,儿童发育疾病研究教育部重点实验室,儿童发育重大疾病国家国际科技合作基地,结构性出生缺陷与器官修复重建重庆市重点实验室,重庆400014

出　　处：《重庆医科大学学报》2024年第11期1457-1464,共8页Journal of Chongqing Medical University

摘　　要：目的:利用机器学习技术对食道闭锁术后出现吻合口漏进行预测,寻找导致术后出现吻合口漏的危险因素,计算相应截断值,并制作交互式网页计算工具,方便医务人员快速计算术后出现吻合口漏的具体风险概率。方法:收集2009年1月至2021年12月在重庆医科大学附属儿童医院胸心外科接受手术治疗的251例Ⅲ型先天性食道闭锁患者的临床资料。包括患儿人口学特征、手术资料和术后资料。本课题组采用支持向量机(support vector machine,SVM)、随机森林(random forest,RF)、逻辑回归模型(logistic regression,LR)、XGboost分类(eXtreme gradient boosting,XGBoost)、高斯朴素贝叶斯(gaussian naive bayes,GNB)这5种机器学习算法来构建预测食道闭锁术后吻合口漏的预测模型。利用受试者工作特征(receiver operating characteristic,ROC)曲线的曲线下面积(area under thecurve,AUC)评价效度,同时综合F1分数、准确率、灵敏度及特异度,HosmerLemeshow检验Brier分数评价校准度及临床决策曲线(decision curve analysis,DCA曲线)对模型的校准度及稳定性进行评价。利用限制性立方样条分别计算相应危险因素的截断值,最后制作交互式网页计算工具,构建术后吻合口漏风险分层系统,方便医务人员快速使用。结果:通过对候选风险因素进行单因素分析、重要度排序、LASSO回归(least absolute shrinkage and selection operator,LASSO)筛选出危险因素为断端距离、是否合并复杂先心、术前蛋白、是否合并肺部感染。在5种机器学习算法中,逻辑回归模型在ROC曲线和DCA性能及校准曲线综合指标方面表现最佳,在逻辑回归模型中,训练集的AUC为0.828,准确度为0.772,F1分数为0.532,验证集的AUC为0.799,准确度为0.765,F1分数为0.544。提示该模型用于预测Ⅲ型先天性食道闭锁术后出现吻合口漏有较好的区分度及校准度。利用限制性立方样条,计算了断端距离及术前蛋白的截断Objective:To predict postoperative anastomotic leak in esophageal atresia using machine learning techniques,to the risk factors for postoperative anastomotic leak,to calculate corresponding cut-off values,to develop an interactive web-based and to help healthcare professionals quickly calculate the specific risk probability of postoperative anastomotic leak.data were collected from 251 patients with typeⅢcongenital esophageal atresia who underwent surgical treatment in our hospital from January 2009 to December 2021,including demographic fea-tures,surgical data,and postoperative data.Five machine learning algorithms,i.e.,support vector machine(SVM),random forest(RF),logistic regression(LR),XGBoost,and Gaussian naive Bayes(GNB),were used to construct a predictive model for anastomotic leak after esophageal atresia repair.The area under the ROC curve(AUC),F1 score,accuracy,sensitivity,and specificity were used to evaluate the validity of the model,the Hosmer-Lemeshow test and Brier score were used to evaluate the degree of calibration,and the decision curve analysis(DCA curve)was used to evaluate the de-gree of calibration and stability.Restricted cubic spline techniques were used to calculate the cut-off value of each risk factor,and then an interactive web-based calculation tool was developed to establish a risk stratification system for postoperative anastomotic leak,which was used to facilitate healthcare professionals in convenient application.Results:The univariate analysis,importance ranking,and LASSO regression were performed for candidate risk factors,and the results showed that the distance between the ends of the esophageal gap,presence or absence of complex congenital heart disease,preoperative protein level,and presence or absence of pulmo-nary infection were the risk factors for postoperative anastomotic leak.Among the five machine learning algorithms,the logistic regres-sion model exhibited the best performance in terms of AUC,DCA,and calibration curve,with an AUC of 0.828,an accuracy of 0.772,and

关 键 词：食道闭锁 机器学习 预测 

分 类 号：R720.5[医药卫生—儿科]