摘要目的:基于极端梯度提升（XGBoost）算法建立重症急性胰腺炎（SAP）早期预测机器学习模型，并探讨其预测效能。方法:采用回顾性队列研究方法，选择2020年1月1日至2021年12月31日苏州大学附属第一医院、苏州大学附属第二医院及苏州大学附属常熟医院收治的急性胰腺炎（AP）患者，根据病历系统与影像系统收集患者的人口学信息、病因、既往史及入院48 h内临床指标和影像学资料，并计算改良CT严重指数评分（MCTSI）、Ranson评分、急性胰腺炎严重程度床旁指数（BISAP）及急性胰腺炎风险评分（SABP）。将苏州大学附属第一医院及苏州大学附属常熟医院的数据集按照8 : 2随机分为训练集和验证集，基于XGBoost算法，在采用五折交叉验证、损失函数进行超参数调整的基础上构建SAP预测模型。将苏州大学附属第二医院的数据集作为独立的测试集，通过受试者工作特征曲线（ROC曲线）评价XGBoost模型的预测效能，并与传统AP相关病情严重程度评分进行比较；同时对特征变量进行重要性排序，采用沙普利加和解释法（SHAP）对模型进行可视化解释。结果:最终共纳入1?183例AP患者，其中129例（10.9%）发生SAP。苏州大学附属第一医院和苏州大学附属常熟医院患者中，训练集786例，验证集197例；苏州大学附属第二医院的200例患者作为测试集。3组数据集分析均显示，进展为SAP的患者存在呼吸功能异常、凝血功能异常、肝肾功能异常、血脂代谢异常等病理表现。基于XGBoost算法构建SAP预测模型；ROC曲线分析显示，该模型预测SAP的准确度达到0.830，ROC曲线下面积（AUC）为0.927，较MCTSI、Ranson、BISAP、SABP等传统评分系统明显提高（准确度分别为0.610、0.690、0.763、0.625，AUC分别为0.689、0.631、0.875、0.770）。基于XGBoost模型的特征变量重要性分析显示，模型中权重排名前10位的指标依次为胸腔积液（0.119）、白蛋白（Alb，0.049）、三酰甘油（TG，0.036）、Ca 2+（0.034）、凝血酶原时间（PT，0.031）、全身炎症反应综合征（SIRS，0.031）、C-反应蛋白（CRP，0.031）、血小板计数（PLT，0.030）、乳酸脱氢酶（LDH，0.029）和碱性磷酸酶（ALP，0.028），说明上述指标对于XGBoost模型预测SAP具有重要意义。基于XGBoost模型的SHAP贡献度分析显示，当患者出现胸腔积液及Alb降低时，SAP发生风险明显增加。 结论:基于机器学习XGBoost算法建立了SAP预测模型，该模型可在入院48 h内对AP患者进展为重症的风险进行预测，且具有良好的准确性。

abstractsObjective:To establish a machine learning model based on extreme gradient boosting (XGBoost) algorithm for early prediction of severe acute pancreatitis (SAP), and explore its predictive efficiency.Methods:A retrospective cohort study was conducted. The patients with acute pancreatitis (AP) who admitted to the First Affiliated Hospital of Soochow University, the Second Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University from January 1, 2020 to December 31, 2021 were enrolled. Demography information, etiology, past history, and clinical indicators and imaging data within 48 hours of admission were collected according to the medical record system and image system, and the modified CT severity index (MCTSI), Ranson score, bedside index for severity in acute pancreatitis (BISAP) and acute pancreatitis risk score (SABP) were calculated. The data sets of the First Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University were randomly divided into training set and validation set according to 8 : 2. Based on XGBoost algorithm, the SAP prediction model was constructed on the basis of hyperparameter adjustment by 5-fold cross validation and loss function. The data set of the Second Affiliated Hospital of Soochow University was served as independent test set. The predictive efficacy of the XGBoost model was evaluated by drawing the receiver operator characteristic curve (ROC curve), and compared it with the traditional AP related severity score; variable importance ranking diagram and Shapley additive explanation (SHAP) diagram were drawn to visually explain the model.Results:A total of 1?183 AP patients were enrolled finally, of which 129 (10.9%) developed SAP. Among the patients from the First Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University, there were 786 patients in the training set and 197 in the validation set; 200 patients from the Second Affiliated Hospital of Soochow University were used as the test set. Analysis of all three datasets showed that patients who advanced to SAP exhibited pathological manifestation such as abnormal respiratory function, coagulation function, liver and kidney function, and lipid metabolism. Based on the XGBoost algorithm, an SAP prediction model was constructed, and ROC curve analysis showed that the accuracy for prediction of SAP reached 0.830, the area under the ROC curve (AUC) was 0.927, which was significantly improved compared with the traditional scoring systems including MCTSI, Ranson, BISAP and SABP, the accuracy was 0.610, 0.690, 0.763, 0.625, and the AUC was 0.689, 0.631, 0.875, and 0.770, respectively. The feature importance analysis based on the XGBoost model showed that the top ten items ranked by the importance of model features were admission pleural effusion (0.119), albumin (Alb, 0.049), triglycerides (TG, 0.036), Ca 2+ (0.034), prothrombin time (PT, 0.031), systemic inflammatory response syndrome (SIRS, 0.031), C-reactive protein (CRP, 0.031), platelet count (PLT, 0.030), lactate dehydrogenase (LDH, 0.029), and alkaline phosphatase (ALP, 0.028). The above indicators were of great significance for the XGBoost model to predict SAP. The SHAP contribution analysis based on the XGBoost model showed that the risk of SAP increased significantly when patients had pleural effusion and decreased Alb. Conclusion:A SAP prediction scoring system was established based on the machine automatic learning XGBoost algorithm, which can predict the SAP risk of patients within 48 hours of admission with good accuracy.