重症急性胰腺炎早期预测模型的建立与评价  

Establishment and evaluation of early prediction models for severe acute pancreatitis

作　　者：王梅[1] 夏瑜 武长美 马良慧 陈艳艳[1] 朱文俊[1] 王兴宇[1] Wang Mei;Xia Yu;Wu Changmei;Ma Lianghui;Chen Yanyan;Zhu Wenjun;Wang Xingyu(Department of Emergency Medicine,The First Affiliated Hospital of Anhui Medical University,Hefei 230032,China;Department of Burn and Trauma Medicine,The First Naval Hospital of Southern Theater of Operations,Zhanjiang 524000,China)

机构地区：[1]安徽医科大学第一附属医院急诊医学科,合肥230032 [2]南部战区海军第一医院烧创伤医学科,湛江524000

出　　处：《中华急诊医学杂志》2024年第10期1398-1406,共9页Chinese Journal of Emergency Medicine

基　　金：安徽省医疗卫生重点专科建设项目(皖卫函[2021]273号)。

摘　　要：目的使用最小绝对收缩与选择算子(least absolute shrinkage and selection operator,LASSO)回归筛选变量,分别构建Logistic回归模型和决策树模型,旨在探索更为简明、高效的重症急性胰腺炎(severe acute pancreatitis,SAP)早期预测模型,为识别高危人群,指导临床治疗,改善预后提供科学依据。方法回顾分析2020年11月至2023年9月安徽医科大学第一附属医院本部及高新院区急诊科和消化内科收治的412名急性胰腺炎患者的临床资料,使用LASSO回归筛选出与SAP发生显著相关的影响因素,分别构建多因素Logistic回归模型和决策树模型,以急性胰腺炎严重程度床旁指数(bedside index for severity in acute pancreatitis,BISAP)为参考,比较和评价模型的预测效能。结果412名研究对象中,SAP发病率为12.14%(n=50),LASSO回归筛选出7个与急性胰腺炎严重程度显著相关的变量,包括:入院呼吸频率、入院疼痛评分、胸腔积液、纤维蛋白降解物、C反应蛋白、血肌酐、血清白蛋白;Logistic回归模型纳入胸腔积液、入院时疼痛评分、血肌酐、白蛋白4项指标。训练集中,模型灵敏度=0.528,特异度=0.984,准确度(95%CI)=0.928(0.892~0.955),Kappa值=0.606,AUC(95%CI)=0.920(0.862~0.979);测试集中,模型灵敏度=0.643,特异度=0.925,准确度(95%CI)=0.891(0.822~0.941),Kappa值=0.519,AUC(95%CI)=0.923(0.861~0.985)。决策树模型包含3个分支,4个终端节点,显示血肌酐、白蛋白和胸腔积液3个因素可以有效预测SAP发生。训练集中,模型灵敏度=0.500,特异度=0.973,准确度(95%CI)=0.914(0.876~0.944),Kappa值=0.544,AUC(95%CI)=0.812(0.731~0.894);测试集中,模型灵敏度=0.500,特异度=0.925,准确度(95%CI)=0.875(0.802~0.928),Kappa值=0.412,AUC(95%CI)=0.709(0.565~0.853)。Delong检验显示:在训练集中,Logistic回归模型AUC大于决策树模型(P<0.01)和BISAP评分(P<0.001),而决策树模型和BISAP评分的AUC差异无统计学意义(P=0.762);在测试集中,Logistic回归模型AUObjective To explore a simplified and efficient early prediction model for severe acute pancreatitis(SAP)using the least absolute shrinkage and selection operator(LASSO)regression,and to construct both logistic regression and decision tree models.The aim is to identify high-risk individuals,guide clinical treatment,and improve patient outcomes.Methods A retrospective analysis was conducted on the clinical data of 412 patients with acute pancreatitis admitted to the Emergency and Gastroenterology Departments of the First Affiliated Hospital of Anhui Medical University and its Hightech Branch from November 2020 to September 2023.LASSO regression was employed to identify factors significantly associated with SAP,followed by the construction of a multivariate logistic regression model and a decision tree model.The predictive performance of these models was evaluated and compared to the bedside index for severity in acute pancreatitis(BISAP).Results Among the 412 patients,the incidence of SAP was 12.14%(n=50).Seven variables significantly associated with SAP severity were identified by LASSO regression,including respiratory rate at admission,pain score at admission,pleural effusion,fibrin degradation products,C-reactive protein,serum creatinine,and serum albumin.The logistic regression model incorporated four variables:pleural effusion,pain score at admission,serum creatinine,and serum albumin.In the training set,the model demonstrated a sensitivity of 0.528,specificity of 0.984,accuracy(95%CI)of 0.928(0.892-0.955),Kappa value of 0.606,and AUC(95%CI)of 0.920(0.862-0.979).In the testing set,the model showed a sensitivity of 0.643,specificity of 0.925,accuracy(95%CI)of 0.891(0.822-0.941),Kappa value of 0.519,and AUC(95%CI)of 0.923(0.861-0.985).The decision tree model comprised three branches and four terminal nodes,indicating that serum creatinine,serum albumin,and pleural effusion could effectively predict SAP occurrence.In the training set,the decision tree model had a sensitivity of 0.500,specificity of 0.973,accurac

关 键 词：急性胰腺炎 LASSO回归 LOGISTIC回归 决策树 预测模型 

分 类 号：R576[医药卫生—消化系统]