HBeAg阴性ALT正常慢性乙型肝炎患者纤维化模型构建  

Establishment of model of liver fibrosis among chronic hepatitis B patients with HBeAg negative and normal ALT level

作　　者：王娟霞[1,2] 孙新策 陈馨悦 魏世博 朱浩宇 连唐悠悠 杜雨峰 WANG Juanxia;SUN Xince;CHEN Xinyue;WEI Shibo;ZHU Haoyu;LIANTANG Youyou;DU Yufeng(The Second Clinical Medical College of Lanzhou University,Lanzhou 730030,Gansu,China;Department of Infectious Diseases,Lanzhou University Second Hospital,Lanzhou 730030,Gansu,China;Department of Epidemiology and Statistics,School of Public Health,Lanzhou University,Lanzhou 730000,Gansu,China)

机构地区：[1]兰州大学第二临床医学院,甘肃兰州730030 [2]兰州大学第二医院感染性疾病科,甘肃兰州730030 [3]兰州大学公共卫生学院流行病与卫生统计学研究所,甘肃兰州7300001

出　　处：《中国现代医生》2023年第35期1-5,共5页China Modern Doctor

基　　金：甘肃省自然科学基金资助项目(22JR5RA1000);兰州大学第二医院萃英学子科研培育计划项目(CYXZ2021-55)。

摘　　要：目的明确HBeAg阴性ALT正常慢性乙型肝炎(chronic hepatitis B,CHB)患者血清指标与肝脏纤维化程度的关系,构建肝纤维化无创预测模型。方法对2012年10月至2021年12月于兰州大学第二医院进行肝脏活检的679例HBeAg阴性ALT正常慢性HBV感染者的术后病理切片进行了回顾性分析,根据活检结果,肝纤维化等级分为无明显纤维化组(S1、观察组)和纤维化组(S2/S3/S4、对照组)。通过LASSO回归筛选自变量,采用限制性立方样条函数探索血清学指标和肝纤维化的曲线关系,采用多因素Logistic回归构建预测模型,绘制受试者操作特征(receiver operator characteristic,ROC)曲线评估模型对纤维化的预测价值。结果48.7%的患者肝组织病理达到明显纤维化程度(S≥2)。血清学指标γ-谷氨酰转肽酶(γ-glutamyl transpeptadase,GGT)、天冬氨酸氨基转移酶(aspartate transaminase,AST)和凝血酶原时间(prothrombin time,PPT)均与肝纤维化呈正相关。GGT+PT+AST预测模型的ROC曲线面积为0.68(95%CI:0.64~0.72),预测价值明显优于使用γ-谷氨酰转肽酶血小板比率、天冬氨酸氨基转移酶血小板比率指数、FIB-4指数构建的预测模型。结论基于GGT+PT+AST构建的预测模型对HBeAg阴性ALT正常的CHB患者纤维化程度具有较高的临床预测价值。Objective To investigate the relationship between serum indexes and the degree of liver fibrosis in chronic hepatitis B(CHB)patients with HBeAg-negative and normal ALT,and to establish a new non-invasive model for predicting liver fibrosis in CHB patients.Methods The clinical data of 679 HBeAg-negative chronic HBV infected patients with normal ALT who underwent liver biopsy from October 2012 to December 2021 were retrospectively analyzed.Among these patients,they were categorized into the control group(S1,observation group)the and significant fibrosis group(S2/S3/S4,control group)based on liver biopsy results.The LASSO regression model was used for covariates selection and the restricted cubic splines model was used to examine nonlinear associations between covariates and outcomes.We used Logistic regression models to establish predictive models.Results Liver biopsy showed that 48.7%of the patients had obvious fibrosis(S≥2).GGT shows a nonlinear relationship with the degree of liver fibrosis.AST and PT show a positive relationship with the liver fibrosis degree,respectively.The area under the ROC curve(AUC)of GGT+PT+AST is 0.68(95%CI:0.64~0.72),and this model performed better than models established using GPR,APRI,and FIB–4.Conclusion The prediction model of GGT+PT+AST has high predictive value on the severity of liver fibrosis among CHB patients whose HBeAg is negative.

关 键 词：HBV感染 肝活检 纤维化 预测模型 

分 类 号：R735.7[医药卫生—肿瘤]