慢性乙型肝炎肝纤维化无创检测模型建立及应用价值验证  

The prediction and validation of liver fibrosis by a noninvasive model and validation in patients with chronic hepatitis B

作　　者：刘卫平[1] 许德军[2] 赵连荣[3] 陆忠华[4] 王玉华[5] 郎振为[6] 王贵强[1] 

机构地区：[1]北京大学第一医院感染疾病科,100034 [2]河南省安阳市第五人民医院 [3]中国医科大学盛京医院 [4]无锡市传染病医院 [5]秦皇岛市第三医院 [6]北京地坛医院

出　　处：《中华内科杂志》2008年第4期308-312,共5页Chinese Journal of Internal Medicine

基　　金：卫生部临床学科重点项目（20010911）;美国CMB基金;北京大学985项目

摘　　要：目的建立慢性乙型肝炎肝纤维化的无创检测模型，并对其应用价值进行验证。方法慢性乙型肝炎190例，模型组（110例）以肝组织学分期为标准，建立纤维化预测模型，在验证组（80例）评价其应用价值。结果该研究共纳入20项指标，在模型组经过logistic分析，建立了结合珠蛋白、γ-谷氨酰转肽酶和血小板计数3项指标构成的模型（FibroScore）。FibroScore判别显著纤维化（S≥2）、严重纤维化（s≥3）、肝硬化（S=4）和任何程度纤维化（S≥1）的曲线下面积（AUC）分别为0．835、0．820、0．843、0．832，准确率分别为75．5％、69．1％、78．2％和75．0％。验证组与模型组的AUC差异无统计学意义。FibroScore对HBeAg阳性或阴性慢性乙型肝炎肝纤维化均具有较高的诊断价值（AUC为0．755～0．857）。以0．18作为除外显著纤维化的界值，阴性预测值为90．0％；以0．70作为诊断显著纤维化的界值，阳性预测值为82．7％。如果对〈0．18和〉10．70的病例不行肝活检，可减少58．4％（111／190）的肝活检，准确率为84．7％（94／111）。结论FibroScore可较准确预测慢性乙型肝炎病人是否存在显著纤维化，而且对HBeAg阳性或阴性慢性乙型肝炎均具有较高的应用价值。Objective To develop a simple model for the noninvasive diagnosis of liver fibrosis in patients with chronic hepatitis B and to testify its diagnostic value. Methods One hundred and ninety patients with chronic hepatitis B who had undergone liver biopsy were divided into 2 groups： one for developing the model （ n = 110） and one for validation （ n = 80）. Histological staging of liver fibrosis, assessed blindly and independently by 2 pathologists, was determined according to Scheuer fibrosis score. Twenty markers involved in the study were analyzed initially in the estimation group to derive a predictive model to discriminate the stages of fibrosis. The model created was then assessed with receiver operating characteristic curve （ROC） analysis. It was also applied to the validation group to test its accuracy. Results Haptoglobin （HPT）, γ-glutamyl transpeptidase （GGT） and platelet were identified by logistic regression analysis as independent factors of fibrosis. A model developed from the above three markers was established to predict the stage of fibrosis（S）. In ROC analysis, the area under curve （AUC） for identifying S≥ 1, S≥ 2, S≥3 and S =4 was 0. 832, 0. 835, 0. 820 and 0. 843 respectively. The model had a similar AUC in the validation group without statistically significant difference. Using a cut-off of 〈 0. 18, significant fibrosis （S≥2） could be excluded in 27 patients of the total patient population （negative predictive value 90% ）. Similarly, applying a cut-off ≥0.70, significant fibrosis could be identified correctly in 67 patients of the total patient population （positive predictive value 82.7% ）. The model had a high level of diagnostic value in patients with HBeAg-positive chronic hepatitis B as well as in patients with HBeAg-negative chronic hepatitis B （ AUC for identifying S ≥ 2, 0. 857 vs 0. 802）. Restricting biopsy to patients with intermediate scores （ ≥0.70 and 〈 0. 18） may prevent liver biopsies in 58.4% of the patients while

关 键 词：肝炎 乙型 慢性 肝纤维化 诊断 预测模型 

分 类 号：R686[医药卫生—骨科学]