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  • 李秀芬,陆伟,潘云鹤,等.GPR、APRI和FIB-4预测HBeAg阴性乙型肝炎相关肝纤维化程度的评价[J].同济大学学报(医学版),2018,39(3):64-69.    [点击复制]
  • LI Xiu-fen,LU Wei,PAN Yun-he,et al.Comparison of GPR, APRI and FIB-4 in predicting severity of liver fibrosis associated with HBeAg-negative chronic hepatitis B[J].Journal of Tongji University(Medical Science),2018,39(3):64-69.   [点击复制]
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GPR、APRI和FIB-4预测HBeAg阴性乙型肝炎相关肝纤维化程度的评价
李秀芬,陆伟,潘云鹤,汪洁,张占卿
0
(复旦大学附属上海市公共卫生临床中心肝胆内科,上海201508;上海市浦东新区南华医院感染科,上海201300;上海市奉贤区古华医院感染科,上海201499)
摘要:
目的 比较GPR、APRI和FIB-4预测乙型肝炎e抗原(HBeAg)阴性乙型肝炎相关肝纤维化程度的性能。方法 HBeAg阴性慢性乙型肝炎患者859例被随机分为两组: 训练集578例、验证集281例。参照Scheuer肝脏组织学评分系统,病理学分期≥S2、≥S3和≥S4分别被定义为显著、严重和晚期纤维化。结果 训练集与验证集显著、严重和晚期纤维化的流行率差异均无统计学意义(P>0.05)。训练集与验证集GPR、APRI和FIB-4预测显著、严重和晚期纤维化的ROC曲线下面积(AUROC)差异均无统计学意义(P>0.05)。对于训练集,GPR和APRI预测显著纤维化及GPR、APRI和FIB-4预测严重和晚期纤维化的AUROC均>0.750(95%CI下限均>0.700);FIB-4预测显著纤维化的AUROC为0.700~0.750(95%CI下限<0.700)。对于验证集,GPR预测显著纤维化,GPR、APRI和FIB-4预测严重纤维化及GPR和FIB-4预测晚期纤维化的AUROC均>0.750(95%CI下限均>0.700);APRI预测显著和晚期纤维化的AUROC为0.700~0.750(95%CI下限均<0.700);FIB-4预测显著纤维化的AUROC<0.700。结论 对于HBeAg阴性者,GPR和APRI预测显著纤维化、GPR和FIB-4预测严重和晚期纤维化的效能均达到了中等水平且均有良好的稳定性,FIB-4预测显著纤维化和APRI预测晚期纤维化的效能相对较低。
关键词:  慢性乙型肝炎  纤维化  无创诊断  GPR  APRI  FIB-4
DOI:10.16118/j.1008-0392.2018.03.012
通信作者:
投稿时间:2017-08-11
录用日期:
基金项目:上海市市级医院新兴前沿技术联合攻关项目(SHDC12016237);国家“十二五”传染病科技重大专项(2013ZX10002005)
Comparison of GPR, APRI and FIB-4 in predicting severity of liver fibrosis associated with HBeAg-negative chronic hepatitis B
LI Xiu-fen,LU Wei,PAN Yun-he,WANG Jie,ZHANG Zhan-qing
(Dept. of Hepatobiliary Medicine, Shanghai Public Health Clinical Center of Fudan University, Shanghai 201508,China;Dept. of Infectious Diseases, Nanhua Hospital of Pudong New Area, Shanghai 201300,China;Dept. of Infectious Diseases, Guhua Hospital of Fengxian District, Shanghai 2014993,China)
Abstract:
Objective To compare GPR (γ-glutamyl transpeptidase to platelet ratio), APRI (aspartate aminotransferase to platelet ratio index) and FIB-4 (fibrosis index based on the 4 factors) in predicting the severity of liver fibrosis associated with HBeAg-negative chronic hepatitis B (CHB). Methods Total 859 patients with HBeAg-negative chronic hepatitis B were randomly divided into training set (n=578) and validation set (n=281). With reference to the Scheuer liver pathological scoring system, pathological stage≥S2, ≥S3 and ≥S4 were defined as significant, extensive and advanced fibrosis. Results There were no significant differences in the prevalence of the significant, extensive and advanced fibrosis between training set and validation set (P> 0.05). There were no significant differences in the areas under the ROC curves (AUROCs) of GPR, APRI and FIB-4 in predicting the significant, extensive and advanced fibrosis between training set and validation set (P> 0.05). In the training set, the AUROCs of GPR and APRI in predicting significant fibrosis, and AUROCs of GPR, APRI and FIB-4 in predicting extensive and advanced fibrosis were greater than 0.750 (the lower limits of 95%CI>0.700); and the AUROC of FIB-4 in predicting significant fibrosis was 0.700-0.750 (the lower limit of 95%CI<0.700). In the validation set, the AUROC of GPR in predicting significant fibrosis, and the AUROCs of GPR, APRI and FIB-4 in predicting extensive fibrosis, and the AUROCs of GPR and FIB-4 in predicting advanced fibrosis were greater than 0.750 (the lower limits of 95%CI>0.700); and the AUROCs of APRI in predicting extensive and advanced fibrosis were 0.700-0.750 (the lower limits of 95%CI<0.700); and the AUROC of FIB-4 in predicting significant fibrosis was <0.700. Conclusion In the framework of HBeAg-negative CHB patients, GPR and APRI in predicting the significant fibrosis, GPR and FIB-4 in predicting extensive and advanced fibrosis have moderate predictive efficiency and good reliability; while FIB-4 in predicting the significant fibrosis and APRI in predicting the advanced fibrosis have relatively low efficiency.
Key words:  chronic hepatitis B  fibrosis  noninvasive diagnosis  gamma-glutamyl transpeptidase-to-platelet ratio  aspartate aminotransferase-to-platelet ratio index  fibrosis index based on the 4 factors

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