A novel predictive model using routinely clinical parameters to predict liver fibrosis in patients with chronic hepatitis B.

Abstract

OBJECTIVES:

Noninvasive models have been established for the assessment of liver fibrosis in patients with chronic hepatitis B(CHB). However, the predictive performance of these established models remains inconclusive. We aimed to develop a novel predictive model for liver fibrosis in CHB based on routinely clinical parameters.

RESULTS:

Platelets(PLT), the standard deviation of red blood cell distribution width(RDW-SD), alkaline phosphatase(ALP) and globulin were independent predictors of significant fibrosis by multivariable analysis. Based on these parameters, a new predictive model namely APRG(ALP/PLT/RDW-SD/globulin) was proposed. The areas under the receiver-operating characteristic curves(AUROCs) of APRG index in predicting significant fibrosis(≥F2), advanced fibrosis(≥F3) and liver cirrhosis(≥F4) were 0.757(95%CI 0.699 to 0.816), 0.763(95%CI 0.711 to 0.816) and 0.781(95%CI 0.728 to 0.835), respectively. The AUROCs of the APRG were significantly higher than that of aspartate transaminase(AST) to PLT ratio index(APRI), RDW to PLT ratio(RPR) and AST to alanine aminotransferase ratio(AAR) to predict significant fibrosis, advanced fibrosis and cirrhosis. The AUROCs of the APRG were also significantly higher than fibrosis-4 score (FIB-4) (0.723, 95%CI 0.663 to 0.783) for cirrhosis(P=0.034) and better than gamma-glutamyl transpeptidase(GGT) to PLT ratio(GPR) (0.657, 95%CI 0.590 to 0.724) for significant fibrosis(P=0.001).

MATERIALS AND METHODS:

308 CHB patients who underwent liver biopsy were enrolled. The diagnostic values of the APRG for liver fibrosis with other noninvasive models were compared.

CONCLUSIONS:

The APRG has a better diagnostic value than conventionally predictive models to assess liver fibrosis in CHB patients. The application of APRG may reduce the need for liver biopsy in CHB patients in clinical practice.

• Authors: Jian Wang, Xiaomin Yan, Yue Yang, Haiyan Chang, Bei Jia, Xiang-An Zhao, Guangmei Chen, Juan Xia, Yong Liu, Yuxin Chen, Guiyang Wang, Li Wang, Zhaoping Zhang, Weimao Ding, Rui Huang, Chao Wu
• Journal: Oncotarget (Oncotarget) Vol. 8 Issue 35 Pg. 59257-59267 (Aug 29 2017) ISSN: 1949-2553 [Electronic] United States
• PMID: 28938634 (Publication Type: Journal Article)