Globulin-platelet model predicts minimal fibrosis and cirrhosis in chronic hepatitis B virus infected patients

World J Gastroenterol. 2012 Jun 14;18(22):2784-92. doi: 10.3748/wjg.v18.i22.2784.

Abstract

Aim: To establish a simple model consisting of the routine laboratory variables to predict both minimal fibrosis and cirrhosis in chronic hepatitis B virus (HBV)-infected patients.

Methods: We retrospectively investigated 114 chronic HBV-infected patients who underwent liver biopsy in two different hospitals. Thirteen parameters were analyzed by step-wise regression analysis and correlation analysis. A new fibrosis index [globulin/platelet (GP) model] was developed, including globulin (GLOB) and platelet count (PLT). GP model = GLOB (g/mL) × 100/PLT (× 10(9)/L). We evaluated the receiver operating characteristics analysis used to predict minimal fibrosis and compared six other available models.

Results: Thirteen clinical biochemical and hematological variables [sex, age, PLT, alanine aminotransferase, aspartate aminotransferase (AST), albumin, GLOB, total bilirubin (T.bil), direct bilirubin (D.bil), glutamyltransferase, alkaline phosphatase, HBV DNA and prothrombin time (PT)] were analyzed according to three stages of liver fibrosis (F0-F1, F2-F3 and F4). Bivariate Spearman's rank correlation analysis showed that six variables, including age, PLT, T.bil, D.bil, GLOB and PT, were correlated with the three fibrosis stages (FS). Correlation coefficients were 0.23, -0.412, 0.208, 0.220, 0.314 and 0.212; and P value was 0.014, < 0.001, 0.026, 0.018, 0.001 and 0.024, respectively. Univariate analysis revealed that only PLT and GLOB were significantly different in the three FS (PLT: F = 11.772, P < 0.001; GLOB: F = 6.612, P = 0.002). Step-wise multiple regression analysis showed that PLT and GLOB were also independently correlated with FS (R(2) = 0.237). By Spearman's rank correlation analysis, GP model was significantly correlated with the three FS (r = 0.466, P < 0.001). The median values in F0-F1, F2-F3 and F4 were 1.461, 1.720 and 2.634. Compared with the six available models (fibrosis index, AST-platelet ratio, FIB-4, fibrosis-cirrhosis index and age-AST model and age-PLT ratio), GP model showed a highest correlation coefficient. The sensitivity and positive predictive value at a cutoff value < 1.68 for predicting minimal fibrosis F0-F1 were 72.4% and 71.2%, respectively. The specificity and negative predictive value at a cutoff value < 2.53 for the prediction of cirrhosis were 84.5% and 96.7%. The area under the curve (AUC) of GP model for predicting minimal fibrosis and cirrhosis was 0.762 [95% confidence interval (CI): 0.676-0.848] and 0.781 (95% CI: 0.638-0.924). Although the differences were not statistically significant between GP model and the other models (P all > 0.05), the AUC of GP model was the largest among the seven models.

Conclusion: By establishing a simple model using available laboratory variables, chronic HBV-infected patients with minimal fibrosis and cirrhosis can be diagnosed accurately, and the clinical application of this model may reduce the need for liver biopsy in HBV-infected patients.

Keywords: Chronic hepatitis B virus; Globulin; Globulin/platelet model; Liver fibrosis; Noninvasive fibrosis biomarker; Platelet.

Publication types

  • Comparative Study
  • Evaluation Study
  • Multicenter Study

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Biomarkers / blood
  • Biopsy
  • China
  • Cross-Sectional Studies
  • Female
  • Hepatitis B, Chronic / blood
  • Hepatitis B, Chronic / complications
  • Hepatitis B, Chronic / diagnosis*
  • Humans
  • Liver / pathology*
  • Liver / virology
  • Liver Cirrhosis / diagnosis*
  • Liver Cirrhosis / pathology
  • Liver Cirrhosis / virology
  • Male
  • Middle Aged
  • Platelet Count*
  • Predictive Value of Tests
  • Prognosis
  • ROC Curve
  • Regression Analysis
  • Retrospective Studies
  • Risk Assessment
  • Risk Factors
  • Sensitivity and Specificity
  • Serum Globulins / analysis*
  • Severity of Illness Index
  • Young Adult

Substances

  • Biomarkers
  • Serum Globulins