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Establishment and validation of a simple noninvasive model to predict significant liver fibrosis in patients with chronic hepatitis B

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Abstract

Background

There have been still few valuable noninvasive models that can be used as indirect markers of liver fibrosis in chronic hepatitis B (CHB) infection.

Methods

In 374 patients with chronic hepatitis B virus infection, the correlation between the conventional parameters and significant fibrosis confirmed by liver biopsy was assessed using univariate analysis and logistic regression. A model was established and assessed by the receiver operating characteristic (ROC) curves. Then it was validated in 108 prospectively enrolled patients. A part of the patients were followed up with cirrhosis as the end point, using survival analysis to assess the prognostic value of the model.

Results

A model named AIAG was constructed consisting of age, international normalized ratio, albumin, and gamma-glutamyltransferase which could discriminate between CHB patients with and without significant fibrosis. The area under ROC curves was 0.842 (95% CI, 0.795–0.888) for the training group (n = 250) and 0.806 (95% CI, 0.730–0.882) for the validation group (n = 124). In the training group, using a cut-off score of <0.32, the presence of significant fibrosis could be excluded with high accuracy (90% negative predictive value); similarly, applying a cut-off score of >0.72, the presence of significant fibrosis could be correctly identified with high accuracy (93% positive predictive value). Similar results have been shown in the internal and external validation groups. In the follow-up study, we found that the AIAG score may have good prognostic values to predict the progression of clinically overt cirrhosis in CHB patients.

Conclusions

AIAG, a simple marker panel consisting of conventional parameters, could easily predict significant fibrosis with a high degree of accuracy.

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References

  1. Lai CL, Ratziu V, Yuen MF, Poynard T. Viral hepatitis B. Lancet. 2003;362:2089–94.

    Article  PubMed  CAS  Google Scholar 

  2. Afdhal NH, Nunes D. Evaluation of liver fibrosis: a concise review. Am J Gastroenterol. 2004;99:1160–74.

    Article  PubMed  Google Scholar 

  3. Lindor KD, Bru C, Jorgensen RA, et al. The role of ultrasonography and automatic-needle biopsy in outpatient percutaneous liver biopsy. Hepatology. 1996;23:1079–83.

    Article  PubMed  CAS  Google Scholar 

  4. Cadranel JF, Rufat P, Degos F. Practices of liver biopsy in France: results of a prospective nationwide survey. For the Group of Epidemiology of the French Association for the Study of the Liver (AFEF). Hepatology. 2000;32:477–81.

    Article  PubMed  CAS  Google Scholar 

  5. Castera L, Pinzani M. Non-invasive assessment of liver fibrosis: are we ready? Lancet. 2010;375:1419–20.

    Article  PubMed  Google Scholar 

  6. Martinez SM, Crespo G, Navasa M, Forns X. Noninvasive assessment of liver fibrosis. Hepatology. 2011;53:325–35.

    Article  PubMed  Google Scholar 

  7. Forns X, Ampurdanes S, Llovet JM, et al. Identification of chronic hepatitis C patients without hepatic fibrosis by a simple predictive model. Hepatology. 2002;36:986–92.

    PubMed  Google Scholar 

  8. Wai CT, Greenson JK, Fontana RJ, et al. A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C. Hepatology. 2003;38:518–26.

    Article  PubMed  Google Scholar 

  9. Adams LA, Bulsara M, Rossi E, et al. Hepascore: an accurate validated predictor of liver fibrosis in chronic hepatitis C infection. Clin Chem. 2005;51:1867–73.

    Article  PubMed  CAS  Google Scholar 

  10. Cales P, Oberti F, Michalak S, et al. A novel panel of blood markers to assess the degree of liver fibrosis. Hepatology. 2005;42:1373–81.

    Article  PubMed  Google Scholar 

  11. Zeng MD, Lu LG, Mao YM, et al. Prediction of significant fibrosis in HBeAg-positive patients with chronic hepatitis B by a noninvasive model. Hepatology. 2005;42:1437–45.

    Article  PubMed  Google Scholar 

  12. Sterling RK, Lissen E, Clumeck N, et al. Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology. 2006;43:1317–25.

    Article  PubMed  CAS  Google Scholar 

  13. The French METAVIR Cooperative Study Group. Intraobserver and interobserver variations in liver biopsy interpretation in patients with chronic hepatitis C. Hepatology. 1994;20:15–20

    Google Scholar 

  14. Ghany MG, Strader DB, Thomas DL, Seeff LB. Diagnosis, management, and treatment of hepatitis C: an update. Hepatology. 2009;49:1335–74.

    Article  PubMed  CAS  Google Scholar 

  15. Hanley JA, McNeil BJ. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology. 1983;148:839–43.

    PubMed  CAS  Google Scholar 

  16. Poynard T, Halfon P, Castera L, et al. Standardization of ROC curve areas for diagnostic evaluation of liver fibrosis markers based on prevalences of fibrosis stages. Clin Chem. 2007;53:1615–22.

    Article  PubMed  CAS  Google Scholar 

  17. Poynard T, Muntenau M, Morra R, et al. Methodological aspects of the interpretation of non-invasive biomarkers of liver fibrosis: a 2008 update. Gastroenterol Clin Biol. 2008;32:8–21.

    Article  PubMed  CAS  Google Scholar 

  18. Lin ZH, Xin YN, Dong QJ, et al. Performance of the aspartate aminotransferase-to-platelet ratio index for the staging of hepatitis C-related fibrosis: an updated meta-analysis. Hepatology. 2011;53(3):726–36.

    Article  PubMed  Google Scholar 

  19. Wai CT, Cheng CL, Wee A, et al. Non-invasive models for predicting histology in patients with chronic hepatitis B. Liver Int. 2006;26:666–72.

    Article  PubMed  Google Scholar 

  20. Sebastiani G, Vario A, Guido M, Alberti A. Sequential algorithms combining non-invasive markers and biopsy for the assessment of liver fibrosis in chronic hepatitis B. World J Gastroenterol. 2007;13:525–31.

    PubMed  Google Scholar 

  21. Vallet-Pichard A, Mallet V, Nalpas B, et al. FIB-4: an inexpensive and accurate marker of fibrosis in HCV infection. Comparison with liver biopsy and fibrotest. Hepatology. 2007;46:32–6.

    Article  PubMed  CAS  Google Scholar 

  22. Mallet V, Dhalluin-Venier V, Roussin C, et al. The accuracy of the FIB-4 index for the diagnosis of mild fibrosis in chronic hepatitis B. Aliment Pharmacol Ther. 2009;29:409–15.

    Article  PubMed  CAS  Google Scholar 

  23. Adler M, Gulbis B, Moreno C, et al. The predictive value of FIB-4 versus FibroTest, APRI, FibroIndex and Forns index to noninvasively estimate fibrosis in hepatitis C and nonhepatitis C liver diseases. Hepatology. 2008;47:762–3.

    Article  PubMed  Google Scholar 

  24. Wu SD, Wang JY, Li L. Staging of liver fibrosis in chronic hepatitis B patients with a composite predictive model: a comparative study. World J Gastroenterol. 2010;16:501–7.

    Article  PubMed  CAS  Google Scholar 

  25. Imbert-Bismut F, Ratziu V, Pieroni L, Charlotte F, Benhamou Y, Poynard T. Biochemical markers of liver fibrosis in patients with hepatitis C virus infection: a prospective study. Lancet. 2001;357:1069–75.

    Article  PubMed  CAS  Google Scholar 

  26. Myers RP, Ratziu V, Imbert-Bismut F, Charlotte F, Poynard T. Biochemical markers of liver fibrosis: a comparison with historical features in patients with chronic hepatitis C. Am J Gastroenterol. 2002;97:2419–25.

    Article  PubMed  CAS  Google Scholar 

  27. Giannini E, Ceppa P, Botta F, et al. Steatosis and bile duct damage in chronic hepatitis C: distribution and relationships in a group of northern Italian patients. Liver. 1999;19:432–7.

    Article  PubMed  CAS  Google Scholar 

  28. Myers RP, Tainturier MH, Ratziu V, et al. Prediction of liver histological lesions with biochemical markers in patients with chronic hepatitis B. J Hepatol. 2003;39:222–30.

    Article  PubMed  CAS  Google Scholar 

  29. Naveau S, Gaude G, Asnacios A, et al. Diagnostic and prognostic values of noninvasive biomarkers of fibrosis in patients with alcoholic liver disease. Hepatology. 2009;49:97–105.

    Article  PubMed  Google Scholar 

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Acknowledgements

This study was supported by the National High Technology Research and Development Program of China (863 Program, no. 2006AA02A411).

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Correspondence to Ji-Yao Wang.

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Wu, Sd., Ni, Yj., Liu, L. et al. Establishment and validation of a simple noninvasive model to predict significant liver fibrosis in patients with chronic hepatitis B. Hepatol Int 6, 360–368 (2012). https://doi.org/10.1007/s12072-011-9328-1

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  • DOI: https://doi.org/10.1007/s12072-011-9328-1

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