Skip to main content

Advertisement

SpringerLink
Log in

Noninvasive serum models to predict significant liver related events in chronic hepatitis C

  • Original Article
  • Published:
Hepatology International Aims and scope Submit manuscript

Abstract

Aim

We aim to compare 20 noninvasive fibrosis scores (NIFS), derived from routine blood tests, for predicting significant liver-related adverse events (SLRE) in patients with chronic hepatitis C (CHC) after anti-viral treatment (AVT) with the goal to identify independent predictors for these outcomes.

Methods

From 1605 patients who received AVT (pegylated interferon and ribavirin) from January 2002 to June 2014, 20 NIFS were calculated from routine blood tests prior to AVT. Areas under the receiver-operating characteristic curve (AUROC) were calculated for each of these NIFS for predicting non-response to AVT and development of SLRE on follow-up.

Results

Mean age was 41.9 ± 9.7 years, and patients were predominantly genotype 4 (65%). After AVT, there were 1089 (67.8%) responders, 482 (30%) non-responders and 34 (2.1%) relapsers. After median follow-up of 6580.5 patient-years, 60 (3.8%) had SLRE, 52 (3.2%) had decompensation, and 11 (0.7%) had hepatocellular carcinoma (HCC). The predictive accuracy of NIFS and liver biopsy (LB) for non-response to AVT was low. FIB-4, FibroQ and King score showed high accuracy for predicting adverse events. For predicting decompensation, HCC and SLRE, FibroQ (0.881), King score (0.905) and FibroQ (0.877) had the highest AUROC, respectively. On multivariate analysis, independent predictors for treatment non-response (age, ALT, GGT, platelet count), HCC (albumin, GGT) and SLRE (albumin, GGT, platelet count) were identified.

Conclusions

Some simple pretreatment blood parameters and NIFS showed high accuracy for predicting development of SLRE post treatment. Application of these simple scores can improve assessment of long-term liver prognosis for CHC.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Lawitz E, Sulkowski MS, Ghalib R, et al. Simeprevir plus sofosbuvir, with or without ribavirin, to treat chronic infection with hepatitis C virus genotype 1 in non-responders to pegylated interferon and ribavirin and treatment-naive patients: the COSMOS randomised study. Lancet 2014;384:1756–1765

  2. Sulkowski MS, Gardiner DF, Rodriguez-Torres M, et al. Daclatasvir plus sofosbuvir for previously treated or untreated chronic HCV infection. N Engl J Med 2014;370:211–221

  3. Alric L, Bonnet D. Grazoprevir + elbasvir for the treatment of hepatitis C virus infection. Expert Opin Pharmacother 2016;17(5):735–742

  4. Zhang X. Direct anti-HCV agents. Acta Pharm Sin B. 2016;6(1):26–31

  5. Boursier J, Brochard C, Bertrais S, et al. Combination of blood tests for significant fibrosis and cirrhosis improves the assessment of liver-prognosis in chronic hepatitis C. Aliment Pharmacol Ther 2014;40(2):178–188

  6. Morgan TR, Ghany MG, Kim HY, HALT-C Trial Group, et al. Outcome of sustained virological responders with histologically advanced chronic hepatitis C. Hepatology 2010;52(3):833–844

  7. Morisco F, Granata R, Stroffolini T, et al. Sustained virological response: a milestone in the treatment of chronic hepatitis C. World J Gastroenterol. 201314;19(18):2793–2798

  8. Tsuda N, Yuki N, Mochizuki K, Nagaoka T, et al. Long-term clinical and virological outcomes of chronic hepatitis C after successful interferon therapy. J Med Virol 2004;74:406–413

  9. Hung CH, Lee CM, Lu SN, et al. Long-term effect of interferon alpha-2b plus ribavirin therapy on incidence of hepatocellular carcinoma in patients with hepatitis C virus related cirrhosis. J Viral Hepat 2006;13:409–414

  10. Arase Y, Ikeda K, Suzuki F, et al. Long-term outcome after interferon therapy in elderly patients with chronic hepatitis C. Intervirology 2007;50:16–23

  11. Annicchiarico BE, Siciliano M, Avolio AW, Grillo RL, Bombardieri G. A 5-year prospective study of the late resolution of chronic hepatitis C after antiviral therapy. Aliment Pharmacol Ther 2007;25:1039–1046

  12. Yu ML, Lin SM, Lee CM, et al. A simple noninvasive index for predicting long-term outcome of chronic hepatitis C after interferon-based therapy. Hepatology. 2006;44(5):1086–1097

  13. Chinnaratha MA, Jeffrey GP, MacQuillan G, et al. Prediction of morbidity and mortality in patients with chronic hepatitis C by non-invasive liver fibrosis models. Liver Int 2014;34(5):720–727

  14. Nunes D, Fleming C, Offner G, et al. Noninvasive markers of liver fibrosis are highly predictive of liver-related death in a cohort of HCV-infected individuals with and without HIV infection. Am J Gastroenterol 2010;105(6):1346–1353

  15. Mayo MJ, Parkes J, Adams-Huet B, et al. Prediction of clinical outcomes in primary biliary cirrhosis by serum enhanced liver fibrosis assay. Hepatology 2008;48:1549–1557

  16. Naveau S, Gaudé 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

  17. Parkes J, Roderick P, Harris S, et al Enhanced liver fibrosis test can predict clinical outcomes in patients with chronic liver disease. Gut 2010;59:1245–1251

  18. Scheuer PJ. Classification of chronic viral hepatitis: a need for reassessment. J Hepatol 1991;13:372–374

  19. Savolainen VT, Liesto K, Männikkö A, et al. Alcohol consumption and alcoholic liver disease: evidence of a threshold level of effects of ethanol. Alcohol Clin Exp Res 1993;17:1112–1117

  20. Thandassery RB, Al Kaabi S, Soofi ME, et al. Mean platelet volume, red cell distribution width to platelet count ratio, globulin platelet index, and 16 other indirect noninvasive fibrosis scores: how much do routine blood tests tell about liver fibrosis in chronic hepatitis C? J Clin Gastroenterol 2016;50(6):518–523

  21. Kleinbaum DG, Kupper LL, Muller KE. Applied Regression Analysis and Other Multivariable Methods, 2nd edn. Boston: PWS-Kent; 1988

  22. Ngo Y, Munteanu M, Messous D, et al. A prospective analysis of the prognostic value of biomarkers (FibroTest) in patients with chronic hepatitis C. Clin Chem 2006;52:1887–1896

  23. Poynard T, Ngo Y, Perazzo H, et al. Prognostic value of liver fibrosis biomarkers: a meta-analysis. Gastroenterol Hepatol (N Y) 2011;7:445–454

  24. Singh S, Fujii LL, Murad MH, et al. Liver stiffness is associated with risk of decompensation, liver cancer, and death in patients with chronic liver diseases: a systematic review and meta-analysis. Clin Gastroenterol Hepatol 2013;11:1573–1584

  25. Vergniol J, Foucher J, Terrebonne E, et al. Noninvasive tests for fibrosis and liver stiffness predict 5-year outcomes of patients with chronic hepatitis C. Gastroenterology 2011;140:1970–1979, 1979 e1971–e1973

  26. Fontana RJ, Dienstag JL, Bonkovsky HL, et al. Serum fibrosis markers are associated with liver disease progression in non-responder patients with chronic hepatitis C. Gut 2010;59:1401–1409

  27. Konerman MA, Yapali S, Lok AS. Systematic review: identifying patients with chronic hepatitis C in need of early treatment and intensive monitoring—predictors and predictive models of disease progression. Aliment Pharmacol Ther. 2014;40(8):863–879

  28. Huang Y, Adams L, MacQuillan G, Speers D, Joseph J, Bulsara M, Jeffrey G. Serum models accurately predict liver related clinical outcomes in chronic hepatitis C. J Gastroenterol Hepatol 2016;31(10):1736–1741

  29. Lee MH, Yang HI, Liu J, Batrla-Utermann R, R.E.V.E.A.L.-HBV Study Group, et al. Prediction models of long-term cirrhosis and hepatocellular carcinoma risk in chronic hepatitis B patients: risk scores integrating host and virus profiles. Hepatology 2013;58(2):546–554

  30. Pang Q, Bi JB, Wang ZX, et al. Simple models based on gamma-glutamyl transpeptidase and platelets for predicting survival in hepatitis B-associated hepatocellular carcinoma. Onco Targets Ther 2016;9:2099–2109

  31. Pang Q, Bi JB, Xu XS, et al. King’s score as a novel prognostic model for patients with hepatitis B-associated hepatocellular carcinoma. Eur J Gastroenterol Hepatol 2015;27(11):1337–1346

  32. Gavilan JC, Ojeda G, Arnedo R, Puerta S. Predictive factors of risk of hepatocellular carcinoma in chronic hepatitis C. Eur J Intern Med 2013;24:846–851

  33. El-Serag HB, Kanwal F, Richardson P, Kramer J. Risk of hepatocellular carcinoma after sustained virological response in Veterans with hepatitis C virus infection. Hepatology 2016;64(1):130–137

Download references

Acknowledgements

We acknowledge Kamron Pourmand, MD (Gastroenterology Fellow, Mount Sinai Hospital, New York), for his assistance with language editing and manuscript proof reading.

Author information

Authors and Affiliations

  1. Division of Gastroenterology, Department of Medicine, Hamad General Hospital, Doha, Qatar

    Ragesh Babu Thandassery & Saad Al Kaabi

  2. Department of Laboratory Medicine and Pathology, Hamad General Hospital, Doha, Qatar

    Madiha E. Soofi

  3. Department of Gastroenterology, University of Arkansas Medical Center, Little Rock, AR, USA

    Benjamin Tharian

  4. Department of Biostatistics, Hamad General Hospital, Doha, Qatar

    Rajvir Singh

Authors
  1. Ragesh Babu Thandassery
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Saad Al Kaabi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Madiha E. Soofi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Benjamin Tharian
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rajvir Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

RBT, concept of study, design of study, data analysis, manuscript writing; SAK, manuscript editing; MES, histopathological evaluation; BT, manuscript editing and statistical analysis; RS, statistical analysis.

Corresponding author

Correspondence to Ragesh Babu Thandassery.

Ethics declarations

Financial support

This study was funded by the Medical Research Committee, Hamad Medical Corp. (project no. 15357/15).

Conflict of interest

Ragesh Babu Thandassery, Saad Al Kaabi, Madiha E Soofi, Benjamin Tharian and Rajvir Singh have no potential conflicts of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Human/animal rights

This was a retrospective study, and no human subjects were directly involved during this study. This article does not contain any studies with animals performed by any of the authors.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Thandassery, R.B., Kaabi, S.A., Soofi, M.E. et al. Noninvasive serum models to predict significant liver related events in chronic hepatitis C. Hepatol Int 11, 401–408 (2017). https://doi.org/10.1007/s12072-017-9800-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12072-017-9800-7

Keywords

Search

Navigation