Development of a non-invasive algorithm with transient elastography (Fibroscan) and serum test formula for advanced liver fibrosis in chronic hepatitis B
Article first published online: 23 FEB 2010
© 2010 Blackwell Publishing Ltd
Alimentary Pharmacology & Therapeutics
Volume 31, Issue 10, pages 1095–1103, May 2010
How to Cite
WONG, G. L. H., WONG, V. W. S., CHOI, P. C. L., CHAN, A. W. H. and CHAN, H. L. Y. (2010), Development of a non-invasive algorithm with transient elastography (Fibroscan) and serum test formula for advanced liver fibrosis in chronic hepatitis B. Alimentary Pharmacology & Therapeutics, 31: 1095–1103. doi: 10.1111/j.1365-2036.2010.04276.x
- Issue published online: 20 APR 2010
- Article first published online: 23 FEB 2010
- Publication data Submitted 8 December 2009 First decision 22 January 2010 Resubmitted 16 February 2010 Accepted 19 February 2010 Epub Accepted Article 23 February 2010
Aliment Pharmacol Ther 31, 1095–1103
Background Non-invasive assessments of liver fibrosis in chronic hepatitis B were well established.
Aim To develop a combined algorithm of liver stiffness measurement (LSM) and serum test formula to predict advanced liver fibrosis in chronic hepatitis B.
Methods We reported an alanine aminotransferase (AST)-based LSM algorithm for liver fibrosis in 156 chronic hepatitis B patients, which formed the training cohort to evaluate the performance of APRI (AST-to-platelet-ratio-index), Forns index, FIB-4 and Fibroindex against liver histology. The best combined LSM-serum formula algorithm would be validated in another cohort of 82 chronic hepatitis B patients.
Results In the training cohort, LSM has the best performance of diagnosing advanced (≥F3) fibrosis [area under the receiver operating characteristics curve (AUROC) 0.88, 95% confidence interval (CI) 0.85–0.91], while Forns index has the best performance among the various serum test formulae (AUROC 0.70, 95% CI 0.62–0.78). In the combined algorithm, low LSM or low Forns index could be used to exclude advanced fibrosis as both of them had high sensitivity (>90%). To confirm advanced fibrosis, agreement between high LSM and high Forns index could improve the specificity (from 99% to 100% and from 87% to 98% in the training and validation cohorts respectively).
Conclusion A combined LSM–Forns algorithm can improve the accuracy to predict advanced liver fibrosis in chronic hepatitis B.