Development of a non-invasive algorithm with transient elastography (Fibroscan) and serum test formula for advanced liver fibrosis in chronic hepatitis B

[Guest guest]

http://www3.interscience.wiley.com/journal/123300937/abstract

Alimentary Pharmacology & Therapeutics

Volume 31 Issue 10, Pages 1095 - 1103

Published Online: 23 Feb 2010

Journal compilation © 2010 Blackwell Publishing Ltd

Development of a non-invasive algorithm with transient elastography (Fibroscan)

and serum test formula for advanced liver fibrosis in chronic hepatitis B

G. L. H. WONG*,†, V. W. S. WONG*,†, P. C. L. CHOI‡, A. W. H. CHAN‡ & H.

L. Y. CHAN*,†

*Institute of Digestive Disease , †Department of Medicine and Therapeutics

and ‡Department of Anatomical and Cellular Pathology, The Chinese University

of Hong Kong, Hong Kong SAR, China

Correspondence to Dr H. L. Y. Chan, Department of Medicine and Therapeutics, 9/F

Prince of Wales Hospital, 30-32 Ngan Shing Street, Shatin, Hong Kong, China.

E-mail: hlychan@...

Copyright Journal compilation © 2010 Blackwell Publishing Ltd

Aliment Pharmacol Ther 31, 1095–1103

ABSTRACT

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.

--------------------------------------------------------------------------------

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

DIGITAL OBJECT IDENTIFIER (DOI)

10.1111/j.1365-2036.2010.04276