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Development of a non‐invasive algorithm with transient elastography (Fibroscan) and serum test formula for advanced liver fibrosis in chronic hepatitis B
Author(s) -
WONG G. L. H.,
WONG V. W. S.,
CHOI P. C. L.,
CHAN A. W. H.,
CHAN H. L. Y.
Publication year - 2010
Publication title -
alimentary pharmacology and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.308
H-Index - 177
eISSN - 1365-2036
pISSN - 0269-2813
DOI - 10.1111/j.1365-2036.2010.04276.x
Subject(s) - transient elastography , medicine , algorithm , gastroenterology , fibrosis , receiver operating characteristic , chronic hepatitis , confidence interval , hepatitis b , cohort , liver fibrosis , immunology , virus , computer science
Aliment Pharmacol Ther 31 , 1095–1103 Summary 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.