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 (> or =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.
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 Bpatients, 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 Bpatients. RESULTS: In the training cohort, LSM has the best performance of diagnosing advanced (> or =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.
Authors: Xin-Wu Cui; Mireen Friedrich-Rust; Chiara De Molo; Andre Ignee; Dagmar Schreiber-Dietrich; Christoph F Dietrich Journal: World J Gastroenterol Date: 2013-10-14 Impact factor: 5.742