G L-H Wong1, H L-Y Chan, P C-L Choi, A W-H Chan, Z Yu, J W-Y Lai, H-Y Chan, V W-S Wong. 1. Institute of Digestive Disease, The Chinese University of Hong Kong, Shatin, Hong Kong, China; Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, Hong Kong, China.
Abstract
BACKGROUND: The accuracy of Enhanced Liver Fibrosis (ELF; ADVIA Centaur, Siemens Healthcare Diagnostics, Tarrytown, NY, USA) in assessing liver fibrosis in chronic hepatitis B (CHB) is to be determined. AIM: To derive and validate a combined ELF-liver stiffness measurement (LSM) algorithm to predict advanced fibrosis in CHB patients. METHODS: Using the data of a previously reported cohort of 238 CHB patients, an ALT-based LSM algorithm for liver fibrosis was used as a training cohort to evaluate the performance of ELF against liver histology. The best combined ELF-LSM algorithm was then validated in new cohort of 85 CHB patients not previously reported. RESULTS: In the training cohort, LSM has better performance of diagnosing advanced (≥F3) fibrosis (area under the receiver operating characteristics curve [AUROC] 0.83, 95% confidence interval [CI 0.76-0.91] than ELF (AUROC 0.69, 95% CI 0.63-0.75). The optimal cut-off values of ELF were 8.4 to exclude advanced fibrosis, and 10.8 to confirm advanced fibrosis. In the training cohort, an ELF ≤ 8.4 had a sensitivity of 95% to exclude advanced fibrosis; an ELF > 10.8 had a specificity of 92% to confirm advanced fibrosis. In the combined algorithm, low ELF or low LSM could be used to exclude advanced fibrosis as both of them had high sensitivity (≥90%). To confirm advanced fibrosis, agreement between high ELF and high LSM could improve the negative predictive value specificity (from 65% and 74% to 80%). CONCLUSIONS: An Enhanced Liver Fibrosis - liver stiffness measurement algorithm could improve the accuracy of prediction of either ELF or LSM alone. Liver biopsy could be correctly avoided in approximately 60% of patients.
BACKGROUND: The accuracy of Enhanced Liver Fibrosis (ELF; ADVIA Centaur, Siemens Healthcare Diagnostics, Tarrytown, NY, USA) in assessing liver fibrosis in chronic hepatitis B (CHB) is to be determined. AIM: To derive and validate a combined ELF-liver stiffness measurement (LSM) algorithm to predict advanced fibrosis in CHB patients. METHODS: Using the data of a previously reported cohort of 238 CHB patients, an ALT-based LSM algorithm for liver fibrosis was used as a training cohort to evaluate the performance of ELF against liver histology. The best combined ELF-LSM algorithm was then validated in new cohort of 85 CHB patients not previously reported. RESULTS: In the training cohort, LSM has better performance of diagnosing advanced (≥F3) fibrosis (area under the receiver operating characteristics curve [AUROC] 0.83, 95% confidence interval [CI 0.76-0.91] than ELF (AUROC 0.69, 95% CI 0.63-0.75). The optimal cut-off values of ELF were 8.4 to exclude advanced fibrosis, and 10.8 to confirm advanced fibrosis. In the training cohort, an ELF ≤ 8.4 had a sensitivity of 95% to exclude advanced fibrosis; an ELF > 10.8 had a specificity of 92% to confirm advanced fibrosis. In the combined algorithm, low ELF or low LSM could be used to exclude advanced fibrosis as both of them had high sensitivity (≥90%). To confirm advanced fibrosis, agreement between high ELF and high LSM could improve the negative predictive value specificity (from 65% and 74% to 80%). CONCLUSIONS: An Enhanced Liver Fibrosis - liver stiffness measurement algorithm could improve the accuracy of prediction of either ELF or LSM alone. Liver biopsy could be correctly avoided in approximately 60% of patients.
Authors: Sophia Swanson; Yifei Ma; Rebecca Scherzer; Greg Huhn; Audrey L French; Michael W Plankey; Carl Grunfeld; William M Rosenberg; Marion G Peters; Phyllis C Tien Journal: J Infect Dis Date: 2015-11-29 Impact factor: 5.226
Authors: S K Sarin; M Kumar; G K Lau; Z Abbas; H L Y Chan; C J Chen; D S Chen; H L Chen; P J Chen; R N Chien; A K Dokmeci; Ed Gane; J L Hou; W Jafri; J Jia; J H Kim; C L Lai; H C Lee; S G Lim; C J Liu; S Locarnini; M Al Mahtab; R Mohamed; M Omata; J Park; T Piratvisuth; B C Sharma; J Sollano; F S Wang; L Wei; M F Yuen; S S Zheng; J H Kao Journal: Hepatol Int Date: 2015-11-13 Impact factor: 6.047