AIM: The Enhanced Liver Fibrosis (ELF) test comprises a logarithmic algorithm combining three serum markers of hepatic extracellular matrix metabolism. We aimed to evaluate the performance of ELF for the diagnosis of liver fibrosis and to compare it with that of liver stiffness measurement (LSM) by FibroScan in non-alcoholic fatty liver disease. METHODS: ELF cut-off values for the diagnosis of advanced fibrosis were obtained using receiver operating characteristic analysis in patients with biopsy-confirmed non-alcoholic fatty liver disease (training set; n = 200). Diagnostic performance was analyzed in the training set and in a validation set (n = 166), and compared with that of LSM in the FibroScan cohort (n = 224). RESULTS: The area under receiver operating characteristic curve was 0.81 for the diagnosis of advanced fibrosis, and the ELF cut-off values were 9.34 with 90.4% sensitivity and 10.83 with 90.6% specificity in the training set, and 89.8% sensitivity and 85.5% specificity in the validation set. There was no significant difference in the area under the receiver operating characteristic curve between ELF and LSM (0.812 and 0.839). A combination of ELF (cut-off 10.83) and LSM (cut-off 11.45) increased the specificity to 97.9% and the positive predictive value, versus ELF alone. Sequential use of the Fibrosis-4 index (cut-off 2.67) and ELF (cut-off 9.34) increased the sensitivity to 95.9%. CONCLUSIONS: ELF can identify advanced liver fibrosis in non-alcoholic fatty liver disease, and its diagnostic accuracy is comparable to that of FibroScan. According to the clinical setting, combinations or sequential procedures using other non-invasive tests complement the diagnostic performance of ELF for the identification of advanced fibrosis.
AIM: The Enhanced Liver Fibrosis (ELF) test comprises a logarithmic algorithm combining three serum markers of hepatic extracellular matrix metabolism. We aimed to evaluate the performance of ELF for the diagnosis of liver fibrosis and to compare it with that of liver stiffness measurement (LSM) by FibroScan in non-alcoholic fatty liver disease. METHODS: ELF cut-off values for the diagnosis of advanced fibrosis were obtained using receiver operating characteristic analysis in patients with biopsy-confirmed non-alcoholic fatty liver disease (training set; n = 200). Diagnostic performance was analyzed in the training set and in a validation set (n = 166), and compared with that of LSM in the FibroScan cohort (n = 224). RESULTS: The area under receiver operating characteristic curve was 0.81 for the diagnosis of advanced fibrosis, and the ELF cut-off values were 9.34 with 90.4% sensitivity and 10.83 with 90.6% specificity in the training set, and 89.8% sensitivity and 85.5% specificity in the validation set. There was no significant difference in the area under the receiver operating characteristic curve between ELF and LSM (0.812 and 0.839). A combination of ELF (cut-off 10.83) and LSM (cut-off 11.45) increased the specificity to 97.9% and the positive predictive value, versus ELF alone. Sequential use of the Fibrosis-4 index (cut-off 2.67) and ELF (cut-off 9.34) increased the sensitivity to 95.9%. CONCLUSIONS: ELF can identify advanced liver fibrosis in non-alcoholic fatty liver disease, and its diagnostic accuracy is comparable to that of FibroScan. According to the clinical setting, combinations or sequential procedures using other non-invasive tests complement the diagnostic performance of ELF for the identification of advanced fibrosis.
Authors: Daniel Bradshaw; Yvonne Gilleece; Sumita Verma; Iga Abramowicz; Stephen Bremner; Nicky Perry Journal: BMJ Open Date: 2020-07-06 Impact factor: 2.692
Authors: Nancy de Los Ángeles Segura-Azuara; Carlos Daniel Varela-Chinchilla; Plinio A Trinidad-Calderón Journal: Front Med (Lausanne) Date: 2022-01-13
Authors: Ferenc Emil Mózes; Jenny A Lee; Emmanuel Anandraj Selvaraj; Arjun Narayan Ajmer Jayaswal; Michael Trauner; Jerome Boursier; Céline Fournier; Katharina Staufer; Rudolf E Stauber; Elisabetta Bugianesi; Ramy Younes; Silvia Gaia; Monica Lupșor-Platon; Salvatore Petta; Toshihide Shima; Takeshi Okanoue; Sanjiv Mahadeva; Wah-Kheong Chan; Peter J Eddowes; Gideon M Hirschfield; Philip Noel Newsome; Vincent Wai-Sun Wong; Victor de Ledinghen; Jiangao Fan; Feng Shen; Jeremy F Cobbold; Yoshio Sumida; Akira Okajima; Jörn M Schattenberg; Christian Labenz; Won Kim; Myoung Seok Lee; Johannes Wiegand; Thomas Karlas; Yusuf Yılmaz; Guruprasad Padur Aithal; Naaventhan Palaniyappan; Christophe Cassinotto; Sandeep Aggarwal; Harshit Garg; Geraldine J Ooi; Atsushi Nakajima; Masato Yoneda; Marianne Ziol; Nathalie Barget; Andreas Geier; Theresa Tuthill; M Julia Brosnan; Quentin Mark Anstee; Stefan Neubauer; Stephen A Harrison; Patrick M Bossuyt; Michael Pavlides Journal: Gut Date: 2021-05-17 Impact factor: 23.059