Accuracy of the Enhanced Liver Fibrosis test, and combination of the Enhanced Liver Fibrosis and non-invasive tests for the diagnosis of advanced liver fibrosis in patients with non-alcoholic fatty liver disease.

combination algorithm non-invasive test sequential algorithm vibration-controlled transient elastography

Journal

Hepatology research : the official journal of the Japan Society of Hepatology
ISSN: 1386-6346
Titre abrégé: Hepatol Res
Pays: Netherlands
ID NLM: 9711801

Informations de publication

Date de publication:
Jun 2020
Historique:
received: 11 01 2020
revised: 14 02 2020
accepted: 18 02 2020
pubmed: 25 2 2020
medline: 25 2 2020
entrez: 25 2 2020
Statut: ppublish

Résumé

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. 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). 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%. 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.

Identifiants

pubmed: 32090397
doi: 10.1111/hepr.13495
doi:

Types de publication

Journal Article

Langues

eng

Pagination

682-692

Subventions

Organisme : Japan Agency for Medical Research and Development
ID : JP19fk0210040
Organisme : Siemens Healthcare Diagnostics

Informations de copyright

© 2020 The Japan Society of Hepatology.

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Auteurs

Chika Inadomi (C)

Division of Metabolism and Endocrinology, Faculty of Medicine, Saga University, Saga, Japan.
Liver Center, Saga University Hospital, Saga, Japan.

Hirokazu Takahashi (H)

Division of Metabolism and Endocrinology, Faculty of Medicine, Saga University, Saga, Japan.
Liver Center, Saga University Hospital, Saga, Japan.

Yuji Ogawa (Y)

Department of Gastroenterology and Hepatology, Yokohama City University Graduate School of Medicine, Yokohama, Japan.

Satoshi Oeda (S)

Division of Metabolism and Endocrinology, Faculty of Medicine, Saga University, Saga, Japan.
Department of Clinical Laboratory Medicine, Saga University Hospital, Saga, Japan.

Kento Imajo (K)

Department of Gastroenterology and Hepatology, Yokohama City University Graduate School of Medicine, Yokohama, Japan.

Yoshihito Kubotsu (Y)

Division of Metabolism and Endocrinology, Faculty of Medicine, Saga University, Saga, Japan.

Kenichi Tanaka (K)

Division of Metabolism and Endocrinology, Faculty of Medicine, Saga University, Saga, Japan.

Takaomi Kessoku (T)

Department of Gastroenterology and Hepatology, Yokohama City University Graduate School of Medicine, Yokohama, Japan.

Michiaki Okada (M)

Division of Metabolism and Endocrinology, Faculty of Medicine, Saga University, Saga, Japan.

Hiroshi Isoda (H)

Liver Center, Saga University Hospital, Saga, Japan.

Takumi Akiyama (T)

Division of Metabolism and Endocrinology, Faculty of Medicine, Saga University, Saga, Japan.

Hideaki Fukushima (H)

Diagnostics Business Area, Siemens Healthcare Diagnostics K.K., Tokyo, Japan.

Masato Yoneda (M)

Department of Gastroenterology and Hepatology, Yokohama City University Graduate School of Medicine, Yokohama, Japan.

Keizo Anzai (K)

Division of Metabolism and Endocrinology, Faculty of Medicine, Saga University, Saga, Japan.

Shinichi Aishima (S)

Department of Pathology & Microbiology, Faculty of Medicine, Saga University, Saga, Japan.

Atsushi Nakajima (A)

Department of Gastroenterology and Hepatology, Yokohama City University Graduate School of Medicine, Yokohama, Japan.

Yuichiro Eguchi (Y)

Liver Center, Saga University Hospital, Saga, Japan.

Classifications MeSH