Authors
Mohammed Eslam, Ahmed M Hashem, Manuel Romero-Gomez, Thomas Berg, Gregory J Dore, Alessandra Mangia, Henry Lik Yuen Chan, William L Irving, David Sheridan, Maria Lorena Abate, Leon A Adams, Martin Weltman, Elisabetta Bugianesi, Ulrich Spengler, Olfat Shaker, Janett Fischer, Lindsay Mollison, Wendy Cheng, Jacob Nattermann, Stephen Riordan, Luca Miele, Kebitsaone Simon Kelaeng, Javier Ampuero, Golo Ahlenstiel, Duncan McLeod, Elizabeth Powell, Christopher Liddle, Mark W Douglas, David R Booth, Jacob George, International Liver Disease Genetics Consortium
Publication date
2016/2/1
Journal
Journal of hepatology
Volume
64
Issue
2
Pages
390-398
Publisher
Elsevier
Description
Background & Aims
The extent of liver fibrosis predicts long-term outcomes, and hence impacts management and therapy. We developed a non-invasive algorithm to stage fibrosis using non-parametric, machine learning methods designed for predictive modeling, and incorporated an invariant genetic marker of liver fibrosis risk.
Methods
Of 4277 patients with chronic liver disease, 1992 with chronic hepatitis C (derivation cohort) were analyzed to develop the model, and subsequently validated in an independent cohort of 1242 patients. The model was assessed in cohorts with chronic hepatitis B (CHB) (n = 555) and non-alcoholic fatty liver disease (NAFLD) (n = 488). Model performance was compared to FIB-4 and APRI, and also to the NAFLD fibrosis score (NFS) and Forns’ index, in those with NAFLD.
Results
Significant fibrosis (⩾F2) was similar in the derivation (48.4%) and validation (47.4%) cohorts. The …
Scholar articles
M Eslam, AM Hashem, M Romero-Gomez, T Berg… - Journal of hepatology, 2016