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Lookup NU author(s): Dr David Sheridan
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Background & Aims: The extent of liver fibrosis predicts long-erm 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 FibroGENE-DT yielded the area under the receiver operating characteristic curve (AUR-OCs) of 0.87, 0.85 and 0.804 for the prediction of fast fibrosis progression, cirrhosis and significant fibrosis risk, respectively, with comparable results in the validation cohort. The model performed well in NAFLD and CHB with AUROCs of 0.791, and 0.726, respectively. The negative predictive value to exclude cirrhosis was >0.96 in all three liver diseases. The AUROC of the FibroGENE-DT performed better than FIB-4, APRI, and NFS and Forns' index in most comparisons.Conclusion: A non-invasive decision tree model can predict liver fibrosis risk and aid decision making. (C) 2015 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.
Author(s): Eslam M, Hashem AM, Romero-Gomez M, Berg T, Dore GJ, Mangia A, Chan HLY, Irving WL, Sheridan D, Abate ML, Adams LA, Weltman M, Bugianesi E, Spengler U, Shaker O, Fischer J, Mollison L, Cheng W, Nattermann J, Riordan S, Miele L, Kelaeng KS, Ampuero J, Ahlenstiel G, McLeod D, Powell E, Liddle C, Douglas MW, Booth DR, George J, ILDGC
Publication type: Article
Publication status: Published
Journal: Journal of Hepatology
Year: 2016
Volume: 64
Issue: 2
Pages: 390-398
Print publication date: 01/02/2016
Online publication date: 01/12/2015
Acceptance date: 09/11/2015
ISSN (print): 0168-8278
ISSN (electronic): 1600-0641
Publisher: Elsevier
URL: http://dx.doi.org/10.1016/j.jhep.2015.11.008
DOI: 10.1016/j.jhep.2015.11.008
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