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Title: FibroGENE: A gene-based model for staging liver fibrosis.
Authors: Eslam, Mohammed
Hashem, Ahmed M
Romero-Gomez, Manuel
Berg, Thomas
Dore, Gregory J
Mangia, Alessandra
Chan, Henry Lik Yuen
Irving, William L
Sheridan, David
Abate, Maria Lorena
Adams, Leon A
Weltman, Martin
Bugianesi, Elisabetta
Spengler, Ulrich
Shaker, Olfat
Fischer, Janett
Mollison, Lindsay
Cheng, Wendy
Nattermann, Jacob
Riordan, Stephen
Miele, Luca
Kelaeng, Kebitsaone Simon
Ampuero, Javier
Ahlenstiel, Golo
McLeod, Duncan
Powell, Elizabeth
Liddle, Christopher
Douglas, Mark W
Booth, David R
George, Jacob
International Liver Disease Genetics Consortium (ILDGC)
Keywords: Chronic hepatitis B;Chronic hepatitis C;Data mining analysis;Fibrosis;IFNL;NASH;Non-alcoholic steatohepatitis
metadata.dc.subject.mesh: Adult
Disease Progression
Genetic Markers
Hepatitis, Chronic
Liver Cirrhosis
Middle Aged
Non-alcoholic Fatty Liver Disease
Patient Acuity
Polymorphism, Single Nucleotide
Predictive Value of Tests
Reproducibility of Results
Research Design
Risk Assessment
Issue Date: 1-Dec-2015
Abstract: 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. 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. 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 (AUROCs) 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. A non-invasive decision tree model can predict liver fibrosis risk and aid decision making.
metadata.dc.identifier.doi: 10.1016/j.jhep.2015.11.008
Appears in Collections:Producción 2020

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