An artificial neural network improves the non-invasive diagnosis of significant fibrosis in HIV/HCV coinfected patients

Salvador Resino, Jose A Seoane, Jose Maria Bellon, Julian Dorado, Fernando Martin-Sanchez, Emilio Alvarez, Jaime Cosin, Juan Carlos Lopez, Guilllermo Lopez, Pilar Miralles, Juan Berenguer

Research output: Contribution to journalArticle (Academic Journal)peer-review

30 Citations (Scopus)

Abstract

Objective: To develop an artificial neural network to predict significant fibrosis (F >= 2) (ANN-SF) in HIV/Hepatitis C (HCV) coinfected patients using clinical data derived from peripheral blood.

Methods: Patients were randomly divided into an estimation group (217 cases) used to generate the ANN and a test group (145 cases) used to confirm its power to predict F >= 2. Liver fibrosis was estimated according to the METAVIR score. Results: The values of the area under the receiver operating characteristic curve (AUC-ROC) of the ANN-SF were 0.868 in the estimation set and 0.846 in the test set. In the estimation set, with a cutoff value of <0.35 to predict the absence of F >= 2, the sensitivity (Se), specificity (Sp), and positive (PPV) and negative predictive values (NPV) were 94.1%, 41.8%, 66.3% and 85.4% respectively. Furthermore, with a cut-off value of >0.75 to predict the presence of F >= 2, the ANN-SF provided Se, Sp, PPV and NPV of 53.8%, 94.9%, 92.8% and 62.8% respectively. In the test set, with a cut-off value of <0.35 to predict the absence of F >= 2, the Se, Sp, PPV and NPV were 91.8%, 51.7%, 72.9% and 81.6% respectively. Furthermore, with a cut-off value of >0.75 to predict the presence of F >= 2, the ANN-SF provided Se, Sp, PPV and NPV of 43.5%, 96.7%, 94.9% and 54.7% respectively.

Conclusion: The ANN-SF accurately predicted significant fibrosis and outperformed other simple non-invasive indices for HIV/HCV coinfected patients. Our data suggest that ANN may be a helpful tool for guiding therapeutic decisions in clinical practice concerning HIV/HCV coinfection. (C) 2010 The British Infection Association. Published by Elsevier Ltd. All rights reserved.

Original languageEnglish
Pages (from-to)77-86
Number of pages10
JournalJournal of Infection
Volume62
Issue number1
DOIs
Publication statusPublished - Jan 2011

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