title: An artificial neural network improves the non-invasive diagnosis of significant fibrosis in HIV/HCV coinfected patients.
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 cut-off 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Laboratory of Molecular Epidemiology of Infectious Diseases, National Centre of Microbiology, Instituto de Salud Carlos III, Carretera Majadahonda-Pozuelo, Km 2.2, Majadahonda, Madrid, Spain. sresino@isciii.es
authors.forename: Salvador
authors.initials: S
authors.lastname: Resino
authors.forename: José Antonio
authors.initials: JA
authors.lastname: Seoane
authors.forename: José María
authors.initials: JM
authors.lastname: Bellón
authors.forename: Julián
authors.initials: J
authors.lastname: Dorado
authors.forename: Fernando
authors.initials: F
authors.lastname: Martin-Sanchez
authors.forename: Emilio
authors.initials: E
authors.lastname: Alvarez
authors.forename: Jaime
authors.initials: J
authors.lastname: Cosín
authors.forename: Juan Carlos
authors.initials: JC
authors.lastname: López
authors.forename: Guilllermo
authors.initials: G
authors.lastname: Lopéz
authors.forename: Pilar
authors.initials: P
authors.lastname: Miralles
authors.forename: Juan
authors.initials: J
authors.lastname: Berenguer
mesh_terms: D000328:Adult; D001706:Biopsy; D003430:Cross-Sectional Studies; D005260:Female; D015658:HIV Infections / Q000150:complications*; D019698:Hepatitis C, Chronic / Q000150:complications* / Q000473:pathology*; D006801:Humans; D008099:Liver / Q000473:pathology; D008103:Liver Cirrhosis / Q000175:diagnosis* / Q000473:pathology / Q000821:virology; D008297:Male; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D012189:Retrospective Studies