An artificial neural network improves the non-invasive diagnosis of significant fibrosis in HIV/HCV coinfected patients.
J Infect
; 62(1): 77-86, 2011 Jan.
Article
en En
| MEDLINE
| ID: mdl-21073895
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.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.
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Infecciones por VIH
/
Hepatitis C Crónica
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Cirrosis Hepática
Tipo de estudio:
Diagnostic_studies
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Observational_studies
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Prevalence_studies
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Prognostic_studies
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Risk_factors_studies
Límite:
Adult
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Female
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Humans
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Male
Idioma:
En
Revista:
J Infect
Año:
2011
Tipo del documento:
Article
País de afiliación:
España