TY - JOUR
T1 - An artificial neural network improves the non-invasive diagnosis of significant fibrosis in HIV/HCV coinfected patients
AU - Resino, Salvador
AU - Seoane, Jose A
AU - Maria Bellon, Jose
AU - Dorado, Julian
AU - Martin-Sanchez, Fernando
AU - Alvarez, Emilio
AU - Cosin, Jaime
AU - Carlos Lopez, Juan
AU - Lopez, Guilllermo
AU - Miralles, Pilar
AU - Berenguer, Juan
PY - 2011/1
Y1 - 2011/1
N2 - 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.
AB - 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.
U2 - 10.1016/j.jinf.2010.11.003
DO - 10.1016/j.jinf.2010.11.003
M3 - Article (Academic Journal)
C2 - 21073895
VL - 62
SP - 77
EP - 86
JO - Journal of Infection
JF - Journal of Infection
SN - 0163-4453
IS - 1
ER -