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.
RCT Entities:
OBJECTIVE: To develop an artificial neural network to predict significant fibrosis (F≥2) (ANN-SF) in HIV/Hepatitis C (HCV) coinfectedpatients 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 coinfectedpatients. Our data suggest that ANN may be a helpful tool for guiding therapeutic decisions in clinical practice concerning HIV/HCV coinfection.
Authors: M Guzmán-Fulgencio; J Berenguer; D Pineda-Tenor; M A Jiménez-Sousa; M García-Álvarez; T Aldámiz-Echevarria; A Carrero; C Diez; F Tejerina; S Vázquez; V Briz; S Resino Journal: Eur J Clin Microbiol Infect Dis Date: 2014-09-19 Impact factor: 3.267
Authors: Daniel Pineda-Tenor; Juan Berenguer; María A Jiménez-Sousa; Ana Carrero; Mónica García-Álvarez; Teresa Aldámiz-Echevarria; Pilar García-Broncano; Cristina Diez; María Guzmán-Fulgencio; Amanda Fernández-Rodríguez; Salvador Resino Journal: AIDS Res Hum Retroviruses Date: 2014-10-29 Impact factor: 2.205
Authors: María Guzmán-Fulgencio; Juan Berenguer; María A Jiménez-Sousa; Daniel Pineda-Tenor; Teresa Aldámiz-Echevarria; Pilar García-Broncano; Ana Carrero; Mónica García-Álvarez; Francisco Tejerina; Cristina Diez; Sonia Vazquez-Morón; Salvador Resino Journal: J Transl Med Date: 2015-06-30 Impact factor: 5.531
Authors: Mónica García-Álvarez; Juan Berenguer; María A Jiménez-Sousa; Daniel Pineda-Tenor; Teresa Aldámiz-Echevarria; Francisco Tejerina; Cristina Diez; Sonia Vázquez-Morón; Salvador Resino Journal: Sci Rep Date: 2017-01-31 Impact factor: 4.379
Authors: Antonio Rivero-Juárez; David Guijo-Rubio; Francisco Tellez; Rosario Palacios; Dolores Merino; Juan Macías; Juan Carlos Fernández; Pedro Antonio Gutiérrez; Antonio Rivero; César Hervás-Martínez Journal: PLoS One Date: 2020-01-10 Impact factor: 3.240
Authors: Luz M Medrano; Juan Berenguer; María A Jiménez-Sousa; Teresa Aldámiz-Echevarria; Francisco Tejerina; Cristina Diez; Lorena Vigón; Amanda Fernández-Rodríguez; Salvador Resino Journal: Sci Rep Date: 2017-10-10 Impact factor: 4.379