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Breath-print analysis by e-nose for classifying and monitoring chronic liver disease: a proof-of-concept study

Since the liver plays a key metabolic role, volatile organic compounds in the exhaled breath might change with type and severity of chronic liver disease (CLD). In this study we analysed breath-prints (BPs) of 65 patients with liver cirrhosis (LC), 39 with non-cirrhotic CLD (NC-CLD) and 56 healthy c...

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Autores principales: De Vincentis, Antonio, Pennazza, Giorgio, Santonico, Marco, Vespasiani-Gentilucci, Umberto, Galati, Giovanni, Gallo, Paolo, Vernile, Chiara, Pedone, Claudio, Antonelli Incalzi, Raffaele, Picardi, Antonio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4857073/
https://www.ncbi.nlm.nih.gov/pubmed/27145718
http://dx.doi.org/10.1038/srep25337
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author De Vincentis, Antonio
Pennazza, Giorgio
Santonico, Marco
Vespasiani-Gentilucci, Umberto
Galati, Giovanni
Gallo, Paolo
Vernile, Chiara
Pedone, Claudio
Antonelli Incalzi, Raffaele
Picardi, Antonio
author_facet De Vincentis, Antonio
Pennazza, Giorgio
Santonico, Marco
Vespasiani-Gentilucci, Umberto
Galati, Giovanni
Gallo, Paolo
Vernile, Chiara
Pedone, Claudio
Antonelli Incalzi, Raffaele
Picardi, Antonio
author_sort De Vincentis, Antonio
collection PubMed
description Since the liver plays a key metabolic role, volatile organic compounds in the exhaled breath might change with type and severity of chronic liver disease (CLD). In this study we analysed breath-prints (BPs) of 65 patients with liver cirrhosis (LC), 39 with non-cirrhotic CLD (NC-CLD) and 56 healthy controls by the e-nose. Distinctive BPs characterized LC, NC-CLD and healthy controls, and, among LC patients, the different Child-Pugh classes (sensitivity 86.2% and specificity 98.2% for CLD vs healthy controls, and 87.5% and 69.2% for LC vs NC-CLD). Moreover, the area under the BP profile, derived from radar-plot representation of BPs, showed an area under the ROC curve of 0.84 (95% CI 0.76–0.91) for CLD, of 0.76 (95% CI 0.66–0.85) for LC, and of 0.70 (95% CI 0.55–0.81) for decompensated LC. By applying the cut-off values of 862 and 812, LC and decompensated LC could be predicted with high accuracy (PPV 96.6% and 88.5%, respectively). These results are proof-of-concept that the e-nose could be a valid non-invasive instrument for characterizing CLD and monitoring hepatic function over time. The observed classificatory properties might be further improved by refining stage-specific breath-prints and considering the impact of comorbidities in a larger series of patients.
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spelling pubmed-48570732016-05-18 Breath-print analysis by e-nose for classifying and monitoring chronic liver disease: a proof-of-concept study De Vincentis, Antonio Pennazza, Giorgio Santonico, Marco Vespasiani-Gentilucci, Umberto Galati, Giovanni Gallo, Paolo Vernile, Chiara Pedone, Claudio Antonelli Incalzi, Raffaele Picardi, Antonio Sci Rep Article Since the liver plays a key metabolic role, volatile organic compounds in the exhaled breath might change with type and severity of chronic liver disease (CLD). In this study we analysed breath-prints (BPs) of 65 patients with liver cirrhosis (LC), 39 with non-cirrhotic CLD (NC-CLD) and 56 healthy controls by the e-nose. Distinctive BPs characterized LC, NC-CLD and healthy controls, and, among LC patients, the different Child-Pugh classes (sensitivity 86.2% and specificity 98.2% for CLD vs healthy controls, and 87.5% and 69.2% for LC vs NC-CLD). Moreover, the area under the BP profile, derived from radar-plot representation of BPs, showed an area under the ROC curve of 0.84 (95% CI 0.76–0.91) for CLD, of 0.76 (95% CI 0.66–0.85) for LC, and of 0.70 (95% CI 0.55–0.81) for decompensated LC. By applying the cut-off values of 862 and 812, LC and decompensated LC could be predicted with high accuracy (PPV 96.6% and 88.5%, respectively). These results are proof-of-concept that the e-nose could be a valid non-invasive instrument for characterizing CLD and monitoring hepatic function over time. The observed classificatory properties might be further improved by refining stage-specific breath-prints and considering the impact of comorbidities in a larger series of patients. Nature Publishing Group 2016-05-05 /pmc/articles/PMC4857073/ /pubmed/27145718 http://dx.doi.org/10.1038/srep25337 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
De Vincentis, Antonio
Pennazza, Giorgio
Santonico, Marco
Vespasiani-Gentilucci, Umberto
Galati, Giovanni
Gallo, Paolo
Vernile, Chiara
Pedone, Claudio
Antonelli Incalzi, Raffaele
Picardi, Antonio
Breath-print analysis by e-nose for classifying and monitoring chronic liver disease: a proof-of-concept study
title Breath-print analysis by e-nose for classifying and monitoring chronic liver disease: a proof-of-concept study
title_full Breath-print analysis by e-nose for classifying and monitoring chronic liver disease: a proof-of-concept study
title_fullStr Breath-print analysis by e-nose for classifying and monitoring chronic liver disease: a proof-of-concept study
title_full_unstemmed Breath-print analysis by e-nose for classifying and monitoring chronic liver disease: a proof-of-concept study
title_short Breath-print analysis by e-nose for classifying and monitoring chronic liver disease: a proof-of-concept study
title_sort breath-print analysis by e-nose for classifying and monitoring chronic liver disease: a proof-of-concept study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4857073/
https://www.ncbi.nlm.nih.gov/pubmed/27145718
http://dx.doi.org/10.1038/srep25337
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