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Exhaled breath profiling for diagnosing acute respiratory distress syndrome
BACKGROUND: The acute respiratory distress syndrome (ARDS) is a common, devastating complication of critical illness that is characterized by pulmonary injury and inflammation. The clinical diagnosis may be improved by means of objective biological markers. Electronic nose (eNose) technology can rap...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4021554/ https://www.ncbi.nlm.nih.gov/pubmed/24767549 http://dx.doi.org/10.1186/1471-2466-14-72 |
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author | Bos, Lieuwe DJ Schultz, Marcus J Sterk, Peter J |
author_facet | Bos, Lieuwe DJ Schultz, Marcus J Sterk, Peter J |
author_sort | Bos, Lieuwe DJ |
collection | PubMed |
description | BACKGROUND: The acute respiratory distress syndrome (ARDS) is a common, devastating complication of critical illness that is characterized by pulmonary injury and inflammation. The clinical diagnosis may be improved by means of objective biological markers. Electronic nose (eNose) technology can rapidly and non–invasively provide breath prints, which are profiles of volatile metabolites in the exhaled breath. We hypothesized that breath prints could facilitate accurate diagnosis of ARDS in intubated and ventilated intensive care unit (ICU) patients. METHODS: Prospective single-center cohort study with training and temporal external validation cohort. Breath of newly intubated and mechanically ventilated ICU-patients was analyzed using an electronic nose within 24 hours after admission. ARDS was diagnosed and classified by the Berlin clinical consensus definition. The eNose was trained to recognize ARDS in a training cohort and the diagnostic performance was evaluated in a temporal external validation cohort. RESULTS: In the training cohort (40 patients with ARDS versus 66 controls) the diagnostic model for ARDS showed a moderate discrimination, with an area under the receiver–operator characteristic curve (AUC–ROC) of 0.72 (95%–confidence interval (CI): 0.63-0.82). In the external validation cohort (18 patients with ARDS versus 26 controls) the AUC–ROC was 0.71 [95%–CI: 0.54 – 0.87]. Restricting discrimination to patients with moderate or severe ARDS versus controls resulted in an AUC–ROC of 0.80 [95%–CI: 0.70 – 0.90]. The exhaled breath profile from patients with cardiopulmonary edema and pneumonia was different from that of patients with moderate/severe ARDS. CONCLUSIONS: An electronic nose can rapidly and non–invasively discriminate between patients with and without ARDS with modest accuracy. Diagnostic accuracy increased when only moderate and severe ARDS patients were considered. This implicates that breath analysis may allow for rapid, bedside detection of ARDS, especially if our findings are reproduced using continuous exhaled breath profiling. TRIAL REGISTRATION: NTR2750, registered 11 February 2011. |
format | Online Article Text |
id | pubmed-4021554 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-40215542014-05-16 Exhaled breath profiling for diagnosing acute respiratory distress syndrome Bos, Lieuwe DJ Schultz, Marcus J Sterk, Peter J BMC Pulm Med Research Article BACKGROUND: The acute respiratory distress syndrome (ARDS) is a common, devastating complication of critical illness that is characterized by pulmonary injury and inflammation. The clinical diagnosis may be improved by means of objective biological markers. Electronic nose (eNose) technology can rapidly and non–invasively provide breath prints, which are profiles of volatile metabolites in the exhaled breath. We hypothesized that breath prints could facilitate accurate diagnosis of ARDS in intubated and ventilated intensive care unit (ICU) patients. METHODS: Prospective single-center cohort study with training and temporal external validation cohort. Breath of newly intubated and mechanically ventilated ICU-patients was analyzed using an electronic nose within 24 hours after admission. ARDS was diagnosed and classified by the Berlin clinical consensus definition. The eNose was trained to recognize ARDS in a training cohort and the diagnostic performance was evaluated in a temporal external validation cohort. RESULTS: In the training cohort (40 patients with ARDS versus 66 controls) the diagnostic model for ARDS showed a moderate discrimination, with an area under the receiver–operator characteristic curve (AUC–ROC) of 0.72 (95%–confidence interval (CI): 0.63-0.82). In the external validation cohort (18 patients with ARDS versus 26 controls) the AUC–ROC was 0.71 [95%–CI: 0.54 – 0.87]. Restricting discrimination to patients with moderate or severe ARDS versus controls resulted in an AUC–ROC of 0.80 [95%–CI: 0.70 – 0.90]. The exhaled breath profile from patients with cardiopulmonary edema and pneumonia was different from that of patients with moderate/severe ARDS. CONCLUSIONS: An electronic nose can rapidly and non–invasively discriminate between patients with and without ARDS with modest accuracy. Diagnostic accuracy increased when only moderate and severe ARDS patients were considered. This implicates that breath analysis may allow for rapid, bedside detection of ARDS, especially if our findings are reproduced using continuous exhaled breath profiling. TRIAL REGISTRATION: NTR2750, registered 11 February 2011. BioMed Central 2014-04-26 /pmc/articles/PMC4021554/ /pubmed/24767549 http://dx.doi.org/10.1186/1471-2466-14-72 Text en Copyright © 2014 Bos et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Bos, Lieuwe DJ Schultz, Marcus J Sterk, Peter J Exhaled breath profiling for diagnosing acute respiratory distress syndrome |
title | Exhaled breath profiling for diagnosing acute respiratory distress syndrome |
title_full | Exhaled breath profiling for diagnosing acute respiratory distress syndrome |
title_fullStr | Exhaled breath profiling for diagnosing acute respiratory distress syndrome |
title_full_unstemmed | Exhaled breath profiling for diagnosing acute respiratory distress syndrome |
title_short | Exhaled breath profiling for diagnosing acute respiratory distress syndrome |
title_sort | exhaled breath profiling for diagnosing acute respiratory distress syndrome |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4021554/ https://www.ncbi.nlm.nih.gov/pubmed/24767549 http://dx.doi.org/10.1186/1471-2466-14-72 |
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