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Detection of Bacterial Colonization in Lung Transplant Recipients Using an Electronic Nose
BACKGROUND. Bacterial colonization (BC) of the lower airways is common in lung transplant recipients (LTRs) and increases the risk of chronic lung allograft dysfunction. Diagnosis often requires bronchoscopy. Exhaled breath analysis using electronic nose (eNose) technology may noninvasively detect B...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10513211/ https://www.ncbi.nlm.nih.gov/pubmed/37745948 http://dx.doi.org/10.1097/TXD.0000000000001533 |
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author | Wijbenga, Nynke de Jong, Nadine L.A. Hoek, Rogier A.S. Mathot, Bas J. Seghers, Leonard Aerts, Joachim G.J.V. Bos, Daniel Manintveld, Olivier C. Hellemons, Merel E. |
author_facet | Wijbenga, Nynke de Jong, Nadine L.A. Hoek, Rogier A.S. Mathot, Bas J. Seghers, Leonard Aerts, Joachim G.J.V. Bos, Daniel Manintveld, Olivier C. Hellemons, Merel E. |
author_sort | Wijbenga, Nynke |
collection | PubMed |
description | BACKGROUND. Bacterial colonization (BC) of the lower airways is common in lung transplant recipients (LTRs) and increases the risk of chronic lung allograft dysfunction. Diagnosis often requires bronchoscopy. Exhaled breath analysis using electronic nose (eNose) technology may noninvasively detect BC in LTRs. Therefore, we aimed to assess the diagnostic accuracy of an eNose to detect BC in LTRs. METHODS. We performed a cross-sectional analysis within a prospective, single-center cohort study assessing the diagnostic accuracy of detecting BC using eNose technology in LTRs. In the outpatient clinic, consecutive LTR eNose measurements were collected. We assessed and classified the eNose measurements for the presence of BC. Using supervised machine learning, the diagnostic accuracy of eNose for BC was assessed in a random training and validation set. Model performance was evaluated using receiver operating characteristic analysis. RESULTS. In total, 161 LTRs were included with 80 exclusions because of various reasons. Of the remaining 81 patients, 16 (20%) were classified as BC and 65 (80%) as non-BC. eNose-based classification of patients with and without BC provided an area under the curve of 0.82 in the training set and 0.97 in the validation set. CONCLUSIONS. Exhaled breath analysis using eNose technology has the potential to noninvasively detect BC. |
format | Online Article Text |
id | pubmed-10513211 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-105132112023-09-22 Detection of Bacterial Colonization in Lung Transplant Recipients Using an Electronic Nose Wijbenga, Nynke de Jong, Nadine L.A. Hoek, Rogier A.S. Mathot, Bas J. Seghers, Leonard Aerts, Joachim G.J.V. Bos, Daniel Manintveld, Olivier C. Hellemons, Merel E. Transplant Direct Lung Transplantation BACKGROUND. Bacterial colonization (BC) of the lower airways is common in lung transplant recipients (LTRs) and increases the risk of chronic lung allograft dysfunction. Diagnosis often requires bronchoscopy. Exhaled breath analysis using electronic nose (eNose) technology may noninvasively detect BC in LTRs. Therefore, we aimed to assess the diagnostic accuracy of an eNose to detect BC in LTRs. METHODS. We performed a cross-sectional analysis within a prospective, single-center cohort study assessing the diagnostic accuracy of detecting BC using eNose technology in LTRs. In the outpatient clinic, consecutive LTR eNose measurements were collected. We assessed and classified the eNose measurements for the presence of BC. Using supervised machine learning, the diagnostic accuracy of eNose for BC was assessed in a random training and validation set. Model performance was evaluated using receiver operating characteristic analysis. RESULTS. In total, 161 LTRs were included with 80 exclusions because of various reasons. Of the remaining 81 patients, 16 (20%) were classified as BC and 65 (80%) as non-BC. eNose-based classification of patients with and without BC provided an area under the curve of 0.82 in the training set and 0.97 in the validation set. CONCLUSIONS. Exhaled breath analysis using eNose technology has the potential to noninvasively detect BC. Lippincott Williams & Wilkins 2023-09-20 /pmc/articles/PMC10513211/ /pubmed/37745948 http://dx.doi.org/10.1097/TXD.0000000000001533 Text en Copyright © 2023 The Author(s). Transplantation Direct. Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND (https://creativecommons.org/licenses/by-nc-nd/4.0/) ), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. |
spellingShingle | Lung Transplantation Wijbenga, Nynke de Jong, Nadine L.A. Hoek, Rogier A.S. Mathot, Bas J. Seghers, Leonard Aerts, Joachim G.J.V. Bos, Daniel Manintveld, Olivier C. Hellemons, Merel E. Detection of Bacterial Colonization in Lung Transplant Recipients Using an Electronic Nose |
title | Detection of Bacterial Colonization in Lung Transplant Recipients Using an Electronic Nose |
title_full | Detection of Bacterial Colonization in Lung Transplant Recipients Using an Electronic Nose |
title_fullStr | Detection of Bacterial Colonization in Lung Transplant Recipients Using an Electronic Nose |
title_full_unstemmed | Detection of Bacterial Colonization in Lung Transplant Recipients Using an Electronic Nose |
title_short | Detection of Bacterial Colonization in Lung Transplant Recipients Using an Electronic Nose |
title_sort | detection of bacterial colonization in lung transplant recipients using an electronic nose |
topic | Lung Transplantation |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10513211/ https://www.ncbi.nlm.nih.gov/pubmed/37745948 http://dx.doi.org/10.1097/TXD.0000000000001533 |
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