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Detection of inspiratory recruitment of atelectasis by automated lung sound analysis as compared to four-dimensional computed tomography in a porcine lung injury model
BACKGROUND: Cyclic recruitment and de-recruitment of atelectasis (c-R/D) is a contributor to ventilator-induced lung injury (VILI). Bedside detection of this dynamic process could improve ventilator management. This study investigated the potential of automated lung sound analysis to detect c-R/D as...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6389194/ https://www.ncbi.nlm.nih.gov/pubmed/29475456 http://dx.doi.org/10.1186/s13054-018-1964-6 |
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author | Boehme, Stefan Toemboel, Frédéric P. R. Hartmann, Erik K. Bentley, Alexander H. Weinheimer, Oliver Yang, Yang Achenbach, Tobias Hagmann, Michael Kaniusas, Eugenijus Baumgardner, James E. Markstaller, Klaus |
author_facet | Boehme, Stefan Toemboel, Frédéric P. R. Hartmann, Erik K. Bentley, Alexander H. Weinheimer, Oliver Yang, Yang Achenbach, Tobias Hagmann, Michael Kaniusas, Eugenijus Baumgardner, James E. Markstaller, Klaus |
author_sort | Boehme, Stefan |
collection | PubMed |
description | BACKGROUND: Cyclic recruitment and de-recruitment of atelectasis (c-R/D) is a contributor to ventilator-induced lung injury (VILI). Bedside detection of this dynamic process could improve ventilator management. This study investigated the potential of automated lung sound analysis to detect c-R/D as compared to four-dimensional computed tomography (4DCT). METHODS: In ten piglets (25 ± 2 kg), acoustic measurements from 34 thoracic piezoelectric sensors (Meditron ASA, Norway) were performed, time synchronized to 4DCT scans, at positive end-expiratory pressures of 0, 5, 10, and 15 cmH(2)O during mechanical ventilation, before and after induction of c-R/D by surfactant washout. 4DCT was post-processed for within-breath variation in atelectatic volume (Δ atelectasis) as a measure of c-R/D. Sound waveforms were evaluated for: 1) dynamic crackle energy (dCE): filtered crackle sounds (600–700 Hz); 2) fast Fourier transform area (FFT area): spectral content above 500 Hz in frequency and above −70 dB in amplitude in proportion to the total amount of sound above −70 dB amplitude; and 3) dynamic spectral coherence (dSC): variation in acoustical homogeneity over time. Parameters were analyzed for global, nondependent, central, and dependent lung areas. RESULTS: In healthy lungs, negligible values of Δ atelectasis, dCE, and FFT area occurred. In lavage lung injury, the novel dCE parameter showed the best correlation to Δ atelectasis in dependent lung areas (R(2) = 0.88) where c-R/D took place. dCE was superior to FFT area analysis for each lung region examined. The analysis of dSC could predict the lung regions where c-R/D originated. CONCLUSIONS: c-R/D is associated with the occurrence of fine crackle sounds as demonstrated by dCE analysis. Standardized computer-assisted analysis of dCE and dSC seems to be a promising method for depicting c-R/D. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13054-018-1964-6) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6389194 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-63891942019-03-19 Detection of inspiratory recruitment of atelectasis by automated lung sound analysis as compared to four-dimensional computed tomography in a porcine lung injury model Boehme, Stefan Toemboel, Frédéric P. R. Hartmann, Erik K. Bentley, Alexander H. Weinheimer, Oliver Yang, Yang Achenbach, Tobias Hagmann, Michael Kaniusas, Eugenijus Baumgardner, James E. Markstaller, Klaus Crit Care Research BACKGROUND: Cyclic recruitment and de-recruitment of atelectasis (c-R/D) is a contributor to ventilator-induced lung injury (VILI). Bedside detection of this dynamic process could improve ventilator management. This study investigated the potential of automated lung sound analysis to detect c-R/D as compared to four-dimensional computed tomography (4DCT). METHODS: In ten piglets (25 ± 2 kg), acoustic measurements from 34 thoracic piezoelectric sensors (Meditron ASA, Norway) were performed, time synchronized to 4DCT scans, at positive end-expiratory pressures of 0, 5, 10, and 15 cmH(2)O during mechanical ventilation, before and after induction of c-R/D by surfactant washout. 4DCT was post-processed for within-breath variation in atelectatic volume (Δ atelectasis) as a measure of c-R/D. Sound waveforms were evaluated for: 1) dynamic crackle energy (dCE): filtered crackle sounds (600–700 Hz); 2) fast Fourier transform area (FFT area): spectral content above 500 Hz in frequency and above −70 dB in amplitude in proportion to the total amount of sound above −70 dB amplitude; and 3) dynamic spectral coherence (dSC): variation in acoustical homogeneity over time. Parameters were analyzed for global, nondependent, central, and dependent lung areas. RESULTS: In healthy lungs, negligible values of Δ atelectasis, dCE, and FFT area occurred. In lavage lung injury, the novel dCE parameter showed the best correlation to Δ atelectasis in dependent lung areas (R(2) = 0.88) where c-R/D took place. dCE was superior to FFT area analysis for each lung region examined. The analysis of dSC could predict the lung regions where c-R/D originated. CONCLUSIONS: c-R/D is associated with the occurrence of fine crackle sounds as demonstrated by dCE analysis. Standardized computer-assisted analysis of dCE and dSC seems to be a promising method for depicting c-R/D. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13054-018-1964-6) contains supplementary material, which is available to authorized users. BioMed Central 2018-02-24 /pmc/articles/PMC6389194/ /pubmed/29475456 http://dx.doi.org/10.1186/s13054-018-1964-6 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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 Boehme, Stefan Toemboel, Frédéric P. R. Hartmann, Erik K. Bentley, Alexander H. Weinheimer, Oliver Yang, Yang Achenbach, Tobias Hagmann, Michael Kaniusas, Eugenijus Baumgardner, James E. Markstaller, Klaus Detection of inspiratory recruitment of atelectasis by automated lung sound analysis as compared to four-dimensional computed tomography in a porcine lung injury model |
title | Detection of inspiratory recruitment of atelectasis by automated lung sound analysis as compared to four-dimensional computed tomography in a porcine lung injury model |
title_full | Detection of inspiratory recruitment of atelectasis by automated lung sound analysis as compared to four-dimensional computed tomography in a porcine lung injury model |
title_fullStr | Detection of inspiratory recruitment of atelectasis by automated lung sound analysis as compared to four-dimensional computed tomography in a porcine lung injury model |
title_full_unstemmed | Detection of inspiratory recruitment of atelectasis by automated lung sound analysis as compared to four-dimensional computed tomography in a porcine lung injury model |
title_short | Detection of inspiratory recruitment of atelectasis by automated lung sound analysis as compared to four-dimensional computed tomography in a porcine lung injury model |
title_sort | detection of inspiratory recruitment of atelectasis by automated lung sound analysis as compared to four-dimensional computed tomography in a porcine lung injury model |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6389194/ https://www.ncbi.nlm.nih.gov/pubmed/29475456 http://dx.doi.org/10.1186/s13054-018-1964-6 |
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