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Latent class analysis of imaging and clinical respiratory parameters from patients with COVID-19-related ARDS identifies recruitment subphenotypes

BACKGROUND: Patients with COVID-19-related acute respiratory distress syndrome (ARDS) require respiratory support with invasive mechanical ventilation and show varying responses to recruitment manoeuvres. In patients with ARDS not related to COVID-19, two pulmonary subphenotypes that differed in rec...

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Autores principales: Filippini, Daan F. L., Di Gennaro, Elisa, van Amstel, Rombout B. E., Beenen, Ludo F. M., Grasso, Salvatore, Pisani, Luigi, Bos, Lieuwe D. J., Smit, Marry R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9700924/
https://www.ncbi.nlm.nih.gov/pubmed/36434629
http://dx.doi.org/10.1186/s13054-022-04251-2
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author Filippini, Daan F. L.
Di Gennaro, Elisa
van Amstel, Rombout B. E.
Beenen, Ludo F. M.
Grasso, Salvatore
Pisani, Luigi
Bos, Lieuwe D. J.
Smit, Marry R.
author_facet Filippini, Daan F. L.
Di Gennaro, Elisa
van Amstel, Rombout B. E.
Beenen, Ludo F. M.
Grasso, Salvatore
Pisani, Luigi
Bos, Lieuwe D. J.
Smit, Marry R.
author_sort Filippini, Daan F. L.
collection PubMed
description BACKGROUND: Patients with COVID-19-related acute respiratory distress syndrome (ARDS) require respiratory support with invasive mechanical ventilation and show varying responses to recruitment manoeuvres. In patients with ARDS not related to COVID-19, two pulmonary subphenotypes that differed in recruitability were identified using latent class analysis (LCA) of imaging and clinical respiratory parameters. We aimed to evaluate if similar subphenotypes are present in patients with COVID-19-related ARDS. METHODS: This is the retrospective analysis of mechanically ventilated patients with COVID-19-related ARDS who underwent CT scans at positive end-expiratory pressure of 10 cmH(2)O and after a recruitment manoeuvre at 20 cmH(2)O. LCA was applied to quantitative CT-derived parameters, clinical respiratory parameters, blood gas analysis and routine laboratory values before recruitment to identify subphenotypes. RESULTS: 99 patients were included. Using 12 variables, a two-class LCA model was identified as best fitting. Subphenotype 2 (recruitable) was characterized by a lower PaO(2)/FiO(2), lower normally aerated lung volume and lower compliance as opposed to a higher non-aerated lung mass and higher mechanical power when compared to subphenotype 1 (non-recruitable). Patients with subphenotype 2 had more decrease in non-aerated lung mass in response to a standardized recruitment manoeuvre (p = 0.024) and were mechanically ventilated longer until successful extubation (adjusted SHR 0.46, 95% CI 0.23–0.91, p = 0.026), while no difference in survival was found (p = 0.814). CONCLUSIONS: A recruitable and non-recruitable subphenotype were identified in patients with COVID-19-related ARDS. These findings are in line with previous studies in non-COVID-19-related ARDS and suggest that a combination of imaging and clinical respiratory parameters could facilitate the identification of recruitable lungs before the manoeuvre. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13054-022-04251-2.
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spelling pubmed-97009242022-11-27 Latent class analysis of imaging and clinical respiratory parameters from patients with COVID-19-related ARDS identifies recruitment subphenotypes Filippini, Daan F. L. Di Gennaro, Elisa van Amstel, Rombout B. E. Beenen, Ludo F. M. Grasso, Salvatore Pisani, Luigi Bos, Lieuwe D. J. Smit, Marry R. Crit Care Research BACKGROUND: Patients with COVID-19-related acute respiratory distress syndrome (ARDS) require respiratory support with invasive mechanical ventilation and show varying responses to recruitment manoeuvres. In patients with ARDS not related to COVID-19, two pulmonary subphenotypes that differed in recruitability were identified using latent class analysis (LCA) of imaging and clinical respiratory parameters. We aimed to evaluate if similar subphenotypes are present in patients with COVID-19-related ARDS. METHODS: This is the retrospective analysis of mechanically ventilated patients with COVID-19-related ARDS who underwent CT scans at positive end-expiratory pressure of 10 cmH(2)O and after a recruitment manoeuvre at 20 cmH(2)O. LCA was applied to quantitative CT-derived parameters, clinical respiratory parameters, blood gas analysis and routine laboratory values before recruitment to identify subphenotypes. RESULTS: 99 patients were included. Using 12 variables, a two-class LCA model was identified as best fitting. Subphenotype 2 (recruitable) was characterized by a lower PaO(2)/FiO(2), lower normally aerated lung volume and lower compliance as opposed to a higher non-aerated lung mass and higher mechanical power when compared to subphenotype 1 (non-recruitable). Patients with subphenotype 2 had more decrease in non-aerated lung mass in response to a standardized recruitment manoeuvre (p = 0.024) and were mechanically ventilated longer until successful extubation (adjusted SHR 0.46, 95% CI 0.23–0.91, p = 0.026), while no difference in survival was found (p = 0.814). CONCLUSIONS: A recruitable and non-recruitable subphenotype were identified in patients with COVID-19-related ARDS. These findings are in line with previous studies in non-COVID-19-related ARDS and suggest that a combination of imaging and clinical respiratory parameters could facilitate the identification of recruitable lungs before the manoeuvre. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13054-022-04251-2. BioMed Central 2022-11-25 /pmc/articles/PMC9700924/ /pubmed/36434629 http://dx.doi.org/10.1186/s13054-022-04251-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Filippini, Daan F. L.
Di Gennaro, Elisa
van Amstel, Rombout B. E.
Beenen, Ludo F. M.
Grasso, Salvatore
Pisani, Luigi
Bos, Lieuwe D. J.
Smit, Marry R.
Latent class analysis of imaging and clinical respiratory parameters from patients with COVID-19-related ARDS identifies recruitment subphenotypes
title Latent class analysis of imaging and clinical respiratory parameters from patients with COVID-19-related ARDS identifies recruitment subphenotypes
title_full Latent class analysis of imaging and clinical respiratory parameters from patients with COVID-19-related ARDS identifies recruitment subphenotypes
title_fullStr Latent class analysis of imaging and clinical respiratory parameters from patients with COVID-19-related ARDS identifies recruitment subphenotypes
title_full_unstemmed Latent class analysis of imaging and clinical respiratory parameters from patients with COVID-19-related ARDS identifies recruitment subphenotypes
title_short Latent class analysis of imaging and clinical respiratory parameters from patients with COVID-19-related ARDS identifies recruitment subphenotypes
title_sort latent class analysis of imaging and clinical respiratory parameters from patients with covid-19-related ards identifies recruitment subphenotypes
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9700924/
https://www.ncbi.nlm.nih.gov/pubmed/36434629
http://dx.doi.org/10.1186/s13054-022-04251-2
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