Research output: Contribution to journal › Article › Academic › peer-review
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. et al.
In: Critical Care, Vol. 26, No. 1, 363, 12.2022.Research output: Contribution to journal › Article › Academic › peer-review
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TY - JOUR
T1 - Latent class analysis of imaging and clinical respiratory parameters from patients with COVID‑19‑related ARDS identifies recruitment subphenotypes
AU - Filippini, Daan F. L.
AU - Di Gennaro, Elisa
AU - van Amstel, Rombout B. E.
AU - Beenen, Ludo F. M.
AU - Grasso, Salvatore
AU - Pisani, Luigi
AU - Bos, Lieuwe D. J.
AU - Smit, Marry R.
N1 - Funding Information: The authors would like to thank the members of the Diagnostic Image Analysis Group (Radboudumc, Nijmegen, The Netherlands) for their support and providing the segmentation algorithm. Publisher Copyright: © 2022, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - 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 cmH2O and after a recruitment manoeuvre at 20 cmH2O. 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 PaO2/ FiO2, 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.
AB - 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 cmH2O and after a recruitment manoeuvre at 20 cmH2O. 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 PaO2/ FiO2, 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.
KW - ARDS
KW - COVID-19
KW - Latent class analysis
KW - Mechanical ventilation
KW - Phenotypes
KW - Radiological data
KW - Recruitment
KW - Respiratory parameters
UR - http://www.scopus.com/inward/record.url?scp=85142496728&partnerID=8YFLogxK
U2 - https://doi.org/10.1186/s13054-022-04251-2
DO - https://doi.org/10.1186/s13054-022-04251-2
M3 - Article
VL - 26
JO - Critical care (London, England)
JF - Critical care (London, England)
SN - 1364-8535
IS - 1
M1 - 363
ER -
ID: 27570461