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

Daan F L Filippini; Elisa Di Gennaro; Rombout B E van Amstel; Ludo F M Beenen; Salvatore Grasso; Luigi Pisani; Lieuwe D J Bos; Marry R Smit

doi:10.1186/s13054-022-04251-2

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

Crit Care. 2022 Nov 25;26(1):363. doi: 10.1186/s13054-022-04251-2.

Authors

Daan F L Filippini¹, Elisa Di Gennaro^{2

3}, Rombout B E van Amstel², Ludo F M Beenen⁴, Salvatore Grasso⁵, Luigi Pisani^{6

7}, Lieuwe D J Bos^{2

8

9}, Marry R Smit²

Affiliations

¹ Department of Intensive Care Medicine, Amsterdam UMC, University of Amsterdam, 1105AZ, Amsterdam, The Netherlands. d.filippini@amsterdamumc.nl.
² Department of Intensive Care Medicine, Amsterdam UMC, University of Amsterdam, 1105AZ, Amsterdam, The Netherlands.
³ Faculty of Medicine, University of Bari, Bari, Italy.
⁴ Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
⁵ Department of Emergency and Organ Transplantation, University of Bari Aldo Moro, Bari, Italy.
⁶ Critical Care Africa Asia Network, Mahidol Oxford Research Unit, Bangkok, Thailand.
⁷ Department of Anesthesia and Intensive Care, Miulli Regional Hospital, Acquiviva Delle Fonti, Bari, Italy.
⁸ Department of Pulmonology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
⁹ Laboratory of Experimental Intensive Care and Anesthesiology (L.E.I.C.A.), University of Amsterdam, Amsterdam, The Netherlands.

Abstract

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.

Keywords: ARDS; COVID-19; Latent class analysis; Mechanical ventilation; Phenotypes; Radiological data; Recruitment; Respiratory parameters.

MeSH terms

COVID-19* / complications
Humans
Latent Class Analysis
Positive-Pressure Respiration / methods
Respiratory Distress Syndrome* / diagnostic imaging
Retrospective Studies