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Annals of Intensive Care
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Open Access 01-12-2024 | Acute Respiratory Distress Syndrome | Research

Identification and validation of respiratory subphenotypes in patients with COVID-19 acute respiratory distress syndrome undergoing prone position

Authors: Mônica R. da Cruz, Pedro Azambuja, Kátia S. C. Torres, Fernanda Lima-Setta, André M. Japiassú, Denise M. Medeiros

Published in: Annals of Intensive Care | Issue 1/2024

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Abstract

Background

The heterogeneity of acute respiratory distress syndrome (ARDS) patients is a challenge for the development of effective treatments. This study aimed to identify and characterize novel respiratory subphenotypes of COVID-19 ARDS, with potential implications for targeted patient management.

Methods

Consecutive ventilated patients with PCR-confirmed COVID-19 infection, in which prone positioning was clinically indicated for moderate or severe ARDS, were included in a prospective cohort. The patients were assigned to development or validation cohorts based on a temporal split. The PaO2/FiO2 ratio, respiratory compliance, and ventilatory ratio were assessed longitudinally throughout the first prone session. The subphenotypes were derived and validated using machine learning techniques. A K-means clustering implementation designed for joint trajectory analysis was utilized for the unsupervised classification of the development cohort. A random forest model was trained on the labeled development cohort and used to validate the subphenotypes in the validation cohort.

Results

718 patients were included in a prospective cohort analysis. Of those, 504 were assigned to the development cohort and 214 to the validation cohort. Two distinct subphenotypes, labeled A and B, were identified. Subphenotype B had a lower PaO2/FiO2 response during the prone session, higher ventilatory ratio, and lower compliance than subphenotype A. Subphenotype B had a higher proportion of females (p < 0.001) and lung disease (p = 0.005), higher baseline SAPS III (p = 0.002) and SOFA (p < 0.001) scores, and lower body mass index (p = 0.05). Subphenotype B had also higher levels of the pro-inflammatory biomarker IL-6 (p = 0.017). Subphenotype B was independently associated with an increased risk of 60-day mortality (OR 1.89, 95% CI 1.51–2.36). Additionally, Subphenotype B was associated with a lower number of ventilator-free days on day 28 (p < 0.001) and a lower hospital length of stay (p < 0.001). The subphenotypes were reproducible in the validation cohort.

Conclusion

Our study successfully identified and validated two distinct subphenotypes of COVID-19 ARDS based on key respiratory parameters. The findings suggest potential implications for better patient stratification, risk assessment, and treatment personalization. Future research is warranted to explore the utility of these novel subphenotypes for guiding targeted therapeutic strategies in COVID-19 ARDS.
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Literature
1.
2.
Botta M, Tsonas AM, Pillay J, Boers LS, Algera AG, Bos LDJ, et al. Ventilation management and clinical outcomes in invasively ventilated patients with COVID-19 (PRoVENT-COVID): a national, multicentre, observational cohort study. Lancet Respir Med. 2021;9(2):139–48.CrossRefPubMed
3.
Bain W, Matute-Bello G. Should we shift the paradigm of preclinical models for ARDS therapies? Thorax. 2019;74(12):1109–10.CrossRefPubMed
4.
5.
Ranieri VM, Rubenfeld GD, Thompson BT, Ferguson ND, Caldwell E, ARDS Definition Task Force, et al. Acute respiratory distress syndrome: the Berlin Definition. JAMA. 2012;307(23):2526–33.PubMed
6.
Amato MBP, Meade MO, Slutsky AS, Brochard L, Costa ELV, Schoenfeld DA, et al. Driving Pressure and Survival in the Acute Respiratory Distress Syndrome. N Engl J Med. 2015;372(8):747–55.CrossRefPubMed
7.
Sinha P, Calfee CS, Beitler JR, Soni N, Ho K, Matthay MA, et al. Physiologic analysis and clinical performance of the ventilatory ratio in acute respiratory distress syndrome. Am J Respir Crit Care Med. 2019;199(3):333–41.CrossRefPubMedPubMedCentral
8.
9.
Greener JG, Kandathil SM, Moffat L, Jones DT. A guide to machine learning for biologists. Nat Rev Mol Cell Biol. 2022;23(1):40–55.CrossRefPubMed
10.
Maslove DM, Tang B, Shankar-Hari M, Lawler PR, Angus DC, Baillie JK, et al. Redefining critical illness. Nat Med. 2022;28(6):1141–8.CrossRefPubMed
11.
Guérin C, Reignier J, Richard JC, Beuret P, Gacouin A, Boulain T, et al. Prone positioning in severe acute respiratory distress syndrome. N Engl J Med. 2013;368(23):2159–68.CrossRefPubMed
12.
Guérin C, Albert RK, Beitler J, Gattinoni L, Jaber S, Marini JJ, et al. Prone position in ARDS patients: why, when, how and for whom. Intensive Care Med. 2020;46(12):2385–96.CrossRefPubMedPubMedCentral
13.
Cronin JN, Camporota L, Formenti F. Mechanical ventilation in COVID-19: a physiological perspective. Exp Physiol. 2022;107(7):683–93.CrossRefPubMed
14.
Scaramuzzo G, Gamberini L, Tonetti T, Zani G, Ottaviani I, Mazzoli CA, et al. Sustained oxygenation improvement after first prone positioning is associated with liberation from mechanical ventilation and mortality in critically ill COVID-19 patients: a cohort study. Ann Intensive Care. 2021;11(1):63.CrossRefPubMedPubMedCentral
15.
Camporota L, Sanderson B, Chiumello D, Terzi N, Argaud L, Rimmelé T, et al. Prone position in COVID-19 and -COVID-19 acute respiratory distress syndrome: an international multicenter observational comparative study*. Crit Care Med. 2022;50(4):633–43.CrossRefPubMed
16.
Grasselli G, Tonetti T, Protti A, Langer T, Girardis M, Bellani G, et al. Pathophysiology of COVID-19-associated acute respiratory distress syndrome: a multicentre prospective observational study. Lancet Respir Med. 2020;8(12):1201–8.CrossRefPubMedPubMedCentral
17.
Chiumello D, Bonifazi M, Pozzi T, Formenti P, Papa GFS, Zuanetti G, et al. Positive end-expiratory pressure in COVID-19 acute respiratory distress syndrome: the heterogeneous effects. Crit Care. 2021;25(1):431.CrossRefPubMedPubMedCentral
18.
19.
Wendel Garcia PD, Caccioppola A, Coppola S, Pozzi T, Ciabattoni A, Cenci S, et al. Latent class analysis to predict intensive care outcomes in Acute Respiratory Distress Syndrome: a proposal of two pulmonary phenotypes. Crit Care. 2021;25(1):154.CrossRefPubMedPubMedCentral
20.
Filippini DFL, Di Gennaro E, van Amstel RBE, Beenen LFM, Grasso S, Pisani L, et al. Latent class analysis of imaging and clinical respiratory parameters from patients with COVID-19-related ARDS identifies recruitment subphenotypes. Crit Care. 2022;26(1):363.CrossRefPubMedPubMedCentral
21.
Bos LDJ, Sjoding M, Sinha P, Bhavani SV, Lyons PG, Bewley AF, et al. Longitudinal respiratory subphenotypes in patients with COVID-19-related acute respiratory distress syndrome: results from three observational cohorts. Lancet Respir Med. 2021;9(12):1377–86.CrossRefPubMedPubMedCentral
22.
Genolini C, Alacoque X, Sentenac M, Arnaud C. kml and kml3d : R packages to cluster longitudinal data. J Stat Soft. 2015;65(4):1–34.CrossRef
23.
Hennig C. Cluster-wise assessment of cluster stability. Comput Stat Data Anal. 2007;52(1):258–71.CrossRef
24.
25.
Liaw A, Wiener M. Classification and regression by randomforest. R News. 2022;2:18–22.
26.
Genolini C, Lacombe A, Écochard R, Subtil F. CopyMean: a new method to predict monotone missing values in longitudinal studies. Comput Methods Programs Biomed. 2016;132:29–44.CrossRefPubMed
27.
Langer T, Brioni M, Guzzardella A, Carlesso E, Cabrini L, PRONA-COVID Group, et al. Prone position in intubated, mechanically ventilated patients with COVID-19: a multi-centric study of more than 1000 patients. Crit Care. 2021;25(1):128.CrossRefPubMedPubMedCentral
28.
Albert RK, Keniston A, Baboi L, Ayzac L, Guérin C. Prone position–induced improvement in gas exchange does not predict improved survival in the acute respiratory distress syndrome. Am J Respir Crit Care Med. 2014;189(4):494–6.CrossRefPubMed
29.
30.
Gattinoni L, Chiumello D, Caironi P, Busana M, Romitti F, Brazzi L, et al. COVID-19 pneumonia: different respiratory treatments for different phenotypes? Intensive Care Med. 2020;46(6):1099–102.CrossRefPubMedPubMedCentral
31.
Ackermann M, Verleden SE, Kuehnel M, Haverich A, Welte T, Laenger F, et al. Pulmonary vascular endothelialitis, thrombosis, and angiogenesis in Covid-19. N Engl J Med. 2020;383(2):120–8.CrossRefPubMedPubMedCentral
32.
Diehl JL, Peron N, Chocron R, Debuc B, Guerot E, Hauw-Berlemont C, et al. Respiratory mechanics and gas exchanges in the early course of COVID-19 ARDS: a hypothesis-generating study. Ann Intensive Care. 2020;10(1):95.CrossRefPubMedPubMedCentral
33.
Lee DW, Gardner R, Porter DL, Louis CU, Ahmed N, Jensen M, et al. Current concepts in the diagnosis and management of cytokine release syndrome. Blood. 2014;124(2):188–95.CrossRefPubMedPubMedCentral
34.
Maddali MV, Churpek M, Pham T, Rezoagli E, Zhuo H, Zhao W, et al. Validation and utility of ARDS subphenotypes identified by machine-learning models using clinical data: an observational, multicohort, retrospective analysis. Lancet Respir Med. 2022;10(4):367–77.CrossRefPubMedPubMedCentral
35.
Altman DG, Vergouwe Y, Royston P, Moons KGM. Prognosis and prognostic research: validating a prognostic model. BMJ. 2009;338:b605–b605.CrossRefPubMed
36.
Grasselli G, Calfee CS, Camporota L, Poole D, Amato MBP, Antonelli M, et al. ESICM guidelines on acute respiratory distress syndrome: definition, phenotyping and respiratory support strategies. Intensive Care Med. 2023;49(7):727–59.CrossRefPubMedPubMedCentral
Metadata
Title
Identification and validation of respiratory subphenotypes in patients with COVID-19 acute respiratory distress syndrome undergoing prone position
Authors
Mônica R. da Cruz
Pedro Azambuja
Kátia S. C. Torres
Fernanda Lima-Setta
André M. Japiassú
Denise M. Medeiros
Publication date
01-12-2024
Publisher
Springer International Publishing
Published in
Annals of Intensive Care / Issue 1/2024
Electronic ISSN: 2110-5820
DOI
https://doi.org/10.1186/s13613-024-01414-y

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