Differential Effect of PEEP Strategies in ARDS Patients: A Bayesian Analysis of Clinical Subphenotypes.

ARDS Bayesian analysis PEEP Subphenotype clinical data clustering critical care machine learning

Journal

Chest
ISSN: 1931-3543
Titre abrégé: Chest
Pays: United States
ID NLM: 0231335

Informations de publication

Date de publication:
18 May 2024
Historique:
received: 28 12 2023
revised: 22 03 2024
accepted: 06 04 2024
medline: 21 5 2024
pubmed: 21 5 2024
entrez: 20 5 2024
Statut: aheadofprint

Résumé

Acute respiratory distress syndrome (ARDS) is a heterogeneous condition with two subphenotypes identified by different methodologies. Our group similarly identified two ARDS subphenotypes using nine routinely available clinical variables. However, whether these are associated with differential response to treatment has yet to be explored. Are there differential responses to positive end-expiratory pressure (PEEP) strategies on 28-day mortality according to subphenotypes in adult patients with ARDS? We evaluated data from two prior ARDS trials (ALVEOLI and ART) that compared different PEEP strategies. We classified patients into one of two subphenotypes as previously described. We assessed the differential effect of PEEP with a Bayesian hierarchical logistic model for the primary outcome of 28-day mortality. We analyzed data from 1559 ARDS patients. Compared to lower PEEP, a higher PEEP strategy resulted in higher 28-day mortality in subphenotype A patients in ALVEOLI (OR, 1.61 [95% CrI 0.90 to 2.94]) and ART (OR 1.73 [ 95% CrI 1.01 to 2.98]), with a probability of harm from higher PEEP in this subphenotype of 94.3% and 97.7% in ALVEOLI and ART, respectively. Higher PEEP was not associated with mortality in subphenotype B patients in each trial (OR, 0.95 [95% CrI, 0.51 to 1.73]) and (OR, 1.00 [95% CrI 0.63 to 1.55]); probability of benefit of 56.4% and 50.7% in ALVEOLI and ART, respectively. These effects were not modified by PaO We found evidence of differential response to PEEP strategies across two ARDS subphenotypes, suggesting possible harm with a higher PEEP strategy in one subphenotype. These observations may assist with predictive enrichment in future clinical trials.

Sections du résumé

BACKGROUND BACKGROUND
Acute respiratory distress syndrome (ARDS) is a heterogeneous condition with two subphenotypes identified by different methodologies. Our group similarly identified two ARDS subphenotypes using nine routinely available clinical variables. However, whether these are associated with differential response to treatment has yet to be explored.
RESEARCH QUESTION OBJECTIVE
Are there differential responses to positive end-expiratory pressure (PEEP) strategies on 28-day mortality according to subphenotypes in adult patients with ARDS?
STUDY DESIGN AND METHODS METHODS
We evaluated data from two prior ARDS trials (ALVEOLI and ART) that compared different PEEP strategies. We classified patients into one of two subphenotypes as previously described. We assessed the differential effect of PEEP with a Bayesian hierarchical logistic model for the primary outcome of 28-day mortality.
RESULTS RESULTS
We analyzed data from 1559 ARDS patients. Compared to lower PEEP, a higher PEEP strategy resulted in higher 28-day mortality in subphenotype A patients in ALVEOLI (OR, 1.61 [95% CrI 0.90 to 2.94]) and ART (OR 1.73 [ 95% CrI 1.01 to 2.98]), with a probability of harm from higher PEEP in this subphenotype of 94.3% and 97.7% in ALVEOLI and ART, respectively. Higher PEEP was not associated with mortality in subphenotype B patients in each trial (OR, 0.95 [95% CrI, 0.51 to 1.73]) and (OR, 1.00 [95% CrI 0.63 to 1.55]); probability of benefit of 56.4% and 50.7% in ALVEOLI and ART, respectively. These effects were not modified by PaO
INTERPRETATION CONCLUSIONS
We found evidence of differential response to PEEP strategies across two ARDS subphenotypes, suggesting possible harm with a higher PEEP strategy in one subphenotype. These observations may assist with predictive enrichment in future clinical trials.

Identifiants

pubmed: 38768777
pii: S0012-3692(24)00630-5
doi: 10.1016/j.chest.2024.04.011
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024. Published by Elsevier Inc.

Auteurs

Matthew T Siuba (MT)

Department of Critical Care Medicine, Respiratory Institute, Cleveland Clinic, Cleveland, Ohio, USA. Electronic address: siubam@ccf.org.

Lucas Bulgarelli (L)

Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.

Abhijit Duggal (A)

Department of Critical Care Medicine, Respiratory Institute, Cleveland Clinic, Cleveland, Ohio, USA.

Alexandre B Cavalcanti (AB)

Hcor Research Institute, São Paulo, Brazil.

Fernando G Zampieri (FG)

Hcor Research Institute, São Paulo, Brazil.

Diego Ariel Rey (DA)

Research Department, Endpoint Health Inc, Palo Alto, California, USA.

Wellington Dos Reis Lucena (WDR)

Research Department, Endpoint Health Inc, Palo Alto, California, USA.

Israel S Maia (IS)

Hcor Research Institute, São Paulo, Brazil.

Denise M Paisani (DM)

Hcor Research Institute, São Paulo, Brazil.

Ligia N Laranjeira (LN)

Hcor Research Institute, São Paulo, Brazil.

Ary Serpa Neto (AS)

Australian and New Zealand Intensive Care Research Centre (ANZIC-RC), School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia; Department of Critical Care, Melbourne Medical School, University of Melbourne, Austin Hospital, Melbourne, Australia; Department of Intensive Care, Austin Hospital, Melbourne, Australia; Department of Critical Care Medicine, Hospital Israelita Albert Einstein, São Paulo, Brazil.

Rodrigo Octávio Deliberato (RO)

Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA; Department of Biomedical Informatics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA.

Classifications MeSH