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

CHEST(2024)

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Abstract
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 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 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 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 PaO2/FiO2 ratio, driving pressure, or the severity of illness for the cohorts. Interpretation 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.
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Key words
Subphenotype,machine learning,ARDS,PEEP,critical care,clinical data,clustering,Bayesian analysis
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