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Critical Care
Published in: Critical Care 1/2021

Open Access 01-12-2021 | Care | Research

Latent class analysis to predict intensive care outcomes in Acute Respiratory Distress Syndrome: a proposal of two pulmonary phenotypes

Authors: Pedro D. Wendel Garcia, Alessio Caccioppola, Silvia Coppola, Tommaso Pozzi, Arianna Ciabattoni, Stefano Cenci, Davide Chiumello

Published in: Critical Care | Issue 1/2021

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Abstract

Background

Acute respiratory distress syndrome remains a heterogeneous syndrome for clinicians and researchers difficulting successful tailoring of interventions and trials. To this moment, phenotyping of this syndrome has been approached by means of inflammatory laboratory panels. Nevertheless, the systemic and inflammatory expression of acute respiratory distress syndrome might not reflect its respiratory mechanics and gas exchange.

Methods

Retrospective analysis of a prospective cohort of two hundred thirty-eight patients consecutively admitted patients under mechanical ventilation presenting with acute respiratory distress syndrome. All patients received standardized monitoring of clinical variables, respiratory mechanics and computed tomography scans at predefined PEEP levels. Employing latent class analysis, an unsupervised structural equation modelling method, on respiratory mechanics, gas-exchange and computed tomography-derived gas- and tissue-volumes at a PEEP level of 5cmH2O, distinct pulmonary phenotypes of acute respiratory distress syndrome were identified.

Results

Latent class analysis was applied to 54 respiratory mechanics, gas-exchange and CT-derived gas- and tissue-volume variables, and a two-class model identified as best fitting. Phenotype 1 (non-recruitable) presented lower respiratory system elastance, alveolar dead space and amount of potentially recruitable lung volume than phenotype 2 (recruitable). Phenotype 2 (recruitable) responded with an increase in ventilated lung tissue, compliance and PaO2/FiO2 ratio (p < 0.001), in addition to a decrease in alveolar dead space (p < 0.001), to a standardized recruitment manoeuvre. Patients belonging to phenotype 2 (recruitable) presented a higher intensive care mortality (hazard ratio 2.9, 95% confidence interval 1.7–2.7, p = 0.001).

Conclusions

The present study identifies two ARDS phenotypes based on respiratory mechanics, gas-exchange and computed tomography-derived gas- and tissue-volumes. These phenotypes are characterized by distinctly diverse responses to a standardized recruitment manoeuvre and by a diverging mortality. Given multicentre validation, the simple and rapid identification of these pulmonary phenotypes could facilitate enrichment of future prospective clinical trials addressing mechanical ventilation strategies in ARDS.
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Metadata
Title
Latent class analysis to predict intensive care outcomes in Acute Respiratory Distress Syndrome: a proposal of two pulmonary phenotypes
Authors
Pedro D. Wendel Garcia
Alessio Caccioppola
Silvia Coppola
Tommaso Pozzi
Arianna Ciabattoni
Stefano Cenci
Davide Chiumello
Publication date
01-12-2021
Publisher
BioMed Central
Published in
Critical Care / Issue 1/2021
Electronic ISSN: 1364-8535
DOI
https://doi.org/10.1186/s13054-021-03578-6

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