• Crit Care · Apr 2021

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

    • Pedro D Wendel Garcia, Alessio Caccioppola, Silvia Coppola, Tommaso Pozzi, Arianna Ciabattoni, Stefano Cenci, and Davide Chiumello.
    • Institute of Intensive Care Medicine, University Hospital of Zurich, Zurich, Switzerland.
    • Crit Care. 2021 Apr 22; 25 (1): 154.

    BackgroundAcute 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.MethodsRetrospective 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.ResultsLatent 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).ConclusionsThe 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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