Abstract
BackgroundThe exploration of subphenotypes in hospitalized COVID-19 patients has garnered substantial attention. Most existing studies operate under the assumption of heterogeneity in COVID-19 patient populations and this assumption can lead to erroneous conclusions. Research QuestionDo plasma biomarker profiles reflective of various pathophysiological pathways provide evidence for heterogeneity in hospitalized patients with COVID-19? Study Design and MethodsThis is a secondary analysis of two prospective observational studies of adult patients hospitalized with COVID-19 related respiratory failure in the general ward and intensive care unit (ICU) of two medical centers and with 44 host response biomarkers available. Parsimonious models were used to allocate and validate ARDS inflammatory subphenotypes. Novel biological subphenotypes were identified using latent profile analysis (LPA) and hierarchical clustering. Heterogeneity of treatment effect (HTE) for corticosteroids was assessed using an interaction term in a logistic regression model. ResultsThe cohort consisted of 162 patients admitted to the ICU and 464 to the ward. Using the parsimonious models in ICU patientsonly 3.1-13% of patients were classified as hyperinflammatory subphenotype. Using de novo subphenotyping techniques, neither clustering or LPA revealed significant evidence for heterogeneity in the ward (p = 0.11-0.13), ICU (p = 0.23-0.88) or combined cohort (p = 0.05-0.88). Adding clinical variables did not alter results in the ICU or combined cohort. Using the combined approach in the ward cohort, indices provided borderline significance for two subphenotypes, and there was good agreement between clustering and LPA (87.9%), but no HTE for corticosteroids was observed between these two classes (p = 0.198). InterpretationSystemic inflammatory subphenotypes derived from ARDS patients do not reflect the variation in severity of COVID-19. Empirical evidence, derived from cluster analysis or latent profile analysis, offers limited support for biological heterogeneity in COVID-19.
Published Version
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