Characterizing COVID-19 clinical phenotypes and associated comorbidities and complication profiles

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  • Lusczek, E.R.
    • Other Affiliation: University of Minnesota
  • Ingraham, N.E.
    • Other Affiliation: University of Minnesota
  • Karam, B.S.
    • Other Affiliation: Medical College of Wisconsin
  • Proper, J.
    • Other Affiliation: University of Minnesota
  • Siegel, L.
    • Other Affiliation: University of Minnesota
  • Helgeson, E.S.
    • Other Affiliation: University of Minnesota
  • Lotfi-Emran, S.
    • Other Affiliation: University of Minnesota
  • Zolfaghari, E.J.
    • Other Affiliation: University of Minnesota Medical School
  • Jones, E.
    • Other Affiliation: University of Minnesota
  • Usher, M.G.
    • Other Affiliation: University of Minnesota
  • Chipman, J.G.
    • Other Affiliation: University of Minnesota
  • Dudley, R.A.
    • Other Affiliation: University of Minnesota
  • Benson, B.
    • Other Affiliation: University of Minnesota
  • Melton, G.B.
    • Other Affiliation: University of Minnesota
  • Charles, A.
    • Affiliation: School of Medicine, Department of Surgery
  • Lupei, M.I.
    • Other Affiliation: University of Minnesota
  • Tignanelli, C.J.
    • Other Affiliation: University of Minnesota
Abstract
  • Purpose Heterogeneity has been observed in outcomes of hospitalized patients with coronavirus disease 2019 (COVID-19). Identification of clinical phenotypes may facilitate tailored therapy and improve outcomes. The purpose of this study is to identify specific clinical phenotypes across COVID-19 patients and compare admission characteristics and outcomes. Methods This is a retrospective analysis of COVID-19 patients from March 7, 2020 to August 25, 2020 at 14 U.S. hospitals. Ensemble clustering was performed on 33 variables collected within 72 hours of admission. Principal component analysis was performed to visualize variable contributions to clustering. Multinomial regression models were fit to compare patient comorbidities across phenotypes. Multivariable models were fit to estimate associations between phenotype and in-hospital complications and clinical outcomes. Results The database included 1,022 hospitalized patients with COVID-19. Three clinical phenotypes were identified (I, II, III), with 236 [23.1%] patients in phenotype I, 613 [60%] patients in phenotype II, and 173 [16.9%] patients in phenotype III. Patients with respiratory comorbidities were most commonly phenotype III (p = 0.002), while patients with hematologic, renal, and cardiac (all p<0.001) comorbidities were most commonly phenotype I. Adjusted odds of respiratory, renal, hepatic, metabolic (all p<0.001), and hematological (p = 0.02) complications were highest for phenotype I. Phenotypes I and II were associated with 7.30- fold (HR:7.30, 95% CI:(3.11-17.17), p<0.001) and 2.57-fold (HR:2.57, 95% CI:(1.10-6.00), p = 0.03) increases in hazard of death relative to phenotype III. Conclusion We identified three clinical COVID-19 phenotypes, reflecting patient populations with different comorbidities, complications, and clinical outcomes. Future research is needed to determine the utility of these phenotypes in clinical practice and trial design.
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  • Article
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  • In Copyright
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  • Attribution 3.0 United States
Journal title
  • PLoS ONE
Journal volume
  • 16
Journal issue
  • 3-Mar-21
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  • Publisher
ISSN
  • 1932-6203
Publisher
  • Public Library of Science

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