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

  1. Elizabeth R. Lusczek
  2. Nicholas E. Ingraham
  3. Basil S. Karam
  4. Jennifer Proper
  5. Lianne Siegel
  6. Erika S. Helgeson
  7. Sahar Lotfi-Emran
  8. Emily J. Zolfaghari
  9. Emma Jones
  10. Michael G. Usher
  11. Jeffrey G. Chipman
  12. R. Adams Dudley
  13. Bradley Benson
  14. Genevieve B. Melton
  15. Anthony Charles
  16. Monica I. Lupei
  17. Christopher J. Tignanelli

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Abstract

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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  1. Version published to 10.1371/journal.pone.0248956
  2. ScreenIT

    SciScore for 10.1101/2020.09.12.20193391: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementIRB: This study was approved by the University of Minnesota institutional review board (STUDY00001489).
    RandomizationFor visualization purposes, these images are provided for a randomly selected dataset in Supplemental Figures 1-4.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Visualizations of comorbidities, complications, and outcomes by clinical phenotype were performed using the circlize package for R.20 Comorbidities and complications were grouped into separate organ systems and the prevalence of each complication/comorbidity type was calculated as a percentage for each phenotype.
    circlize
    suggested: (circlize, RRID:SCR_002141)
    All analyses were conducted using R version 3.6.321 and Stata version 16.1 (StataCorp).
    StataCorp
    suggested: (Stata, RRID:SCR_012763)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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  3. Version published to 10.1101/2020.09.12.20193391v1 on medRxiv