Factors associated with disease severity and mortality among patients with COVID-19: A systematic review and meta-analysis

  1. Vignesh Chidambaram
  2. Nyan Lynn Tun
  3. Waqas Z. Haque
  4. Marie Gilbert Majella
  5. Ranjith Kumar Sivakumar
  6. Amudha Kumar
  7. Angela Ting-Wei Hsu
  8. Izza A. Ishak
  9. Aqsha A. Nur
  10. Samuel K. Ayeh
  11. Emmanuella L. Salia
  12. Ahsan Zil-E-Ali
  13. Muhammad A. Saeed
  14. Ayu P. B. Sarena
  15. Bhavna Seth
  16. Muzzammil Ahmadzada
  17. Eman F. Haque
  18. Pranita Neupane
  19. Kuang-Heng Wang
  20. Tzu-Miao Pu
  21. Syed M. H. Ali
  22. Muhammad A. Arshad
  23. Lin Wang
  24. Sheriza Baksh
  25. Petros C. Karakousis
  26. Panagis Galiatsatos

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Abstract

Understanding the factors associated with disease severity and mortality in Coronavirus disease (COVID-19) is imperative to effectively triage patients. We performed a systematic review to determine the demographic, clinical, laboratory and radiological factors associated with severity and mortality in COVID-19.

Methods

We searched PubMed, Embase and WHO database for English language articles from inception until May 8, 2020. We included Observational studies with direct comparison of clinical characteristics between a) patients who died and those who survived or b) patients with severe disease and those without severe disease. Data extraction and quality assessment were performed by two authors independently.

Results

Among 15680 articles from the literature search, 109 articles were included in the analysis. The risk of mortality was higher in patients with increasing age, male gender (RR 1.45, 95%CI 1.23–1.71), dyspnea (RR 2.55, 95%CI 1.88–2.46), diabetes (RR 1.59, 95%CI 1.41–1.78), hypertension (RR 1.90, 95%CI 1.69–2.15). Congestive heart failure (OR 4.76, 95%CI 1.34–16.97), hilar lymphadenopathy (OR 8.34, 95%CI 2.57–27.08), bilateral lung involvement (OR 4.86, 95%CI 3.19–7.39) and reticular pattern (OR 5.54, 95%CI 1.24–24.67) were associated with severe disease. Clinically relevant cut-offs for leukocytosis(>10.0 x10 9 /L), lymphopenia(< 1.1 x10 9 /L), elevated C-reactive protein(>100mg/L), LDH(>250U/L) and D-dimer(>1mg/L) had higher odds of severe disease and greater risk of mortality.

Conclusion

Knowledge of the factors associated of disease severity and mortality identified in our study may assist in clinical decision-making and critical-care resource allocation for patients with COVID-19.

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  1. Version published to 10.1371/journal.pone.0241541
  2. ScreenIT

    SciScore for 10.1101/2020.08.07.20166868: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    RandomizationCase reports, case series, and randomized control trials were excluded from this review.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    We searched PubMed, Embase and the WHO COVID-19 database by using the search strategy included in the supplementary document (Section I).
    Embase
    suggested: (EMBASE, RRID:SCR_001650)
    For PubMed and Embase, an initial search on March 26, 2020 was conducted, and updated multiple times with the final update performed on May 7, 2020.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Literature Screening: The COVIDENCE platform was used for conducting this systematic review[13].
    COVIDENCE
    suggested: (Covidence, RRID:SCR_016484)
    All analyses were carried out using the meta package in Stata (StataCorp, version 16) [17].
    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: We detected the following sentences addressing limitations in the study:
    Although this systematic review is able to delineate important parameters associated with disease severity and mortality in COVID-19, our study has a few limitations. First, we included current published articles related to the highly dynamic information available on COVID-19. As this pandemic has not impacted all regions within the same time frame, there is a potential timing bias, whereby the majority of patients described are from early-hit regions, which may not be representative of other patient populations, based on sociodemographic characteristics and pre-existing conditions. Although a pooled analysis of effect sizes adjusted for potential confounders would have been desirable, most studies did not uniformly report estimates adjusted for the same parameters. Time points of evaluation of laboratory, radiological and disease severity assessment were not clearly defined in many of the studies, which precluded the calculation of risk ratio of severe disease. We did not take into account the critical care interventions and strategies that potentially impact the course of the disease and/or survival, as such interventions (e.g. mechanical ventilation, extracorporeal membrane oxygenation, convalescent plasma) vary across different regions. Further investigation is warranted to evaluate which interventions impact the morbidity and mortality of patients with COVID-19. Future studies may investigate if individual and contextual-level sociodemographic factors are associated with...

    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

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.08.07.20166868v1 on medRxiv