Evaluating clinical characteristics studies produced early in the Covid-19 pandemic: A systematic review

  1. Lakshmi Manoharan
  2. Jonathan W. S. Cattrall
  3. Carlyn Harris
  4. Katherine Newell
  5. Blake Thomson
  6. Mark G. Pritchard
  7. Peter G. Bannister
  8. Louise Sigfrid
  9. Tom Solomon
  10. Peter W. Horby
  11. Gail Carson
  12. Piero Olliaro

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

Clinical characterisation studies have been essential in helping inform research, diagnosis and clinical management efforts, particularly early in a pandemic. This systematic review summarises the early literature on clinical characteristics of patients admitted to hospital, and evaluates the quality of evidence produced during the initial stages of the pandemic.

Methods

MEDLINE, EMBASE and Global Health databases were searched for studies published from January 1 st 2020 to April 28 th 2020. Studies which reported on at least 100 hospitalised patients with Covid-19 of any age were included. Data on clinical characteristics were independently extracted by two review authors. Study design specific critical appraisal tools were used to evaluate included studies: the Newcastle Ottawa scale for cohort and cross sectional studies, Joanna Briggs Institute checklist for case series and the Cochrane collaboration tool for assessing risk of bias in randomised trials.

Results

The search yielded 78 studies presenting data on 77,443 people. Most studies (82%) were conducted in China. No studies included patients from low- and middle-income countries. The overall quality of included studies was low to moderate, and the majority of studies did not include a control group. Fever and cough were the most commonly reported symptoms early in the pandemic. Laboratory and imaging findings were diverse with lymphocytopenia and ground glass opacities the most common findings respectively. Clinical data in children and vulnerable populations were limited.

Conclusions

The early Covid-19 literature had moderate to high risk of bias and presented several methodological issues. Early clinical characterisation studies should aim to include different at-risk populations, including patients in non-hospital settings. Pandemic preparedness requires collection tools to ensure observational studies are methodologically robust and will help produce high-quality data early on in the pandemic to guide clinical practice and public health policy.

Review registration

Available at https://osf.io/mpafn

Article activity feed

  1. Version published to 10.1371/journal.pone.0251250
  2. ScreenIT

    SciScore for 10.1101/2020.07.31.20165738: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Search strategy: The following databases were searched from inception to 28 April 2020 for relevant studies: MEDLINE; EMBASE and Global Health.
    MEDLINE
    suggested: (MEDLINE, RRID:SCR_002185)
    EMBASE
    suggested: (EMBASE, RRID:SCR_001650)
    The electronic database results were supplemented with a Google Scholar search on the 28 April 2020 with the first 100 results screened for inclusion.
    Google Scholar
    suggested: (Google Scholar, RRID:SCR_008878)
    The data extraction and synthesis were performed using Microsoft Excel.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    There were limitations to this review, we focused on hospitalised patients due to limited data being available from primary care settings. We only included studies with at least 100 patients to ensure robustness. Hospitalised patients are likely to represent the more severe end of the clinical spectrum, presenting with a more advanced clinical picture compared to cases in the community. As reports emerge of the elderly with severe disease being cared for in care homes, this population may be under-represented in our data. New symptoms that have recently become significant may not have been documented consistently, and the data biased by different countries’ admission policies. Furthermore, evaluating the progression of clinical characteristics was challenging as many studies did not report on the day of illness on which results were recorded.

    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 10.1101/2020.07.31.20165738 on medRxiv