Long Covid in adults discharged from UK hospitals after Covid-19: A prospective, multicentre cohort study using the ISARIC WHO Clinical Characterisation Protocol

  1. Louise Sigfrid
  2. Thomas M. Drake
  3. Ellen Pauley
  4. Edwin C. Jesudason
  5. Piero Olliaro
  6. Wei Shen Lim
  7. Annelies Gillesen
  8. Colin Berry
  9. David J. Lowe
  10. Joanne McPeake
  11. Nazir Lone
  12. Daniel Munblit
  13. Muge Cevik
  14. Anna Casey
  15. Peter Bannister
  16. Clark D. Russell
  17. Lynsey Goodwin
  18. Antonia Ho
  19. Lance Turtle
  20. Margaret E. O'Hara
  21. Claire Hastie
  22. Chloe Donohue
  23. Rebecca G. Spencer
  24. Cara Donegan
  25. Alison Gummery
  26. Janet Harrison
  27. Hayley E. Hardwick
  28. Claire E. Hastie
  29. Gail Carson
  30. Laura Merson
  31. J. Kenneth Baillie
  32. Peter Openshaw
  33. Ewen M. Harrison
  34. Annemarie B. Docherty
  35. Malcolm G. Semple
  36. Janet T. Scott

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Abstract

No abstract available

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  1. Version published to 10.1016/j.lanepe.2021.100186
  2. ScreenIT

    SciScore for 10.1101/2021.03.18.21253888: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data from responses were entered onto a Research Electronic Capture (REDCap) Database system hosted at the University of Oxford and linked with data documented during the admission with acute Covid-19 for the analysis.
    REDCap
    suggested: (REDCap, RRID:SCR_003445)

    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:
    There are several limitations to our study. First, we were not able to follow all the cases that were discharged from hospital, either because they did not give permission or because they did not respond to repeated requests for information. We attempted to reach non-responders to the survey via telephone follow-up to limit potential for selection bias, but not all could be reached. It is possible that those who did not respond might have been well and therefore uninterested in responding, but it could also be that some were too unwell to respond, had died or moved away. Our results may therefore not be fully representative of with the entire population of those hospitalised with Covid-19. Secondly, we did not include patients hospitalised with other non-Covid-19 illness or a contemporaneous control group, therefore it is unknown if the changes in our outcomes e.g. quality of life, are specific to recovery from Covid-19 or may be linked to other aspects of life during the pandemic. Thirdly, patients only completed the survey at one timepoint, limiting comparison across repeat measures. This also meant retrospective measures asking patients to rate outcomes before their Covid-19 illness were included, which are open to recall bias. Finally, as our study focussed on hospitalised patients primarily from the first wave of infection in the UK, our data cannot be generalised to those with disease managed in the community who comprise the majority of individuals affected by Covid-19...

    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 10.1101/2021.03.18.21253888 on medRxiv