Cognitive deficits in people who have recovered from COVID-19

  1. Adam Hampshire
  2. William Trender
  3. Samuel R Chamberlain
  4. Amy E. Jolly
  5. Jon E. Grant
  6. Fiona Patrick
  7. Ndaba Mazibuko
  8. Steve CR Williams
  9. Joseph M Barnby
  10. Peter Hellyer
  11. Mitul A Mehta

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Abstract

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  3. Version published to 10.1016/j.eclinm.2021.101044
  4. ScreenIT

    SciScore for 10.1101/2020.10.20.20215863: (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 pre-processing: All processing and analysis steps were conducted in MATLAB by AH with assistance from WT.
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)
    Questionnaire items analysed in this study are outlined in Supplementary Table S1 Materials and Methods:
    Methods
    suggested: None

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Normal limitations pertaining to inferences about cause and effect from cross-sectional studies apply3,20. One might posit that people with lower cognitive ability have higher risk of catching the virus. We consider such a relationship plausible; however, it would not explain why the observed deficits varied in scale with respiratory symptom severity. We also note that the large and socioeconomically diverse nature of the cohort enabled us to include many potentially confounding variables in our analysis. Nonetheless, we emphasise that longitudinal research, including follow-up of this cohort, is required to further confirm the cognitive impact of COVID-19 infection and determine deficit longevity as a function of respiratory symptom severity, and other symptoms. It also is plausible that cognitive deficits associated with COVID-19 are no different to other respiratory illnesses. The observation of significant cognitive deficit associated with positive biological verification of having had COVID-19, i.e., relative to suspected COVID-19, goes some way to mitigate this possibility. Further work is required to interrelate the deficits to underlying neurological changes, and to disambiguate the associated pathological processes and cross compare to other respiratory viruses. A fuller understanding of the marked deficits that our study shows will enable better preparedness in the post-pandemic recovery challenges.

    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.
    • Thank you for including a protocol registration statement.

    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.

  5. ScreenIT

    SciScore for 10.1101/2020.10.20.20215863: (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 pre-processing | All processing and analysis steps were conducted in MATLAB by AH with assistance from WT.
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)

    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:

    Normal limitations pertaining to inferences about cause and effect from cross-sectional studies apply3,20. One might posit that people with lower cognitive ability have higher risk of catching the virus. We consider such a relationship plausible; however, it would not explain why the observed deficits varied in scale with respiratory symptom severity. We also note that the large and socioeconomically diverse nature of the cohort enabled us to include many potentially confounding variables in our analysis. Nonetheless, we emphasise that longitudinal research, including follow-up of this cohort, is required to further confirm the cognitive impact of COVID-19 infection and determine deficit longevity as a function of respiratory symptom severity, and other symptoms. It also is plausible that cognitive deficits associated with COVID-19 are no different to other respiratory illnesses. The observation of significant cognitive deficit associated with positive biological verification of having had COVID-19, i.e., relative to suspected COVID-19, goes some way to mitigate this possibility. Further work is required to interrelate the deficits to underlying neurological changes, and to disambiguate the associated pathological processes and cross compare to other respiratory viruses. A fuller understanding of the marked deficits that our study shows will enable better preparedness in the post-pandemic recovery challenges. Materials and Methods Study promotion | The Great British Intellige...

    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.

    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.

  6. Version published to 10.1101/2020.10.20.20215863v1 on medRxiv