COVID-19 in London, a Case Series Demonstrating Late Improvement in Survivors

  1. Ahmed Al-Hindawi
  2. Jagdish Sokhi
  3. Joshua Cuddihy
  4. Christopher Lockie
  5. Linsey Christie
  6. Roger Davies
  7. Suveer Singh
  8. Marcela Vizcaychipi
  9. Michelle Hayes
  10. Alice Sisson
  11. Richard Keays

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Abstract

Objective To determine whether the trajectories of survivors and non-survivors are different in patients admitted to intensive care in London. Design In this case series of 15 survivors and 16 non-survivors, data from admission to discharge was collected and aligned to lowest PaO2/FiO2 ratio where aggregation and trends were demonstrated. Setting Single centre case-series in London, Intensive Care. Participants All non-survivors were included (n=16). A biased set of survivors (n=15) who were demonstrated an unexpected and rapid recovery after a prolonged period of mechanical ventilatory support. Results Respiratory failure trajectories of survivors and non-survivors were similar once aligned indicating, from a respiratory function perspective, it is difficult to identify survivors from non-survivors with some survivors improving late in their disease (day 20 - 30 from symptom onset) Non-survivors are admitted earlier in their disease (p < 0.05) and had worse organ failure requirements prior to the nadir of their respiratory funciton (p < 0.05) compard to survivors. Conclusion Analysis of multiple factors fails to differentiate between survivors and non-survivors. Even when faced with multiorgan failure, perseverance until discharge must be advocated as late improvements do occur in survivors.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.16.20103853: (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

    No key resources detected.

    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:
    Limitations of this case series include a small sample size in both survivors and non-survivors with selection bias occurring in the survivor cohort.

    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.

  2. Version published to 10.1101/2020.05.16.20103853v1 on medRxiv