Remote home monitoring (virtual wards) for confirmed or suspected COVID-19 patients: a rapid systematic review

  1. Cecilia Vindrola-Padros
  2. Kelly E Singh
  3. Manbinder S Sidhu
  4. Theo Georghiou
  5. Chris Sherlaw-Johnson
  6. Sonila M Tomini
  7. Matthew Inada-Kim
  8. Karen Kirkham
  9. Allison Streetly
  10. Nathan Cohen
  11. Naomi J Fulop

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

No abstract available

Article activity feed

  1. Version published to 10.1016/j.eclinm.2021.100965
  2. Version published to 10.1101/2020.10.07.20208587v3 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.10.07.20208587: (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
    We searched for literature indexed in the following databases: MEDLINE, CINAHL PLUS, EMBASE, TRIP, medRxiv and Web of Science.
    MEDLINE
    suggested: (MEDLINE, RRID:SCR_002185)
    EMBASE
    suggested: (EMBASE, RRID:SCR_001650)
    Results were combined into Mendeley and duplicates were removed.
    Mendeley
    suggested: (Mendeley Data, RRID:SCR_002750)
    Data extraction and management: The included articles were analysed using a data extraction form developed in REDCap (Research Electronic Data Capture) that extracted data on: the design and general characteristics of the model, patient populations, main reported process and clinical outcomes and its potential economic impact.
    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:
    Similarly to other reviews on remote patient monitoring in other conditions, another limitation was the lack of attention placed on the implementation of the models and the failure to identify the programme theories guiding their design, factors that acted as barriers and facilitators and the extent to which the pathways were implemented according to their original plans24. This could be due to the limited evidence on COVID-19 and the management of patients with this disease at the time of designing and implementing these models as well as the general limited use of programme theories in the design of healthcare interventions that has already been documented in the literature25. Emerging international evidence has indicated that lower thresholds for oxygen saturation, are associated with worse patient outcomes2,26. In the case of our review, even though some authors argued that pulse oximetry identified the need for hospitalisation when using a cut-off of 92%16, we could not reach conclusions in relation to patient safety and the degree to which remote home monitoring models can conclusively identify cases of deterioration at an earlier stage in the disease trajectory. The main reasons were lack of standardised reporting across articles in relation to these outcome measures and how these were measured, as well as the limitation that none of the articles used comparators. Issues with using pulse oximetry were also highlighted such as: patient physiological measures needed to b...

    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.

  4. Version published to 10.1101/2020.10.07.20208587v2 on medRxiv
  5. Version published to 10.1101/2020.10.07.20208587v1 on medRxiv