CHA2DS2-VASc score on admission to predict mortality in COVID-19 patients: A meta-analysis

  1. Prattay Guha Sarkar
  2. Amit Kumar
  3. Debdutta Bandyopadhyay

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Abstract

Background

CHA2DS2-VASc score is used in non-valvular AF patients to predict thromboembolic risk. Recent studies have tried to evaluate CHA2DS2-VASc score on admission in COVID-19 patients to predict mortality.

Methods

We conducted a literature search on 14 April 2021 to retrieve all published studies, pre-prints and grey literature related to the predictive power of CHA2DS2-VASc score in COVID-19 patients of admission and mortality. Screening of studies and data extraction was done by two authors independently. We used the Quality in Prognosis Studies (QUIPS) tool for the methodological quality assessment of the included studies.

Results

Five studies involving 5,941 patients reported the predictive value of CHA2DS2-VASc score for mortality in COVID-19 patients. The pooled sensitivity (SEN), specificity (SPE) and area under curve were 0.72 (95% CI 0.63-0.79), 0.74 (95% CI 0.67-0.81) and 0.80 (95% CI 0.76-0.83).

Conclusions

CHA2DS2-VASc score at admission has good predictive value for mortality in patients with COVID-19 infection and can help clinicians identify potentially severe cases early. Early initiation of effective management in these cases may help in reducing overall mortality due to COVID-19.

Trial registry

We prospectively registered this meta-analysis on PROSPERO database (Reg number: CRD42021248398).

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  1. ScreenIT

    SciScore for 10.1101/2021.06.07.21258402: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Selection of studies: We reviewed PubMed, Google scholar, Scirius, Medline, Liliacs, Cochrane, CINAHIL, Plos and SIGLE databases through April 14, 2021.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Google scholar
    suggested: (Google Scholar, RRID:SCR_008878)
    Medline
    suggested: (MEDLINE, RRID:SCR_002185)
    All the statistical analyses was completed using software STATA version 13.
    STATA
    suggested: (Stata, RRID:SCR_012763)

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

    Results from scite Reference Check: We found no unreliable references.

    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 10.1101/2021.06.07.21258402v1 on medRxiv