Neutrophil-to-lymphocyte ratio on admission to predict the severity and mortality of COVID-19 patients: A meta-analysis

  1. Daniel Martin Simadibrata
  2. Julius Calvin
  3. Alya Darin Wijaya
  4. Naufal Arkan Abiyyu Ibrahim

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.ajem.2021.01.006
  2. ScreenIT

    SciScore for 10.1101/2020.09.14.20191098: (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
    , Ovid EMBASE, SCOPUS, and the Cochrane Library); preprints were searched from three databases (MedRxiv, BioRxiv, and SSRN); and grey literature was searched from two databases (WHO COVID-19 Global Research Database, and the Centers for Disease Control and Prevention COVID-19 Research Article Database).
    EMBASE
    suggested: (EMBASE, RRID:SCR_001650)
    Cochrane Library)
    suggested: None
    BioRxiv
    suggested: (bioRxiv, RRID:SCR_003933)
    3 (Cochrane Collaboration) and Stata version 16, and a meta-analysis of studies was performed.
    Cochrane Collaboration
    suggested: None
    Statistical heterogeneity was determined using the Cochrane chi-square and I2 with cut-off values for I2 of greater than 50% to be considered significantly heterogeneous.
    Cochrane chi-square
    suggested: None

    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 some limitations to this meta-analysis. First, we acknowledge that most of the included studies were primarily conducted in China. Thus, our data might have less clinical relevance in other countries, especially in countries with higher cases and death rates, such as in the United States of America and Europe. Second, we included preprints in the meta-analysis. However, the preprints included had low risks of bias, and further sensitivity analysis to only peer-reviewed studies showed similar results to when preprints were included. Lastly, the studies included in our meta-analysis were all retrospective, except for one, which was a prospective cohort study.14 Our meta-analysis demonstrated that NLR on admission is predictive of the severity and mortality in COVID-19 patients, where higher NLR levels are associated with poor outcomes. To date, no optimal cut-off value has been validated across different populations. Therefore, prior to clinical use, further studies should be developed to obtain an exact consensus cut-off value with the optimal sensitivity and specificity. However, our findings support the use of NLR levels to perform early risk stratification in clinical settings, thus allowing patients with higher NLR to be prioritized for healthcare resource allocation.

    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 10.1101/2020.09.14.20191098v1 on medRxiv