Can we predict the severe course of COVID-19 - a systematic review and meta-analysis of indicators of clinical outcome?

  1. Stephan Katzenschlager
  2. Alexandra J. Zimmer
  3. Claudius Gottschalk
  4. Jürgen Grafeneder
  5. Stephani Schmitz
  6. Sara Kraker
  7. Marlene Ganslmeier
  8. Amelie Muth
  9. Alexander Seitel
  10. Lena Maier-Hein
  11. Andrea Benedetti
  12. Jan Larmann
  13. Markus A. Weigand
  14. Sean McGrath
  15. Claudia M. Denkinger

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Abstract

COVID-19 has been reported in over 40million people globally with variable clinical outcomes. In this systematic review and meta-analysis, we assessed demographic, laboratory and clinical indicators as predictors for severe courses of COVID-19.

Methods

This systematic review was registered at PROSPERO under CRD42020177154. We systematically searched multiple databases (PubMed, Web of Science Core Collection, MedRvix and bioRvix) for publications from December 2019 to May 31 st 2020. Random-effects meta-analyses were used to calculate pooled odds ratios and differences of medians between (1) patients admitted to ICU versus non-ICU patients and (2) patients who died versus those who survived. We adapted an existing Cochrane risk-of-bias assessment tool for outcome studies.

Results

Of 6,702 unique citations, we included 88 articles with 69,762 patients. There was concern for bias across all articles included. Age was strongly associated with mortality with a difference of medians (DoM) of 13.15 years (95% confidence interval (CI) 11.37 to 14.94) between those who died and those who survived. We found a clinically relevant difference between non-survivors and survivors for C-reactive protein (CRP; DoM 69.10 mg/L, CI 50.43 to 87.77), lactate dehydrogenase (LDH; DoM 189.49 U/L, CI 155.00 to 223.98), cardiac troponin I (cTnI; DoM 21.88 pg/mL, CI 9.78 to 33.99) and D-Dimer (DoM 1.29mg/L, CI 0.9 to 1.69). Furthermore, cerebrovascular disease was the co-morbidity most strongly associated with mortality (Odds Ratio 3.45, CI 2.42 to 4.91) and ICU admission (Odds Ratio 5.88, CI 2.35 to 14.73).

Discussion

This comprehensive meta-analysis found age, cerebrovascular disease, CRP, LDH and cTnI to be the most important risk-factors that predict severe COVID-19 outcomes and will inform clinical scores to support early decision-making.

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  1. Version published to 10.1371/journal.pone.0255154
  2. ScreenIT

    SciScore for 10.1101/2020.11.09.20228858: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    RandomizationCross-sectional studies, cohort studies, randomized and non-randomized controlled trials were included.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Search strategy: Medline [PubMed] and Web of Science Core Collection as well as preprint databases (bioRxiv and medRxiv) were searched.
    Medline
    suggested: (MEDLINE, RRID:SCR_002185)
    bioRxiv
    suggested: (bioRxiv, RRID:SCR_003933)
    The full search strategy used for PubMed is presented in the supplementary file S1.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Assessment of study quality: To analyze risk of bias in individual studies, we evaluated the studies using an approach adapted from an existing Cochrane tool by Higgins et al. [31] for systematic reviews that assessed indicators of outcomes.
    Cochrane tool
    suggested: None

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Strengths and limitations of this study: Our study provides a comprehensive review of the data from both pre-print and peer-reviewed sources with a broad geographic distribution and assesses the different categories of risk factors from symptoms, co-morbidities, laboratory values to clinical complications. Correlating the indicators to the two clinical outcomes death and ICU admission has both strength and limitations. While ICU admission is a clinical decision, it is, especially early on in a new disease, sometimes a measure of precaution. This might weaken the association of indicators with clinical outcomes. At the same time, when capacity of ICU beds is exhausted, triage decisions might have been made based on age and co-morbidities to not admit to the ICU, thus strengthening an association of an indicator beyond what would be expected under routine conditions. We also assessed the association with hospitalization and intubation (see Supplement), but here confounding factors seemed to be even more pronounced, and data are further limited. In addition, with improving care and novel therapies certain associations might be less pronounced. We did not observe improved survival of antiviral therapy in the studies included, suggesting that this effect might not yet have occurred in the timeframe of studies included here. Additional limitations primarily relate to data quality of the included studies. Our quality assessment of studies clearly indicated that substantial bias was ...

    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 10.1101/2020.11.09.20228858v1 on medRxiv