Prognostic factors for adverse outcomes in patients with COVID-19: a field-wide systematic review and meta-analysis

  1. Vanesa Bellou
  2. Ioanna Tzoulaki
  3. Maarten van Smeden
  4. Karel G.M. Moons
  5. Evangelos Evangelou
  6. Lazaros Belbasis

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Abstract

The individual prognostic factors for coronavirus disease 2019 (COVID-19) are unclear. For this reason, we aimed to present a state-of-the-art systematic review and meta-analysis on the prognostic factors for adverse outcomes in COVID-19 patients.

Methods

We systematically reviewed PubMed from 1 January 2020 to 26 July 2020 to identify non-overlapping studies examining the association of any prognostic factor with any adverse outcome in patients with COVID-19. Random-effects meta-analysis was performed, and between-study heterogeneity was quantified using I 2 statistic. Presence of small-study effects was assessed by applying the Egger's regression test.

Results

We identified 428 eligible articles, which were used in a total of 263 meta-analyses examining the association of 91 unique prognostic factors with 11 outcomes. Angiotensin-converting enzyme inhibitors, obstructive sleep apnoea, pharyngalgia, history of venous thromboembolism, sex, coronary heart disease, cancer, chronic liver disease, COPD, dementia, any immunosuppressive medication, peripheral arterial disease, rheumatological disease and smoking were associated with at least one outcome and had >1000 events, p<0.005, I 2 <50%, 95% prediction interval excluding the null value, and absence of small-study effects in the respective meta-analysis. The risk of bias assessment using the Quality in Prognosis Studies tool indicated high risk of bias in 302 out of 428 articles for study participation, 389 articles for adjustment for other prognostic factors and 396 articles for statistical analysis and reporting.

Conclusions

Our findings could be used for prognostic model building and guide patient selection for randomised clinical trials.

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  1. Version published to 10.1183/13993003.02964-2020
  2. ScreenIT

    SciScore for 10.1101/2020.05.13.20100495: (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
    8 We systematically searched PubMed from January 1, 2020 to April 19, 2020 to identify observational studies examining risk factors of adverse clinical outcomes in patients diagnosed with COVID-19.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)

    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:
    Some limitations should be considered for the interpretation of our findings. A majority of the effect estimates are unadjusted odds ratios, indicating that potential confounding cannot be excluded. Moreover, a majority of the studies included in our meta-analyses were based on samples from China. There is need for independent observational studies to examine the external validity of our findings. Also, the observed association of some biomarkers with severity and progression of disease might be a result of reverse causation. This means that the observed differences in the biomarkers could be attributed to the natural history of COVID-19. Furthermore, a third of the meta-analyses presented large between-study heterogeneity, but sources of heterogeneity could not be explored due to the small number of observational studies in each meta-analysis. Potential sources of heterogeneity include different methods of risk factor measurement, varying inclusion criteria between studies as well as random error due to the small sample size of the studies. Our article systematically identified all the published observational studies examining risk factors for adverse clinical outcomes in patients with COVID-19. The systematic review was further complemented by meta-analysis on more than 250 associations. The risk factors that presented the strongest epidemiological evidence were related to age, sex, respiratory and flu-like symptoms and clinical signs, major comorbidities, pleural effusion,...

    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 10.1101/2020.05.13.20100495v1 on medRxiv