The role of comorbidities and clinical predictors of severe disease in COVID-19: a systematic review and meta-analysis

  1. Reza Tabrizi
  2. Kamran B Lankarani
  3. Peyman Nowrouzi-sohrabi
  4. Mojtaba Shabani-Borujeni
  5. Shahla Rezaei
  6. Mahnaz Hosseini-bensenjan
  7. Sina vakili
  8. Seyed Taghi Heydari
  9. Mohammad Ali Ashraf

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Abstract

Background

COVID_19 is unpredictable due to non-specific symptoms and clinical course diversity in different individuals. We analyzed studies regarding the factors associated with severe status of the disease to identify unique findings in severely affected patients.

Methods

We systematically searched the electronic databases, including PubMed, Scopus, EMBASE, Web of Science, and Google Scholar from inception to 12 th of March 2020. Cochrane’s Q and I-square statistics were used to assess the existence of heterogeneity between the included studies. We used the random-effects model to pool the odds ratios (ORs) at 95% confidence intervals (CIs).

Results

Seventeen articles out of 3009 citations were included. These contained 3189 patients, of whom 732 were severely affected (severe group) and 3189 were in non-severe group. Using the random-effects model, our meta-analyses showed that the odds of comorbidities, including COPD, DM, HTN, CVD, CKD, and symptoms, including dyspnea, dizziness, anorexia, and cough, were significantly higher among the severe group compared with the non-severe group. There were no significant changes in odds of CVA, liver disease, immunodeficiency/immunosuppression, fever, fatigue, myalgia, headache, diarrhea, sore throat, nasal congestion, sputum, nausea, vomiting, chest pain between the two groups.

Conclusions

Early recognition and intervention can be critical in management, and might stop progression to severe disease. Predictive symptoms and comorbidities can be used as a predictor in patients who are at risk of severe disease.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.21.20074633: (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
    Search Strategy: We systematically searched online databases, including PubMed, Scopus, EMBASE, Web of Science, and Google Scholar from inception to 12th of March 2020.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    EMBASE
    suggested: (EMBASE, RRID:SCR_001650)
    Google Scholar
    suggested: (Google Scholar, RRID:SCR_008878)
    The following both medical subject headings (MeSH) and keywords were used in the search strategy: “2019-nCoV disease” OR “2019 novel coronavirus disease” OR “COVID-19” OR “COVID19” OR “2019 novel coronavirus infection” OR “coronavirus disease 2019” OR “coronavirus disease-19” OR “2019-nCoV infection” OR “2019-nCoV” OR “2019 novel coronavirus” OR “2019 coronavirus” OR “novel coronavirus” OR (2019 AND
    MeSH
    suggested: (MeSH, RRID:SCR_004750)
    STATA version 12.0
    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: We detected the following sentences addressing limitations in the study:
    The present study had some limitations that should be acknowledged. First, definitions of severity across the included studies were inconsistent. However, overall results showed that the odds of death were 10.56 times higher among severe than the non-severe group. This means that the chance of death was significantly higher in the severe group, which implies that patients were in a more critical situation in severe than the non-severe group. second, our analysis was based on small number of studies in some outcomes, however, the relevant results should be interpreted with caution. Third, most of the included studies were from China and may have population biases. Fourth, Heterogeneity across included studies on some outcomes was another limitation and sensitivity analysis was done to address this limitation.

    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

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  2. Version published to 10.1101/2020.04.21.20074633v1 on medRxiv