Incidence, clinical characteristics and prognostic factor of patients with COVID-19: a systematic review and meta-analysis

  1. Chaoqun Ma
  2. Jiawei Gu
  3. Pan Hou
  4. Liang Zhang
  5. Yuan Bai
  6. Zhifu Guo
  7. Hong Wu
  8. Bili Zhang
  9. Pan Li
  10. Xianxian Zhao

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Abstract

Background

Recently, Coronavirus Disease 2019 (COVID-19) outbreak started in Wuhan, China. Although the clinical features of COVID-19 have been reported previously, data regarding the risk factors associated with the clinical outcomes are lacking.

Objectives

To summary and analyze the clinical characteristics and identify the predictors of disease severity and mortality.

Methods

The PubMed, Web of Science Core Collection, Embase, Cochrane and MedRxiv databases were searched through February 25, 2020. Meta-analysis of Observational Studies in Epidemiology (MOOSE) recommendations were followed. We extracted and pooled data using random-e□ects meta-analysis to summary the clinical feature of the confirmed COVID-19 patients, and further identify risk factors for disease severity and death. Heterogeneity was evaluated using the I 2 method and explained with subgroup analysis and meta-regression.

Results

A total of 30 studies including 53000 patients with COVID-19 were included in this study, the mean age was 49.8 years (95% CI, 47.5-52.2 yrs) and 55.5% were male. The pooled incidence of severity and mortality were 20.2% (95% CI, 15.1-25.2%) and 3.1% (95% CI, 1.9-4.2%), respectively. The predictor for disease severity included old age (≥ 50 yrs, odds ratio [OR] = 2.61; 95% CI, 2.29-2.98), male (OR =1.348, 95% CI, 1.195-1.521), smoking (OR =1.734, 95% CI, 1.146-2.626) and any comorbidity (OR = 2.635, 95% CI, 2.098-3.309), especially chronic kidney disease (CKD, OR = 6.017; 95% CI, 2.192-16.514), chronic obstructive pulmonary disease (COPD, OR = 5.323; 95% CI, 2.613-10.847) and cerebrovascular disease (OR = 3.219; 95% CI, 1.486-6.972). In terms of laboratory results, increased lactate dehydrogenase (LDH), C-reactive protein (CRP) and D-dimer and decreased blood platelet and lymphocytes count were highly associated with severe COVID-19 (all for P < 0.001). Meanwhile, old age (≥ 60 yrs, RR = 9.45; 95% CI, 8.09-11.04), followed by cardiovascular disease (RR = 6.75; 95% CI, 5.40-8.43) hypertension (RR = 4.48; 95% CI, 3.69-5.45) and diabetes (RR = 4.43; 95% CI, 3.49-5.61) were found to be independent prognostic factors for the COVID-19 related death.

Conclusions

To our knowledge, this is the first evidence-based medicine research to explore the risk factors of prognosis in patients with COVID-19, which is helpful to identify early-stage patients with poor prognosis and adapt effective treatment.

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    SciScore for 10.1101/2020.03.17.20037572: (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 variableData analysis: Firstly, to obtain summary e□ect estimate for each clinical variable, including case severity rate (CSR), case fatality rate (CFR), male proportion, mean age, pooled value of lymphocyte count and timeline of COVID-19 confirmed patients, random e□ects meta-analysis was used because high variability between studies was expected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Search strategy and study selection: PubMed, Embase, Cochrane, the Web of Science Core Collection (Clarivate Analytics), and MedRxiv databases were used for searching articles published until February 25, 2020 using the following keywords: “coronavirus”, “nCoV”, “HCoV”, “Wuhan”, “2019” “SARS-CoV-2”, “COVID*”, “NCP*”, “China”, “clinical”, “outcome”, “sever*”, “death”, “fatali*” and “mortalit*” alone and in combination.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Embase
    suggested: (EMBASE, RRID:SCR_001650)
    Cochrane
    suggested: (Cochrane Library, RRID:SCR_013000)
    MedRxiv
    suggested: (medRxiv, RRID:SCR_018222)
    All analyses were performed using Review Manager (version5.3), Stata (version 15) and R (version 3.5.3), RStudio (version 1.2.1335) and Comprehensive Meta-analysis (version 3.3).
    RStudio
    suggested: (RStudio, RRID:SCR_000432)

    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.

    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 10.1101/2020.03.17.20037572v1 on medRxiv