Clinical and epidemiological characteristics of Coronavirus Disease 2019 (COVID-19) patients

  1. Shing Cheng Tan

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Abstract

Background

Numerous groups have reported the clinical and epidemiological characteristics of Coronavirus Disease 2019 (COVID-19) cases; however, the data remained inconsistent. This paper aimed to pool the available data to provide a more complete picture of the characteristics of COVID-19 patients.

Methods

A systematic review and pooled analysis was performed. Eligible studies were identified from database and hand searches up to March 2, 2020. Data on clinical (including laboratory and radiological) and epidemiological (including demographic) characteristics of confirmed COVID-19 cases were extracted and combined by simple pooling.

Results

Of 644 studies identified, 69 studies (involving 48,926 patients) were included in the analysis. The average age of the patients was 49.16 years. A total of 51.46% of the patients were men and 52.32% were non-smokers. Hypertension (50.82%) and diabetes (20.89%) were the most frequent comorbidities observed. The most common symptoms were fever (83.21%), cough (61.74%), and myalgia or fatigue (30.22%). Altered levels of blood and biochemical parameters were observed in a proportion of the patients. Most of the patients (78.50%) had bilateral lung involvements, and 5.86% showed no CT findings indicative of viral pneumonia. Acute respiratory distress syndrome (28.36%), acute cardiac injury (7.89%) and acute kidney injury (7.60%) were the most common complications recorded.

Conclusions

Clinical and epidemiological characteristics of COVID-19 patients were mostly heterogeneous and non-specific. This is the most comprehensive report of the characteristics of COVID-19 patients to date. The information presented is important for improving our understanding of the spectrum and impact of this novel disease.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.02.20050989: (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 and selection criteria: Three separate searches were performed on PubMed database on March 2, 2020, using the keywords “COVID-19”, “2019-nCoV” and “SARS-CoV-2”.
    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:
    A few limitations exist in the present work. First, there were instances where a same patient was described in multiple reports (15, 96). Efforts have been made to identify such patients by meticulously reviewing the descriptions of the patients in each report. However, since all patients were anonymized, it was challenging to identify all overlapping patients. Thus, there is a possibility that some overlapping patients were not removed from our analysis and their characteristics were overreported. Besides, since patient-level data were not reported in most of the studies, median values and standard deviations, which understandably provide more meaningful information, could not be computed. Finally, as mentioned above, various comparisons among the patients (e.g. severe vs. mild, and death vs. survivor) could not be analyzed due to insufficient data available.

    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 10.1101/2020.04.02.20050989v1 on medRxiv