The influence of comorbidity on the severity of COVID-19 disease: A systematic review and analysis

  1. Nazar Zaki
  2. Elfadil A. Mohamed
  3. Sahar Ibrahim
  4. Gulfaraz Khan

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Abstract

Background

A novel form of coronavirus disease (SARS-CoV-2) has spread rapidly across the world. What risk factors influence the severity of the disease is of considerable importance.

Aim

This research offers a systematic review and meta-analysis of the correlation between common clinical conditions and comorbidities and the severity of COVID-19.

Methodology

Two independent researchers searched Europe PMC, Google Scholar, and PubMed databases for articles related to influence comorbidities have on the progress of the disease. A search engine was also created to screen a further 59,000 articles in COVID-19 Open Research Dataset (CORD-19). Random-effects modeling was used to pool 95% confidence intervals (CIs) and odds ratios (ORs). The significance of all comorbidities and clinical conditions to the severity of the disease was evaluated by employing machine-learning techniques. Publication bias was assessed by using funnel-plots and Egger's test. Heterogeneity was tested using I 2 .

Results

The meta-analysis incorporated 12 studies spanning 4,101 confirmed COVID-19 patients who were admitted to Chinese hospitals. The prevalence of the most commonly associated co-morbidities and their corresponding odds ratio for disease severity were as follows: coronary heart disease (OR 2.97 [CI: 1.99-4.45], p < 0.0001), cancer (OR 2.65 [CI: 1.12-6.29], p < 0.03), cardiovascular disease (OR 2.89 [CI: 1.90-4.40], p < 0.0001), COPD (OR 3.24 [CI: 1.66-6.32], p = 0.0), and kidney disease (OR 2.2.4 [CI: 1.01-4.99], p = 0.05) with low or moderate level of heterogeneity. The most frequently exhibited clinical symptoms were fever (OR 1.37 [CI: 1.01-1.86], p = 0.04), myalgia/fatigue (OR 1.31 [CI: 1.11-1.55], p = 0.0018), and dyspnea (OR 3.61, [CI: 2.57-5.06], p = <0.0001). No significant associations between disease severity and liver disease, smoking habits, and other clinical conditions, such as a cough, respiratory/ARDS, diarrhea or chest tightness/pain were found. The meta-analysis also revealed that the incubation period was positively associated with disease severity.

Conclusion

Existing comorbidities, including COPD, cardiovascular disease, and coronary heart disease, increase the severity of COVID-19. Some studies found a statistically significant association between comorbidities such as diabetes and hypertension and disease severity. However, these studies may be biased due to substantial heterogeneity.

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  1. Version published to 10.1101/2020.06.18.20134478v2 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.06.18.20134478: (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 items: Data was compiled on paper reference number, author name, publication venue, month and year of publication, basic information (number of patients, median age, number of male and female under severe/non-severe conditions), number of severe/non-severe COVID-19 patients with comorbidities (diabetes, hypertension, coronary heart disease, cancer, liver disease, COPD, kidney disease, cardiovascular disease), number of severe/non-severe COVID-19 patients with symptoms (cough, respiratory/ARDS, fever, myalgia/fatigue, dyspnea, diarrhea, chest tightness/pain), and other information such as incubation period, smoking status, and deaths).

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Annoy is a C++ library that has Python bindings for searching for documents within a space that make a close match to a specific query.
    Python
    suggested: (IPython, RRID:SCR_001658)
    Selection of evidence sources: In addition to the automated search conducted using the search engine, an independent search was undertaken by two reviewers (NZ and EA), who examined the PubMed, Europe PMC, and Google Scholar databases.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Google Scholar
    suggested: (Google Scholar, RRID:SCR_008878)
    The study was performed according to the process outlined in the Cochrane Handbook for Systematic Reviews of Interventions Version 6.0 [
    Cochrane Handbook
    suggested: None
    Machine learning techniques based on regression, e.g., support vector machine (SVM) [11], linear regression, multi-perceptron [12], random forest [13], and attribute selection techniques, such as Classifier Subset Evaluator, Correlation Ranking Filter, and Relief Ranking Filter [14] as implemented in WEKA [15], were employed to determine how useful and significant different variables were in terms of the prediction of levels of severe instances of COVID-19.
    WEKA
    suggested: (Weka, RRID:SCR_001214)

    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:
    Limitations: This scoping review and meta-analysis have certain limitations that should be addressed within future studies. First, only patients admitted to Chinese hospitals were included in the studies under review. As such, the findings cannot be generalized across a wider population. Second, the presence of multiple comorbidities within single patients was not considered (a limitation that is consistent across the existing studies in this context). Third, the results are only up to date as of May 20th, 2020. Due to the rapidly changing nature of the COVID-19 pandemic, significant additional data has been made available since that date. Finally, this meta-analysis does not include laboratory, radiographic, clinical, or demographic data. 4.3. Conclusions: Existing comorbidity, including coronary heart disease, COPD, and cardiovascular disease, along with clinical conditions, such as myalgia/fatigue and dyspnea, are associated with a higher risk of patients developing a more severe form of COVID-19. As more evidence becomes available in the shape of trustworthy published results from other regions/nations, further studies should be undertaken in this area.

    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 10.1101/2020.06.18.20134478v1 on medRxiv