An integrated clinical and genetic model for predicting risk of severe COVID-19: A population-based case–control study

  1. Gillian S. Dite
  2. Nicholas M. Murphy
  3. Richard Allman

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Abstract

Up to 30% of people who test positive to SARS-CoV-2 will develop severe COVID-19 and require hospitalisation. Age, gender, and comorbidities are known to be risk factors for severe COVID-19 but are generally considered independently without accurate knowledge of the magnitude of their effect on risk, potentially resulting in incorrect risk estimation. There is an urgent need for accurate prediction of the risk of severe COVID-19 for use in workplaces and healthcare settings, and for individual risk management. Clinical risk factors and a panel of 64 single-nucleotide polymorphisms were identified from published data. We used logistic regression to develop a model for severe COVID-19 in 1,582 UK Biobank participants aged 50 years and over who tested positive for the SARS-CoV-2 virus: 1,018 with severe disease and 564 without severe disease. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC). A model incorporating the SNP score and clinical risk factors (AUC = 0.786; 95% confidence interval = 0.763 to 0.808) had 111% better discrimination of disease severity than a model with just age and gender (AUC = 0.635; 95% confidence interval = 0.607 to 0.662). The effects of age and gender are attenuated by the other risk factors, suggesting that it is those risk factors–not age and gender–that confer risk of severe disease. In the whole UK Biobank, most are at low or only slightly elevated risk, but one-third are at two-fold or more increased risk. We have developed a model that enables accurate prediction of severe COVID-19. Continuing to rely on age and gender alone (or only clinical factors) to determine risk of severe COVID-19 will unnecessarily classify healthy older people as being at high risk and will fail to accurately quantify the increased risk for younger people with comorbidities.

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  1. ScreenIT

    SciScore for 10.1101/2020.09.30.20204453: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementConsent: All participants provided written informed consent to the UK Biobank before data collection began.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    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:
    Our study does have some limitations. We used source of test result as a proxy for severity of disease. Therefore, there is considerable opportunity for misclassification of disease severity but this would be likely to attenuate the magnitude of the associations. Townsend deprivation score, BMI and current smoking status were taken from the baseline assessment data and may not represent the participants’ current status. This may have contributed to these variables not being statistically significant. Until mid-May, testing for COVID-19 in the UK was limited to those who had recognisable symptoms and were essential workers, contacts of known cases, hospitalised or had returned from overseas.22 Therefore, many asymptomatic or very mild cases from the first wave of the pandemic will not have been identified in this dataset. Nevertheless, our results remain applicable to those who develop symptoms that warrant medical attention. While the vast majority of UK Biobank participants are at low or only slightly elevated risk of severe COVID-19 (Figure 3), we can identify those who are likely to be at substantially increased risk. Our risk prediction test for severe COVID-19 in people aged 50 years or older has great potential for wide-reaching benefits in managing the risk for essential workers, in healthcare settings and in workplaces that seek to re-open safely. The test will also enable individuals to make informed choices based on their personal risk. However, key to understanding...

    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 10.1371/journal.pone.0247205
  3. Version published to 10.1101/2020.09.30.20204453v1 on medRxiv