Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study

  1. Mohammad A. Dabbah
  2. Angus B. Reed
  3. Adam T. C. Booth
  4. Arrash Yassaee
  5. Aleksa Despotovic
  6. Benjamin Klasmer
  7. Emily Binning
  8. Mert Aral
  9. David Plans
  10. Davide Morelli
  11. Alain B. Labrique
  12. Diwakar Mohan

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Abstract

The COVID-19 pandemic has created an urgent need for robust, scalable monitoring tools supporting stratification of high-risk patients. This research aims to develop and validate prediction models, using the UK Biobank, to estimate COVID-19 mortality risk in confirmed cases. From the 11,245 participants testing positive for COVID-19, we develop a data-driven random forest classification model with excellent performance (AUC: 0.91), using baseline characteristics, pre-existing conditions, symptoms, and vital signs, such that the score could dynamically assess mortality risk with disease deterioration. We also identify several significant novel predictors of COVID-19 mortality with equivalent or greater predictive value than established high-risk comorbidities, such as detailed anthropometrics and prior acute kidney failure, urinary tract infection, and pneumonias. The model design and feature selection enables utility in outpatient settings. Possible applications include supporting individual-level risk profiling and monitoring disease progression across patients with COVID-19 at-scale, especially in hospital-at-home settings.

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  1. Version published to 10.1038/s41598-021-95136-x
  2. Version published to 10.1101/2021.02.08.21251343v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.02.08.21251343: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Ethical approval was granted by the national research ethics committee (REC 16/NW/0274) for the overall UK Biobank cohort.
    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:
    While the use of the UKB is a key strength in the development of the model, there are associated limitations which may impact the generalizability of the model. The UKB cohort trends towards being healthier and wealthier than the general population, which poses a notable limitation when modeling noncommunicable diseases49. As COVID-19 acquisition, however, is determined by exposure, this limitation is minimised in our investigation. Separately, the UKB COVID-19 data subset is less likely to capture asymptomatic or non-severe cases, in part as such individuals may not have received a test or sought medical treatment, but predominantly owing to UKB’s enrichment for older age resulting in lesser rates of such presentation. The restricted age distribution (51-85 years) may further limit generalization of our findings to outside of this age range, however, ONS figures show those aged 50+ have accounted for 98.06% of all COVID-19-related deaths in England and Wales50. Although age is clearly an important feature, our sensitivity analysis (Supplementary Figure 2) demonstrated negligible performance drop, likely because much of the risk associated with older age is captured within other included features. One reason for using uniform leave-one-out (LOO) training is to overcome such issues of feature reliance and generalize the model as much as possible. The F-score in Figure 2B illustrates this robustness, however, this must be tested on a separate representative dataset for a conclu...

    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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  4. Version published to 10.1101/2021.02.08.21251343v1 on medRxiv