Development of a COVID-19 Application Ontology for the ACT Network

  1. Shyam Visweswaran
  2. Malarkodi J. Samayamuthu
  3. Michele Morris
  4. Griffin M. Weber
  5. Douglas MacFadden
  6. Philip Trevvett
  7. Jeffrey G. Klann
  8. Vivian Gainer
  9. Barbara Benoit
  10. Shawn N. Murphy
  11. Lav Patel
  12. Nebojsa Mirkovic
  13. Yuliya Borovskiy
  14. Robert D. Johnson
  15. Matthew C. Wyatt
  16. Amy Y. Wang
  17. Robert W. Follett
  18. Ngan Chau
  19. Wenhong Zhu
  20. Mark Abajian
  21. Amy Chuang
  22. Neil Bahroos
  23. Phillip Reeder
  24. Donglu Xie
  25. Jennifer Cai
  26. Elaina R. Sendro
  27. Robert D. Toto
  28. Gary S. Firestein
  29. Lee M. Nadler
  30. Steven E. Reis

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Abstract

Clinical data networks that leverage large volumes of data in electronic health records (EHRs) are significant resources for research on coronavirus disease 2019 (COVID-19). Data harmonization is a key challenge in seamless use of multisite EHRs for COVID-19 research. We developed a COVID-19 application ontology in the national Accrual to Clinical Trials (ACT) network that enables harmonization of data elements that that are critical to COVID-19 research. The ontology contains over 50,000 concepts in the domains of diagnosis, procedures, medications, and laboratory tests. In particular, it has computational phenotypes to characterize the course of illness and outcomes, derived terms, and harmonized value sets for SARS-CoV-2 laboratory tests. The ontology was deployed and validated on the ACT COVID-19 network that consists of nine academic health centers with data on 14.5M patients. This ontology, which is freely available to the entire research community on GitHub at https://github.com/shyamvis/ACT-COVID-Ontology , will be useful for harmonizing EHRs for COVID-19 research beyond the ACT network.

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  1. Version published to 10.1101/2021.03.15.21253596v3 on medRxiv
  2. Version published to 10.1101/2021.03.15.21253596v2 on medRxiv
  3. Version published to 10.1093/jamiaopen/ooab036
  4. ScreenIT

    SciScore for 10.1101/2021.03.15.21253596: (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

    No key resources detected.

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    A limitation of the ontology is that the inclusion of codes and terms in the computable phenotypes is based on a limited number of sites though we obtained feedback from nine large healthcare systems across the United States. Thus, the heterogeneity of coding that we observed is likely to be limited. As we deploy the ontology across all 50 sites in the ACT network, we will solicit feedback and update the ontology iteratively. Another limitation is that new codes for COVID-19 are being continually introduced, especially LOINC codes for new SARS-CoV-2 tests. Therefore, it is critical to keep the ontology updated with the newest codes. We plan to develop and publish new versions of the ontology on a regular basis. Though we primarily developed the COVID-19 ontology for the ACT network, the ontology is has been leveraged by other COVID-19 research groups. In particular, the computable phenotypes that characterize the severity COVID-19 and disease outcomes have be beneficial in standardizing these computable phenotype definitions beyond the ACT network [12]. Since the publication of the ontology, the 4CE, the N3C, and the PCORnet consortia have adapted the ontology for their own use.

    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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  5. Version published to 10.1101/2021.03.15.21253596v1 on medRxiv