Evidence map›Paper›PMID 36227072›Full record

Observational studyJournal of the American Medical Informatics Association : JAMIA2022

Transforming and evaluating the UK Biobank to the OMOP Common Data Model for COVID-19 research and beyond.

Vaclav Papez, Maxim Moinat, Erica A Voss, Sofia Bazakou, Anne Van Winzum, Alessia Peviani, Stefan Payralbe, Michael Kallfelz, Folkert W Asselbergs, Daniel Prieto-Alhambra and 2 more

Erratum issuedAbstract readObservational Study
In one paragraph

Observational study in Journal of the American Medical Informatics Association : JAMIA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 29 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

29 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Observational
  4. Article
  5. Article
  6. Using ontologies to facilitate healthcare process mining and analysis.Journal of intelligent information systems · 2026
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Centralized and Federated Models for the Analysis of Clinical Data.Annual review of biomedical data science · 2024
    Review
  18. Review
  19. Review
  20. Cross Biobank Comparison of Phenomic Profiles.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Vaclav PapezInstitute of Health Informatics, University College London, London, UK.ORCID 0000-0002-2123-7993
Maxim MoinatThe Hyve, Utrecht, The Netherlands.
Erica A VossDepartment of Epidemiology, Janssen Research & Development LLC, Raritan, New Jersey, USA.ORCID 0000-0002-0651-0613
Sofia BazakouThe Hyve, Utrecht, The Netherlands.
Anne Van WinzumThe Hyve, Utrecht, The Netherlands.
Alessia PevianiThe Hyve, Utrecht, The Netherlands.ORCID 0000-0003-0889-8575
Stefan PayralbeThe Hyve, Utrecht, The Netherlands.
Michael KallfelzOdysseus Data Services GmbH, Berlin, Germany.
Folkert W AsselbergsInstitute of Health Informatics, University College London, London, UK.
Daniel Prieto-AlhambraErasmus Medical Center Rotterdam, Rotterdam, The Netherlands.
Richard J B DobsonInstitute of Health Informatics, University College London, London, UK.
Spiros DenaxasInstitute of Health Informatics, University College London, London, UK.ORCID 0000-0001-9612-7791

Funding

BHF Data Science Centre led by Health Data Research UK SP/19/3/34678Chief Scientist OfficeDepartment of HealthMedical Research Council MC_PC_17228Medical Research Council MC_PC_20030Medical Research Council MC_PC_20051Medical Research Council MC_PC_20059Medical Research Council MC_QA137853Medical Research Council MR/K006584/1Wellcome Trust
6 · The paper itself

Abstract

objectiveThe coronavirus disease 2019 (COVID-19) pandemic has demonstrated the value of real-world data for public health research. International federated analyses are crucial for informing policy makers. Common data models (CDMs) are critical for enabling these studies to be performed efficiently. Our objective was to convert the UK Biobank, a study of 500 000 participants with rich genetic and phenotypic data to the Observational Medical Outcomes Partnership (OMOP) CDM. MATERIALS AND

methodsWe converted UK Biobank data to OMOP CDM v. 5.3. We transformedparticipant research data on diseases collected at recruitment and electronic health records (EHRs) from primary care, hospitalizations, cancer registrations, and mortality from providers in England, Scotland, and Wales. We performed syntactic and semantic validations and compared comorbidities and risk factors between source and transformed data.

resultsWe identified 502 505 participants (3086 with COVID-19) and transformed 690 fields (1 373 239 555 rows) to the OMOP CDM using 8 different controlled clinical terminologies and bespoke mappings. Specifically, we transformed self-reported noncancer illnesses 946 053 (83.91% of all source entries), cancers 37 802 (70.81%), medications 1 218 935 (88.25%), and prescriptions 864 788 (86.96%). In EHR, we transformed 13 028 182 (99.95%) hospital diagnoses, 6 465 399 (89.2%) procedures, 337 896 333 primary care diagnoses (CTV3, SNOMED-CT), 139 966 587 (98.74%) prescriptions (dm+d) and 77 127 (99.95%) deaths (ICD-10). We observed good concordance across demographic, risk factor, and comorbidity factors between source and transformed data. DISCUSSION AND

conclusionOur study demonstrated that the OMOP CDM can be successfully leveraged to harmonize complex large-scale biobanked studies combining rich multimodal phenotypic data. Our study uncovered several challenges when transforming data from questionnaires to the OMOP CDM which require further research. The transformed UK Biobank resource is a valuable tool that can enable federated research, like COVID-19 studies.

Indexed as

Biological Specimen BanksCOVID-19Databases, FactualElectronic Health RecordsHumansUnited Kingdomcommon data modelelectronic health recordsmedical ontologiesOMOPphenotyping

Identifiers

PMID36227072
PMCPMC9619789

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.