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Lookup NU author(s): Philip Darke, Dr Sophie Cassidy, Professor Mike Catt, Professor Roy Taylor, Professor Paolo MissierORCiD, Professor Jaume Bacardit
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. Primary care EHR data are often of clinical importance to cohort studies however they require careful handling. Challenges include determining the periods during which EHR data were collected. Participants are typically censored when they deregister from a medical practice, however, cohort studies wish to follow participants longitudinally including those that change practice. Using UK Biobank as an exemplar, we developed methodology to infer continuous periods of data collection and maximize follow-up in longitudinal studies. This resulted in longer follow-up for around 40% of participants with multiple registration records (mean increase of 3.8 years from the first study visit). The approach did not sacrifice phenotyping accuracy when comparing agreement between self-reported and EHR data. A diabetes mellitus case study illustrates how the algorithm supports longitudinal study design and provides further validation. We use UK Biobank data, however, the tools provided can be used for other conditions and studies with minimal alteration.
Author(s): Darke P, Cassidy S, Catt M, Taylor R, Missier P, Bacardit J
Publication type: Article
Publication status: Published
Journal: Journal of the American Medical Informatics Association : JAMIA
Year: 2022
Volume: 29
Issue: 3
Pages: 546-552
Print publication date: 01/03/2022
Online publication date: 13/12/2021
Acceptance date: 23/11/2021
Date deposited: 24/02/2022
ISSN (electronic): 1527-974X
Publisher: Oxford University Press
URL: https://doi.org/10.1093/jamia/ocab260
DOI: 10.1093/jamia/ocab260
PubMed id: 34897458
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