Validation of UK Biobank data for mental health outcomes: A pilot study using secondary care electronic health records.


Journal

International journal of medical informatics
ISSN: 1872-8243
Titre abrégé: Int J Med Inform
Pays: Ireland
ID NLM: 9711057

Informations de publication

Date de publication:
04 2022
Historique:
received: 01 03 2021
revised: 20 12 2021
accepted: 20 01 2022
pubmed: 16 2 2022
medline: 5 4 2022
entrez: 15 2 2022
Statut: ppublish

Résumé

UK Biobank (UKB) is widely employed to investigate mental health disorders and related exposures; however, its applicability and relevance in a clinical setting and the assumptions required have not been sufficiently and systematically investigated. Here, we present the first validation study using secondary care mental health data with linkage to UKB from Oxford - Clinical Record Interactive Search (CRIS) focusing on comparison of demographic information, diagnostic outcome, medication record and cognitive test results, with missing data and the implied bias from both resources depicted. We applied a natural language processing model to extract information embedded in unstructured text from clinical notes and attachments. Using a contingency table we compared the demographic information recorded in UKB and CRIS. We calculated the positive predictive value (PPV, proportion of true positives cases detected) for mental health diagnosis and relevant medication. Amongst the cohort of 854 subjects, PPVs for any mental health diagnosis for dementia, depression, bipolar disorder and schizophrenia were 41.6%, and were 59.5%, 12.5%, 50.0% and 52.6%, respectively. Self-reported medication records in UKB had general PPV of 47.0%, with the prevalence of frequently prescribed medicines to each typical mental health disorder considerably different from the information provided by CRIS. UKB is highly multimodal, but with limited follow-up records, whereas CRIS offers a longitudinal high-resolution clinical picture with more than ten years of observations. The linkage of both datasets will reduce the self-report bias and synergistically augment diverse modalities into a unified resource to facilitate more robust research in mental health.

Identifiants

pubmed: 35168089
pii: S1386-5056(22)00018-1
doi: 10.1016/j.ijmedinf.2022.104704
pmc: PMC8889024
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

104704

Informations de copyright

Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.

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Auteurs

Zhenpeng Li (Z)

Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK.

Andrey Kormilitzin (A)

Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK.

Marco Fernandes (M)

Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK.

Nemanja Vaci (N)

Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK.

Qiang Liu (Q)

Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK.

Danielle Newby (D)

Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK.

Sarah Goodday (S)

Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK; 4youandme, Seattle, WA 98121-1031, USA.

Tanya Smith (T)

NIHR Biomedical Research Centre, Oxford Health NHS Foundation Trust, Warneford Hospital, Warneford Lane, Oxford OX3 7JX, UK.

Alejo J Nevado-Holgado (AJ)

Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK.

Laura Winchester (L)

Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK. Electronic address: laura.winchester@psych.ox.ac.uk.

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