Clinical prediction models in epidemiological studies: lessons from the application of QRISK3 to UK Biobank Data

Journal article

Statistical models for clinical risk prediction are often derived using data from primary care databases; however, they are frequently used outside of clinical settings. The use of prediction models in epidemiological studies without external validation may lead to inaccurate results. We use the example of applying the QRISK3 model to data from the United Kingdom (UK) Biobank study to illustrate the challenges and provide suggestions for future authors. The QRISK3 model is recommended by the National Institute for Health and Care Excellence (NICE) as a tool to aid cardiovascular risk prediction in English and Welsh primary care patients aged between 40 and 74. QRISK3 has not been externally validated for use in studies where data is collected for more general scientific purposes, including the UK Biobank study. This lack of external validation is important as the QRISK3 scores of participants in UK Biobank have been used and reported in several publications. This paper outlines: (i) how various publications have used QRISK3 on UK Biobank data and (ii) the ways that the lack of external validation may affect the conclusions from these publications. We then propose potential solutions for addressing these challenges; for example, model recalibration and considering alternative models, for the application of traditional statistical models such as QRISK3, in cohorts without external validation.

Authors: Parsons, RE; Colopy, GW; Clifton, DA; Clifton, L

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: School of Statistics, Renmin University of China
• Journal: Journal of Data Science
• Volume: 20
• Issue: 1
• Pages: 1-13
• Publication date: 2022-02-08
• Acceptance date: 2022-01-27
• DOI: 10.6339/22-jds1037
• EISSN: 1683-8602
• ISSN: 1680-743X
• Language: English
• Keywords: cardiovascular; resources and infrastructure (aetiology); FFR; information and computing sciences; health and social care services research; research design and methodologies (health services); good health and well being; generic health relevance; data management and data science; aetiology