Imperial College London

Ruthie Parsons

Faculty of MedicineSchool of Public Health

Research Postgraduate
 
 
 
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Contact

 

+44 (0)20 7594 3515r.parsons21

 
 
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Location

 

Medical SchoolSt Mary's Campus

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Summary

 

Publications

Publication Type
Year
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2 results found

Parsons RE, Liu X, Collister JA, Clifton DA, Cairns BJ, Clifton Let al., 2023, Independent external validation of the QRISK3 cardiovascular disease risk prediction model using UK Biobank, Heart, Vol: 109, Pages: 1690-1697, ISSN: 1355-6037

Objective To externally evaluate the performance of QRISK3 for predicting 10 year risk of cardiovascular disease (CVD) in the UK Biobank cohort.Methods We used data from the UK Biobank, a large-scale prospective cohort study of 403 370 participants aged 40–69 years recruited between 2006 and 2010 in the UK. We included participants with no previous history of CVD or statin treatment and defined the outcome to be the first occurrence of coronary heart disease, ischaemic stroke or transient ischaemic attack, derived from linked hospital inpatient records and death registrations.Results Our study population included 233 233 women and 170 137 men, with 9295 and 13 028 incident CVD events, respectively. Overall, QRISK3 had moderate discrimination for UK Biobank participants (Harrell’s C-statistic 0.722 in women and 0.697 in men) and discrimination declined by age (<0.62 in all participants aged 65 years or older). QRISK3 systematically overpredicted CVD risk in UK Biobank, particularly in older participants, by as much as 20%.Conclusions QRISK3 had moderate overall discrimination in UK Biobank, which was best in younger participants. The observed CVD risk for UK Biobank participants was lower than that predicted by QRISK3, particularly for older participants. It may be necessary to recalibrate QRISK3 or use an alternate model in studies that require accurate CVD risk prediction in UK Biobank.

Journal article

Parsons RE, Colopy GW, Clifton DA, Clifton Let al., 2022, Clinical prediction models in epidemiological studies: lessons from the application of QRISK3 to UK biobank data, Journal of Data Science, Vol: 20, Pages: 1-13, ISSN: 1680-743X

<jats:p>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.</jats:p>

Journal article

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