Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study
BMJ 2017; 357 doi: https://doi.org/10.1136/bmj.j2099 (Published 23 May 2017) Cite this as: BMJ 2017;357:j2099
- Julia Hippisley-Cox, professor of clinical epidemiology and general practice1,
- Carol Coupland, professor of medical statistics in primary care1,
- Peter Brindle, evaluation and implementation theme lead, NIHR CLAHRC West2
- 1Division of Primary Care, University Park, Nottingham NG2 7RD, UK
- 2Bristol Primary Clinical Commissioning Group and The National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care West (NIHR CLAHRC West) at University Hospitals Bristol NHS Foundation Trust, UK, UK
- Correspondence to: J Hippisley-Cox julia.hippisley-cox{at}nottingham.ac.uk
- Accepted 21 April 2017
Abstract
Objectives To develop and validate updated QRISK3 prediction algorithms to estimate the 10 year risk of cardiovascular disease in women and men accounting for potential new risk factors.
Design Prospective open cohort study.
Setting General practices in England providing data for the QResearch database.
Participants 1309 QResearch general practices in England: 981 practices were used to develop the scores and a separate set of 328 practices were used to validate the scores. 7.89 million patients aged 25-84 years were in the derivation cohort and 2.67 million patients in the validation cohort. Patients were free of cardiovascular disease and not prescribed statins at baseline.
Methods Cox proportional hazards models in the derivation cohort to derive separate risk equations in men and women for evaluation at 10 years. Risk factors considered included those already in QRISK2 (age, ethnicity, deprivation, systolic blood pressure, body mass index, total cholesterol: high density lipoprotein cholesterol ratio, smoking, family history of coronary heart disease in a first degree relative aged less than 60 years, type 1 diabetes, type 2 diabetes, treated hypertension, rheumatoid arthritis, atrial fibrillation, chronic kidney disease (stage 4 or 5)) and new risk factors (chronic kidney disease (stage 3, 4, or 5), a measure of systolic blood pressure variability (standard deviation of repeated measures), migraine, corticosteroids, systemic lupus erythematosus (SLE), atypical antipsychotics, severe mental illness, and HIV/AIDs). We also considered erectile dysfunction diagnosis or treatment in men. Measures of calibration and discrimination were determined in the validation cohort for men and women separately and for individual subgroups by age group, ethnicity, and baseline disease status.
Main outcome measures Incident cardiovascular disease recorded on any of the following three linked data sources: general practice, mortality, or hospital admission records.
Results 363 565 incident cases of cardiovascular disease were identified in the derivation cohort during follow-up arising from 50.8 million person years of observation. All new risk factors considered met the model inclusion criteria except for HIV/AIDS, which was not statistically significant. The models had good calibration and high levels of explained variation and discrimination. In women, the algorithm explained 59.6% of the variation in time to diagnosis of cardiovascular disease (R2, with higher values indicating more variation), and the D statistic was 2.48 and Harrell’s C statistic was 0.88 (both measures of discrimination, with higher values indicating better discrimination). The corresponding values for men were 54.8%, 2.26, and 0.86. Overall performance of the updated QRISK3 algorithms was similar to the QRISK2 algorithms.
Conclusion Updated QRISK3 risk prediction models were developed and validated. The inclusion of additional clinical variables in QRISK3 (chronic kidney disease, a measure of systolic blood pressure variability (standard deviation of repeated measures), migraine, corticosteroids, SLE, atypical antipsychotics, severe mental illness, and erectile dysfunction) can help enable doctors to identify those at most risk of heart disease and stroke.
Footnotes
A simple web calculator to implement the QRISK3 algorithms can be accessed at www.qrisk.org/Open source software is also available for download.
We thank the EMIS practices that contribute to QResearch, and EMIS and the University of Nottingham for expertise in establishing, developing, and supporting the QResearch database, and the Office for National Statistics for providing the mortality data. The Hospital Episode Statistics data in this analysis are reused by permission from NHS Digital, which retains the copyright. ONS and NHS Digital bear no responsibility for the analysis or interpretation of the data.
Contributors: JHC initiated the study, developed the research question, undertook the literature review, extracted and manipulated the data, performed the primary data analysis, and wrote the first draft of the paper. CC contributed to the refinement of the research question, design, analysis, interpretation, and drafting of the paper. PB contributed to the development of the research question, design, interpretation, and drafting of the paper.
Funding: No external funding was received for this study.
Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: JHC is professor of clinical epidemiology at the University of Nottingham and codirector of QResearch a not-for-profit organisation that is a joint partnership between the University of Nottingham and Egton Medical Information Systems (leading commercial supplier of IT for 55% of general practices in the UK). JHC is also a paid director of ClinRisk, which produces open and closed source software to ensure the reliable and updatable implementation of clinical risk algorithms within clinical computer systems to help improve patient care. CC is associate professor of medical statistics at the University of Nottingham and a paid consultant statistician for ClinRisk. PB is partly funded by Health Research Collaboration for Leadership in Applied Health Research and Care West (NIHR CLAHRC West), Bristol Clinical Commissioning Group and the West of England Academic Health Science Network.. This work and any views expressed within it are solely those of the authors and not of any affiliated bodies or organisations.
Ethical approval: The study was reviewed in accordance with the QResearch agreement with East Midlands-Derby Research Ethics Committee (reference 03/4/021).
Data sharing: The algorithms presented in this paper will be released as open source software under the GNU lesser GPL v3. The open source software allows use without charge under the terms of the GNU lesser public license version 3. Closed source software can be licensed at a fee.
Transparency: The lead author (JHC) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.
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