Derivation, validation, and evaluation of a new QRISK model to estimate lifetime risk of cardiovascular disease: cohort study using QResearch database
BMJ 2010; 341 doi: https://doi.org/10.1136/bmj.c6624 (Published 09 December 2010) Cite this as: BMJ 2010;341:c6624
- Julia Hippisley-Cox, professor of clinical epidemiology and general practice1,
- Carol Coupland, associate professor in medical statistics1,
- John Robson, senior lecturer general practice2,
- Peter Brindle, research and evaluation programme director3
- 1Division of Primary Care, University Park, Nottingham NG2 7RD, UK
- 2Centre for Health Sciences, Queen Mary’s School of Medicine and Dentistry, London E1 2AT, UK
- 3Avon Primary Care Research Collaborative, NHS Bristol, South Plaza, Bristol BS1 3NX, UK
- Correspondence to: Julia Hippisley-Cox juliahippisleycox{at}gmail.com
- Accepted 15 November 2010
Abstract
Objective To develop, validate, and evaluate a new QRISK model to estimate lifetime risk of cardiovascular disease.
Design Prospective cohort study with routinely collected data from general practice. Cox proportional hazards models in the derivation cohort to derive risk equations accounting for competing risks. Measures of calibration and discrimination in the validation cohort.
Setting 563 general practices in England and Wales contributing to the QResearch database.
Subjects Patients aged 30–84 years who were free of cardiovascular disease and not taking statins between 1 January 1994 and 30 April 2010: 2 343 759 in the derivation dataset, and 1 267 159 in the validation dataset.
Main outcomes measures Individualised estimate of lifetime risk of cardiovascular disease accounting for smoking status, ethnic group, systolic blood pressure, ratio of total cholesterol:high density lipoprotein cholesterol, body mass index, family history of coronary heart disease in first degree relative aged <60 years, Townsend deprivation score, treated hypertension, rheumatoid arthritis, chronic renal disease, type 2 diabetes, and atrial fibrillation. Age-sex centile values for lifetime cardiovascular risk compared with 10 year risk estimated using QRISK2 (2010).
Results Across all the 1 267 159 patients in the validation dataset, the 50th, 75th, 90th, and 95th centile values for lifetime risk were 31%, 39%, 50%, and 57% respectively. Of the 10% of patients in the validation cohort classified at highest risk with either the lifetime risk model or the 10 year risk model, only 18 385(14.5%) were at high risk on both measures. Patients identified as high risk with the lifetime risk approach were more likely to be younger, male, from ethnic minority groups, and have a positive family history of premature coronary heart disease than those identified with the 10 year QRISK2 score. The lifetime risk calculator is available at www.qrisk.org/lifetime/.
Conclusions Compared with using a 10 year QRISK2 score, a lifetime risk score will tend to identify patients for intervention at a younger age. Although lifestyle interventions at an earlier age could be advantageous, there would be small gains under the age of 65, and medical interventions carry risks as soon as they are initiated. Research is needed to examine closely the cost effectiveness and acceptability of such an approach.
Footnotes
The lifetime risk calculator is available at www.qrisk.org/lifetime/ (free for non-commercial research, educational, and personal use).
We thank the EMIS practices which contribute to the QResearch database, and EMIS for expertise in establishing, developing, and supporting the database.
Contributors: JH-C initiated the study; undertook the literature review, data extraction, data manipulation, and primary data analysis; and wrote the first draft of the paper. CC contributed to the design, analysis, interpretation, and drafting of the paper. JR and PB contributed to the development of core ideas, the analysis plan, interpretation of the results, and the drafting of the paper.
Funding: There was no external funding.
Competing interests: JH-C 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 EMIS (leading commercial supplier of information technology for 60% of general practices in the UK). JH-C is also 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 consultant statistician for ClinRisk. JR and PB have received no financial support for undertaking this work. JR and PB were previously members of the NICE Guideline Development Group for Lipid Modification, of which JR was chair. This work and any views expressed within it are solely those of the co-authors and not of any affiliated bodies or organisations. There are no other relationships or activities that could have influenced the submitted work.
Ethical approval: The project was reviewed in accordance with the QResearch agreement with Trent Multi-Centre Research Ethics Committee.
Data sharing: The patient level data from QResearch are specifically licensed according to its governance framework. See www.qresearch.org for further details. The Read codes groups used are available from the authors on request. The lifetime risk algorithm will be published as open source software under the GNU Lesser Public Licence.
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