Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study
- PMID: 17615182
- PMCID: PMC1925200
- DOI: 10.1136/bmj.39261.471806.55
Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study
Abstract
Objective: To derive a new cardiovascular disease risk score (QRISK) for the United Kingdom and to validate its performance against the established Framingham cardiovascular disease algorithm and a newly developed Scottish score (ASSIGN).
Design: Prospective open cohort study using routinely collected data from general practice.
Setting: UK practices contributing to the QRESEARCH database.
Participants: The derivation cohort consisted of 1.28 million patients, aged 35-74 years, registered at 318 practices between 1 January 1995 and 1 April 2007 and who were free of diabetes and existing cardiovascular disease. The validation cohort consisted of 0.61 million patients from 160 practices.
Main outcome measures: First recorded diagnosis of cardiovascular disease (incident diagnosis between 1 January 1995 and 1 April 2007): myocardial infarction, coronary heart disease, stroke, and transient ischaemic attacks. Risk factors were age, sex, smoking status, systolic blood pressure, ratio of total serum cholesterol to high density lipoprotein, body mass index, family history of coronary heart disease in first degree relative aged less than 60, area measure of deprivation, and existing treatment with antihypertensive agent.
Results: A cardiovascular disease risk algorithm (QRISK) was developed in the derivation cohort. In the validation cohort the observed 10 year risk of a cardiovascular event was 6.60% (95% confidence interval 6.48% to 6.72%) in women and 9.28% (9.14% to 9.43%) in men. Overall the Framingham algorithm over-predicted cardiovascular disease risk at 10 years by 35%, ASSIGN by 36%, and QRISK by 0.4%. Measures of discrimination tended to be higher for QRISK than for the Framingham algorithm and it was better calibrated to the UK population than either the Framingham or ASSIGN models. Using QRISK 8.5% of patients aged 35-74 are at high risk (20% risk or higher over 10 years) compared with 13% when using the Framingham algorithm and 14% when using ASSIGN. Using QRISK 34% of women and 73% of men aged 64-75 would be at high risk compared with 24% and 86% according to the Framingham algorithm. UK estimates for 2005 based on QRISK give 3.2 million patients aged 35-74 at high risk, with the Framingham algorithm predicting 4.7 million and ASSIGN 5.1 million. Overall, 53 668 patients in the validation dataset (9% of the total) would be reclassified from high to low risk or vice versa using QRISK compared with the Framingham algorithm.
Conclusion: QRISK performed at least as well as the Framingham model for discrimination and was better calibrated to the UK population than either the Framingham model or ASSIGN. QRISK is likely to provide more appropriate risk estimates to help identify high risk patients on the basis of age, sex, and social deprivation. It is therefore likely to be a more equitable tool to inform management decisions and help ensure treatments are directed towards those most likely to benefit. It includes additional variables which improve risk estimates for patients with a positive family history or those on antihypertensive treatment. However, since the validation was performed in a similar population to the population from which the algorithm was derived, it potentially has a "home advantage." Further validation in other populations is therefore required.
Conflict of interest statement
Figures
Comment in
-
Cardiovascular risk models.BMJ. 2007 Jul 21;335(7611):107-8. doi: 10.1136/bmj.39262.643090.47. Epub 2007 Jul 6. BMJ. 2007. PMID: 17616541 Free PMC article.
-
The QRISK was less likely to overestimate cardiovascular risk than the Framingham or ASSIGN equations.ACP J Club. 2008 Jan-Feb;148(1):25. ACP J Club. 2008. PMID: 18171012 No abstract available.
-
NICE on lipid modification: NICE has overestimated cardiovascular risk.BMJ. 2008 Jun 14;336(7657):1323-4. doi: 10.1136/bmj.39605.518287.3A. BMJ. 2008. PMID: 18556279 Free PMC article. No abstract available.
Similar articles
-
Performance of the QRISK cardiovascular risk prediction algorithm in an independent UK sample of patients from general practice: a validation study.Heart. 2008 Jan;94(1):34-9. doi: 10.1136/hrt.2007.134890. Epub 2007 Oct 4. Heart. 2008. PMID: 17916661
-
Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2.BMJ. 2008 Jun 28;336(7659):1475-82. doi: 10.1136/bmj.39609.449676.25. Epub 2008 Jun 23. BMJ. 2008. PMID: 18573856 Free PMC article.
-
An independent external validation and evaluation of QRISK cardiovascular risk prediction: a prospective open cohort study.BMJ. 2009 Jul 7;339:b2584. doi: 10.1136/bmj.b2584. BMJ. 2009. PMID: 19584409 Free PMC article.
-
Evidence review for CVD risk assessment tools: primary prevention: Cardiovascular disease: risk assessment and reduction, including lipid modification: Evidence review A.London: National Institute for Health and Care Excellence (NICE); 2023 May. London: National Institute for Health and Care Excellence (NICE); 2023 May. PMID: 38723137 Free Books & Documents. Review.
-
Framingham and European risk algorithms: implications for African Americans.Rev Cardiovasc Med. 2004;5 Suppl 3:S34-41. Rev Cardiovasc Med. 2004. PMID: 15303084 Review.
Cited by
-
Prediction of cardiovascular markers and diseases using retinal fundus images and deep learning: a systematic scoping review.Eur Heart J Digit Health. 2024 Sep 10;5(6):660-669. doi: 10.1093/ehjdh/ztae068. eCollection 2024 Nov. Eur Heart J Digit Health. 2024. PMID: 39563905 Free PMC article. Review.
-
A classification system for identifying persons with an unknown cardiovascular disease (CVD) status for a multiracial/ ethnic Caribbean population.PeerJ. 2024 Oct 22;12:e17948. doi: 10.7717/peerj.17948. eCollection 2024. PeerJ. 2024. PMID: 39465157 Free PMC article.
-
An Approach for Combining Clinical Judgment with Machine Learning to Inform Medical Decision Making: Analysis of Nonemergency Surgery Strategies for Acute Appendicitis in Patients with Multiple Long-Term Conditions.Med Decis Making. 2024 Nov;44(8):944-960. doi: 10.1177/0272989X241289336. Epub 2024 Oct 23. Med Decis Making. 2024. PMID: 39440442 Free PMC article.
-
Improving Cardiovascular Disease Prediction With Machine Learning Using Mental Health Data: A Prospective UK Biobank Study.JACC Adv. 2024 Sep 25;3(9):101180. doi: 10.1016/j.jacadv.2024.101180. eCollection 2024 Sep. JACC Adv. 2024. PMID: 39372477 Free PMC article.
-
The essential role of dual-energy x-ray absorptiometry in the prediction of subclinical cardiovascular disease.Front Cardiovasc Med. 2024 Aug 29;11:1377299. doi: 10.3389/fcvm.2024.1377299. eCollection 2024. Front Cardiovasc Med. 2024. PMID: 39280034 Free PMC article. Review.
References
-
- British Heart Foundation. Coronary heart disease statistics London: BHF, 2007
-
- Department of Health. National service framework for coronary heart disease London: DoH, 2000
-
- NCEP Expert Panel. Detection, evaluation and treatment of high blood cholesterol in adults (adult treatment panel III): executive summary 3, 1-30. Report of the national cholesterol education program (NCEP): Bethesda, MD: National Institute of Health/National Heart, Lung and Blood Institute, 2001
-
- Mann J, Crooke M, Fear H, Hay D, Jackson R, Neutze J, et al. Guidelines for detection and management of dyslipidaemia. Scientific Committee of the National Heart Foundation of New Zealand. NZ Med J 1993;106:133-41. - PubMed
-
- Anderson KM, Odell PM, Wilson PWF, Kannel WB. Cardiovascular disease risk profiles. Am Heart J 1991;121:293-8. - PubMed
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Other Literature Sources
Miscellaneous