BMJ  2008;336:1475-1482 (28 June), doi:10.1136/bmj.39609.449676.25 (published 23 June 2008)

Research

Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2

¹ Division of Primary Care, Tower Building, University Park, Nottingham NG2 7RD, ² Centre for Health Sciences, Queen Mary’s School of Medicine and Dentistry, London E1 2AT, ³ Medway Primary Care Trust, Unit 2, Gillingham, Kent ME7 0NJ, ⁴ Division of Community Health Sciences: GP Section, University of Edinburgh, Edinburgh EH8 9DX, ⁵ Avon Primary Care Research Collaborative, Bristol Primary Care Trust, Bristol BS2 8EE

Objective To develop and validate version two of the QRISK cardiovascular disease risk algorithm (QRISK2) to provide accurate estimates of cardiovascular risk in patients from different ethnic groups in England and Wales and to compare its performance with the modified version of Framingham score recommended by the National Institute for Health and Clinical Excellence (NICE).

Design Prospective open cohort study with routinely collected data from general practice, 1 January 1993 to 31 March 2008.

Setting 531 practices in England and Wales contributing to the national QRESEARCH database.

Participants 2.3 million patients aged 35-74 (over 16 million person years) with 140 000 cardiovascular events. Overall population (derivation and validation cohorts) comprised 2.22 million people who were white or whose ethnic group was not recorded, 22 013 south Asian, 11 595 black African, 10 402 black Caribbean, and 19 792 from Chinese or other Asian or other ethnic groups.

Main outcome measures First (incident) diagnosis of cardiovascular disease (coronary heart disease, stroke, and transient ischaemic attack) recorded in general practice records or linked Office for National Statistics death certificates. Risk factors included self assigned ethnicity, age, sex, smoking status, systolic blood pressure, ratio of total serum cholesterol:high density lipoprotein cholesterol, body mass index, family history of coronary heart disease in first degree relative under 60 years, Townsend deprivation score, treated hypertension, type 2 diabetes, renal disease, atrial fibrillation, and rheumatoid arthritis.

Results The validation statistics indicated that QRISK2 had improved discrimination and calibration compared with the modified Framingham score. The QRISK2 algorithm explained 43% of the variation in women and 38% in men compared with 39% and 35%, respectively, by the modified Framingham score. Of the 112 156 patients classified as high risk (that is, 20% risk over 10 years) by the modified Framingham score, 46 094 (41.1%) would be reclassified at low risk with QRISK2. The 10 year observed risk among these reclassified patients was 16.6% (95% confidence interval 16.1% to 17.0%)—that is, below the 20% treatment threshold. Of the 78 024 patients classified at high risk on QRISK2, 11 962 (15.3%) would be reclassified at low risk by the modified Framingham score. The 10 year observed risk among these patients was 23.3% (22.2% to 24.4%)—that is, above the 20% threshold. In the validation cohort, the annual incidence rate of cardiovascular events among those with a QRISK2 score of 20% was 30.6 per 1000 person years (29.8 to 31.5) for women and 32.5 per 1000 person years (31.9 to 33.1) for men. The corresponding figures for the modified Framingham equation were 25.7 per 1000 person years (25.0 to 26.3) for women and 26.4 (26.0 to 26.8) for men). At the 20% threshold, the population identified by QRISK2 was at higher risk of a CV event than the population identified by the Framingham score.

Conclusions Incorporating ethnicity, deprivation, and other clinical conditions into the QRISK2 algorithm for risk of cardiovascular disease improves the accuracy of identification of those at high risk in a nationally representative population. At the 20% threshold, QRISK2 is likely to be a more efficient and equitable tool for treatment decisions for the primary prevention of cardiovascular disease. As 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 advised.

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