title: 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).
  
  
  METHODS
  Prospective open cohort study using routinely collected data from general practice.
  
  
  METHODS
  UK practices contributing to the QRESEARCH database.
  
  
  METHODS
  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.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Tower Building, University Park, Nottingham NG2 7RD. julia.hippisley-cox@nottingham.ac.uk
authors.forename: Julia
authors.initials: J
authors.lastname: Hippisley-Cox
authors.forename: Carol
authors.initials: C
authors.lastname: Coupland
authors.forename: Yana
authors.initials: Y
authors.lastname: Vinogradova
authors.forename: John
authors.initials: J
authors.lastname: Robson
authors.forename: Margaret
authors.initials: M
authors.lastname: May
authors.forename: Peter
authors.initials: P
authors.lastname: Brindle
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D002318:Cardiovascular Diseases / Q000209:etiology* / Q000401:mortality / Q000517:prevention & control; D015331:Cohort Studies; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011446:Prospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D006113:United Kingdom / Q000453:epidemiology