Literature DB >> 17615182

Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study.

Julia Hippisley-Cox1, Carol Coupland, Yana Vinogradova, John Robson, Margaret May, Peter Brindle.   

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.

Entities:  

Mesh:

Year:  2007        PMID: 17615182      PMCID: PMC1925200          DOI: 10.1136/bmj.39261.471806.55

Source DB:  PubMed          Journal:  BMJ        ISSN: 0959-8138


  23 in total

1.  The use of fractional polynomials to model continuous risk variables in epidemiology.

Authors:  P Royston; G Ambler; W Sauerbrei
Journal:  Int J Epidemiol       Date:  1999-10       Impact factor: 7.196

2.  Explaining trends in inequities: evidence from Brazilian child health studies.

Authors:  C G Victora; J P Vaughan; F C Barros; A C Silva; E Tomasi
Journal:  Lancet       Date:  2000-09-23       Impact factor: 79.321

3.  Developing a prognostic model in the presence of missing data: an ovarian cancer case study.

Authors:  Taane G Clark; Douglas G Altman
Journal:  J Clin Epidemiol       Date:  2003-01       Impact factor: 6.437

4.  A new measure of prognostic separation in survival data.

Authors:  Patrick Royston; Willi Sauerbrei
Journal:  Stat Med       Date:  2004-03-15       Impact factor: 2.373

Review 5.  A systematic review and economic evaluation of statins for the prevention of coronary events.

Authors:  S Ward; M Lloyd Jones; A Pandor; M Holmes; R Ara; A Ryan; W Yeo; N Payne
Journal:  Health Technol Assess       Date:  2007-04       Impact factor: 4.014

6.  Cardiovascular risk prediction tools for populations in Asia.

Authors:  F Barzi; A Patel; D Gu; P Sritara; T H Lam; A Rodgers; M Woodward
Journal:  J Epidemiol Community Health       Date:  2007-02       Impact factor: 3.710

7.  European guidelines on cardiovascular disease prevention in clinical practice. Third Joint Task Force of European and Other Societies on Cardiovascular Disease Prevention in Clinical Practice.

Authors:  Guy De Backer; Ettore Ambrosioni; Knut Borch-Johnsen; Carlos Brotons; Renata Cifkova; Jean Dallongeville; Shah Ebrahim; Ole Faergeman; Ian Graham; Giuseppe Mancia; Volkert Manger Cats; Kristina Orth-Gomér; Joep Perk; Kalevi Pyörälä; José L Rodicio; Susana Sans; Vedat Sansoy; Udo Sechtem; Sigmund Silber; Troels Thomsen; David Wood
Journal:  Eur Heart J       Date:  2003-09       Impact factor: 29.983

8.  Stroke risk profile: adjustment for antihypertensive medication. The Framingham Study.

Authors:  R B D'Agostino; P A Wolf; A J Belanger; W B Kannel
Journal:  Stroke       Date:  1994-01       Impact factor: 7.914

9.  Family burden of cardiovascular mortality: risk implications for offspring in a national register linkage study based upon the Malmö Preventive Project.

Authors:  P M Nilsson; J-A Nilsson; G Berglund
Journal:  J Intern Med       Date:  2004-02       Impact factor: 8.989

10.  Predictive accuracy of the Framingham coronary risk score in British men: prospective cohort study.

Authors:  Peter Brindle; Jonathan Emberson; Fiona Lampe; Mary Walker; Peter Whincup; Tom Fahey; Shah Ebrahim
Journal:  BMJ       Date:  2003-11-29
View more
  276 in total

1.  Obese schizophrenia spectrum patients have significantly higher 10-year general cardiovascular risk and vascular ages than obese individuals without severe mental illness.

Authors:  Joseph C Ratliff; Laura B Palmese; Erin L Reutenauer; Vinod H Srihari; Cenk Tek
Journal:  Psychosomatics       Date:  2012-06-02       Impact factor: 2.386

2.  Inclusion of stroke as an outcome and risk equivalent in risk scores for primary and secondary prevention of vascular disease.

Authors:  Mandip S Dhamoon; Mitchell S V Elkind
Journal:  Circulation       Date:  2010-05-11       Impact factor: 29.690

Review 3.  Initiation of statin therapy: are there age limits?

Authors:  Dipan A Desai; Sammy Zakaria; Pamela Ouyang
Journal:  Curr Atheroscler Rep       Date:  2012-02       Impact factor: 5.113

4.  QRISK or Framingham?

Authors:  John Robson; Julia Hippisley-Cox; Carol Coupland
Journal:  Br J Clin Pharmacol       Date:  2012-09       Impact factor: 4.335

5.  Association between family history and coronary heart disease death across long-term follow-up in men: the Cooper Center Longitudinal Study.

Authors:  Justin M Bachmann; Benjamin L Willis; Colby R Ayers; Amit Khera; Jarett D Berry
Journal:  Circulation       Date:  2012-05-23       Impact factor: 29.690

6.  Common clinical practice versus new PRIM score in predicting coronary heart disease risk.

Authors:  Ruth Frikke-Schmidt; Anne Tybjærg-Hansen; Peter Schnohr; Gorm B Jensen; Børge G Nordestgaard
Journal:  Atherosclerosis       Date:  2010-07-27       Impact factor: 5.162

7.  Cardiovascular risk models.

Authors:  Luc Bonneux
Journal:  BMJ       Date:  2007-07-06

Review 8.  Socioeconomic status and cardiovascular disease: risks and implications for care.

Authors:  Alexander M Clark; Marie DesMeules; Wei Luo; Amanda S Duncan; Andy Wielgosz
Journal:  Nat Rev Cardiol       Date:  2009-09-22       Impact factor: 32.419

9.  C-reactive protein and parental history improve global cardiovascular risk prediction: the Reynolds Risk Score for men.

Authors:  Paul M Ridker; Nina P Paynter; Nader Rifai; J Michael Gaziano; Nancy R Cook
Journal:  Circulation       Date:  2008-11-09       Impact factor: 29.690

10.  Improving global vascular risk prediction with behavioral and anthropometric factors. The multiethnic NOMAS (Northern Manhattan Cohort Study).

Authors:  Ralph L Sacco; Minesh Khatri; Tatjana Rundek; Qiang Xu; Hannah Gardener; Bernadette Boden-Albala; Marco R Di Tullio; Shunichi Homma; Mitchell S V Elkind; Myunghee C Paik
Journal:  J Am Coll Cardiol       Date:  2009-12-08       Impact factor: 24.094

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.