Cited by 1,459Health Promotion and Cardiovascular PreventionCardiovascular Health and Risk FactorsLipoproteins and Cardiovascular Health

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

Open access

Hippisley-Cox, Julia, Coupland, Carol, Vinogradova, Yana, Robson, John, Minhas, Rubin, Sheikh, Aziz, Brindle, Peter·2008·BMJ

DOI 10.1136/bmj.39609.449676.25PMC PMC2440904

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Abstract

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, >or=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 >or=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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“One further explanation for the limited benefit in terms of QRISK2 score is the effect of smoking, which makes a major contribution to overall CVD risk.(195) In this trial we observed a non-significant trend towards a greater chance of being a smoker in the intervention group than in the usual care group after 12 mont…”
Background
The role of dietary and socioeconomic status assessment on the predictive ability of the HellenicSCORE.
Vlismas, K., Panagiotakos, D. B., Pitsavos, C. S., Chrysohoou, C. A., Skoumas, Y., Stavrinos, V., & Stefanadis, C. (2011) · PubMed · Cited by 9
Is the Population Detected by Screening in China Truly at High Risk of Stroke?
Wang, Y., Wang, J., Cheng, J., Liang, X., Li, X., & Lu, W. (2018) · Journal of Stroke and Cerebrovascular Diseases · doi:10.1016/j.jstrokecerebrovasdis.2018.03.009 · Cited by 9
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Cardoso, R. N., Blumenthal, R. S., Kopecky, S. L., López-Jiménez, F. A., & Martin, S. S. (2019) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2018.08.011 · Cited by 10
Building Global Research Capacity in Public Health: The Case of a Science Gateway for Physical Activity Lifelong Modelling and Simulation
Anagnostou, A., Taylor, S. J. E., Groen, D., Suleimenova, D., Anokye, N., Bruno, R., & Barbera, R. (2019) · Winter Simulation Conference · doi:10.1109/wsc40007.2019.9004845 · Cited by 9
“2008) and Type 2 Diabetes (QDiabetes) (Hippisley-Cox et al. 2017) risks. Incidence data from literature is used to estimate the probabilities of depression and MSI (Rait et al. 2009; Wijlaars et al. 2012). The above, however, do not factor in the physical activity level of an individual and its effect on health condit…”
Method
Family History for Cardio-Metabolic Diseases: A Predictor of Major Adverse Cardiovascular Events in Men with Erectile Dysfunction
Rastrelli, G., Yannas, D., Mucci, B., Corona, G., & Maggi, M. (2020) · The Journal of Sexual Medicine · doi:10.1016/j.jsxm.2020.08.012 · Cited by 9
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Chen, Y., Chen, P., Chang, C., Lin, C., Chen, H., Lan, J., Chang, S., & Chen, D. (2020) · Life · doi:10.3390/life10120330 · Cited by 9
“The global 10-year risk for a heart attack or stroke was estimated by calculating the QRISK-2 scores [29,30].”
Method
A Systematic Review: Do the Use of Machine Learning, Deep Learning, and Artificial Intelligence Improve Patient Outcomes in Acute Myocardial Ischemia Compared to Clinician-Only Approaches?
Panjiyar, B. K., Davydov, G., Nashat, H., Ghali, S., Afifi, S., Suryadevara, V., Habab, Y., Hutcheson, A., & Franchini, A. P. A. (2023) · Cureus · doi:10.7759/cureus.43003 · Cited by 10
Randomised controlled trial of additional lifestyle support for the reduction of cardiovascular disease risk through primary care in Stoke-on-Trent, UK
Davey, R., Cochrane, T., Iqbal, Z., Rajaratnam, G., Chambers, R. M., Mawby, Y., Picariello, L., Leese, C., & Ryder, N. (2010) · Contemporary Clinical Trials · doi:10.1016/j.cct.2010.04.002 · Cited by 9
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Vikhireva, O., Broda, G., Kubínová, R., Malyutina, S. K., Pająk, A., Tamošiūnas, A., Škodová, Z., Симонова, Г. И., Bobák, M., & Pikhart, H. (2014) · PLoS ONE · doi:10.1371/journal.pone.0094344 · Cited by 9
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Do traditional risk stratification models for cerebrovascular events apply in irradiated head and neck cancer patients?
Gujral, D. M., Shah, B. N., Chahal, N., Bhattacharyya, S., Senior, R., Harrington, K. J., & Nutting, C. (2015) · QJM · doi:10.1093/qjmed/hcv120 · Cited by 9
Scores de risco cardiovascular: utilidade e limitações
Rocha, E. (2016) · Revista Portuguesa de Cardiologia · doi:10.1016/j.repc.2015.11.002 · Cited by 11
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Clark, C. E., Boddy, K., Warren, F. C., Taylor, R. S., Aboyans, V., Cloutier, L., McManus, R. J., Shore, A. C., & Campbell, J. L. (2017) · BMJ Open · doi:10.1136/bmjopen-2017-016844 · Cited by 9
A Prediction Model with Lifestyle in Addition to Previously Known Risk Factors Improves Its Predictive Ability for Cardiovascular Death
Nishimoto, M., Tagawa, M., Matsui, M., Eriguchi, M., Samejima, K., Iseki, K., Iseki, C., Asahi, K., Yamagata, K., Konta, T., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-49003-5 · Cited by 9
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Shalnova, S. A., Drapkina, O. M., Васильевна, К. А., Yarovaya, E. B., Kutsenko, V. A., Metelskaya, V. A., Владимировна, К. А., Balanova, Y. А., Litinskaya, O. A., & Pokrovskaya, M. S. (2021) · CARDIOVASCULAR THERAPY AND PREVENTION · doi:10.15829/1728-8800-2021-2980 · Cited by 9
Using Administrative Databases to Calculate Framingham Scores Within a Large Health Care Organization
Ekundayo, O. J., Vassar, S. D., Williams, L. S., Bravata, D. M., & Cheng, E. M. (2011) · Stroke · doi:10.1161/strokeaha.110.603340 · Cited by 9
Vascular risk screening: possible or too much, too soon?
Wierzbicki, A. S., & Reynolds, T. (2009) · International Journal of Clinical Practice · doi:10.1111/j.1742-1241.2009.02111.x · Cited by 9
Academic primary care: at a tipping point?
Hobbs, R. F., & Taylor, C. J. (2014) · British Journal of General Practice · doi:10.3399/bjgp14x679543 · Cited by 9
Symptoms and risk factors to identify people with suspected cancer in primary care
Hippisley‐Cox, J., & Coupland, C. A. C. (2013) · British Journal of General Practice · doi:10.3399/bjgp13x664135 · Cited by 9
Self-citation
Establishing the independence and clinical importance of non-alcoholic fatty liver disease as a risk factor for cardiovascular disease
Chimakurthi, C. R., & Rowe, I. A. C. (2016) · Journal of Hepatology · doi:10.1016/j.jhep.2016.06.034 · Cited by 9
Prediction of the onset of cardiovascular diseases from electronic health records using multi-task gated recurrent unitsPreprint
Andreotti, F., Heldt, F. S., Abu‐Jamous, B., Li, M., Javer, A., Carr, O., Jovanović, S., Lipunova, N., Irving, B., Khan, R. T., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.08491 · Cited by 9
“r the task of predicting CVD events from a sequence of n days containing hospital observations, the following approaches are evaluated. 2.2.1. BASELINE CLINICAL APPROACH The QRISK2 (QRISK) algorithm (Hippisley-Cox et al., 2008) is the UK primary care standard clinical assessment tool, which calculates 10-year risk of…”
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Blanchflower, D. G., & Bryson, A. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3909603 · Cited by 9
Communicating cardiovascular risk: Systematic review of qualitative evidence
Lorenc, T., Stokes, G., Fulbright, H., Sutcliffe, K., & Sowden, A. J. (2024) · Patient Education and Counseling · doi:10.1016/j.pec.2024.108231 · Cited by 8
Patients who discontinued statin treatment: a protocol for cohort study using primary care data
Vinogradova, Y., Coupland, C. A. C., Brindle, P. M., & Hippisley‐Cox, J. (2015) · BMJ Open · doi:10.1136/bmjopen-2015-008701 · Cited by 8
Self-citation
Blood Pressure Variability in Pregnancy: an Opportunity to Develop Improved Prognostic and Risk Assessment Tools
Vermunt, J. V., Kennedy, S., & Garovic, V. D. (2020) · Current Hypertension Reports · doi:10.1007/s11906-019-1014-z · Cited by 8
Factors predicting statin prescribing for primary prevention: a historical cohort study
Finnikin, S., Willis, B. H., Ryan, R. P., Evans, T., & Marshall, T. P. (2020) · British Journal of General Practice · doi:10.3399/bjgp20x714065 · Cited by 8
Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration. Translation in to Russian
Moons, K. G. M., Altman, D. G., Reitsma, J. B., loannidis, J. P. A., Macaskill, P., Steyerberg, E. W., Vickers, A. J., Ransohoff, D. F., & Collins, P. G. S. (2022) · Digital Diagnostics · doi:10.17816/dd110794 · Cited by 10
Self‐Report Tool for Identification of Individuals With Coronary Atherosclerosis: The Swedish CardioPulmonary BioImage Study
Bergström, G. M. L., Hagberg, E., Björnson, E., Adiels, M., Bonander, C., Strömberg, U., Andersson, J. S. O., Brunström, M., Carlhäll, C., Engström, G., et al. (2024) · Journal of the American Heart Association · doi:10.1161/jaha.124.034603 · Cited by 7
“Family history of myocardial infarction is included in some, 30,31 but not all, 1 of the previous clinical risk algorithms.”
Measuring the clinical dimensions of negative symptoms through the Positive and Negative Syndrome Scale
Wolpe, N., Perrottelli, A., Giuliani, L., Yang, Z., Rekhi, G., Jones, P. B., Bernardo, M., García‐Portilla, M. P., Kaiser, S., Robert, G., et al. (2025) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2024.12.016 · Cited by 8
Development of a high-performing, cost-effective and inclusive Afrocentric predictive model for stroke: a meta-analysis approach
Nweke, M., Oyirinnaya, P., Nwoha, P. U., Mitha, S. B., Mshunqane, N., Govender, N., Ukwuoma, M., & Ibeneme, S. C. (2025) · BMC Neurology · doi:10.1186/s12883-025-04229-x · Cited by 9
“T h e i m p l i c a t i o n o f t h e c r i t i c i s m v e r s u s o u r fi n d i n g s Q R I S K [ 65 ] A g e , s e x , s m o k i n g , s y s t o l i c b l o o d p r e ss u r e , t h e r a t i o o f t o t a l s e r u m c h o l e s t e r o l t o H D L , B M I , f a m i l y h i s t o r y o f c o r…”
Influential
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Dhaliwal, S. S., Goh, L. G. H., Welborn, T. A., Thompson, P. L., Maycock, B. R., Kerr, D. A., Lee, A. H., Bertolatti, D., Clark, K., Naheed, R., et al. (2014) · International Journal of Women s Health · doi:10.2147/ijwh.s55225 · Cited by 8
Using the Payoff Time in Decision-Analytic Models: A Case Study for Using Statins in Primary Prevention
Thompson, A., Guthrie, B., & Payne, K. (2017) · Medical Decision Making · doi:10.1177/0272989x17700846 · Cited by 8
Ten-year risk prediction models of complications and mortality of Chinese patients with diabetes mellitus in primary care in Hong Kong: a study protocol
Wan, E. Y. F., Yu, E. Y. T., Chin, W. Y., Fung, C. S. C., Kwok, R. L. P., Chao, D. V. K., Chan, K. H., Hui, E. M., Tsui, W. W. S., Tan, K. C. B., et al. (2018) · BMJ Open · doi:10.1136/bmjopen-2018-023070 · Cited by 8
Mortality prediction enhancement in end-stage renal disease: A machine learning approach
Macias, E., Morell, A., Serrano, J., Vicario, J. L., & Ibeas, J. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100351 · Cited by 8
Biomarkers in essential hypertension
Pattanaik, S. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-815923-1.00008-0 · Cited by 10
A pilot study of a nurse-led integrated care review (the INCLUDE review) for people with inflammatory rheumatological conditions in primary care: feasibility study findings
Hider, S., Bucknall, M., Jinks, C., Cooke, K., Cooke, K., Desilva, E. E., Finney, A. G., Healey, E. L., Herron, D., Machin, A. R., et al. (2021) · Pilot and Feasibility Studies · doi:10.1186/s40814-020-00750-7 · Cited by 7
Endorsement of the TRIPOD statement and the reporting of studies developing contrast-induced nephropathy prediction models for the coronary angiography/percutaneous coronary intervention population: a cross-sectional study
Miao, S., Chen, P., Li, D., Shen, S., & Wen, A. (2022) · BMJ Open · doi:10.1136/bmjopen-2021-052568 · Cited by 8
A cross-sectional study evaluating cardiovascular risk and statin prescribing in the Canadian Primary Care Sentinel Surveillance Network database
Johnston, I. S., Miles, B., Soós, B., Garies, S., Perez, G., Queenan, J. A., Drummond, N., & Singer, A. G. (2022) · BMC Family Practice · doi:10.1186/s12875-022-01735-6 · Cited by 8
“The importance of SES to CVD risk, including pharmacotherapy management, has been described in other countries such as the United Kingdom, [7, 8] but is not well studied in a Canadian primary care context.”
Background
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Authors: Hippisley-Cox, J., Coupland, C., Vinogradova, Y., Robson, J., Minhas, R., Sheikh, A., & Brindle, P.
Affiliations: University of Nottingham (GB)
Queen Mary University of London (GB)
Medway NHS Foundation Trust (GB)
University of Edinburgh (GB)
ORCID: Coupland, Carol
Vinogradova, Yana
Robson, John
Sheikh, Aziz
Brindle, Peter
Venue: BMJ
Publisher: BMJ
Published: 2008-06-23
Volume: 336
Issue: 7659
Pages: 1475-1482
ISSN: 0007-1447 (ISSN-L)
0959-8138
1468-5833
DOI: 10.1136/bmj.39609.449676.25
PMC: PMC2440904
OpenAlex: W2055838663
Cited by: 1,459
References: 55
Citations by year: 2026: 25 · 2025: 49 · 2024: 58 · 2023: 86 · 2022: 100 · 2021: 108 · 2020: 108 · 2019: 106 · 2018: 94 · 2017: 102 · 2016: 101 · 2015: 95 · 2014: 90 · 2013: 64 · 2012: 80
FWCI: 62.43
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.bmj.org/licenses/tdm/1.0/terms-and-conditions.html (tdm)
Locations: BMJ (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
HighWire Press Open Archive (submittedVersion)
Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
Funders: University of Nottingham
Topics: Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health Promotion and Cardiovascular Prevention
Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Risk Factors
Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Lipoproteins and Cardiovascular Health
Keywords: QRISK2, cardiovascular risk prediction, risk reclassification, coronary artery disease, stroke, transient ischemic attack, ethnic groups, South Asian patients, risk discrimination and calibration, cardiovascular disease prevention, NICE guidelines, QResearch database, England and Wales, general practice records, family history of coronary heart disease
MeSH terms: Adult, Aged, Algorithms, Cardiovascular Diseases, England, Female, Humans, Male, Middle Aged, Prognosis, Wales, Epidemiologic Studies, Age Distribution, Sex Distribution

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

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