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

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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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Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study
Yusuf, S., Hawken, S., Ôunpuu, S., Dans, T., Avezum, A., Lanas, F., McQueen, M., Budaj, A., Pais, P., Varigos, J., et al. (2004) · The Lancet · doi:10.1016/s0140-6736(04)17018-9 · Cited by 12,026
Missing data: our view of the state of the art
Schafer, J. L., & Graham, J. W. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.2.147 · Cited by 11,638
General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study
D’Agostino, R. B., Vasan, R. S., Pencina, M. J., Wolf, P. A., Cobain, M., Massaro, J. M., & Kannel, W. B. (2008) · Circulation · doi:10.1161/circulationaha.107.699579 · Cited by 7,491
Impact of Atrial Fibrillation on the Risk of Death: The Framingham Heart Study
Benjamin, E. J., Wolf, P. A., D’Agostino, R. B., Silbershatz, H., Kannel, W. B., & Levy, D. (1998) · Circulation · doi:10.1161/01.cir.98.10.946 · Cited by 4,575
Kidney Disease as a Risk Factor for Development of Cardiovascular Disease: A Statement From the American Heart Association Councils on Kidney in Cardiovascular Disease, High Blood Pressure Research, Clinical Cardiology, and Epidemiology and Prevention
Sarnak, M. J., Levey, A. S., Schoolwerth, A. C., Coresh, J., Culleton, B., Hamm, L. L., McCullough, P. A., Kasiske, B. L., Kelepouris, E., Klag, M. J., et al. (2003) · Circulation · doi:10.1161/01.cir.0000095676.90936.80 · Cited by 3,768
Multiple Imputation of Missing Values
Royston, P. (2004) · The Stata Journal: Promoting communications on statistics and Stata · doi:10.1177/1536867x0400400301 · Cited by 2,340 · cited 1×
“We used fractional polynomials to model non-linear risk relations with continuous variables where appropriate.”
Cardiovascular disease risk profiles
Anderson, K. M., Odell, P. M., Wilson, P. W. F., & Kannel, W. B. (1991) · American Heart Journal · doi:10.1016/0002-8703(91)90861-b · Cited by 2,163
Temporal Relations of Atrial Fibrillation and Congestive Heart Failure and Their Joint Influence on Mortality: The Framingham Heart Study
Wang, T. J., Larson, M. G., Levy, D., Vasan, R. S., Leip, E. P., Wolf, P. A., D’Agostino, R. B., Murabito, J. M., Kannel, W. B., & Benjamin, E. J. (2003) · Circulation · doi:10.1161/01.cir.0000072767.89944.6e · Cited by 1,970
Probability of stroke: a risk profile from the Framingham Study.
Wolf, P. A., D'Agostino, R. B., Belanger, A. J., & Kannel, W. B. (1991) · Stroke · doi:10.1161/01.str.22.3.312 · Cited by 1,782
Relation of central obesity and insulin resistance with high diabetes prevalence and cardiovascular risk in South Asians
McKeigue, P. M., Shah, B., & Marmot, M. G. (1991) · The Lancet · doi:10.1016/0140-6736(91)91164-p · Cited by 1,618

Show all 55 references

Research works printed but not linked: 3

Gail M, Pfeiffer R. On evaluating models of absolute risk. Biostatistics 2005;6:227-39.
Erika Graf CSWSMS. Assessment and comparison of prognostic classification schemes for survival data. Stat Med 1999;18:2529-45.
Maradit-Kremers H, Crowson C, Nicola P, Ballman K, Roger V, Jacobsend S, et al. Increased unrecognized coronary heart disease and sudden deaths in rheumatoid arthritis: a population-based cohort study. Arthritis Rheum 2005;52:402-11.

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Aspirin for prophylactic use in the primary prevention of cardiovascular disease and cancer: a systematic review and overview of reviews
Sutcliffe, P. A., Connock, M. J., Gurung, T., Freeman, K., Johnson, S. A., Kandala, N., Grove, A., Gurung, B., Morrow, S., & Clarke, A. (2013) · Health Technology Assessment · doi:10.3310/hta17430 · Cited by 99
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Cardiovascular risk prediction in type 2 diabetes: a comparison of 22 risk scores in primary care settings
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“[59] Additionally, QRISK2,[60] the only cardiovascular screening tool currently recommended for use in the UK by the National Institute for Health and Clinical Excellence, [61] whether for individual patients or to prioritise patients for programmes such as NHS PLOS ONE | https://doi.org/10.1371/journal.pone.0178945 J…”
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Rheumatoid Arthritis: Atherosclerosis Imaging and Cardiovascular Risk Assessment Using Machine and Deep Learning–Based Tissue Characterization
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Self-citation
The New Pooled Cohort Equations Risk Calculator
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Putting polypills into practice: challenges and lessons learned
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Personalized medicine—a modern approach for the diagnosis and management of hypertension
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Identifying patients with suspected lung cancer in primary care: derivation and validation of an algorithm
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Self-citation
Development of scoring system for risk stratification in clinical medicine: a step-by-step tutorial
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Validation of an Integrated Risk Tool, Including Polygenic Risk Score, for Atherosclerotic Cardiovascular Disease in Multiple Ethnicities and Ancestries
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“The number of people with a follow-up exceeding 10 years was also larger than that in the studies for ASSIGN, Framingham and QRISK1 [5,6,8].”
Influential
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Background
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Self-citation
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“However, a few studies have previously recommended modifications to the existing FRS to improve its accuracy [31].”
Background
Derivation and Validation of a New Cardiovascular Risk Score for People With Type 2 Diabetes
Elley, C. R., Robinson, E., Kenealy, T. W., Bramley, D. M., & Drury, P. (2010) · Diabetes Care · doi:10.2337/dc09-1444 · Cited by 88
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“org/publicdomain/ze risk of in-hospital mortality following heart valve surgery and to predict the risk of developing cardiovascular disease within the next 10 years [3, 4].”
Background
The value of aspartate aminotransferase and alanine aminotransferase in cardiovascular disease risk assessment
Weng, S. F., Kai, J., Guha, I. N., & Qureshi, N. (2015) · Open Heart · doi:10.1136/openhrt-2015-000272 · Cited by 84
“This approach follows the same imputation procedures utilised in the original QRISK2 algorithm.(3) A descriptive analysis was performed, reporting number (%), mean (SD) and median (IQR) for categorical, normal continuous and non-normal continuous variables, respectively.”
Method
Association of 10‐Year and Lifetime Predicted Cardiovascular Disease Risk With Subclinical Atherosclerosis in South Asians: Findings From the Mediators of Atherosclerosis in South Asians Living in America (MASALA) Study
Kandula, N. R., Kanaya, A. M., Liu, K., Lee, J. Y., Herrington, D. M., Hulley, S. B., Persell, S. D., Lloyd‐Jones, D. M., & Huffman, M. D. (2014) · Journal of the American Heart Association · doi:10.1161/jaha.114.001117 · Cited by 85
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Benefits and Harms of Antihypertensive Treatment in Low-Risk Patients With Mild Hypertension
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“8%) had available risk factor information to calculate a QRISK2 score.(2) It was possible to estimate a QRISK2 score for the remaining”
Influential
Barriers to Routine Risk-Score Use for Healthy Primary Care Patients
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“Although patients were not directly involved with the design of the specific research questions in this study, the hypotheses tested were developed in the context of clinical experience that depression and hypertension may interact to impact on CVD. UK Biobank has an active and ongoing programme of participant involve…”
Influential
Stroke risk prediction using machine learning: a prospective cohort study of 0.5 million Chinese adults
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“841] [8-35] 1722 Journal of the American Medical Informatics Association, 2021, Vol.”
Background
Data mining for censored time-to-event data: a Bayesian network model for predicting cardiovascular risk from electronic health record data
Bandyopadhyay, S., Wolfson, J. A., Vock, D. M., Vazquez‐Benitez, G., Adomavičius, G., Elidrisi, M., Johnson, P. E., & O’Connor, P. J. (2014) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0386-6 · Cited by 89
“Recent systematic reviews found that there are over 100 risk models produced between 1999 and 2009 [Cooney et al., 2009, 2010, Matheny et al., 2011] including the well-known Framingham [D’Agostino et al., 2008], SCORE [Conroy et al., 2003], ASSIGN-SCORE [Woodward et al., 2007], QRISK1 [Hippisley-Cox et al., 2007, 2008…”
Universal screening at age 1–2 years as an adjunct to cascade testing for familial hypercholesterolaemia in the UK: A cost-utility analysis
McKay, A. J., Hogan, H., Humphries, S. E., Marks, D., Ray, K. K., & Miners, A. H. (2018) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2018.05.047 · Cited by 84
The Maudsley environmental risk score for psychosis
Vassos, E., Sham, P. C., Kempton, M. J., Trotta, A., Stilo, S. A., Gayer-Anderson, C. E., Forti, M. D., Lewis, C. M., Murray, R. M., & Morgan, C. (2019) · Psychological Medicine · doi:10.1017/s0033291719002319 · Cited by 88
Consensus statement on management of dyslipidemia in Indian subjects
Chandra, K. S., Bansal, M., Nair, T., Iyengar, S. S., Gupta, R., Manchanda, S. C., Mohanan, P. P., Rao, V. D., Manjunath, C. N., Sawhney, J. P. S., et al. (2014) · Indian Heart Journal · doi:10.1016/j.ihj.2014.12.001 · Cited by 86
Lipids, lipid modifying agents and cardiovascular risk: a review of the evidence
Preiss, D., & Sattar, N. A. (2008) · Clinical Endocrinology · doi:10.1111/j.1365-2265.2008.03490.x · Cited by 80
Socioeconomic deprivation: an important, largely unrecognized risk factor in primary prevention of cardiovascular disease
Kimenai, D. M., Pirondini, L., Gregson, J., Prieto, D., Pocock, S. J., Perel, P., Hamilton, T., Welsh, P., Campbell, A., Porteous, D. J., et al. (2022) · Circulation · doi:10.1161/circulationaha.122.060042 · Cited by 76
“30 In addition, incorporation of socioeconomic status to the Framingham Risk Score was evaluated in the Atherosclerosis Risk in Communities study.”
Method
Reducing health inequalities through general practice
Gkiouleka, A., Wong, G., Sowden, S. L., Bambra, C. L., Siersbaek, R., Manji, S., Moseley, A., Harmston, R. R., Kuhn, I. L., & Ford, J. A. (2023) · The Lancet Public Health · doi:10.1016/s2468-2667(23)00093-2 · Cited by 89
Prediction and diagnosis of depression using machine learning with electronic health records data: a systematic review
Nickson, D., Meyer, C., Walasek, L., & Toro, C. T. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02341-x · Cited by 99
“…led to cardiovascular risk prediction becoming established in routine clinical care and the UK QRISK versions 2 and 3 show significant improvements in discrimination performance over the Framingham Risk Score and atherosclerotic cardiovascular disease (ASCVD) score methods [34] that preceded them.”
Method
Polygenic risk score adds to a clinical risk score in the prediction of cardiovascular disease in a clinical setting
Samani, N. J., Beeston, E., Greengrass, C., Riveros-Mckay, F., Dębiec, R., Lawday, D., Wang, Q., Budgeon, C. A., Braund, P. S., Bramley, R., et al. (2024) · European Heart Journal · doi:10.1093/eurheartj/ehae342 · Cited by 86
Cardiovascular risk in rheumatoid arthritis: assessment, management and next steps
Zegkos, T., Kitas, G. D., & Dimitroulas, T. (2016) · Therapeutic Advances in Musculoskeletal Disease · doi:10.1177/1759720x16643340 · Cited by 81
South Asians and coronary disease: is there discordance between effects on incidence and prognosis?
Zaman, M., Philipson, P., Chen, R., Farag, A., Shipley, M. J., Marmot, M. G., Timmis, A., & Hemingway, H. (2013) · Heart · doi:10.1136/heartjnl-2012-302925 · Cited by 81
Handling missing predictor values when validating and applying a prediction model to new patients
Hoogland, J., van Barreveld, M., Debray, T. P. A., Reitsma, J. B., Verstraelen, T. E., Dijkgraaf, M. G. W., & Zwinderman, A. H. (2020) · Statistics in Medicine · doi:10.1002/sim.8682 · Cited by 90
Socioeconomic Status and Coronary Heart Disease Risk Prediction
Fiscella, K. A. (2008) · JAMA · doi:10.1001/jama.2008.792 · Cited by 78
Cardiovascular risk age: concepts and practicalities
Cooney, M. T., Vartiainen, E. A., Laatikainen, T. K., Bacquer, D. D., McGorrian, C., Dudina, A. L., Graham, I. M., & on behalf of the SCORE and FINRISK investigators (2012) · Heart · doi:10.1136/heartjnl-2011-301478 · Cited by 79

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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