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Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2
Open accessAbstract
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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“Many risk assessment systems are available and have been comprehensively reviewed, including different Framingham models, 41 Systemic Coronary Risk Estimation (SCORE), 42 ASSIGN (CV risk estimation model from the Scottish Intercollegiate Guidelines Network), 43 Q-Risk, 44 Prospective Cardiovascular Munster Study (PROCAM), 45 Reynolds, 46,47 CUORE, 48 the Pooled Cohort equations 49 and Globorisk. 5”
From 2016 ESC/EAS Guidelines for the Management of Dyslipidaemias (2016) · cites this paper as [44]
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Cardiovascular Disease Risk Assessment Tools in HIV-Infected Patients - Are They Adequate?
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“ISSN: 2155-6113 JAR an open access journal transient ischemic attack [15].”
BackgroundPredicting cardiovascular disease
Holt, T. (2016) · BMJ · doi:10.1136/bmj.i2621 · Cited by 6
Interpretation and Use of Another Statin Guideline
Greenland, P., & Bonow, R. O. (2016) · JAMA Cardiology · doi:10.1001/jamacardio.2016.4950 · Cited by 6
Personalized Medicine for Cardiovascular Disease Risk in Artificial Intelligence FrameworkPreprint
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“Risk assessment and clinical management of people with CVD has long included weight-management (Hippisley-Cox et al., 2008)”
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“Hippisley-Cox J, Coupland C, Vinogradova Y, et al. Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2.”
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Comparative performance of cardiovascular risk prediction models in people living with HIV
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AI in Healthcare: Implications for Family Medicine and Primary Care
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“Currently, several CVD risk prediction models, such as the Framingham risk score (FRS) [32], Systematic Coronary Risk Estimation risk charts [33], and QRISK2 scoring system [34], are available.”
Liu, X., Luo, Z., Jing, F., Ren, H., Li, C., Wang, L., & Chen, T. (2024) · Journal of Psychosomatic Research · doi:10.1016/j.jpsychores.2024.112030 · Cited by 6
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Validation of QRISK2 (2014) in patients with diabetes
Hippisley‐Cox, J., Coupland, C. A. C., & Brindle, P. M. (2014) · Nottingham ePrints (University of Nottingham) · Cited by 5
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Strategies for treating lipids for prevention: Risk stratification models with and without imaging
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“Model based scoring systems have been developed to calculate the risk of coronary events [8, 9, 10].”
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Impact of Obesity on Cardiovascular Disease
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Cardiac-gated Slit Lamp Videography as a Novel Approach to Assessing a Microcirculatory Network
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“All patients will have a “Q-RISK” score calculated, a well validated risk assessment tool used to predict the risk of having a myocardial infarction or cerebrovascular accident over the next ten years [14].”
Method
Metadata
- 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)
- Venue
- BMJ
- Publisher
- BMJ
- Published
- 2008-06-23
- Volume
- 336
- Issue
- 7659
- Pages
- 1475-1482
- ISSN
- 0007-1447 (ISSN-L)
- 0959-8138
- 1468-5833
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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