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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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“QRISK2, however, was derived using a large South Asian population (n = 17,102) (Hippisley-Cox et al. 2008) and so would be expected to take account of differences in intrinsic CVD risk amongst South Asians.”
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“The 10-year CVD risk was assessed by the QRISK2 score, provided by the University of Nottingham, UK [24] at http://www.”
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“Similar to the PCRE, there are several other popular conventional cardiovascular risk calculators such as Framingham risk score, Reynold’s risk score, the systematic coronary risk evaluation calculator, and QRISK3 [169–174].”
MethodFan, L., Liu, J., Zhang, Y., Zhang, C., Shi, B., Hu, X., Chen, W., Yin, W., & Wang, J. (2022) · Theranostics · doi:10.7150/thno.73336 · Cited by 21
Guzmán, M. G., Medraño, J., Rallón, N. I., Echeverria-Urabayen, A., Benito, J. M., Restrepo, C., García–Álvarez, M., Vispo, E., Román, J. S., Sánchez‐Piedra, C., et al. (2011) · Journal of Infection · doi:10.1016/j.jinf.2011.08.006 · Cited by 22
Robson, J. P., Ayerbe, L., Mathur, R., Addo, J., & Wragg, A. (2015) · BMJ Open · doi:10.1136/bmjopen-2014-007251 · Cited by 22
“Additional clinical data used in the analyses included long-term CV risk, assessed with QRisk2 10-year CV risk scores.(17) The QRisk2 score for prediction of CV disease within 10 years was stratified into lower (0–9%), medium (10–19%) and high risk (≥20%).”
BackgroundMethodInfluentialSelf-citation'Prosiect Sir Gar': workplace-based cardiovascular disease and diabetes risk assessments
Gray, B. J., Bracken, R. M., Thomas, M., Williams, S. P., Williams, M., Rice, S., Stephens, J. W., & the Prosiect Sir Gar Group (2014) · Occupational Medicine · doi:10.1093/occmed/kqu103 · Cited by 22
“Newer CVD and diabetes risk prediction models such as QRISK2 [15] and QDiabetes [16] take account of the degree of socio-economic deprivation in their calculations because alongside clinically significant markers such as hypertension, hyperlipidaemia, impaired glucose tolerance and obesity socio-economic status indepe…”
BackgroundMethodMerry, A. H. H., Boer, J. M. A., Schouten, L. J., Ambergen, T., Steyerberg, E. W., Feskens, E. J. M., Verschuren, W. M. M., Gorgels, A. P. M., & van den Brandt, P. A. (2011) · European Journal of Preventive Cardiology · doi:10.1177/1741826711410256 · Cited by 22
Alageel, S., Wright, A. J., & Gulliford, M. C. (2016) · Preventive Medicine · doi:10.1016/j.ypmed.2016.08.025 · Cited by 25
“QRISK2 is a score that estimates 10-years CVD risk by looking into several variables that affects cardiovascular risk, including: age, sex, ethnicity, family history, smoking, blood pressure, total cholesterol/HDL cholesterol ratio, BMI and comorbidities (diabetes, atrial fibrillation, chronic kidney disease and rheum…”
BackgroundMethodUeda, P., Lung, T. W.-C., Clarke, P., & Danaei, G. (2017) · British Journal of General Practice · doi:10.3399/bjgp17x692141 · Cited by 22
Sweeney, T. E., Quispe, R., Das, T. M., Juraschek, S. P., Martin, S. S., & Michos, E. D. (2021) · Expert Review of Precision Medicine and Drug Development · doi:10.1080/23808993.2021.1930531 · Cited by 24
Cardiovascular risk assessment in children following kidney transplantation
Dégi, A. A., Kerti, A., Kis, É., Cseprekál, O., Tory, K., Szabo, A. J., & Reusz, G. (2012) · Pediatric Transplantation · doi:10.1111/j.1399-3046.2012.01730.x · Cited by 22
Is primary care research important and relevant to GPs?
Hobbs, R. F. (2019) · British Journal of General Practice · doi:10.3399/bjgp19x705149 · Cited by 22
Cardiovascular risk reduction following metabolic and bariatric surgery.
Albaugh, V. L., Kindel, T. L., Nissen, S. E., & Aminian, A. (2021) · Surgical Clinics of North America · doi:10.1016/j.suc.2020.12.012 · Cited by 21
Gold, N., Durlik, C., Sanders, J. G., Thompson, K., & Chadborn, T. (2019) · BMC Public Health · doi:10.1186/s12889-019-7754-5 · Cited by 21
Cardiovascular risk assessment in rheumatoid arthritis – controversies and the new approach
Bonek, K., & Głuszko, P. (2016) · Reumatologia/Rheumatology · doi:10.5114/reum.2016.61214 · Cited by 24
“Pharmacological intervention (application of statins) is recommended in patients with the threshold of 20% of the end event over the following 10 years [39].”
BackgroundInfluentialOverview of guidelines for the management of dyslipidemia: EU perspectives
Pallarés‐Carratalá, V., Esteban-Giner, M. J., & Galvañ, V. G. (2016) · Vascular Health and Risk Management · doi:10.2147/vhrm.s89038 · Cited by 21
Jebb, S. A., Astbury, N. M., Tearne, S., Nickless, A., & Aveyard, P. N. (2017) · BMJ Open · doi:10.1136/bmjopen-2017-016709 · Cited by 23
“Participants will also be asked to self-report items required to determine cardiovascular risk score using QRISK2.”
MethodInfluentialMehta, S., Jackson, R., Pylypchuk, R. D., Poppe, K., Wells, S., & Kerr, A. (2018) · International Journal of Epidemiology · doi:10.1093/ije/dyy137 · Cited by 21
Berry, A., Drake, R., Webb, R. T., Ashcroft, D. M., Carr, M., & Yung, A. R. (2018) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2018.00685 · Cited by 20
Yiannoullou, P., Summers, A., Goh, S. C., Fullwood, C., Khambalia, H. A., Moinuddin, Z. M., Shapey, I. M., Naish, J. H., Miller, C., Augustine, T., et al. (2019) · Diabetes Care · doi:10.2337/dc18-2111 · Cited by 23
“It is unlikely that this affected the performance of the QRISK2 calculator in our cohort, because QRISK2was developed using BMI as a continuous variable (10).”
BackgroundMethodResultInfluentialDanese, M. D., Pemberton-Ross, P. J., Catterick, D., & Villa, G. (2020) · European Journal of Preventive Cardiology · doi:10.1177/2047487319899212 · Cited by 21
Cubbon, R. M., Yuldasheva, N. Y., Viswambharan, H., Mercer, B. N., Baliga, V., Stephen, S. L., Askham, J. M., Sukumar, P., Skromna, A., Mughal, R. S., et al. (2014) · Stem Cells · doi:10.1002/stem.1766 · Cited by 21
Mossakowska, T. J., Saunders, C. L., Corbett, J., MacLure, C., Winpenny, E. M., Dujso, E., & Payne, R. (2017) · European Journal of Public Health · doi:10.1093/eurpub/ckx216 · Cited by 24
“guidelines and the National Institute for Health and Care Excellence (NICE) recommends the QRISK2 tool.(12) There is also regional variation within countries; some regions in Spain recommend a Framingham-based tool or the REGICOR (REgistre GIroní del COR, Girona Heart Registry) calculator, which is based on a calibrat…”
MethodBrennan, P., McNeil, A. J., Jing, M., Awuah, A., Moore, J. S., Mailey, J. A., Finlay, D., Blighe, K., McLaughlin, J. A., McNeil, A. J., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-87315-7 · Cited by 20
Jowett, S. M., Kodabuckus, S., Ford, G. A., Hobbs, R. F., Lown, M. T., Mant, J., Payne, R., McManus, R. J., Sheppard, J. P., & for the OPTiMISE investigators (2022) · Hypertension · doi:10.1161/hypertensionaha.121.18726 · Cited by 20
“19 In the absence of robust published estimates in this older population, an assumption of greater CVD risk was applied to those with CVD conditions by applying a multiplier of 1.5, based on expert clinical opinion.”
InfluentialMisra, S., Lyngdoh, T., & Mulchandani, R. (2022) · Indian Heart Journal · doi:10.1016/j.ihj.2022.07.009 · Cited by 20
Xia, Y., Liu, Z., Hu, B., Rangarajan, S., Tse, L. A., Li, Y., Wang, J., Hu, L., Wang, Y., Xiang, Q., et al. (2023) · Environment International · doi:10.1016/j.envint.2023.107829 · Cited by 20
2022 Saudi Guidelines for the Management of Dyslipidemia
Alrahimi, J. S., AlSaif, S. M., Alasnag, M. A., Awan, Z. A., Almutairi, F., Mudaiheem, H. Y. A., Gencer, B., Catapano, A. L., Mach, F., & Tash, A. A. (2023) · Heart Views · doi:10.4103/heartviews.heartviews_102_22 · Cited by 21
Prediction models for risk classification in cardiovascular disease
Petretta, M., & Cuocolo, A. (2012) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-012-2254-1 · Cited by 21
Loudon, B. L., Gollop, N. D., Carter, P. R., Uppal, H., CHANDRAN, S. R., & Potluri, R. (2016) · International Journal of Cardiology · doi:10.1016/j.ijcard.2016.06.188 · Cited by 20
Thompson, D., Mackay, T., Matthews, M., Edwards, J., Peters, N. S., & Connolly, S. B. (2017) · JMIR mhealth and uhealth · doi:10.2196/mhealth.6998 · Cited by 20
“[8].”
Importance of Endogenous Fibrinolysis in Platelet Thrombus Formation
Gue, Y. X., & Gorog, D. A. (2017) · International Journal of Molecular Sciences · doi:10.3390/ijms18091850 · Cited by 20
“Currently in the UK, the screening for cardiovascular disease is done thorough risk scores (such as QRisk2) [56] to allow primary care practitioners to start primary prevention treatment for patients to avoid future thrombotic events.”
BackgroundMethodDietary intake, nutritional status and mental wellbeing of homeless adults in Reading, UK
Fallaize, R., Seale, J. V., Mortin, C., Armstrong, L., & Lovegrove, J. A. (2017) · British Journal Of Nutrition · doi:10.1017/s0007114517002495 · Cited by 22
“CVD, for which diet is a key modi fi able factor, is reported as the leading cause of mortality in homeless adults between 45 and 65 years (15,16) .”
InfluentialWiernik, E., Meneton, P., Empana, J. P., Siemiatycki, J., Hoertel, N., Vulser, H., Nabi, H., Limosin, F., Czernichow, S., Goldberg, M., et al. (2018) · International Journal of Cardiology · doi:10.1016/j.ijcard.2018.02.033 · Cited by 20
Saeed, A. A. (2012) · Balkan Medical Journal · doi:10.5152/balkanmedj.2012.035 · Cited by 20
Malati, T., & Mahesh, M. R. U. (2009) · Indian Journal of Clinical Biochemistry · doi:10.1007/s12291-009-0063-5 · Cited by 20
Dyslipidemia in Special Ethnic Populations
Pu, J., Romanelli, R. J., Zhao, B., Azar, K. M. J., Hastings, K. G., Nimbal, V. C., Fortmann, S. P., & Palaniappan, L. P. (2016) · Endocrinology and Metabolism Clinics of North America · doi:10.1016/j.ecl.2015.09.013 · Cited by 19
Machine Learning Models for Multidimensional Clinical Data
Orphanidou, C., & Wong, D. (2017) · Scalable computing and communications · doi:10.1007/978-3-319-58280-1_8 · Cited by 20
Wilson, S. R., Lin, F. Y.-H., & Min, J. K. (2011) · Current Cardiology Reports · doi:10.1007/s11886-011-0191-4 · Cited by 19
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
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