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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Gourdy, P., Schiele, F., Halimi, J., Kownator, S., Hadjadj, S., & Valensi, P. E. (2023) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2023.1227769 · Cited by 34
“Of note, the ASCVD risk calculated by QRISK2 is likely to be underestimated in individuals from deprived areas and overestimated for those from affluent areas (22).”
Background
Clinical management of older persons with haemophilia
Hermans, C. R. J. R., de Moerloose, P., & Dolan, G. (2013) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2013.07.005 · Cited by 31
Dislipidemias: un reto pendiente en prevención cardiovascular. Documento de consenso CEIPC/SEA
Royo‐Bordonada, M. Á., Bejarano, J. M. L., Núñez-Cortés, J. M., Álvarez, F. V., Cuixart, C. B., Camafort, M., Guijarro, C., Zarzosa, C. D. P., Pedro‐Botet, J., & Nocito, A. D. S. (2011) · Medicina Clínica · doi:10.1016/j.medcli.2011.02.008 · Cited by 30
Renal Association Clinical Practice Guideline on Detection, Monitoring and Management of Patients with CKD
MACGREGOR, M. S., & Taal, M. W. (2011) · Nephron Clinical Practice · doi:10.1159/000328062 · Cited by 31
Promoting smoking cessation in Pakistani and Bangladeshi men in the UK: pilot cluster randomised controlled trial of trained community outreach workers
Begh, R. A., Aveyard, P. N., Upton, P., Bhopal, R. S., White, M. J., Amos, A., Prescott, R. J., Bedi, R., Barton, P. M., Fletcher, M. J., et al. (2011) · Trials · doi:10.1186/1745-6215-12-197 · Cited by 33
Self-citation
Are Genetic Tests for Atherosclerosis Ready for Routine Clinical Use?
Paynter, N. P., Ridker, P. M., & Chasman, D. I. (2016) · Circulation Research · doi:10.1161/circresaha.115.306360 · Cited by 31
“blood pressure, and a family history of angina or heart attack in a first-degree relative under 60.(28) These models target a range of related CVD outcomes, and all have strong baseline”
Background
Deep learning–based prediction model of occurrences of major adverse cardiac events during 1-year follow-up after hospital discharge in patients with AMI using knowledge mining
Kim, Y. J., Saqlain, M., & Lee, J. Y. (2019) · Personal and Ubiquitous Computing · doi:10.1007/s00779-019-01248-7 · Cited by 32
“Currently, Framingham risk score (FRS) [3 – 5], QRISK [6, 7], TIMI [8, 9], and GRACE [10 – 12] models are available in regression-based cardiovascular risk prediction models.”
Method
Predicting the risk of stroke among patients with type 2 diabetes: a systematic review and meta-analysis of C-statistics
Chowdhury, M., Yeasmin, F., Rabi, D. M., Ronksley, P. E., & Turin, T. C. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-025579 · Cited by 31
Cardiovascular risk scores: qualitative study of how primary care practitioners understand and use them
Liew, S. M., Blacklock, C., Hislop, J., Glasziou, P., & Mant, D. (2013) · British Journal of General Practice · doi:10.3399/bjgp13x668195 · Cited by 31
Global Cardiovascular Risk Assessment: Strengths and Limitations
Redón, J. (2016) · High Blood Pressure & Cardiovascular Prevention · doi:10.1007/s40292-016-0139-2 · Cited by 33
Risk classification in primary prevention of CVD according to QRISK2 and JBS3 ‘heart age’, and prevalence of elevated high-sensitivity C reactive protein in the UK cohort of the EURIKA study
Johns, I., Moschonas, K., Medina, J., Ossei‐Gerning, N., Kassianos, G. C., & Halcox, J. (2018) · Open Heart · doi:10.1136/openhrt-2018-000849 · Cited by 34
“The most frequently used risk algorithm is the QRISK2 risk assessor, which is calibrated using UK patient data and assesses an extensive array of risk factors, incorporating body mass index (BMI), ethnicity, an area of residence–based deprivation index, type 2 diabetes mellitus, antihypertensive treatment status, fami…”
Method
Cardiovascular Disease Risk in a Semirural Community in Malaysia
Chia, Y. C., & Pengal, S. (2009) · Asia Pacific Journal of Public Health · doi:10.1177/1010539509343973 · Cited by 32
Monitoring and Managing Cardiovascular Risk in Immune Mediated Inflammatory Diseases
Anyfanti, P., Dara, A., Angeloudi, E., Bekiari, E., Dimitroulas, T., & Kitas, G. D. (2021) · Journal of Inflammation Research · doi:10.2147/jir.s276986 · Cited by 33
Cardiometabolic risk estimation using exposome data and machine learning
Atehortúa, A., Gkontra, P., Camacho, M., Díaz, O., Bulgheroni, M., Simonetti, V., Chadeau‐Hyam, M., Felix, J. F., Sebert, S. P., & Lekadir, K. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105209 · Cited by 32
Implementing Cardiovascular Risk Reduction in Patients with Cardiovascular Disease and Diabetes Mellitus
Nandish, S., Wyatt, J., Bailon, O., Smith, M., OLIVEROS, R. A., & CHILTON, R. J. (2011) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2011.03.015 · Cited by 29
Bridging Income Generation with Group Integrated Care for cardiovascular risk reduction: Rationale and design of the BIGPIC study
Vedanthan, R., Kamano, J. H., Lee, H., Andama, B., Bloomfield, G. S., DeLong, A. K., Edelman, D. E., Finkelstein, E. A., Hogan, J. W., Horowitz, C. R., et al. (2017) · American Heart Journal · doi:10.1016/j.ahj.2017.03.012 · Cited by 30
“A key secondary outcome measure is one-year change in overall CVD risk as measured by the QRISK2 score, which has been validated for calculating 10-year CVD event risk for Black Africans.(72,73) Other secondary outcome measures will include: (1) percentage of participants with controlled BP; (2) change in individual C…”
Method
Replacement of dietary saturated fat with unsaturated fats increases numbers of circulating endothelial progenitor cells and decreases numbers of microparticles: findings from the randomized, controlled Dietary Intervention and VAScular function (DIVAS) study
Weech, M., Altowaijri, H., Mayneris‐Perxachs, J., Vafeiadou, K., Madden, J., Todd, S., Jackson, K. G., Lovegrove, J. A., & Yaqoob, P. (2018) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/nqy018 · Cited by 30
Strengthening Referral Networks for Management of Hypertension Across the Health System (STRENGTHS) in western Kenya: a study protocol of a cluster randomized trial
Mercer, T., Njuguna, B., Bloomfield, G. S., Dick, J. J., Finkelstein, E. A., Kamano, J. H., Mwangi, A., Yebei, V. N., Pastakia, S. D., Valente, T. W., et al. (2019) · Trials · doi:10.1186/s13063-019-3661-4 · Cited by 30
DAPSA, carotid plaque and cardiovascular events in psoriatic arthritis: a longitudinal study
Lam, S. H. M., Cheng, I. T., Li, E. K., Wong, P. C. H., Lee, J., Yip, R. M.-L., Yim, C.-W., Ying, S. K. Y., Li, M., Li, T. K., et al. (2020) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2020-217595 · Cited by 30
Proactive approaches to identifying dementia and dementia risk; a qualitative study of public attitudes and preferences
Robinson, L., Dickinson, C., Magklara, E., Newton, L., Prato, L., & Bamford, C. (2018) · BMJ Open · doi:10.1136/bmjopen-2017-018677 · Cited by 26
“34 With electronic vascular disease risk assessment tools such as Q-RISK2, which allow health-care professionals to quickly calculate an individual’s future risk, 38 39 now embedded in routine primary care practice, similar methods have been used to develop a dementia risk tool.”
Genetic Risk Factors for Type 2 Diabetes: Insights from the Emerging Genomic Evidence
Ntzani, E., & Kavvoura, F. K. (2012) · Current Vascular Pharmacology · doi:10.2174/157016112799305030 · Cited by 29
Review of tools of cardiovascular disease risk stratification: interpretation, customisation and application in clinical practice.
Chia, Y. C. (2011) · PubMed · Cited by 29
The ESC Textbook of Preventive Cardiology
Nixdorff, U., Achenbach, S., Bengel, F., Faggiano, P., Fernández, S., Heiß, C., Mengden, T., Mureddu, F., Nagel, E. C., Püntmann, V. O., et al. (2015) · Oxford University Press eBooks · doi:10.1093/med/9780199656653.001.0001 · Cited by 31
Machine Learning Prediction of Biomarkers from SNPs and of Disease Risk from Biomarkers in the UK Biobank
Widén, E., Raben, T. G., Lello, L., & Hsu, S. (2021) · Genes · doi:10.3390/genes12070991 · Cited by 33
“Examples of such scores include Framingham [26,27], SCORE [28], ASSIGN–SCORE [29], QRISK1 [30], QRISK2 [31], QRISK3 [32], PROCAM [33], Pooled Cohort Studies Equations [34–36], CUORE [37], Globorisk [38], Reynolds risk score [39,40], World Health Organization (WHO) risk chart [41,42], MyRisk_stroke calculator [43], NIP…”
Method
The Framingham Study on Cardiovascular Disease Risk and Stress-Defenses: A Historical Review
Abohelwa, M., Kopel, J. J., Shurmur, S. W., Ansari, M. M., Awasthi, S., & Awasthi, S. (2023) · Journal of Vascular Diseases · doi:10.3390/jvd2010010 · Cited by 43
Italian cardiovascular mortality charts of the CUORE project: are they comparable with the SCORE charts?
Donfrancesco, C., Palmieri, L., Cooney, M. T., Vanuzzo, D., Panico, S., Cesana, G., Ferrario, M., Pilotto, L., Graham, I. M., & Giampaoli, S. (2010) · European Journal of Cardiovascular Prevention & Rehabilitation · doi:10.1097/hjr.0b013e328334ea70 · Cited by 29
Cardiovascular screening in rheumatoid arthritis: a cross-sectional primary care database study
Monk, H. L., Müller, S., Mallen, C. D., & Hider, S. (2013) · BMC Family Practice · doi:10.1186/1471-2296-14-150 · Cited by 29
“QRISK2 is the only validated CVD risk assessment tool to include RA as an independent risk factor for CVD [7], and indeed other UK guidance from the Joint British Societies Guidance 2 [8] fails to mention rheumatoid arthritis, further contributing to the lack of awareness of the increased risk of CVD in patients with…”
Background
Being Human: A Qualitative Interview Study Exploring Why a Telehealth Intervention for Management of Chronic Conditions Had a Modest Effect
O’Cathain, A., Drabble, S. J., Foster, A., Horspool, K., Edwards, L., Thomas, C., & Salisbury, C. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5879 · Cited by 34
“The primary outcome was response to treatment defined as maintenance or reduction in 10-year risk of CVD (measured by QRISK2 score [19]) after 12 months.”
QRISK or Framingham for predicting cardiovascular risk?
Jackson, R., Marshall, R., Kerr, A., Riddell, T., & Wells, S. J. (2009) · BMJ · doi:10.1136/bmj.b2673 · Cited by 29
Strategies for Cardiovascular Risk Assessment and Prevention Over the Life Course
Vasan, R. S., & Kannel, W. B. (2009) · Circulation · doi:10.1161/circulationaha.109.881995 · Cited by 29
A Machine Learning-Based Applied Prediction Model for Identification of Acute Coronary Syndrome (ACS) Outcomes and Mortality in Patients during the Hospital Stay
Sherazi, S. W. A., Zheng, H., & Lee, J. Y. (2023) · Sensors · doi:10.3390/s23031351 · Cited by 29
Multidimensional individualised Physical ACTivity (Mi-PACT) – a technology-enabled intervention to promote physical activity in primary care: study protocol for a randomised controlled trial
Peacock, O. J., Western, M. J., Batterham, A. M., Stathi, A., Standage, M., Tapp, A., Bennett, P., & Thompson, D. (2015) · Trials · doi:10.1186/s13063-015-0892-x · Cited by 30
The Use of Primary Prevention Statin Therapy in Those Predisposed to Atherosclerosis
Garshick, M. S., & Underberg, J. A. (2017) · Current Atherosclerosis Reports · doi:10.1007/s11883-017-0685-7 · Cited by 28
Response bias to a randomised controlled trial of a lifestyle intervention in people at high risk of cardiovascular disease: a cross-sectional analysis
Bayley, A., Ståhl, D., Ashworth, M., Cook, D., Whincup, P., Treasure, J., Greenough, A., Ridge, K., Winkley, K., & Ismail, K. (2018) · BMC Public Health · doi:10.1186/s12889-018-5939-y · Cited by 18
Reducing risk of Type 2 diabetes in HIV : a mixed‐methods investigation of the STOP ‐Diabetes diet and physical activity intervention
Duncan, A., Peters, B. S., Rivas, C. A., & Goff, L. M. (2019) · Diabetic Medicine · doi:10.1111/dme.13927 · Cited by 31
Novel Biomarkers of Early Atherosclerotic Changes for Personalised Prevention of Cardiovascular Disease in Cervical Cancer and Human Papillomavirus Infection
Tonhajzerová, I., Olexova, L. B., Jurko, A., Spronck, B., Jurko, T., Sekaninova, N., Višňovcová, Z., Mestanikova, A., Kúdela, E., & Mestanik, M. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20153720 · Cited by 28
“, the Atherosclerotic Cardiovascular Disease risk score from the American Heart Association and the American College of Cardiology [66], United Kingdom QRISK2 model [67], European SCORE risk estimator [68], Framingham risk score [69], and others [70].”
Method
Enhanced motivational interviewing for reducing weight and increasing physical activity in adults with high cardiovascular risk: the MOVE IT three-arm RCT
Ismail, K., Stahl, D. R., Bayley, A., Twist, K., Stewart, K., Ridge, K., Britneff, E., Ashworth, M. J., de Zoysa, N., Rundle, J., et al. (2019) · Health Technology Assessment · doi:10.3310/hta23690 · Cited by 29
Integration of estimated glomerular filtration rate biomarker in image-based cardiovascular disease/stroke risk calculator: a south Asian-Indian diabetes cohort with moderate chronic kidney disease
Viswanathan, V., Jamthikar, A. D., Gupta, D., Puvvula, A., Khanna, N. N., Saba, L., Višković, K., Mavrogeni, S. I., Turk, M., Laird, J. R., et al. (2020) · International Angiology · doi:10.23736/s0392-9590.20.04338-2 · Cited by 28
What are the cost-savings and health benefits of improving detection and management for six high cardiovascular risk conditions in England? An economic evaluation
Thomas, C., Brennan, A., Goka, E., Squires, H., Brenner, G., Bagguley, D., Woods, H. B., Gillett, M., Leaviss, J., Clowes, M., et al. (2020) · BMJ Open · doi:10.1136/bmjopen-2020-037486 · Cited by 28
Implications of incidental findings from lung screening for primary care: data from a UK pilot
Bartlett, E., Belsey, J., Derbyshire, J., Morris, R. K., Chen, M., Addis, J., Martins, M., Ridge, C. A., Desai, S. R., Mirsadraee, S., et al. (2021) · npj Primary Care Respiratory Medicine · doi:10.1038/s41533-021-00246-8 · Cited by 28
Behavioural interventions to promote physical activity in a multiethnic population at high risk of diabetes: PROPELS three-arm RCT
Khunti, K., Griffin, S. J., Brennan, A., Dallosso, H. M., Davies, M. J., Eborall, H., Edwardson, C. L., Gray, L. J., Hardeman, W., Heathcote, L., et al. (2021) · Health Technology Assessment · doi:10.3310/hta25770 · Cited by 33
Identifying patients with suspected renal tract cancer in primary care: derivation and validation of an algorithm
Hippisley‐Cox, J., & Coupland, C. A. C. (2012) · British Journal of General Practice · doi:10.3399/bjgp12x636074 · Cited by 27
Self-citation
Use of ethnic identifiers to narrow health inequality gaps
van Apeldoorn, J. A. N., Agyemang, C. O., & van Charante, E. P. M. (2022) · The Lancet Regional Health - Europe · doi:10.1016/j.lanepe.2022.100411 · Cited by 28
“9 Recently, the COVID-19 pandemic demonstrated the need for collecting ethnicity data; countries like the UK could detect clear ethnic disparities early, which fuelled subsequent policy directives.”
Should Your Family History of Coronary Heart Disease Scare You?
Prabhakaran, D., & Jeemon, P. (2012) · Mount Sinai Journal of Medicine A Journal of Translational and Personalized Medicine · doi:10.1002/msj.21348 · Cited by 28
Aspirin and other non-steroidal anti-inflammatory drug prescriptions and survival after the diagnosis of head and neck and oesophageal cancer
Macfarlane, T. V., Murchie, P., & Watson, M. C. (2015) · Cancer Epidemiology · doi:10.1016/j.canep.2015.10.030 · Cited by 28
Reference effect measures for quantifying, comparing and visualizing variation from random and fixed effects in non-normal multilevel models, with applications to site variation in medical procedure use and outcomes
Glorioso, T. J., Grunwald, G. K., Ho, P. M., & Maddox, T. M. (2018) · BMC Medical Research Methodology · doi:10.1186/s12874-018-0517-7 · Cited by 27
The Burden of Atherosclerotic Cardiovascular Disease in South Asians Residing in Canada: A Reflection From the South Asian Heart Alliance
Bainey, K. R., Gupta, M., Ali, I. S., Bangalore, S., Chiu, M., Kaila, K., Kaul, P., Khan, N. A., King‐Shier, K. M., Palaniappan, L. P., et al. (2019) · CJC Open · doi:10.1016/j.cjco.2019.09.004 · Cited by 29
“The modified Framingham risk score (advocated by the United Kingdom) applies a correction factor to the Framingham risk score in SAs.(77,78) Other scoring algorithms, including the ETHRISK score, American College of Cardiology/American Heart Association ASCVD, score, and Reynolds score, have yet to be independently va…”
Method
Monogenic and Polygenic Models of Coronary Artery Disease
Muse, E. D., Chen, S.-F., & Torkamani, A. (2021) · Current Cardiology Reports · doi:10.1007/s11886-021-01540-0 · Cited by 27
Artificial Intelligence Applications in Cardio-Oncology: A Comprehensive Review
Guha, A., Shah, V., Nahle, T., Singh, S., Kunhiraman, H. H., Shehnaz, F., Nain, P., Makram, O. M., Mahmoudi, M., Al-Kindi, S. G., et al. (2025) · Current Cardiology Reports · doi:10.1007/s11886-025-02215-w · Cited by 33
“In the general population, there are several algorithms used to estimate ASCVD risk, such as the Framingham Risk Score [37], Pooled Cohort ASCVD Risk Equations [38 ], Prospective CV Munster (PROCAM) calculator [ 39], QRISK2 [40], and ASSIGN-SCORE [41 ].”

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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