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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You, J., Guo, Y., Kang, J.-J., Wang, H.-F., Yang, M., Feng, J.-F., Yu, J.-T., & Cheng, W. (2023) · Stroke and vascular neurology · doi:10.1136/svn-2023-002332 · Cited by 66
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Method
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Self-citation
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Method
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Payne, R. (2012) · British Journal of Clinical Pharmacology · doi:10.1111/j.1365-2125.2012.04219.x · Cited by 70
A machine learning–based 1-year mortality prediction model after hospital discharge for clinical patients with acute coronary syndrome
Sherazi, S. W. A., Jeong, Y. J., Jae, M. H., Bae, J., & Lee, J. Y. (2019) · Health Informatics Journal · doi:10.1177/1460458219871780 · Cited by 62
Telehealth for patients at high risk of cardiovascular disease: pragmatic randomised controlled trial
Salisbury, C., O’Cathain, A., Thomas, C., Edwards, L., Gaunt, D., Dixon, P., Hollinghurst, S. P., Nicholl, J. P., Large, S., Yardley, L., et al. (2016) · BMJ · doi:10.1136/bmj.i2647 · Cited by 64
A systematic review of predictive risk models for diabetes complications based on large scale clinical studies
Lagani, V., Koumakis, L., Chiarugi, F., Lakasing, E., & Tsamardinos, I. (2012) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2012.11.003 · Cited by 63
Variations by ethnicity in referral and treatment pathways for IAPT service users in South London
Harwood, H., Rhead, R. D., Chui, Z., Bakolis, I., Connor, L., Gazard, B., Hall, J., MacCrimmon, S., Rimes, K. A., Woodhead, C., et al. (2021) · Psychological Medicine · doi:10.1017/s0033291721002518 · Cited by 66
“…from the analysis entirely (complete case analysis) (Osborn et al., 2015), or by performing single imputation of missing values with the White ethnic group (Hippisley-Cox et al., 2008) − these methods generally lead to biased estimates of association and standard errors (Sterne et al., 2009).”
Method
Missing data was handled inconsistently in UK prediction models: a review of method used
Tsvetanova, A., Sperrin, M., Peek, N. B., Buchan, I. E., Hyland, S. L., & Martin, G. P. (2021) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2021.09.008 · Cited by 63
Cardiovascular risk prediction in HIV ‐infected patients: comparing the Framingham, atherosclerotic cardiovascular disease risk score ( ASCVD ), Systematic Coronary Risk Evaluation for the N etherlands (SCORE ‐ NL) and Data Collection on Adverse Events of Anti‐ HIV Drugs ( D : A : D ) risk prediction models
Krikke, M., Hoogeveen, R., Hoepelman, A. I. M., Visseren, F. L. J., & Arends, J. (2015) · HIV Medicine · doi:10.1111/hiv.12300 · Cited by 63
Rheumatoid Arthritis and Incidence of Twelve Initial Presentations of Cardiovascular Disease: A Population Record-Linkage Cohort Study in England
Pujades‐Rodríguez, M., Duyx, B., Thomas, S. L., Stogiannis, D., Rahman, A., Smeeth, L. L., & Hemingway, H. (2016) · PLoS ONE · doi:10.1371/journal.pone.0151245 · Cited by 63
Challenges of developing a cardiovascular risk calculator for patients with rheumatoid arthritis
Crowson, C. S., Rollefstad, S., Kitas, G. D., van Riel, P. L. C. M., Gabriel, S. E., & Semb, A. G. (2017) · PLoS ONE · doi:10.1371/journal.pone.0174656 · Cited by 65
“CVD risk was calculated for the purpose of comparison using risk algorithms designed for use in the general population, including Framingham risk score (FRS) for general CVD risk, American College of Cardiology/American Heart Association (ACC/AHA) Pooled Cohort Equation, QRISK2 and Systematic COronoary Risk Evaluation…”
MethodInfluential
Predictive Utility of Cardiovascular Risk Prediction Algorithms in Inflammatory Rheumatic Diseases: A Systematic Review
Colaco, K., Ocampo, V., Ayala, A. P., Harvey, P., Gladman, D. D., Piguet, V., & Eder, L. (2019) · The Journal of Rheumatology · doi:10.3899/jrheum.190261 · Cited by 63
“Unlike these scores, which are based solely on traditional risk factors, the QRISK2 algorithm includes RA as an independent risk factor(26).”
Background
Impact of age and sex on primary preventive treatment for cardiovascular disease in the West Midlands, UK: cross sectional study
Sheppard, J. P., Singh, S. P., Fletcher, K., McManus, R. J., & Mant, J. (2012) · BMJ · doi:10.1136/bmj.e4535 · Cited by 62
Carotid intima–media thickness and the prediction of vascular events
Robertson, C. M., Gerry, F., Fowkes, R., & Price, J. F. (2012) · Vascular Medicine · doi:10.1177/1358863x12445103 · Cited by 62
Statins and Cardiovascular Primary Prevention in CKD
Major, R., Cheung, C. K., Gray, L. J., & Brunskill, N. J. (2015) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.07460714 · Cited by 62
“The QRisk2 score (24) does provide a fixed CKD adjustment without consideration of staging.”
Background
Optimal strategies for monitoring lipid levels in patients at risk or with cardiovascular disease: a systematic review with statistical and cost-effectiveness modelling
Perera, R., McFadden, E. C., McLellan, J., Lung, T. W.-C., Clarke, P., Pérez, T., Fanshawe, T. R., Dalton, A., Farmer, A. A., Glasziou, P., et al. (2015) · Health Technology Assessment · doi:10.3310/hta191000 · Cited by 63
Managing Cardiovascular Disease Risk in Rheumatoid Arthritis: Clinical Updates and Three Strategic Approaches
Chodara, A. M., Wattiaux, A., & Bartels, C. M. (2017) · Current Rheumatology Reports · doi:10.1007/s11926-017-0643-y · Cited by 62
Systematic Coronary Risk Evaluation (SCORE)
Graham, I. M., Angelantonio, E. D., Visseren, F. L. J., Bacquer, D. D., Ference, B. A., Timmis, A., Halle, M., Vardas, P. E., Huculeci, R., & Cooney, M. T. (2021) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2021.04.052 · Cited by 60
Chapter 4: Other complications of CKD: CVD, medication dosage, patient safety, infections, hospitalizations, and caveats for investigating complications of CKD
(2012) · Kidney International Supplements · doi:10.1038/kisup.2012.67 · Cited by 62
Comparative accuracy of different risk scores in assessing cardiovascular risk in Indians: A study in patients with first myocardial infarction
Bansal, M., Kasliwal, R. R., & Trehan, N. K. (2014) · Indian Heart Journal · doi:10.1016/j.ihj.2014.10.399 · Cited by 59
Increased Dolutegravir Peak Concentrations in People Living With Human Immunodeficiency Virus Aged 60 and Over, and Analysis of Sleep Quality and Cognition
Elliot, E. R., Wang, X., Singh, S., Simmons, B., Vera, J. H., Miller, R. F., Fitzpatrick, C., Moyle, G. J., McClure, M. O., & Boffito, M. (2018) · Clinical Infectious Diseases · doi:10.1093/cid/ciy426 · Cited by 61
Artificial intelligence framework for predictive cardiovascular and stroke risk assessment models: A narrative review of integrated approaches using carotid ultrasound
Jamthikar, A. D., Gupta, D., Saba, L., Khanna, N. N., Višković, K., Mavrogeni, S. I., Laird, J. R., Sattar, N. A., Johri, A. M., Pareek, G., et al. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104043 · Cited by 64
Application of ensemble machine learning algorithms on lifestyle factors and wearables for cardiovascular risk prediction
Huang, W., Ying, T. W., Chin, W. L. C., Baskaran, L., Marcus, O. E. H., Yeo, K. K., & Kiong, N. S. (2022) · Scientific Reports · doi:10.1038/s41598-021-04649-y · Cited by 63
Tocilizumab: Therapy and safety management
Pham, T., Claudepierre, P., Constantin, A., Bandt, M. D., Fautrel, B., Gossec, L., Gottenberg, J., Goupille, P., Guillaume, S., HACHULLA, E., et al. (2010) · Joint Bone Spine · doi:10.1016/s1297-319x(10)70001-4 · Cited by 60
The Agewell trial: a pilot randomised controlled trial of a behaviour change intervention to promote healthy ageing and reduce risk of dementia in later life
Clare, L., Nelis, S. M., Jones, I. R., Hindle, J. V., Thom, J. M., Nixon, J., Cooney, J., Jones, C. L., Edwards, R. T., & Whitaker, C. J. (2015) · BMC Psychiatry · doi:10.1186/s12888-015-0402-4 · Cited by 65
“Assessment of physical health, which included anthropometric data, blood pressure and blood sample, provided indices of Body Mass Index (BMI), body fat percentage, and cholesterol level, and yielded the QRISK2 score [49] indicating percentage risk for cardiovascular disease over the next 10 years.”
The efficiency of cardiovascular risk assessment: do the right patients get statin treatment?
van Staa, T. P., Smeeth, L. L., Ng, E. S. W., Goldacre, B. M., & Gulliford, M. C. (2013) · Heart · doi:10.1136/heartjnl-2013-303698 · Cited by 59
Atherosclerosis in systemic lupus erythematosus
Croca, S. C., & Rahman, A. (2017) · Best Practice & Research Clinical Rheumatology · doi:10.1016/j.berh.2017.09.012 · Cited by 60
Landmark Models for Optimizing the Use of Repeated Measurements of Risk Factors in Electronic Health Records to Predict Future Disease Risk
Paige, E., Barrett, J., Stevens, D., Keogh, R. H., Sweeting, M. J., Nazareth, I., Petersen, I., & Wood, A. (2018) · American Journal of Epidemiology · doi:10.1093/aje/kwy018 · Cited by 60
“CVD risk assessment tools, such as the Framingham risk model (36) and QRISK2 (4), are now integrated into electronic primary care record systems, but are not purposefully designed for pre-screening use.”
BackgroundMethodInfluential
Social deprivation and prognostic benefits of cardiac surgery: observational study of 44 902 patients from five hospitals over 10 years
Pagano, D., Freemantle, N., Bridgewater, B., Howell, N., Ray, D., Jackson, M., Fabri, B. M., Au, J., Keenan, D., Kirkup, B., et al. (2009) · BMJ · doi:10.1136/bmj.b902 · Cited by 59
The impact of ethnicity on asthma care
Davidson, E. M., Liu, J., & Sheikh, A. (2010) · Primary Care Respiratory Journal · doi:10.4104/pcrj.2010.00013 · Cited by 59
Self-citation
Cardiovascular risk according to educational status in India
Gupta, R., Kaul, V., Agrawal, A., Guptha, S., & Gupta, V. P. (2010) · Preventive Medicine · doi:10.1016/j.ypmed.2010.08.014 · Cited by 59
The role of inflammation and cardiovascular disease risk on microvascular and macrovascular endothelial function in patients with rheumatoid arthritis: a cross-sectional and longitudinal study
Sandoo, A., Kitas, G. D., Carroll, D., & Zanten, J. V. (2012) · Arthritis Research & Therapy · doi:10.1186/ar3847 · Cited by 63
Machine-learning based identification of undiagnosed dementia in primary care: a feasibility study
Jammeh, E., Camille, B. C., Stephen, W. P., Escudero, J., Αναστασίου, Α., Zhao, P., Chenore, T., Zajicek, J., & Ifeachor, E. (2018) · BJGP Open · doi:10.3399/bjgpopen18x101589 · Cited by 57
“Patients who had any of the Read codes in the list assigned to them any time during the study period were assumed to have been diagnosed with dementia.”
Stroke and Bleeding Risk in Atrial Fibrillation: Navigating the Alphabet Soup of Risk‐Score Acronyms (CHADS 2 , CHA 2 DS 2 ‐VASc, R 2 CHADS 2 , HAS‐BLED, ATRIA, and More)
Dzeshka, M. S., Lane, D. A., & Lip, G. Y. H. (2014) · Clinical Cardiology · doi:10.1002/clc.22294 · Cited by 57
Analysis of lung cancer risk model (PLCOM2012 and LLPv2) performance in a community-based lung cancer screening programme
Lebrett, M. B., Balata, H., Evison, M., Colligan, D., Duerden, R., Elton, P., Greaves, M., Howells, J., Irion, K., Karunaratne, D., et al. (2020) · Thorax · doi:10.1136/thoraxjnl-2020-214626 · Cited by 57
Cardiovascular disease assessment in rheumatoid arthritis: a guide to translating knowledge of cardiovascular risk into clinical practice
Semb, A. G., Rollefstad, S., van Riel, P. L. C. M., Kitas, G. D., Matteson, E. L., & Gabriel, S. E. (2014) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2013-204792 · Cited by 57
The Prevalence of Atherosclerosis in Those with Inflammatory Connective Tissue Disease by Race, Age and Traditional Risk Factors
Alenghat, F. J. (2016) · Scientific Reports · doi:10.1038/srep20303 · Cited by 57
Statin initiations and QRISK2 scoring in UK general practice: a THIN database study
Finnikin, S., Ryan, R. P., & Marshall, T. P. (2017) · British Journal of General Practice · doi:10.3399/bjgp17x693485 · Cited by 56
Nonlinear model for the carotid artery disease 10‐year risk prediction by fusing conventional cardiovascular factors to carotid ultrasound image phenotypes: A Japanese diabetes cohort study
Khanna, N. N., Jamthikar, A. D., Araki, T., Gupta, D., Piga, M., Saba, L., Carcassi, C., Nicolaides, A. N., Laird, J. R., Suri, H. S., et al. (2019) · Echocardiography · doi:10.1111/echo.14242 · Cited by 55
Inflammation beyond the Joints: Rheumatoid Arthritis and Cardiovascular Disease.
Urman, A. R., Taklalsingh, N., Sorrento, C., & McFarlane, I. M. (2018) · Europe PMC (PubMed Central) · Cited by 55
Cardiovascular risk assessment tools in Asia
Zhang, Y., Miao, H., Chia, Y. C., Buranakitjaroen, P., Siddique, S., Shin, J., Turana, Y., Park, S., Tsoi, K., Chen, C., et al. (2022) · Journal of Clinical Hypertension · doi:10.1111/jch.14336 · Cited by 57
Cardiovascular disease and type 1 diabetes: prevalence, prediction and management in an ageing population
Lee, S. I., Patel, M., Jones, C. M., & Narendran, P. (2015) · Therapeutic Advances in Chronic Disease · doi:10.1177/2040622315598502 · Cited by 58
Predicting the risk of coronary heart disease
Dent, T. H. S. (2010) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2010.06.019 · Cited by 57
Personalized Cardiovascular Disease Prediction and Treatment—A Review of Existing Strategies and Novel Systems Medicine Tools
Björnson, E., Borén, J., & Mardinoğlu, A. (2016) · Frontiers in Physiology · doi:10.3389/fphys.2016.00002 · Cited by 55
“…the Framingham risk score (Wilson et al., 1998), Reynolds risk score (Ridker et al., 2007), Pan European score (SCORE; Conroy et al., 2003), ASSIGN Scottish algorithm (Woodward et al., 2007), and QRISK2 UK algorithm (Hippisley-Cox et al., 2008) have been developed (Simmonds andWald, 2012).”
Method
Normal values for myocardial deformation within the right heart measured by feature-tracking cardiovascular magnetic resonance imaging
Liu, B., Dardeer, A. M., Moody, W. E., Edwards, N. C., Hudsmith, L., & Steeds, R. P. (2017) · International Journal of Cardiology · doi:10.1016/j.ijcard.2017.10.106 · Cited by 55
“TheQRISK®2 score iswidely usedwithin theNational Health Service to predict the risk of an individual developing cardiovascular disease over the next 10 years [8]; this was calculated for each patient using the QRISK®2-2017 calculator.”
Development of a Cardiovascular Disease Risk Prediction Model Using the Suita Study, a Population-Based Prospective Cohort Study in Japan
Nakai, M., Watanabe, M., Kokubo, Y., Nishimura, K., Higashiyama, A., Takegami, M., Nakao, Y. M., Okamura, T., & Miyamoto, Y. (2020) · Journal of Atherosclerosis and Thrombosis · doi:10.5551/jat.48843 · Cited by 55
Best practice for prevention and treatment of cardiovascular disease through an equity lens: a review
Beauchamp, A., Peeters, A., Tonkin, A. M., & Turrell, G. (2010) · European Journal of Cardiovascular Prevention & Rehabilitation · doi:10.1097/hjr.0b013e328339cc99 · Cited by 54
Trials in primary care: statistical issues in the design, conduct and evaluation of complex interventions
Lancaster, G., Campbell, M. J., Eldridge, S. M., Farrin, A. J., Marchant, M., Müller, S., Perera, R., Peters, T., Prevost, A. T., & Rait, G. (2010) · Statistical Methods in Medical Research · doi:10.1177/0962280209359883 · Cited by 54
Cardiovascular Risk Stratification and Management in Pre-Diabetes
Færch, K., Vistisen, D., Johansen, N. B., & Jørgensen, M. E. (2014) · Current Diabetes Reports · doi:10.1007/s11892-014-0493-1 · Cited by 60

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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