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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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“(v) In the United Kingdom, the National Institute for Health and Care Excellence (NICE) recommends the utilization of the QRISK2 for cardiovascular risk estimation [20].”
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“Ethnicity is frequently missing from routinely collected primary care records and we assigned missing data into the white category in keeping with the local population and previous studies [15].”
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“Table 1 shows that overall severity of depression and anxiety as measured by the PHQ-9 and GAD-7 were inversely associated with higher cardiovascular risk score on QRISK2 [25] classified as a 10-year risk > 10% [26].”
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“Among these algorithms, only QRISK-2 considers RA as a predictive variable for CVD risk [6].”
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A simple risk score using routine data for predicting cardiovascular disease in primary care
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“The original QRISK and subsequent QRISK2 models to assess the risk for CVD based on more than million patients were published in 2007 and 2008, respectively (26, 27).”
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“Family history of AMI or cardiovascular disease were not included in the analysis because of the very low rates of recording in primary care.(24) Indicators of psychosocial factors such as job strain and lack of social support, fruit and vegetable intake, and physical activity were not included in the analysis because…”
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“RA, family history of angina or heart attack, atrial fibrillation, chronic kidney disease and type 1 diabetes mellitus are included among its variables [11].”
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“QRISK 2 [51] and SCORE [52] for primary prevention of CVD, the GRACE score [53, 54] in acute coronary syndromes and, euroSCORE [33] in cardiac surgery.”
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“Many risk prediction models and algorithms are being developed to assess the risks of developing CVD, such as systematic coronary risk evaluation (SCORE) [8], cardiovascular risk score (QRISK2) [9-11], Framingham [12], and Reynolds risk score [13].”
BackgroundSample size determination for prediction models via learning‐type curves
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“current blood pressure, current blood lipids, and current smoking status).(5,6) Therefore, downgrading age in risk prediction scores would downgrade the best current measure we have of cumulative exposure to modifiable CVD risk factors.”
BackgroundErectile dysfunction and testosterone deficiency as cardiovascular risk factors?
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Bauernfreund, Y., Launders, N., Favarato, G., Hayes, J., Osborn, D., & Sampson, E. L. (2022) · Acta Psychiatrica Scandinavica · doi:10.1111/acps.13480 · Cited by 12
Soran, H., Schofield, J. D., & Durrington, P. N. (2014) · Current Opinion in Lipidology · doi:10.1097/mol.0000000000000097 · Cited by 12
“In other words, estimates of CVD risk based on QRISK2, even if accurate, are likely to disenfranchise much of the currently treated population unless they are used with the LDL-C treatment response in the calculation of NNT as a statin indication.”
BackgroundMethodInfluentialYe, S., Wang, Y. C., Shimbo, D., Newman, J. D., Levitan, E. B., & Muntner, P. (2013) · Journal of the American Society of Hypertension · doi:10.1016/j.jash.2013.12.006 · Cited by 12
Stevanović, J., Postma, M. J., & Pechlivanoglou, P. (2012) · European Journal of Preventive Cardiology · doi:10.1177/2047487312448994 · Cited by 11
Trends in the burden of varicella in UK general practice
Walker, J. L., Andrews, N. J., Mathur, R., Smeeth, L. L., & THOMAS, S. L. (2017) · Epidemiology and Infection · doi:10.1017/s0950268817001649 · Cited by 12
“We did not classify those with no recorded ethnicity as being from a white ethnic group, an approach adopted by some researchers on the basis that, if the study population reflected the UK population, at least 93% of these individuals would be likely to be of white ethnicity [12].”
Sivritepe, R., Basat, S., & Ortaboz, D. (2018) · The Aging Male · doi:10.1080/13685538.2018.1499080 · Cited by 12
“systolic blood pressure, and body mass index [20].”
BackgroundMao, L., He, J., Gao, X., Guo, H., Wang, K., Zhang, X., Yang, W., Zhang, J., Li, S., Hu, Y., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0202665 · Cited by 12
Direito, A., Rawstorn, J. C., Mair, J. L., Daryabeygi‐Khotbehsara, R., Maddison, R., & Tai, E. S. (2019) · Digital Health · doi:10.1177/2055207619890480 · Cited by 10
CHD Risk Minimization through Lifestyle Control: Machine Learning Gateway
He, X., Matam, B. R., Bellary, S., Ghosh, G., & Chattopadhyay, A. K. (2020) · Scientific Reports · doi:10.1038/s41598-020-60786-w · Cited by 11
Abundance of undiagnosed cardiometabolic risk within the population of a long-stay prison in the UK
Gray, B. J., Craddock, C., Couzens, Z., Bain, E., Dunseath, G. J., Shankar, A. G., Luzio, S. D., & Perrett, S. E. (2020) · European Journal of Public Health · doi:10.1093/eurpub/ckaa187 · Cited by 14
Riley, V., Ellis, N., Cowap, L., Grogan, S., Cottrell, E., Crone, D., Chambers, R., Clark-Carter, D., Fedorowicz, S. E., & Gidlow, C. (2020) · BMC Family Practice · doi:10.1186/s12875-020-01315-6 · Cited by 11
Stroke Risk Calculators in the Era of Electronic Health Records Linked to Administrative Databases
Richards, A. K., & Cheng, E. M. (2012) · Stroke · doi:10.1161/strokeaha.111.649798 · Cited by 12
A model for measuring the health burden of classic congenital adrenal hyperplasia in adults
Hummel, S., Sadler, S., Whitaker, M. J., Ara, R., Dixon, S., & Ross, R. J. M. (2016) · Clinical Endocrinology · doi:10.1111/cen.13060 · Cited by 12
Simple screening tools predict death and cardiovascular events in patients with rheumatic disease
Breunig, M., Kleinert, S., Lehmann, S., Kneitz, C., Feuchtenberger, M., Tony, H.-P., Ce, A., Ertl, G., & Störk, S. (2017) · Scandinavian Journal of Rheumatology · doi:10.1080/03009742.2017.1337924 · Cited by 11
“Despite extensive efforts to adopt cardiovascular risk scores (5, 28, 29), no routine test has gained widespread acceptance in predicting CVD in RD patients (30).”
BackgroundJeffery, A., Bhanu, C., Walters, K. R., Wong, I. C. K., Osborn, D., & Hayes, J. (2022) · The British Journal of Psychiatry · doi:10.1192/bjp.2022.160 · Cited by 11
“…17,18 93% of more individuals with missing ethnicity would be expected to be of White ethnicity; this approach is in line with other research studies using the CPRD. 23 We did not include comorbidities or diabetes treatment stage because of collinearity with our main exposure of polypharmacy.”
Countermeasures for Maintaining Cardiovascular Health in Space Missions
Kumar, A., Pramanik, J., Panchal, L., & Prajapati, B. G. (2023) · Current Cardiology Reviews · doi:10.2174/1573403x19666230330083225 · Cited by 11
“…Cardiovascular Disease Estimates the lifetime risk and CVD-free life expectancy [59] QRISK Estimates the lifetime risk prediction of cardiovascular disease [60] QRISK 2 Estimates the risk prediction of cardiovascular disease [61] QRISK 3 Estimates the risk prediction of cardiac disease and stroke.”
BackgroundPredicting Cardiovascular Disease Risk in Tobacco Users Using Machine Learning Algorithms
Khimani, A., Hornback, A. M., Jain, N., Avula, P., Jaishankar, A., & Wang, M. D. (2024) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc53108.2024.10782885 · Cited by 15
“…there are well established CVD risk assessment tools available, such as Framingham Risk Score (FRS) [4], Pooled Cohort Equations (PCE) [5], Systematic Coronary Risk Evaluation (SCORE) [6], and QRISK2 score [7] that consider medical risk factors to predict future risk of cardiovascular events.”
Waldron, C.-A., Gallacher, J., van der Weijden, T., Newcombe, R. G., & Elwyn, G. J. (2010) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-10-41 · Cited by 10
Total cardiovascular disease risk assessment
Cooney, M. T., Cooney, H. C., Dudina, A. L., & Graham, I. M. (2011) · Current Opinion in Cardiology · doi:10.1097/hco.0b013e3283499f06 · Cited by 13
Development, implementation and yield of a cardiometabolic health check
Godefrooij, M. B., van de Kerkhof, R. M., Wouda, P. J., Vening, R. A., Knottnerus, J. A., Dinant, G., & Spigt, M. (2011) · Family Practice · doi:10.1093/fampra/cmr068 · Cited by 10
Calculation of changes in life expectancy based on proportional hazards model of an intervention
Kulinskaya, E., Gitsels, L. A., Bakbergenuly, I., & Wright, N. R. (2020) · Insurance Mathematics and Economics · doi:10.1016/j.insmatheco.2020.04.006 · Cited by 11
“The most significant changes in statins eligibility criteria were in 2006, when the threshold of cardiac risk at which to prescribe statins was lowered to a QRISK2 ≥ 20% (previously ≥ 40%) (Hippisley-Cox et al., 2008); in 2007, when statins became first line treatment for CVD survivors (NICE, 2013); and in 2014, when…”
BackgroundSingleton, R. K., Heath, A. K., Clasen, J. L., Scélo, G., Johansson, M., Calvez‐Kelm, F. L., Weiderpass, E., Liedberg, F., Ljungberg, B., Harbs, J., et al. (2021) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-20-1438 · Cited by 11
“The discrimination of both the EPIC and Usher–Smith models is not substantially lower than the Pooled Cohort Equations or Qrisk which are used in practice for prediction of cardiovascular diseases (16, 17); however, compared with cardiovascular diseases, the 10-year risk of RCC is relatively low.”
MethodAge-associated decline of monocyte insulin sensitivity in diabetic and healthy individuals
Nakamura, S., Mori, K., Okuma, H., Sekine, T., Miyazaki, A., & Tsuchiya, K. (2021) · Diabetes and Vascular Disease Research · doi:10.1177/1479164121989281 · Cited by 13
Hypertension mediated kidney and cardiovascular damage and risk stratification: Redefining concepts
Márquez, D. F., Rodríguez‐Sánchez, E., Segura, J., Ruilope, L. M., & Ruiz‐Hurtado, G. (2021) · Nefrología · doi:10.1016/j.nefro.2021.10.003 · Cited by 13
CALMS: Modelling the long-term health and economic impact of Covid-19 using agent-based simulation
Mintram, K. S., Anagnostou, A., Anokye, N., Okine, E., Groen, D., Saha, A., Abubakar, N. T., Islam, T., Daroge, H., Ghorbani, M., et al. (2022) · PLoS ONE · doi:10.1371/journal.pone.0272664 · Cited by 13
Schoofs, M. C. A., Akkermans, R., de Grauw, W. J., Schalk, B. W. M., van Dis, I., Tjin-A-Ton, J., Bischoff, E. W. M. A., & Biermans, M. C. J. (2022) · British Journal of General Practice · doi:10.3399/bjgp.2021.0726 · Cited by 11
A Deep Survival Learning Approach for Cardiovascular Risk Estimation in Patients With Sleep Apnea
Blanchard, M., Feuilloy, M., Sabil, A., Gervès‐Pinquié, C., Gagnadoux, F., & Girault, J. (2022) · IEEE Access · doi:10.1109/access.2022.3231743 · Cited by 11
Predicting the risk of cardiovascular disease in people exposed to moderate to high levels of dioxin
Chang, J.-W., Chen, H., Chang, C., Su, H.-J. J., Liao, P.-C., & Lee, C.-C. (2011) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2011.10.046 · Cited by 11
Ghouri, N., Purves, D., Deans, K. A., LOGAN, G. R. M., McConnachie, A., Wilson, J., Gill, J. M. R., & Sattar, N. A. (2015) · PLoS ONE · doi:10.1371/journal.pone.0123317 · Cited by 11
“SCORE—and thus identification of other markers to identify the increased CVD risk in South Asians is a clinical concern [9].”
BackgroundJackson, R., Kerr, A., & Wells, S. (2013) · Circulation · doi:10.1161/circulationaha.113.002650 · Cited by 11
Biomarkers in predicting cardiovascular risk: new prospects of troponin I
Васильевна, К. А., Myrzamatova, A. О., & Drapkina, O. M. (2020) · CARDIOVASCULAR THERAPY AND PREVENTION · doi:10.15829/1728-8800-2020-2584 · Cited by 11
“Многие системы оценки риска ССЗ доступны для использования у условно здоровых людей, включая Фремингемскую шкалу [2], SCORE (Systematic Coronary Risk Evaluation) [3], Q2-Risk (Cardiovascular disease risk algorithm) [4], PROCAM (Prospective Cardiovascular Munster Study), Globorisk [5] и др.”
To Establish the Reference Intervals of Lipid Profile in Punjab
Kaur, V., Verma, M., Kaur, A., Gupta, S., & Singh, K. (2012) · Indian Journal of Clinical Biochemistry · doi:10.1007/s12291-012-0208-9 · Cited by 11
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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