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 cardiovascular risk assessment systems are available and have been comprehensively reviewed, including Framingham models, SCORE, ASSIGN, Q-Risk, PROCAM, Reynolds, CUORE, the Pooled Cohort equations, and Globorisk.
“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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“example, after development of the QRISK2 model using routinely collected data from 355 primary care practices, Hippisley-Cox et al.(5) immediately evaluated the model’s performance”
MethodHypertension and cardiovascular risk: General aspects
Kjeldsen, S. E. (2018) · Pharmacological Research · doi:10.1016/j.phrs.2017.11.003 · Cited by 694 · see citing works
Foley, C. N., Staley, J. R., Breen, P. G., Sun, B. B., Kirk, P., Burgess, S., & Howson, J. M. M. (2021) · Nature Communications · doi:10.1038/s41467-020-20885-8 · Cited by 542 · see citing works
Machine learning prediction in cardiovascular diseases: a meta-analysis
Krittanawong, C., Virk, H. H., Bangalore, S., Wang, Z., Johnson, K. W., Pinotti, R., Zhang, H.-J., Kaplin, S. L., Narasimhan, B., Kitai, T., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-72685-1 · Cited by 447 · see citing works
Hippisley‐Cox, J., & Coupland, C. A. C. (2010) · BMJ · doi:10.1136/bmj.c2197 · Cited by 511 · see citing works
Self-citation2016 European Guidelines on cardiovascular disease prevention in clinical practice
Piepoli, M. F., Hoes, A. W., Agewall, S., Albus, C., Brotons, C., Catapano, A. L., Cooney, M. T., Corrà, U., Cosyns, B., Deaton, C., et al. (2016) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2016.05.037 · Cited by 488 · see citing works
Management of hypertension: summary of NICE guidance
Krause, T., Lovibond, K., Caulfield, M., McCormack, T., & Williams, B. (2011) · British medical journal · doi:10.1136/bmj.d4891 · Cited by 511 · see citing works
Hippisley-Cox, J., & Coupland, C. (2009) · British medical journal · doi:10.1136/bmj.b4229 · Cited by 500 · see citing works
Self-citationElliott, J., Bodinier, B., Bond, T. A., Chadeau‐Hyam, M., Εvangelou, Ε., Moons, K. G. M., Dehghan, A., Muller, D. C., Elliott, P., & Tzoulaki, I. (2020) · JAMA · doi:10.1001/jama.2019.22241 · Cited by 471 · see citing works
Socioeconomic status and cardiovascular disease: risks and implications for care
Clark, A. M., DesMeules, M., Luo, W., Duncan, A. S., & Wielgosz, A. (2009) · Nature Reviews Cardiology · doi:10.1038/nrcardio.2009.163 · Cited by 533 · see citing works
The Swedish CArdioPulmonary BioImage Study: objectives and design
Bergström, G. M. L., Berglund, G., Blomberg, A., Brandberg, J., Engström, G., Engvall, J. E., Eriksson, M., Fairé, U. D., Flinck, A., HANSSON, M. G., et al. (2015) · Journal of Internal Medicine · doi:10.1111/joim.12384 · Cited by 456 · see citing works
Volgman, A. S., Palaniappan, L. S., Aggarwal, N. T., Gupta, M., Khandelwal, A., Krishnan, A. V., Lichtman, J. H., Mehta, L. S., Patel, H. N., Shah, K. S., et al. (2018) · Circulation · doi:10.1161/cir.0000000000000580 · Cited by 552 · see citing works
Value and Limitations of Existing Scores for the Assessment of Cardiovascular Risk
Cooney, M. T., Dudina, A. L., & Graham, I. M. (2009) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2009.07.020 · Cited by 436 · see citing works
RETRACTED ARTICLE: Artificial intelligence and machine learning in precision and genomic medicine
Quazi, S. (2022) · Medical Oncology · doi:10.1007/s12032-022-01711-1 · Cited by 455 · see citing works
Retracted2013 ESH/ESC Guidelines for the management of arterial hypertension
Mancia, G., Fagard, R. H., Narkiewicz, K., Redón, J., Zanchetti, A., Böhm, M., Christiaens, T. C., Cífková, R., Backer, G. D., Dominiczak, A. F., et al. (2013) · Blood Pressure · doi:10.3109/08037051.2013.812549 · Cited by 425 · see citing works
Pavlou, M., Ambler, G., Seaman, S. R., Iorio, M. D., & Omar, R. (2015) · Statistics in Medicine · doi:10.1002/sim.6782 · Cited by 421 · see citing works
“Since last decade, risk models that are commonly used in practice such as the ‘QRISK-2’ and the ‘Framingham’ calculator for the risk of cardiovascular disease [4, 5] and the ‘HCM-SCD calculator’ for the risk of sudden cardiac death in patients with hypertrophic”
MethodRole of artificial intelligence in patient safety outcomes: systematic literature review
Choudhury, A., & Asan, O. (2020) · JMIR Medical Informatics · doi:10.2196/18599 · Cited by 440 · see citing works
Hippisley‐Cox, J., Coupland, C. A. C., Robson, J. P., Sheikh, A., & Brindle, P. M. (2009) · BMJ · doi:10.1136/bmj.b880 · Cited by 370 · see citing works
Self-citationHippisley-Cox, J., Coupland, C., Robson, J., & Brindle, P. (2010) · British medical journal · doi:10.1136/bmj.c6624 · Cited by 366 · see citing works
Self-citationCarbone, M., Sharp, S. J., Flack, S., Paximadas, D., Spiess, K., Adgey, C., Griffiths, L., Lim, R., Trembling, P., Williamson, K., et al. (2016) · Hepatology · doi:10.1002/hep.28017 · Cited by 354 · see citing works
“Following convention,((15,16)) we randomly allocated 60% of these UDCA-treated participants to a derivation cohort and the remaining 40% to a validation cohort.”
Hippisley-Cox, J., & Coupland, C. (2012) · British medical journal · doi:10.1136/bmj.e3427 · Cited by 357 · see citing works
Self-citationGrundy, S. M. (2013) · Journal of clinical lipidology · doi:10.1016/j.jacl.2013.10.001 · Cited by 349 · see citing works
SCORE2-Diabetes: 10-year cardiovascular risk estimation in type 2 diabetes in Europe
SCORE2-Diabetes Working Group and the ESC Cardiovascular Risk Collaboration, Pennells, L., Kaptoge, S., Østergaard, H. B., Read, S. H., Carinci, F., Franch-Nadal, J., Petitjean, C., Taylor, O., Hageman, S. H. J., et al. (2023) · European Heart Journal · doi:10.1093/eurheartj/ehad260 · Cited by 329 · see citing works
“The discrimination ability reported for SCORE2-Diabetes, was somewhat lower than that previously reported for CVD risk scores in the general population.(4,5,37,38) However, higher C-indices are expected to be seen in general population cohorts given the wider age range and heterogeneity in risk factor values.”
ResultPersonalised risk communication for informed decision making about taking screening tests
Edwards, A., Naik, G., Ahmed, H., Elwyn, G. J., Pickles, T., Hood, K., & Playle, R. (2013) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd001865.pub3 · Cited by 331 · see citing works
Cardiovascular comorbidity in rheumatic diseases
Nurmohamed, M. T., Heslinga, M., & Kitas, G. D. (2015) · Nature Reviews Rheumatology · doi:10.1038/nrrheum.2015.112 · Cited by 331 · see citing works
Grundy, S. M., Arai, H., Barter, P. J., Bersot, T. P., Betteridge, D. J., Carmena, R., Cuevas, A. M., Davidson, M. H., Genest, J. J., Kesäniemi, Y. A., et al. (2013) · Journal of clinical lipidology · doi:10.1016/j.jacl.2013.12.005 · Cited by 328 · see citing works
Valtorta, N. K., Kanaan, M., Gilbody, S., & Hanratty, B. (2018) · European Journal of Preventive Cardiology · doi:10.1177/2047487318792696 · Cited by 323 · see citing works
Comparisons of established risk prediction models for cardiovascular disease: systematic review
Siontis, G. C. M., Tzoulaki, I., Siontis, K. C., & Ioannidis, J. P. A. (2012) · BMJ · doi:10.1136/bmj.e3318 · Cited by 300 · see citing works
Greaves, C. J., Gillison, F., Stathi, A., Bennett, P., Reddy, P., Dunbar, J., Perry, R. A., Messom, D., Chandler, R., Francis, M., et al. (2015) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-014-0159-z · Cited by 299 · see citing works
Rabar, S., Harker, M., O'Flynn, N., Wierzbicki, A. S., & On behalf of the Guideline Development Group (2014) · BMJ · doi:10.1136/bmj.g4356 · Cited by 292 · see citing works
Talmud, P. J., Hingorani, A. D., Cooper, J. A., Marmot, M. G., Brunner, E. J., Meena Kumari, B., Mika Kivimaki, B., & Humphries, S. E. (2010) · BMJ · doi:10.1136/bmj.b4838 · Cited by 294 · see citing works
Arts, E., Popa, C. D., den Broeder, A. A., Semb, A. G., Toms, T. E., Kitas, G. D., van Riel, P. L., & Fransen, J. (2014) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2013-204024 · Cited by 279 · see citing works
Backholer, K., Peters, S. A. E., Bots, S. H., Peeters, A., Huxley, R., & Woodward, M. (2016) · Journal of Epidemiology & Community Health · doi:10.1136/jech-2016-207890 · Cited by 266 · see citing works
Cardiovascular disease risk factors in chronic kidney disease: A systematic review and meta-analysis
Major, R., Cheng, M. R. I., Grant, R. A., Shantikumar, S., Xu, G., Oozeerally, I., Brunskill, N. J., & Gray, L. J. (2018) · PLoS ONE · doi:10.1371/journal.pone.0192895 · Cited by 254 · see citing works
Alexander, M., Loomis, A. K., van der Lei, J., Duarte-Salles, T., Prieto-Alhambra, D., Ansell, D., Pasqua, A., Lapi, F., Rijnbeek, P., Mosseveld, M., et al. (2019) · BMJ · doi:10.1136/bmj.l5367 · Cited by 254 · see citing works
Myocardial strain measurement with feature-tracking cardiovascular magnetic resonance: normal values
Taylor, R. J., Moody, W. E., Umar, F., Edwards, N. C., Taylor, T. J., Stegemann, B., Townend, J., Hor, K. N., Steeds, R. P., Mazur, W. M., et al. (2015) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jev006 · Cited by 251 · see citing works
Collins, P. G. S., & Altman, D. G. (2010) · BMJ · doi:10.1136/bmj.c2442 · Cited by 250 · see citing works
Collins, P. G. S., & Altman, D. G. (2012) · BMJ · doi:10.1136/bmj.e4181 · Cited by 245 · see citing works
Liu, J., Davidson, E., Bhopal, R., White, M., Johnson, M., Netto, G., Deverill, M., & Sheikh, A. (2012) · Health Technology Assessment · doi:10.3310/hta16440 · Cited by 237 · see citing works
Self-citationLangrish, J. P., Mills, N. L., Chan, J. K. K., Leseman, D. L. A. C., Aitken, R. J., Fokkens, P. H. B., Cassee, F. R., Li, J., Donaldson, K., Newby, D. E., et al. (2009) · Particle and Fibre Toxicology · doi:10.1186/1743-8977-6-8 · Cited by 237 · see citing works
INDO AMERICAN JOURNAL OF PHARMACEUTICAL SCIENCES
Baru, P. C. R. (2014) · ADVANCED SCIENCES INDEX · Cited by 236 · see citing works
Tonelli, M., Wanner, C., & for the Kidney Disease: Improving Global Outcomes Lipid Guideline Development Work Group Members* (2014) · Annals of Internal Medicine · doi:10.7326/m13-2453 · Cited by 226 · see citing works
Generating high-fidelity synthetic patient data for assessing machine learning healthcare software
Tucker, A., Wang, Z., Rotalinti, Y., & Myles, P. R. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00353-9 · Cited by 230 · see citing works
Ramos, R., Comas-Cufí, M., Martí-Lluch, R., Balló, E., Ponjoan, A., Alves-Cabratosa, L., Blanch, J., Marrugat, J., Elosua, R., Grau, M., et al. (2018) · BMJ · doi:10.1136/bmj.k3359 · Cited by 215 · see citing works
“Tools exist to predict CVD risk in patients aged up to 79 years, 19 or even 84 years, 41 but older populations are heterogeneous, with diverse life expectancy, different degrees of frailty or special comorbid conditions, and use of drug combinations.”
BackgroundNishimura, K., Okamura, T., Watanabe, M., Nakai, M., Takegami, M., Higashiyama, A., Kokubo, Y., Okayama, A., & Miyamoto, Y. (2014) · Journal of Atherosclerosis and Thrombosis · doi:10.5551/jat.19356 · Cited by 228 · see citing works
Cooney, M. T., Dudina, A., D'Agostino, R., & Graham, I. M. (2010) · Circulation · doi:10.1161/circulationaha.109.852756 · Cited by 222 · see citing works
Black, C., Sharma, P., Scotland, G., McCullough, K., McGurn, D., Robertson, L. M., Fluck, N., MacLeod, A. M., McNamee, P., Prescott, G. J., et al. (2010) · Health Technology Assessment · doi:10.3310/hta14210 · Cited by 221 · see citing works
Hippisley‐Cox, J., & Coupland, C. A. C. (2017) · BMJ · doi:10.1136/bmj.j5019 · Cited by 218 · see citing works
“10 29-32 An initial analysis was performed based on patients with complete data.”
MethodSelf-citationvan Dieren, S., Beulens, J. W. J., Kengne, A. P., Peelen, L. M., Rutten, G. E. H. M., Woodward, M., van der Schouw, Y. T., & Moons, K. G. M. (2011) · Heart · doi:10.1136/heartjnl-2011-300734 · Cited by 217 · see citing works
Atherosclerotic cardiovascular disease prevention in rheumatoid arthritis
Semb, A. G., Ikdahl, E., Wibetoe, G., Crowson, C. S., & Rollefstad, S. (2020) · Nature Reviews Rheumatology · doi:10.1038/s41584-020-0428-y · Cited by 216 · see citing works
Should people at low risk of cardiovascular disease take a statin?
Abramson, J. D., Rosenberg, H. G., Jewell, N., & Wright, J. M. (2013) · BMJ · doi:10.1136/bmj.f6123 · Cited by 208 · see citing works
Hippisley-Cox, J., & Coupland, C. (2012) · British Journal of General Practice · doi:10.3399/bjgp12x616355 · Cited by 208 · see citing works
Self-citationZhao, J., Feng, Q., Wu, P., Lupu, R. A., Wilke, R. A., Wells, Q. S., Denny, J. C., & Wei, W. (2019) · Scientific Reports · doi:10.1038/s41598-018-36745-x · Cited by 208 · see citing works
Two Risk-Scoring Systems for Predicting Incident Diabetes Mellitus in U.S. Adults Age 45 to 64 Years
Kahn, H. S., Cheng, Y. J., Thompson, T. J., Imperatore, G., & Gregg, E. W. (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-150-11-200906020-00002 · Cited by 204 · see citing works
Sterne, J. A. C., Bodalia, P. N., Bryden, P. A., Davies, P. A., López-López, J. A., Okoli, G. N., Thom, H. H. Z., Caldwell, D. M., Dias, S., Eaton, D., et al. (2017) · Health Technology Assessment · doi:10.3310/hta21090 · Cited by 201 · see citing works
Basu, S., Sussman, J. B., Berkowitz, S. A., Hayward, R. A., & Yudkin, J. S. (2017) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(17)30221-8 · Cited by 206 · see citing works
Sex Differences in Peripheral Artery Disease
Pabón, M. A., Cheng, S. C., Altin, S. E., Sethi, S. S., Nelson, M. D., Moreau, K. L., Hamburg, N. M., & Hess, C. N. (2022) · Circulation Research · doi:10.1161/circresaha.121.320702 · Cited by 197 · see citing works
Collins, P. G. S., & Altman, D. G. (2009) · BMJ · doi:10.1136/bmj.b2584 · Cited by 192 · see citing works
The NHS Health Check in England: an evaluation of the first 4 years
Robson, J., Dostal, I., Sheikh, A., Eldridge, S., Madurasinghe, V., Griffiths, C., Coupland, C., & Hippisley-Cox, J. (2016) · BMJ Open · doi:10.1136/bmjopen-2015-008840 · Cited by 184 · see citing works
Self-citationCollins, G. S., Ogundimu, E. O., Cook, J. A., Manach, Y. L., & Altman, D. G. (2016) · Statistics in Medicine · doi:10.1002/sim.6986 · Cited by 185 · see citing works
“For convenience and simplicity, the models were developed using a subset of variables that were chosen from the total list of predictors contained in the QRISK2 [28] and QFracture [29] risk prediction models.”
MethodFrench, D., Cameron, E., Benton, J. S., Deaton, C., & Harvie, M. (2017) · Annals of Behavioral Medicine · doi:10.1007/s12160-017-9895-z · Cited by 179 · see citing works
Hippisley‐Cox, J., & Coupland, C. A. C. (2010) · BMC Family Practice · doi:10.1186/1471-2296-11-49 · Cited by 178 · see citing works
Self-citationRisk prediction models for contrast induced nephropathy: systematic review
Silver, S. A., Shah, P. S., Chertow, G. M., Harel, S., Wald, R., & Harel, Z. (2015) · BMJ · doi:10.1136/bmj.h4395 · Cited by 179 · see citing works
Hippisley‐Cox, J., & Coupland, C. A. C. (2015) · BMJ Open · doi:10.1136/bmjopen-2015-007825 · Cited by 177 · see citing works
“independent studies have examined other risk algorithms,(85) 86 88 89 they have demonstrated comparable performance compared with the validation in the QResearch database.(19) 80 84 We (or another academic team) intend to conduct a separate validation on an independent database using a different clinical system (CPRD)…”
MethodResultSelf-citationCurrent and future perspectives on the management of polypharmacy
Molokhia, M., & Majeed, A. (2017) · BMC Family Practice · doi:10.1186/s12875-017-0642-0 · Cited by 184 · see citing works
“cular risk, such as QRISK in the UK [23, 24].”
BackgroundWestwood, M. E., Al, M., Burgers, L. T., Redekop, K., Lhachimi, S. K., Armstrong, N., Raatz, H., Misso, K., Severens, J. L., & Kleijnen, J. (2013) · Health Technology Assessment · doi:10.3310/hta17090 · Cited by 175 · see citing works
Cardiovascular risk estimation in older persons: SCORE O.P
Cooney, M. T., Selmer, R., Lindman, A., Tverdal, A., Menotti, A., Thomsen, T., DeBacker, G., De Bacquer, D., Tell, G. S., Njolstad, I., et al. (2016) · European Journal of Preventive Cardiology · doi:10.1177/2047487315588390 · Cited by 172 · see citing works
Risk scoring for the primary prevention of cardiovascular disease
Karmali, K. N., Persell, S. D., Perel, P., Lloyd‐Jones, D. M., Berendsen, M. A., & Huffman, M. D. (2017) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd006887.pub4 · Cited by 172 · see citing works
Systemic inflammatory response and neuromuscular involvement in amyotrophic lateral sclerosis
Lu, C., Allen, K., Oei, F., Leoni, E., Kühle, J., Tree, T. I. M., Fratta, P., Sharma, N., Sidle, K. C. L., Howard, R., et al. (2016) · Neurology Neuroimmunology & Neuroinflammation · doi:10.1212/nxi.0000000000000244 · Cited by 172 · see citing works
“QRISK2, a cerebrovascular disease risk score, was included in the statistical analyses.(12)”
MethodDrury, P. L., Ting, R., Zannino, D., Ehnholm, C., Flack, J., Whiting, M., Fassett, R., Ansquer, J.-C., Dixon, P., Davis, T. M. E., et al. (2011) · Diabetologia · doi:10.1007/s00125-010-1854-1 · Cited by 195 · see citing works
Solomon, D. H., Greenberg, J. D., Curtis, J. R., Liu, M., Farkouh, M. E., Tsao, P., Kremer, J. M., & Etzel, C. J. (2015) · Arthritis & Rheumatology · doi:10.1002/art.39195 · Cited by 169 · see citing works
“rigorously or in such a large cohort with RA (18,19).”
Background
- 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 › Surgery › 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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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