Cited by 1,459Health Promotion and Cardiovascular PreventionCardiovascular Health and Risk FactorsLipoproteins and Cardiovascular Health

The DOI matches its Crossref record; 54 of the 55 references it cites were found in a registry or an index.

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

Open this paper in CoChat — free account Open PDF

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1136/bmj.39609.449676.25

Cites

54 of 54 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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.

Sorted by citations

Items 601–700 of 1,000

Characterization of Portuguese Centenarian Eating Habits, Nutritional Biomarkers, and Cardiovascular Risk: A Case Control Study
da Silva, A. P., Valente, A., Chaves, C., Matos, A., Gil, Á., Santos, A. C., Gorjão-Clara, J. P., & Bicho, M. (2018) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2018/5296168 · Cited by 19
“org), based on age, gender, ethnicity, smoking habits, diabetes status, angina or heart attack in a 1st degree relative age below 60 years, chronic kidney disease (stage 4 or 5), atrial fibrillation, hypertension, and rheumatoid arthritis and also based on cholesterol/HDL ratio, systolic blood pressure, and body mass…”
BackgroundMethodInfluential
A Novel Afrocentric Stroke Risk Assessment Score: Models from the Siren Study
Akpa, O. M., Sarfo, F. S., Owolabi, M. O., Akpalu, A. K., Wahab, K., Obiako, R., Komolafe, M. A., Owolabi, L. F., Osaigbovo, G. O., Ogbole, G. I., et al. (2021) · Journal of Stroke and Cerebrovascular Diseases · doi:10.1016/j.jstrokecerebrovasdis.2021.106003 · Cited by 19
Metabolic syndrome and cardiovascular disease after haematopoietic cell transplantation (HCT) in adults: an EBMT cross-sectional non-interventional study
Greenfield, D., Salooja, N., Peczynski, C., van der Werf, S. M., Schoemans, H. M., Hill, K., Cortelezzi, A., Stanghellini, M. T. L., Özkurt, Z. N., Arat, M., et al. (2021) · Bone Marrow Transplantation · doi:10.1038/s41409-021-01414-7 · Cited by 19
The importance of blood pressure thresholds versus predicted cardiovascular risk on subsequent rates of cardiovascular disease: a cohort study in English primary care
Herrett, E. L., Strongman, H., Gadd, S., Tomlinson, L. A., Nitsch, D., Bhaskaran, K., Williamson, E., van Staa, T. P., Sofat, R., Timmis, A., et al. (2022) · The Lancet Healthy Longevity · doi:10.1016/s2666-7568(21)00281-6 · Cited by 21
Circulating Extracellular Vesicles Are Strongly Associated With Cardiovascular Risk Markers
Zhou, R., Bozbaş, E., Allen‐Redpath, K., & Yaqoob, P. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.907457 · Cited by 19
Disease-modifying anti-rheumatic drugs improve the cardiovascular profile in patients with rheumatoid arthritis
Giachi, A., Cugno, M., & Gualtierotti, R. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.1012661 · Cited by 20
Prescribing in general practice for people with coronary heart disease; equity by age, sex, ethnic group and deprivation
Mathur, R., Badrick, E., Boomla, K., Bremner, S. A., Hull, S., & Robson, J. P. (2011) · Ethnicity and Health · doi:10.1080/13557858.2010.540312 · Cited by 18
Self-citation
Body Composition Indices and Predicted Cardiovascular Disease Risk Profile among Urban Dwellers in Malaysia
Su, T. T., Amiri, M., Hairi, F. M., Thangiah, N., Dahlui, M., & Majid, H. A. (2015) · BioMed Research International · doi:10.1155/2015/174821 · Cited by 23
Assessing the impact of football-based health improvement programmes: stay onside, avoid own goals and score with the evaluation!
Pringle, A., Hargreaves, J., Lozano, L., McKenna, J., & Zwolinsky, S. (2014) · Soccer and Society · doi:10.1080/14660970.2014.920620 · Cited by 19
Stroke risk estimation across nine European countries in the MORGAM project
Borglykke, A., Andreasen, A. H., Kuulasmaa, K., Sans, S., Ducimetière, P., Vanuzzo, D., Ferrario, M., Palmieri, L., Karvanen, J., Tunstall-Pedoe, H., et al. (2010) · Heart · doi:10.1136/hrt.2010.207555 · Cited by 19
Women Up, Men Down: The Clinical Impact of Replacing the Framingham Risk Score with the Reynolds Risk Score in the United States Population
Tattersall, M. C., Gangnon, R. E., Karmali, K. N., & Keevil, J. G. (2012) · PLoS ONE · doi:10.1371/journal.pone.0044347 · Cited by 22
An Evidence-Based Guide to Cholesterol-Lowering Guidelines
Waters, D. D., & Boekholdt, S. M. (2016) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2016.10.019 · Cited by 19
“3 million patients aged 35-74 years with 140,000 CV events, and showed superior discrimination and calibration in British patients compared with the modified FRS.(17) QRISK2 includes the Townsend index, a marker of social deprivation, which is derived from the patient’s postal code.”
MethodInfluential
Association between Negatively Charged Low-Density Lipoprotein L5 and Subclinical Atherosclerosis in Rheumatoid Arthritis Patients
Chang, C., Chen, C., Chen, Y., Hsieh, T., Li, J., Shen, M.-Y., Lan, J., & Chen, D. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8020177 · Cited by 19
“Global 10-year risk for a heart attack or stroke was estimated by calculating the QRISK-2 scores [28] on the website: https://www.”
Method
Inflammation and Vascular Ageing: From Telomeres to Novel Emerging Mechanisms
Chiriacò, M., Georgiopoulos, G. A., Duranti, E., Antonioli, L., Puxeddu, I., Nannipieri, M., Rosada, J., Blandizzi, C., Taddei, S., Virdis, A., et al. (2019) · High Blood Pressure & Cardiovascular Prevention · doi:10.1007/s40292-019-00331-7 · Cited by 20
Family history of cardiovascular disease and risk of premature coronary heart disease: A matched case-control study
Chacko, M., Sarma, P. S., Harikrishnan, S., Zachariah, G. A., & Jeemon, P. (2020) · Wellcome Open Research · doi:10.12688/wellcomeopenres.15829.1 · Cited by 19
Mind the gap: difference between Framingham heart age and real age increases with age in HIV-positive individuals–a clinical cohort study
Davies, T.-L., Gompels, M. M., Johnston, S. L., Bovill, B. A., & May, M. (2013) · BMJ Open · doi:10.1136/bmjopen-2013-003245 · Cited by 19
The development and validation of an index to predict 10-year mortality risk in a longitudinal cohort of older English adults
Kobayashi, L. C., Jackson, S. E., Lee, S. J., Wardle, J., & Steptoe, A. (2016) · Age and Ageing · doi:10.1093/ageing/afw199 · Cited by 19
“Risk prediction scores are used extensively in the cardiovascular context, but the plethora of cardiovascular risk calculators remain invalidated and are restricted to a single clinical category [1, 2].”
Background
Comparison of non-traditional biomarkers, and combinations of biomarkers, for vascular risk prediction in people with type 2 diabetes: The Edinburgh Type 2 Diabetes Study
Price, A. H., Weir, C. J., Welsh, P. I., McLachlan, S., Strachan, M. W. J., Sattar, N. A., & Price, J. F. (2017) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2017.07.009 · Cited by 21
“TIA [1]: ICD-10 code for TIA (G45, G659) as primary diagnosis on discharge record; or [2] self-report of stroke or non-primary ICD-10 discharge”
MethodInfluential
Psoriasis, psoriatic arthritis and cardiovascular risk: are we closer to a clinical recommendation?
Kristensen, S. L., McInnes, I. B., & Sattar, N. A. (2014) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2014-206617 · Cited by 19
“Systemic in ﬂ ammation levels are generally much lower in psoriasis than in RA, as assessed at least by serum C-reactive protein, suggesting that C-reactive protein is not serving as an appropriate surrogate in this condition for in ﬂ ammation, or that other, as yet imperfectly appreciated, pathways may contribute to…”
BackgroundInfluential
Variation in the Proportion of Adults in Need of Blood Pressure-Lowering Medications by Hypertension Care Guideline in Low- and Middle-Income Countries: A Cross-Sectional Study of 1 037 215 Individuals From 50 Nationally Representative Surveys.
Sudharsanan, N., Theilmann, M., Kirschbaum, T. K., Manne‐Goehler, J., Azadnajafabad, S., Bovet, P., Chen, S., Damasceno, A. A. M., Neve, J. D., Dorobanţu, M., et al. (2021) · PubMed · doi:10.1161/circulationaha.120.051620 · Cited by 18
Assessing the Accuracy of Cardiovascular Disease Prediction Using Female-Specific Risk Factors in Women Aged 45 to 69 Years in the UK Biobank Study
Doust, J. A., Baneshi, M. R., Chung, H., Wilson, L. F., & Mishra, G. D. (2024) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.123.010842 · Cited by 18
“…included in 3 contemporary CVD risk calculators: (1) the Pooled Cohort Equation - Atherosclerotic Cardiovascular Disease (PCE-ASCVD) calculator developed in the United States, 10 (2) the Qrisk2 equation developed in England and Wales, 11 and (3) the PREDICT equation developed in New Zealand.”
Mind-Body Connection: Cardiovascular Sequelae of Psychiatric Illness
Minhas, S., Patel, J., Malik, M. I., Hana, D., Hassan, F., & Khouzam, R. N. (2021) · Current Problems in Cardiology · doi:10.1016/j.cpcardiol.2021.100959 · Cited by 18
Estimating the potential population impact of stepwise screening strategies for identifying and treating individuals at high risk of Type 2 diabetes: a modelling study
Chamnan, P., Simmons, R. K., Khaw, K., Wareham, N. J., & Griffin, S. J. (2012) · Diabetic Medicine · doi:10.1111/j.1464-5491.2012.03609.x · Cited by 18
Renal Association Clinical Practice Guideline on Cardiovascular Disease in CKD
Holt, S. G., & Goldsmith, D. J. A. (2011) · Nephron Clinical Practice · doi:10.1159/000328065 · Cited by 18
Do novel biomarkers add to existing scores of total cardiovascular risk?
Backer, G. D., Graham, I. M., & Cooney, M. T. (2012) · European Journal of Preventive Cardiology · doi:10.1177/2047487312448988 · Cited by 18
Cardiovascular education for people with rheumatoid arthritis: what can existing patient education programmes teach us?
John, H., Carroll, D., & Kitas, G. D. (2011) · Lara D. Veeken · doi:10.1093/rheumatology/ker191 · Cited by 18
A systematic scoping review of the domains and innovations in secondary uses of digitised health-related data
Robertson, A. R. R., Nurmatov, U., Sood, H. S., Cresswell, K. M., Smith, P. A., & Sheikh, A. (2016) · Journal of Innovation in Health Informatics · doi:10.14236/jhi.v23i3.841 · Cited by 17
Self-citation
The Expanded Risk Score in Rheumatoid Arthritis (ERS-RA): performance of a disease-specific calculator in comparison with the traditional prediction scores in the assessment of the 10-year risk of cardiovascular disease in patients with rheumatoid arthritis
Salaffi, F., Carotti, M., Carlo, M. D., Tardella, M., Lato, V., Becciolini, A., Favalli, E. G., & Giovagnoni, A. (2018) · Swiss Medical Weekly · doi:10.4414/smw.2018.14656 · Cited by 18
The utility of long-term blood pressure variability for cardiovascular risk prediction in primary care
Stevens, S., McManus, R. J., & Stevens, R. (2018) · Journal of Hypertension · doi:10.1097/hjh.0000000000001923 · Cited by 17
The impact of cardiorespiratory fitness on classical cardiovascular disease risk factors in rheumatoid arthritis: a cross-sectional and longitudinal study
Cooney, J., Ahmad, Y. A., Moore, J. P., Sandoo, A., & Thom, J. M. (2019) · Rheumatology International · doi:10.1007/s00296-019-04431-4 · Cited by 18
Association of Electronegative LDL with Macrophage Foam Cell Formation and CD11c Expression in Rheumatoid Arthritis Patients
Chang, C., Chen, P., Lan, J., Chang, S., Hsieh, T., Liao, P.-J., Chen, C., & Chen, D. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21165883 · Cited by 18
“Global 10-year risk for a heart attack or stroke was estimated by calculating the QRISK-2 scores [41] on the website: https://www.”
Method
Metabolic syndrome and its components as predictors of stroke in middle-aged and elderly Chinese people
Jia, Z., Wu, S., Zhou, Y., Wang, W., Liu, X., Wang, L., Wang, Y., Wang, Y., Liang, W., Zhao, X., et al. (2011) · Neurological Research · doi:10.1179/016164111x13007856083882 · Cited by 18
Predicted 10-year risk of cardiovascular disease is influenced by the risk equation adopted: a cross-sectional analysis
Gray, B. J., Bracken, R. M., Turner, D., Morgan, K., Mellalieu, S. D., Thomas, M., Williams, S. P., Williams, M., Rice, S., Stephens, J. W., et al. (2014) · British Journal of General Practice · doi:10.3399/bjgp14x681805 · Cited by 18
Chronic kidney disease: towards a risk-based approach
Taal, M. W. (2016) · Clinical Medicine · doi:10.7861/clinmedicine.16-6-s117 · Cited by 18
Refined selection of individuals for preventive cardiovascular disease treatment with a transformer-based risk model
Rao, S., Li, Y., Mamouei, M., Salimi‐Khorshidi, G., Wamil, M., Nazarzadeh, M., Yau, C., Collins, P. G. S., Jackson, R., Vickers, A. J., et al. (2025) · The Lancet Digital Health · doi:10.1016/j.landig.2025.03.005 · Cited by 20
Socioeconomic status and modification of atherosclerotic cardiovascular disease risk prediction: epidemiological analysis using data from the atherosclerosis risk in communities study
Henderson, K., Kaufman, B. G., Rotter, J., Stearns, S. C., Sueta, C. A., Foraker, R. E., Ho, P. M., & Chang, P. P. (2022) · BMJ Open · doi:10.1136/bmjopen-2021-058777 · Cited by 18
“19 25 Such discrepancies have important implications globally and for the USA, creating uncertainty regarding the importance of incorporating SES into ASCVD risk prediction models and the value of SES as a marker to identify individuals in need of additional ASCVD primary prevention interventions…”
Background
Impact of Changes in Inflammation on Estimated Ten‐Year Cardiovascular Risk in Rheumatoid Arthritis
Yu, Z., Yang, N., Everett, B. M., Frits, M. L., Iannaccone, C. K., Coblyn, J. S., Weinblatt, M. E., Shadick, N. A., Solomon, D. H., & Liao, K. P. (2018) · Arthritis & Rheumatology · doi:10.1002/art.40532 · Cited by 17
Level of agreement between frequently used cardiovascular risk calculators in people living with HIV
Dhillon, S., Sabin, C. A., Alagaratnam, J., Bagkeris, E., Post, F. A., Boffito, M., Anderson, J., Vera, J. H., Williams, I. G., Johnson, M. A., et al. (2019) · HIV Medicine · doi:10.1111/hiv.12731 · Cited by 18
Sex Differences in the Assessment of Cardiovascular Risk in Primary Health Care: A Systematic Review
Hyun, K. K., Millett, E. R. C., Redfern, J., Brieger, D. B., Peters, S. A. E., & Woodward, M. (2019) · Heart Lung and Circulation · doi:10.1016/j.hlc.2019.04.005 · Cited by 17
Entropy-Based Measures of Hypnopompic Heart Rate Variability Contribute to the Automatic Prediction of Cardiovascular Events
Yan, X., Zhang, L., Li, J., Du, D., & Hou, F. (2020) · Entropy · doi:10.3390/e22020241 · Cited by 17
“However, in the existing systems of cardiovascular risk evaluation, such as QRISK2 risk score [23], and Framingham risk score [24], HRV characteristics haven’t been taken into consideration.”
Method
Cardiovascular risk profiles and the uptake of the NHS Healthcheck programme in male prisoners in six UK prisons: an observational cross-sectional survey
Packham, C., Butcher, E., Williams, M., Miksza, J. K., Morriss, R. K., & Khunti, K. K. (2020) · BMJ Open · doi:10.1136/bmjopen-2019-033498 · Cited by 17
An economic evaluation of adaptive e-learning devices to promote weight loss via dietary change for people with obesity
Miners, A., Harris, J., Felix, L., Murray, E., Michie, S., & Edwards, P. (2012) · BMC Health Services Research · doi:10.1186/1472-6963-12-190 · Cited by 20
Cardiometabolic disease in South Asians: A global health concern in an expanding population
Das, A., Ambale‐Venkatesh, B., Lima, J. A. C., Freedman, J. E., Spahillari, A., Das, R., Das, S., Shah, R. V., & Murthy, V. L. (2016) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2016.08.001 · Cited by 17
Visit-to-visit blood pressure variability and cardiovascular outcomes: a systematic review and dose-response meta-analysis
Lukitasari, M., Jonnagaddala, J., Liaw, S., & Jalaludin, B. B. (2025) · European Journal of Preventive Cardiology · doi:10.1093/eurjpc/zwaf546 · Cited by 16
Fuzzy Decision Support System for Coronary Artery Disease Diagnosis Based on Rough Set Theory
Setiawan, N. A. (2014) · International Journal of Rough Sets and Data Analysis · doi:10.4018/ijrsda.2014010105 · Cited by 16
Controversies Regarding Lipid Management and Statin Use for Cardiovascular Risk Reduction in Patients With CKD
Markossian, T. W., Burge, N. J., Ling, B., Schneider, J., Pacold, I. V., Bansal, V., Leehey, D. J., Stroupe, K. T., Chang, A. R., & Kramer, H. (2016) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2015.12.030 · Cited by 16
Derivation and validation of SIDIAP-FHP score: A new risk model predicting cardiovascular disease in familial hypercholesterolemia phenotype
Ramos, R., Masana, L., Comas‐Cufí, M., García-Gil, M., Martí‐Lluch, R., Ponjoan, A., Plana, N., Alves‐Cabratosa, L., Marrugat, J., Elosúa, R., et al. (2019) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2019.10.016 · Cited by 18
Comparing antibiotic prescribing between clinicians in UK primary care: an analysis in a cohort study of eight different measures of antibiotic prescribing
van Staa, T. P., Li, Y., Gold, N., Chadborn, T., Welfare, W., Palin, V., Ashcroft, D. M., & Bircher, J. (2022) · BMJ Quality & Safety · doi:10.1136/bmjqs-2020-012108 · Cited by 16
Drivers with and without Obesity Respond Differently to a Multi-Component Health Intervention in Heavy Goods Vehicle Drivers
Ruettger, K. B., Clemes, S. A., Chen, Y., Edwardson, C. L., Guest, A. J., Gilson, N. D., Gray, L. J., Johnson, V., Paine, N. J., Sherry, A. P., et al. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph192315546 · Cited by 16
First-line immunosuppression in neuromuscular diseases
Foster, M. A., Lunn, M. P., & Carr, A. S. (2023) · Practical Neurology · doi:10.1136/pn-2023-003708 · Cited by 16
Ancestry, ethnicity, and race: explaining inequalities in cardiometabolic disease
Eastwood, S. V., Hemani, G., Watkins, S., Scally, A., Smith, G. D., & Chaturvedi, N. (2024) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2024.04.002 · Cited by 19
“Risk prediction for CVD includes an ethnicity correction factor, recognizing differential absolute CVD risks [51,52], although performance remains suboptimal [53].”
The impact of social deprivation on coronary revascularisation treatment outcomes within the National Health Service in England and Wales
Matata, B. M., Shaw, M., Grayson, A. D., McShane, J., Lucy, J. M., Fisher, M., & Jackson, M. (2015) · European Journal of Preventive Cardiology · doi:10.1177/2047487314567000 · Cited by 16
Is the high-risk strategy to prevent cardiovascular disease equitable? A pharmacoepidemiological cohort study
Wallach‐Kildemoes, H., Diderichsen, F., Krasnik, A., Lange, T., & Andersen, M. T. (2012) · BMC Public Health · doi:10.1186/1471-2458-12-610 · Cited by 16
Cardiovascular risk assessments in chronic myeloid leukemia allow identification of patients at high risk of cardiovascular events during treatment with nilotinib
Breccia, M., Colafigli, G., Molica, M., & Alimena, G. (2015) · American Journal of Hematology · doi:10.1002/ajh.23976 · Cited by 16
“Another prediction algorithm for cardiovascular disease is the QRisk2 [8] that uses tradi-”
MethodInfluential
Limitations for health research with restricted data collection from UK primary care
Strongman, H., Williams, R., Meeraus, W. H., Murray‐Thomas, T., Campbell, J., Carty, L., Dedman, D., Gallagher, A. M., Oyinlola, J. O., Kousoulis, A. A., et al. (2019) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.4765 · Cited by 19
Engagement with advice to reduce cardiovascular risk following a health check programme: A qualitative study
Alageel, S., Gulliford, M. C., Wright, A. J., Khoshaba, B., & Burgess, C. C. (2019) · Health Expectations · doi:10.1111/hex.12991 · Cited by 15
PARTICULATE MATTER AIR POLLUTION EFFECTS ON THE INCIDENCE OF HEART DISEASES AMONG THE URBAN POPULATION
Викторович, Т. М., & Artamonova, G. V. (2014) · Annals of the Russian academy of medical sciences · doi:10.15690/vramn.v69.i3-4.996 · Cited by 16
Introducing the Canadian Women’s Heart Health Alliance ATLAS on the Epidemiology, Diagnosis, and Management of Cardiovascular Diseases in Women
Norris, C. M., Yip, C. Y. Y., Nerenberg, K. A., Jaffer, S., Grewal, J., Levinsson, A., & Mulvagh, S. L. (2020) · CJC Open · doi:10.1016/j.cjco.2020.02.004 · Cited by 19
Effectiveness of the Ready to Reduce Risk (3R) complex intervention for the primary prevention of cardiovascular disease: a pragmatic randomised controlled trial
Byrne, J. L., Dallosso, H. M., Rogers, S., Gray, L. J., Waheed, G., Patel, P., Gupta, P., Doherty, Y., Davies, M. J., & Khunti, K. K. (2020) · BMC Medicine · doi:10.1186/s12916-020-01664-0 · Cited by 16
“In the UK, the recommendation is to offer a high-intensity statin for the primary prevention of CVD to people who have a 10% or greater 10year risk of developing CVD, assessed by using the QRISK2 assessment tool [6]; in the most recent guidance published in the USA, a high-intensity statin is recommended, without any…”
Method
Cardiovascular risk and mortality prediction in patients suspected of sleep apnea: a model based on an artificial intelligence system
Blanchard, M., Feuilloy, M., Gervès‐Pinquié, C., Trzépizur, W., Meslier, N., Goupil, F., Pigeanne, T., Racineux, J.-L., Balusson, F., Oger, E., et al. (2021) · Physiological Measurement · doi:10.1088/1361-6579/ac2a8f · Cited by 18
Toward Clinical Applications of Carotid Ultrasound: Intima-Media Thickness, Plaque Area, and Three-Dimensional Phenotypes
Spence, J. D., & Rundek, T. (2011) · Springer eBooks · doi:10.1007/978-1-84882-688-5_25 · Cited by 17
Statins: Cholesterol guidelines and Indian perspective
Menon, A., Kotwal, N., Singh, Y., & Girish, R. (2015) · Indian Journal of Endocrinology and Metabolism · doi:10.4103/2230-8210.163105 · Cited by 16
“Pharmacokinetic studies of statins in Indian population Pharmacogenomic study for prevalence of SLCO1B1 gene variants in Indian population Adverse events data of patients already on high intensity statins Large studies on applicability of cardiovascular risk calculators to Indian population (Framingham, QRISK 2,[49] a…”
Background
Risk of Cardiovascular Disease among Diabetic Patients in Manipur, Northeast India
Tungdim, M. G., Ginzaniang, T., Kabui, G. P., Verma, D., & Kapoor, S. (2014) · Journal of Anthropology · doi:10.1155/2014/421439 · Cited by 16
“Several multivariate risk prediction algorithms [7–9] have been developed to predict future CVD risk but their use has lagged in primary care [10].”
The impact of different point-of-care testing lipid analysers on cardiovascular disease risk assessment
Whitehead, S. J., Ford, C., & Gama, R. M. (2014) · Journal of Clinical Pathology · doi:10.1136/jclinpath-2013-202123 · Cited by 16
Ethnic differences in the cost-effectiveness of targeted and mass screening for high cardiovascular risk in the UK: cross-sectional study
Baker, J., Mitchell, R., Lawson, K. D., & Pell, J. P. (2013) · Heart · doi:10.1136/heartjnl-2013-304625 · Cited by 16
3-year risk prediction of Coronary Heart Disease in hypertension patients: A preliminary study
Chen, R., Yang, Y., Miao, F., Cai, Y., Lin, D., Zheng, J., & Li, Y. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8037041 · Cited by 17
“Multiple studies indicated that an elevated triglycerides, low-density lipoprotein cholesterol (LDL-C), or TC would contribute greatly to a higher risk of CHD [9,10,16-19].”
BackgroundInfluential
Genome-Wide Association Studies and Risk Scores for Coronary Artery Disease: Sex Biases
Byars, S., & Inouye, M. (2018) · Advances in experimental medicine and biology · doi:10.1007/978-3-319-77932-4_38 · Cited by 16
Comparison of Recommendations and Use of Cardiovascular Risk Equations by Health Technology Assessment Agencies and Clinical Guidelines
Betts, M. B., Milev, S., Hoog, M. M., Jung, H., Milenković, D., Qian, Y., Tai, M., Kutikova, L., Villa, G., & Edwards, C. (2018) · Value in Health · doi:10.1016/j.jval.2018.08.003 · Cited by 16
Multi‐domain analysis of microvascular flow motion dynamics in NAFLD
Chipperfield, A. J., Thanaj, M., Scorletti, E., Byrne, C. D., & Clough, G. F. (2019) · Microcirculation · doi:10.1111/micc.12538 · Cited by 16
Lifetime risk predictions for cardiovascular diseases: Competing risks analyses on a population-based cohort in Sweden
Stenling, A., Häggström, C., Norberg, M., & Norström, F. (2020) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2020.08.014 · Cited by 16
Electronic Health Record Technology
Cresswell, K. M., & Sheikh, A. (2012) · JAMA · doi:10.1001/jama.2012.3520 · Cited by 15
Self-citation
Growth Differentiation Factor-15 as a Potent Predictor of Long-Term Mortality among Subjects with Osteoarthritis
Arnold, N., Rehm, M., Büchele, G., Peter, R. S., Brenner, R. E., Günther, K., Brenner, H., Köenig, W., & Rothenbacher, D. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9103107 · Cited by 15
Primary care use by men with symptoms of possible prostate cancer: A multi‐method study with an ethnically diverse sample in London
Martins, T., Walter, F. M., Penfold, C., Abel, G., & Hamilton, W. T. (2021) · European Journal of Cancer Care · doi:10.1111/ecc.13482 · Cited by 17
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. (2022) · Nefrología (English Edition) · doi:10.1016/j.nefroe.2021.10.008 · Cited by 18
Sex-based differences in risk factors for incident myocardial infarction and stroke in the UK Biobank
Remfry, E., Ardissino, M., McCracken, C., Szabó, L. E., Neubauer, S., Harvey, N. C., Mamas, M. A., Robson, J. P., Petersen, S. E., & Raisi‐Estabragh, Z. (2023) · European Heart Journal - Quality of Care and Clinical Outcomes · doi:10.1093/ehjqcco/qcad029 · Cited by 15
“Lower socioeconomic status (SES) is known to be associated with CVD, 33 although in a previous study on the UK Biobank no sex diﬀerence was found.”
Self-citation
Evidence supporting the choice of a new cardiovascular risk equation for Australia
Brown, S., Banks, E., Woodward, M., Raffoul, N., Jennings, G., & Paige, E. (2023) · The Medical Journal of Australia · doi:10.5694/mja2.52052 · Cited by 15
MSGene: a multistate model using genetic risk and the electronic health record applied to lifetime risk of coronary artery disease
Urbut, S., Yeung, M., Khurshid, S., Cho, S. J., Schuermans, A., German, J., Taraszka, K., Paruchuri, K., Fahed, A., Ellinor, P., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-49296-9 · Cited by 16
“Existingscores 26,39 implythatthelevelsofcovariateswillstay ﬁ xedover the life course or require recalculation, which ignores the information within transitions through the life course.”
Overview of cardiac toxicity from radiation therapy
Chin, V., Finnegan, R., Keall, P., Otton, J. M., Delaney, G. P., & Vinod, S. K. (2024) · Journal of Medical Imaging and Radiation Oncology · doi:10.1111/1754-9485.13757 · Cited by 15
Experiences of outreach workers in promoting smoking cessation to Bangladeshi and Pakistani men: longitudinal qualitative evaluation
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) · BMC Public Health · doi:10.1186/1471-2458-11-452 · Cited by 16
Self-citation
Classical risk factors of cardiovascular disease among Chinese male steel workers: a prospective cohort study for 20 years
Ji, J., Pan, E., Li, J., Chen, J., Cao, J., Sun, D., Lu, X., Chen, S., Gu, D., Duan, X., et al. (2011) · BMC Public Health · doi:10.1186/1471-2458-11-497 · Cited by 15
Consensus statement on the management of dyslipidemia in Indian subjects: A different perspective
Enas, E. A., Dharmarajan, T. S., & Varkey, B. (2015) · Indian Heart Journal · doi:10.1016/j.ihj.2015.03.020 · Cited by 16
“(such as angina and transient ischemic attack).(41,42) As result,”
Background
Evaluation of risk factors for cardiovascular diseases among Saudi diabetic patients attending primary health care service
Ahmed, A. A., Alsharief, E., & Alsharief, A. (2013) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2013.06.012 · Cited by 15
Carotid Intima-media Thickness Determined Vascular Age and the Framingham Risk Score
Adolphe, A., Huang, X., & Cook, L. S. (2011) · Critical Pathways in Cardiology A Journal of Evidence-Based Medicine · doi:10.1097/hpc.0b013e31823a31f0 · Cited by 15
Relationships of different types of event to cardiovascular death in trials of antihypertensive treatment
Zambon, A., Arfè, A., Corrao, G., & Zanchetti, A. (2013) · Journal of Hypertension · doi:10.1097/hjh.0000000000000077 · Cited by 15
“Unauth Journal of Hypertension the full guideline [13], in which they find a comparison of the Framingham, ASSIGN and QRISK scores applied to a large UK population, the results of which appear to favour QRISK [14], which defines cardiovascular outcomes as myocardial infarction, coronary heart disease, stroke and trans…”
Result
Novel biomarkers with potential for cardiovascular risk reclassification
Mallikethi‐Reddy, S., Briasoulis, Α., Akintoye, E. O., & Afonso, L. (2016) · Biomarkers · doi:10.1080/1354750x.2016.1201540 · Cited by 16
“The QRISK2 score added variables such as diabetes, chronic renal disease, atrial fibrillation, ethnicity and socioeconomic status to the modified Framingham/ATP III model, and accurately predicted cardiovascular risk in different ethnic groups from England and Wales (Hippisley-Cox et al., 2008).”
Method
A novel cardiovascular death prediction model for Chinese individuals: A prospective cohort study of 381,963 study participants
Hu, W., Lee, J. H., Tsai, M., & Wen, C.-P. (2017) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2017.07.016 · Cited by 14
Current practice of usual clinic blood pressure measurement in people with and without diabetes: a survey and prospective ‘mystery shopper’ study in UK primary care
Stevens, S., McManus, R. J., & Stevens, R. (2018) · BMJ Open · doi:10.1136/bmjopen-2017-020589 · Cited by 15
“In particular, the recommended cardiovascular risk calculator in the UK, QRISK2 15 was derived using such data.”
Method
Cardiorespiratory fitness as a predictor of short‐term and lifetime estimated cardiovascular disease risk
Swainson, M. G., Ingle, L., & Carroll, S. (2019) · Scandinavian Journal of Medicine and Science in Sports · doi:10.1111/sms.13468 · Cited by 15
“UK clinical practice is now routinely adopting the QRISK prediction model, a UK-specific and validated predictor of 10-year CVD risk in representative cohorts (9), but European alternative risk algorithms (SCORE) are also advised (10).”
Method
Evaluating Cardiovascular Disease (CVD) risk scores for participants with known CVD and non-CVD in a multiracial/ethnic Caribbean sample
Hosein, A., Stoute, V., Chadee, S., & Singh, N. R. (2020) · PeerJ · doi:10.7717/peerj.8232 · Cited by 15
“These weights, based on established population data, which are used to calculate a risk score, which is used in further analyses (Hippisley-Cox et al., 2008; Sullivan, Massaro & D’Agostino, 2004; Woodward, Brindle & Tunstall-Pedoe, 2007).”
BackgroundMethodInfluential
Cardiovascular disease and depression in psoriatic arthritis: Multidimensional comorbidities requiring multidisciplinary management
So, H., & Tam, L. (2021) · Best Practice & Research Clinical Rheumatology · doi:10.1016/j.berh.2021.101689 · Cited by 17
Digital health RCT interventions for cardiovascular disease risk reduction: a systematic review and meta-analysis
Devani, R., Kirubakaran, A., & Molokhia, M. (2022) · Health and Technology · doi:10.1007/s12553-022-00651-0 · Cited by 15
“Framingham Risk Score (FRS) was the most common risk score—reported in 4 of the included studies; D’Agostino risk score (based on FRS) and QRISK2 (an earlier version of QRISK3 previously used in UK Primary Care) reported by Gonzalez-Sanchez [26] and Salisbury [27] respectively.”
MethodInfluential
Design and baseline characteristics of the PerfectFit study: a multicenter cluster-randomized trial of a lifestyle intervention in employees with increased cardiovascular risk
Kouwenhoven-Pasmooij, T. A., Djikanović, B., Robroek, S. J., Helmhout, P. H., Alex Burdorf, A., & Hunink, M. G. M. (2015) · BMC Public Health · doi:10.1186/s12889-015-2059-9 · Cited by 16
“Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, Minhas R, Sheikh A, et al. Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2.”
BackgroundInfluential
Communicating Cardiovascular Disease Risk to People with Psoriasis: What Techniques do Practitioners Use?
Keyworth, C., Nelson, P. A., Chew-Graham, C. A., Kane, K., Pearce, C. J., Griffiths, C. E. M., Chisholm, A., & Cordingley, L. (2015) · International Journal of Behavioral Medicine · doi:10.1007/s12529-015-9517-8 · Cited by 15
Statin prescribing for prevention of cardiovascular disease amongst people with severe mental illness: Cohort study in UK primary care
Blackburn, R. M., Osborn, D., Walters, K. R., Nazareth, I., & Petersen, I. (2017) · Schizophrenia Research · doi:10.1016/j.schres.2017.05.028 · Cited by 15
“However, differences in age and sex (key predictors of CVD (D'Agostino et al., 2008; Hippisley-Cox et al., 2008)) were controlled for at both the study design and analysis stage and data on diabetes and antihypertensive medication were fully observed, thereby improving control for confounding.”
High-sensitivity troponin I for cardiovascular risk stratification in the general asymptomatic population: Perspectives from Asia-Pacific
Lam, C. S. P., Castillo, R., Ho, D. T., Kasliwal, R. R., Khurana, R., Naik, S. R., Omland, T., Parsonage, W. A., Phrommintikul, A., Tobing, D., et al. (2019) · International Journal of Cardiology · doi:10.1016/j.ijcard.2019.01.107 · Cited by 15
“Current CVD risk stratification strategies cited by group members include risk factor counting and estimation based on experience, the American College of Cardiology/ American Heart Association (ACC/AHA) risk calculator [11], the Framingham risk score [12,13], SCORE (Systematic Coronary Risk Evaluation) risk charts [1…”
Background
Cardiovascular risk assessment models: Have we found the perfect solution yet?
Abidov, A., & Chehab, O. (2019) · Journal of Nuclear Cardiology · doi:10.1007/s12350-019-01642-x · Cited by 15
Lifetime cardiovascular risk is associated with a multimarker score of systemic oxidative status in young adults independently of traditional risk factors
Rodríguez‐Sánchez, E., Navarro‐García, J. A., Aceves‐Ripoll, J., González‐Lafuente, L., Corbacho-Alonso, N., Martínez, P., Calvo‐Bonacho, E., Álvarez‐Llamas, G., Barderas, M. G., Ruilope, L. M., et al. (2019) · Translational research · doi:10.1016/j.trsl.2019.06.002 · Cited by 15
Personalized E-Coaching in Cardiovascular Risk Reduction: A Randomized Controlled Trial
Khanji, M. Y., Balawon, A., Boubertakh, R., Hofstra, L., Narula, J., Hunink, M. G. M., Pugliese, F., & Petersen, S. E. (2019) · Annals of Global Health · doi:10.5334/aogh.2496 · Cited by 15
Gamma-glutamyl transferase and cardiovascular risk in nonalcoholic fatty liver disease: The Gut and Obesity Asia initiative
Pitisuttithum, P., Chan, W., Goh, G. B., Fan, J., Song, M. J., Charatcharoenwitthaya, P., Duseja, A. K., Dan, Y.-Y., Imajo, K., Nakajima, A., et al. (2020) · World Journal of Gastroenterology · doi:10.3748/wjg.v26.i19.2416 · Cited by 15
Chapter 2: Pharmacological cholesterol-lowering treatment in adults
(2013) · Kidney International Supplements · doi:10.1038/kisup.2013.34 · Cited by 16

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

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.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks