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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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“This definition is consistent with the one used in the most common risk scores used in clinical practice.(18,19) Information on family history of CVD was considered missing for patients who reported that they did not know their family history of CVD or who did not fill in the corresponding questionnaire item.”
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Prevention of cardiovascular disease among people living with HIV in sub-Saharan Africa
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“For the use of the accurate use of the QRISK2/3 score we would need more information on kidney disease, rheumatoid arthritis and atrial fibrillation [64,65].”
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“6 mmol/L).(27) Ten-year CVD risk was calculated using the QRISK calculator (http://www.”
Knowledge and perception of cardiovascular disease risk among patients with rheumatoid arthritis
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BackgroundChoi, D., Pavlou, M., Omar, R., Arts, M. P., Balabaud, L., Buchowski, J. M., Bünger, C. E., Chung, C. K., Coppes, M. H., Depreitere, B., et al. (2018) · European Journal of Cancer · doi:10.1016/j.ejca.2018.11.011 · Cited by 47
“To address these deficiencies, we sought to develop a risk calculator similar to those for stroke [14] and cardiovascular disease [15] that is individualised for the patient.”
MethodKalra, D. K., Vijayaraghavan, K., Sikand, G., Desai, N. R., Joshi, P. H., Mehta, A., Karmally, W., Vani, A. K., Sitafalwalla, S. J., Puri, R., et al. (2021) · Journal of clinical lipidology · doi:10.1016/j.jacl.2021.03.007 · Cited by 50
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“…the literature and selected the common components between Framingham risk scores [6], SCORE CVD death risk score [7], QRISK risk calculator [12], Reynolds CVD risk score [8], ACC/AHA pooled cohort hard CVD risk calculator [9], JBS3 risk score [10], MESA risk score [11], and China-PAR risk…”
BackgroundAdapting the severe mental illness physical Health Improvement Profile for use in primary care
Hardy, S., & Gray, R. J. (2010) · International Journal of Mental Health Nursing · doi:10.1111/j.1447-0349.2010.00686.x · Cited by 46
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Cardiovascular disease in rheumatoid arthritis: a step forward
Myasoedova, E., & Gabriel, S. E. (2010) · Current Opinion in Rheumatology · doi:10.1097/bor.0b013e3283379b91 · Cited by 46
Using Body Mass Index Data in the Electronic Health Record to Calculate Cardiovascular Risk
Green, B. B., Anderson, M. L., Cook, A. J., Catz, S. L., Fishman, P. A., McClure, J. B., & Reid, R. (2012) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2011.12.009 · Cited by 46
Hung, C.-Y., Lin, C., Lan, T., Peng, G.-S., & Lee, C.-C. (2019) · PLoS ONE · doi:10.1371/journal.pone.0213007 · Cited by 44
“817 QRISK2, 2008 [53] age 35–74, men 35,595 0.”
MethodResultInfluentialPrimary prevention of acute cardiovascular events by influenza vaccination: an observational study
Davidson, J. A., Banerjee, A., Douglas, I., Leyrat, C., Pebody, R. G., McDonald, H. I., Herrett, E. L., Forbes, H. J., Smeeth, L. L., & Warren‐Gash, C. (2022) · European Heart Journal · doi:10.1093/eurheartj/ehac737 · Cited by 47
“We only included persistent and diagnosed hypertension, defined by coded CPRD diagnoses.35 QRISK2 uses many risk factors to estimate an individual’s absolute 10-year risk of CVD.37 The risk factors considered are age, sex, ethnicity, deprivation score for area of residence, family history of coronary heart disease in…”
BackgroundMethodInfluentialCai, Y., Gong, D.-X., Tang, L.-Y., Cai, Y. H., Li, H., Jing, T.-C., Gong, M., Hu, W., Zhang, Z.-W., Zhang, X., et al. (2024) · Journal of Medical Internet Research · doi:10.2196/47645 · Cited by 54
“All the 3 versions of QRISK set the end points as CVDs, which are defined as a composite outcome of CHD, stroke, and transient ischemic attacks [5,111,137].”
de la Iglesia, B., Potter, J. F., Poulter, N. R., Robins, M. M., & Skinner, J. S. (2010) · Heart · doi:10.1136/hrt.2010.203364 · Cited by 45
Isolated Systolic Hypertension in Young People Is Not Spurious and Should Be Treated
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Pasea, L., Chung, S., Pujades‐Rodríguez, M., Moayyeri, A., Denaxas, S., Fox, K. A. A., Wallentin, L. C., Pocock, S., Timmis, A., Banerjee, A., et al. (2017) · European Heart Journal · doi:10.1093/eurheartj/ehw683 · Cited by 45
“42 We present a web-based tool providing personalised risk predictions at http://www.caliberresearch.org/prolonged_dapt_ benefits_harms_risks (10 January 2017).”
MethodIncluding Socioeconomic Status in Coronary Heart Disease Risk Estimation
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Cardiovascular risk assessment tools in Asia
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Barber, C. E. H., Smith, A., Esdaile, J. M., Barnabe, C. C. M., Martin, L. O., Faris, P., Hazlewood, G. S., Noormohamed, R., Alvarez, N., Mancini, G. B. J., et al. (2014) · Arthritis Care & Research · doi:10.1002/acr.22419 · Cited by 49
“One NICE QI (30) addressed CV risk assessment using a calculator that adjusted for RA, but the recommended calculator (QRISK [47]) is specific to the UK and is not endorsed in guidelines outside of the UK.”
MethodDyslipidemia in Special Ethnic Populations
Pu, J., Romanelli, R. J., Zhao, B., Azar, K. M. J., Hastings, K. G., Nimbal, V. C., Fortmann, S. P., & Palaniappan, L. P. (2015) · Cardiology Clinics · doi:10.1016/j.ccl.2015.01.005 · Cited by 45
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“The QRISK2 (24) was calculated to estimate the risk of an individual having a cardio-”
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Using genetics to predict the natural history of asthma?
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Health outcomes after myocardial infarction: A population study of 56 million people in England
Hall, M., Smith, L., Wu, J., Hayward, C., Batty, J. A., Lambert, P. C., Hemingway, H., & Gale, C. P. (2024) · PLoS Medicine · doi:10.1371/journal.pmed.1004343 · Cited by 48
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“For Healthlines CVD, participants were aged 40–74, with 20 % or greater risk of having a cardiovascular event in the next 10 years (QRISK2 score [19]) and had to have at least 1 of 3 modifiable risk factors (systolic blood pressure of 140 or greater; a body mass index (BMI) ≥ 30; or current smoker).”
BackgroundImprovement in Cardiovascular Risk Prediction with Electronic Health Records
Pike, M., Decker, P. A., Larson, N. B., Sauver, J. L. S., Takahashi, P. Y., Roger, V. L., Rocca, W. A., Miller, V. M., Olson, J. E., Pathak, J., et al. (2016) · Journal of Cardiovascular Translational Research · doi:10.1007/s12265-016-9687-z · Cited by 47
“One of the first studies compared the performance of the QRISKII and the modified FRS equation recommended by NICE [21] in the national QResearch® database [9].”
Influential2016 ESC/EAS Guidelines for the Management of Dyslipidaemias
Catapano, A. L., Graham, I. M., Backer, G. D., Wiklund, O., Chapman, M. J., Drexel, H., Hoes, A. W., Jennings, C. S., Landmesser, U., Pedersen, T. R., et al. (2016) · Kardiologia Polska · doi:10.5603/kp.2016.0157 · Cited by 48
Thomas, C., Sadler, S., Breeze, P. R., Squires, H., Gillett, M., & Brennan, A. (2017) · BMJ Open · doi:10.1136/bmjopen-2016-014953 · Cited by 44
“First, disease risk is influenced by subgroup; for example, both ethnicity and socioeconomic status are parameters in the QRISK equations that are used in the model to determine CVD risk.(22) This means that even if a given individual reduces their metabolic risk factors through the DPP they may still be at high risk…”
MethodPatient experience of NHS health checks: a systematic review and qualitative synthesis
Usher‐Smith, J. A., Harte, E., MacLure, C., Martin, A., Saunders, C. L., Meads, C., Walter, F. M., Griffin, S. J., & Mant, J. (2017) · BMJ Open · doi:10.1136/bmjopen-2017-017169 · Cited by 42
Targeted lung cancer screening selects individuals at high risk of cardiovascular disease
Balata, H., Knight, S., Barber, P. V., Colligan, D., Crosbie, E. J., Duerden, R., Elton, P., Evison, M., Greaves, M. D., Howells, J., et al. (2018) · Lung Cancer · doi:10.1016/j.lungcan.2018.08.006 · Cited by 42
“In the UK, the National Institute for Health and Care Excellence (NICE) recommends primary prevention of CVD in individuals with a 10-year risk of cardiovascular event ≥10% (4-6).”
BackgroundEffect of carotid image-based phenotypes on cardiovascular risk calculator: AECRS1.0
Khanna, N. N., Jamthikar, A. D., Gupta, D., Araki, T., Piga, M., Saba, L., Carcassi, C., Nicolaides, A. N., Laird, J. R., Suri, H. S., et al. (2019) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-019-01975-2 · Cited by 42
Generating and Evaluating Synthetic UK Primary Care Data: Preserving Data Utility & Patient Privacy
Wang, Z., Myles, P. R., & Tucker, A. (2019) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms.2019.00036 · Cited by 42
“For example, a predefined set of variables such as cardiovascular risk factors [24].”
BackgroundChacko, M., Sarma, P. S., Harikrishnan, S., Zachariah, G. A., & Jeemon, P. (2020) · Wellcome Open Research · doi:10.12688/wellcomeopenres.15829.2 · Cited by 51
Bressington, D., Mui, J., Tse, M.-L., Gray, R., Cheung, E. F. C., & Chien, W.-T. (2016) · BMC Psychiatry · doi:10.1186/s12888-016-1121-1 · Cited by 40
“The version 2 of QRISK® (QRISK®2-2015) [27, 28] was used to calculate the 10-year CVD risk score of each participant.”
MethodInfluentialAbeles, R. D., Mullish, B. H., Forlano, R., Kimhofer, T., Adler, M., Tzallas, A. T., Γιαννακέας, Ν., Yee, M. S., Mayet, J., Goldin, R. D., et al. (2019) · Alimentary Pharmacology & Therapeutics · doi:10.1111/apt.15192 · Cited by 41
“Non‐alcoholic fatty liver disease (NAFLD) is estimated to affect 25% of the world's population and represents a spectrum of liver disease that ranges from simple steatosis (SS) to steatohepatitis (NASH), found in 30%‐70% on biopsy, with or without fibrosis.1 Approximately 41% of patients with NASH will experience prog…”
BackgroundInfluentialCuadrado‐Godia, E., Jamthikar, A. D., Gupta, D., Khanna, N. N., Araki, T., Maniruzzaman, M., Saba, L., Nicolaides, A. N., Sharma, A., Omerzu, T., et al. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.03.020 · Cited by 41
Subclinical Atherosclerosis: Evolving Role of Carotid Intima-Media Thickness
Mookadam, F., Moustafa, S., Lester, S. J., & Warsame, T. A. (2010) · Preventive Cardiology · doi:10.1111/j.1751-7141.2010.00072.x · Cited by 40
Skin Autofluorescence and All-Cause Mortality in Stage 3 CKD
Fraser, S. D., Roderick, P. J., McIntyre, N. J., Harris, S., McIntyre, C. W., Fluck, R., & Taal, M. W. (2014) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.09510913 · Cited by 41
“When considering cardiovascular risk, for United Kingdom populations, the QRisk2 score seems to achieve better predictive accuracy for CVD than the Framingham score by incorporating other comorbidities, including CKD (although CKD is treated as a simple dichotomous variable in QRisk2) (41,42).”
Prediction of cardiovascular disease risk based on major contributing features
Peng, M., Hou, F. F., Cheng, Z.-X., Shen, T., Liu, K., Zhao, C., & Zheng, W. (2023) · Scientific Reports · doi:10.1038/s41598-023-31870-8 · Cited by 48
Variations in the organization and delivery of the 'NHS health check' in primary care
Nicholas, J. M., Burgess, C. C., Dodhia, H., Miller, J. M., Fuller, F., Cajeat, E., & Gulliford, M. C. (2012) · Journal of Public Health · doi:10.1093/pubmed/fds062 · Cited by 40
Coronary heart disease in Indian Asians
Tan, S.-T., Scott, W. R., Panoulas, V. F., Sehmi, J., Zhang, W., Scott, J., Elliott, P., Chambers, J. C., & Kooner, J. S. (2014) · Global Cardiology Science and Practice · doi:10.5339/gcsp.2014.4 · Cited by 40
Sheppard, J. P., Fletcher, K., McManus, R. J., & Mant, J. (2013) · British Journal of General Practice · doi:10.3399/bjgp14x676447 · Cited by 40
Hirata, T., Arai, Y., Takayama, M., Abe, Y., Ohkuma, K., & Takebayashi, T. (2017) · Journal of Atherosclerosis and Thrombosis · doi:10.5551/jat.37911 · Cited by 40
Shen, J., Lam, S. H. M., Shang, Q., Wong, C. K., Li, E. K., Wong, P. C. H., Kun, E. W. L., Cheng, I. T., Li, M., Li, T. K., et al. (2017) · The Journal of Rheumatology · doi:10.3899/jrheum.170025 · Cited by 39
Koye, D. N., Ling, J. Z. J., Dibato, J. E., Khunti, K., Montvida, O., & Paul, S. K. (2020) · Diabetes Care · doi:10.2337/dc20-0417 · Cited by 43
“Overweight 3,417 (20) 8,537 (23) 17,157 (27) 24,455 (33) 21,237 (39) 74,803 (30)”
BackgroundMethodInfluentialPrediction of Cardiovascular Disease Risk Accounting for Future Initiation of Statin Treatment
Xu, Z., Arnold, M., Stevens, D., Kaptoge, S. K., Pennells, L. A. M., Sweeting, M. J., Barrett, J., Angelantonio, E. D., & Wood, A. (2021) · American Journal of Epidemiology · doi:10.1093/aje/kwab031 · Cited by 43
“ischemic attack, matching the definition used by QRISK algorithm (8,15), which is”
MethodEthnic differences in SCORE cardiovascular risk in Oslo, Norway
Kumar, B., Selmer, R. M., Lindman, A. S., Tverdal, A., Falster, K., & Meyer, H. E. (2009) · European Journal of Cardiovascular Prevention & Rehabilitation · doi:10.1097/hjr.0b013e3283294b07 · Cited by 40
Chapter 5: Referral to specialists and models of care
(2012) · Kidney International Supplements · doi:10.1038/kisup.2012.68 · Cited by 42
Cardoso, P., Young, K., Nair, A. T. N., Hopkins, R., McGovern, A. P., Haider, E., Karunaratne, P., Donnelly, L. A., Mateen, B. A., Sattar, N. A., et al. (2024) · Diabetologia · doi:10.1007/s00125-024-06099-3 · Cited by 40
Kearns, B., Gallagher, H., & de Lusignan, S. (2013) · BMC Nephrology · doi:10.1186/1471-2369-14-49 · Cited by 39
Vedanthan, R., Kamano, J. H., Yebei, V. N., DeLong, A. K., Were, M. C., Finkelstein, E. A., Menya, D., Akwanalo, C. O., Bloomfield, G. S., Binanay, C. A., et al. (2014) · Trials · doi:10.1186/1745-6215-15-143 · Cited by 39
“We will evaluate the incremental cost-effectiveness of each intervention arm, both in terms of costs per unit decrease in BP and in terms of costs per unit reduction in CVD risk by extrapolating one-year BP reductions to CVD risk reductions based on the QRISKTM2-2011 CVD risk calculator (University of Nottingham, Nott…”
MethodThe provision of NHS health checks in a community setting: an ethnographic account
Riley, R. A., Coghill, N. F., Montgomery, A. A., Feder, G. S., & Horwood, J. P. (2015) · BMC Health Services Research · doi:10.1186/s12913-015-1209-1 · Cited by 39
Dixon, P., Hollinghurst, S. P., Edwards, L., Thomas, C., Gaunt, D., Foster, A., Large, S., Montgomery, A. A., & Salisbury, C. (2016) · BMJ Open · doi:10.1136/bmjopen-2016-012352 · Cited by 39
Shahid, S. U., Shabana, S., Cooper, J. A., Beaney, K. E., Li, K., Rehman, A., & Humphries, S. E. (2017) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2017.01.024 · Cited by 40
“Systematic Coronary Risk Evaluation (SCORE) system [8], the Reynolds risk score [9] and QRISK2 [10].”
MethodCorrelates and assessment of excess cardiovascular risk in bronchiectasis
Saleh, A., Kwok, B., Brown, J., & Hurst, J. R. (2017) · European Respiratory Journal · doi:10.1183/13993003.01127-2017 · Cited by 41
“It estimates the 10-year absolute risk of myocardial infarction and stroke and the relative risk of these outcomes compared to a healthy individual of the same age, gender and ethnicity [11].”
BackgroundMethodPredicting Bleeding Risk to Guide Aspirin Use for the Primary Prevention of Cardiovascular Disease
Selak, V., Jackson, R., Poppe, K., Wu, B., Harwood, M. L. N., Grey, C., Pylypchuk, R. D., Mehta, S., Kerr, A., & Wells, S. (2019) · Annals of Internal Medicine · doi:10.7326/m18-2808 · Cited by 40
Merry, A. H. H., Boer, J. M. A., Schouten, L. J., Feskens, E. J. M., Verschuren, W. M. M., Gorgels, A. P. M., & van den Brandt, P. A. (2011) · BMC Cardiovascular Disorders · doi:10.1186/1471-2261-11-13 · Cited by 40
Chronic diseases in Europe's migrant and ethnic minorities: challenges, solutions and a vision
Bhopal, R. (2009) · European Journal of Public Health · doi:10.1093/eurpub/ckp024 · Cited by 39
(2012) · Kidney International Supplements · doi:10.1038/kisup.2012.72 · Cited by 39
Spatola, C. A. M., Manzoni, G. M., Castelnuovo, G., Malfatto, G., Facchini, M., Goodwin, C. L., Baruffi, M., & Molinari, E. (2014) · Health and Quality of Life Outcomes · doi:10.1186/1477-7525-12-22 · Cited by 39
“Discussion As shown by large epidemiological studies [76,77] cardiovascular risk can be described as multi-factorial and preventable because a greater percentage of the overall risk is comprised of modifiable risk factors [1-3].”
BackgroundGoh, L. G. H., Welborn, T. A., & Dhaliwal, S. S. (2014) · BMC Women s Health · doi:10.1186/1472-6874-14-118 · Cited by 40
“body mass index (BMI) (which is also found in a simpler version of the general CVD risk score model), family history (also found in the Reynolds and ASSIGN model), Townsend deprivation score (a measure of social deprivation is also found in the ASSIGN model), use of antihypertensive medication (also found in the gener…”
MethodResultLi, Y., Sperrin, M., Belmonte, M., Pate, A., Ashcroft, D. M., & van Staa, T. P. (2019) · Scientific Reports · doi:10.1038/s41598-019-47712-5 · Cited by 39
Arpaïa, P., Cuocolo, R., Donnarumma, F., Espósito, A., Moccaldi, N., Natalizio, A., & Prevete, R. (2020) · Measurement · doi:10.1016/j.measurement.2020.108551 · Cited by 39
Yu, K.-H., Chen, H.-H., Cheng, T.-T., Jan, Y.-J., Weng, M.-Y., Lin, Y.-J., Chen, H.-A., Cheng, J.-T., Huang, K.-Y., Li, K.-J., et al. (2022) · Medicine · doi:10.1097/md.0000000000028501 · Cited by 38
“[63] A serial study revealed that corticosteroids and NSAIDs are risk factors for CVD.[45,46,62] The British Society of Rheumatology guidelines outlined that corticosteroid may worsen the CVD risk,[41] and the EULAR guidelines recommend the use of the lowest dose of corticosteroids.”
BackgroundInfluentialMortality in COVID-19 among women on hormone replacement therapy: a retrospective cohort study
Dambha‐Miller, H., Hinton, W., Wilcox, C. R., Joy, M., Feher, M. D., & de Lusignan, S. (2022) · Family Practice · doi:10.1093/fampra/cmac041 · Cited by 38
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
About these checks
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