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Items: 1 to 20 of 152

1.

Predicting the 30-year risk of cardiovascular disease: the framingham heart study.

Pencina MJ, D'Agostino RB Sr, Larson MG, Massaro JM, Vasan RS.

Circulation. 2009 Jun 23;119(24):3078-84. doi: 10.1161/CIRCULATIONAHA.108.816694. Epub 2009 Jun 8.

2.

Sex-specific differences in the predictive value of cholesterol homeostasis markers and 10-year cardiovascular disease event rate in Framingham Offspring Study participants.

Matthan NR, Zhu L, Pencina M, D'Agostino RB, Schaefer EJ, Lichtenstein AH.

J Am Heart Assoc. 2013 Feb 19;2(1):e005066. doi: 10.1161/JAHA.112.005066.

3.

General cardiovascular risk profile for use in primary care: the Framingham Heart Study.

D'Agostino RB Sr, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, Kannel WB.

Circulation. 2008 Feb 12;117(6):743-53. doi: 10.1161/CIRCULATIONAHA.107.699579. Epub 2008 Jan 22.

4.

Predictive accuracy of the 'Framingham's general CVD algorithm' in a Middle Eastern population: Tehran Lipid and Glucose Study.

Bozorgmanesh M, Hadaegh F, Azizi F.

Int J Clin Pract. 2011 Mar;65(3):264-73. doi: 10.1111/j.1742-1241.2010.02529.x.

PMID:
21314863
5.

Risk functions for prediction of cardiovascular disease in elderly Australians: the Dubbo Study.

Simons LA, Simons J, Friedlander Y, McCallum J, Palaniappan L.

Med J Aust. 2003 Feb 3;178(3):113-6.

6.

Circulating Proneurotensin Concentrations and Cardiovascular Disease Events in the Community: The Framingham Heart Study.

Januzzi JL Jr, Lyass A, Liu Y, Gaggin H, Trebnick A, Maisel AS, D'Agostino RB Sr, Wang TJ, Massaro J, Vasan RS.

Arterioscler Thromb Vasc Biol. 2016 Aug;36(8):1692-7. doi: 10.1161/ATVBAHA.116.307847. Epub 2016 Jun 16.

7.

Cardiovascular risk prediction models for people with severe mental illness: results from the prediction and management of cardiovascular risk in people with severe mental illnesses (PRIMROSE) research program.

Osborn DP, Hardoon S, Omar RZ, Holt RI, King M, Larsen J, Marston L, Morris RW, Nazareth I, Walters K, Petersen I.

JAMA Psychiatry. 2015 Feb;72(2):143-51. doi: 10.1001/jamapsychiatry.2014.2133.

8.

Usefulness of the left ventricular myocardial contraction fraction in healthy men and women to predict cardiovascular morbidity and mortality.

Chuang ML, Gona P, Salton CJ, Yeon SB, Kissinger KV, Blease SJ, Levy D, O'Donnell CJ, Manning WJ.

Am J Cardiol. 2012 May 15;109(10):1454-8. doi: 10.1016/j.amjcard.2012.01.357. Epub 2012 Feb 28.

9.

Prediction of First Cardiovascular Disease Event in Type 1 Diabetes Mellitus: The Steno Type 1 Risk Engine.

Vistisen D, Andersen GS, Hansen CS, Hulman A, Henriksen JE, Bech-Nielsen H, Jørgensen ME.

Circulation. 2016 Mar 15;133(11):1058-66. doi: 10.1161/CIRCULATIONAHA.115.018844. Epub 2016 Feb 17.

10.

Quantifying cardiometabolic risk using modifiable non-self-reported risk factors.

Marino M, Li Y, Pencina MJ, D'Agostino RB Sr, Berkman LF, Buxton OM.

Am J Prev Med. 2014 Aug;47(2):131-40. doi: 10.1016/j.amepre.2014.03.006. Epub 2014 Jun 17.

11.

Observed versus predicted cardiovascular events and all-cause death in HIV infection: a longitudinal cohort study.

De Socio GV, Pucci G, Baldelli F, Schillaci G.

BMC Infect Dis. 2017 Jun 12;17(1):414. doi: 10.1186/s12879-017-2510-x.

12.

Performance of Framingham cardiovascular disease (CVD) predictions in the Rotterdam Study taking into account competing risks and disentangling CVD into coronary heart disease (CHD) and stroke.

van Kempen BJ, Ferket BS, Kavousi M, Leening MJ, Steyerberg EW, Ikram MA, Witteman JC, Hofman A, Franco OH, Hunink MG.

Int J Cardiol. 2014 Feb 15;171(3):413-8. doi: 10.1016/j.ijcard.2013.12.036. Epub 2013 Dec 27.

13.

Cardiovascular risk prediction in a population with the metabolic syndrome: Framingham vs. UKPDS algorithms.

Zomer E, Liew D, Owen A, Magliano DJ, Ademi Z, Reid CM.

Eur J Prev Cardiol. 2014 Mar;21(3):384-90. doi: 10.1177/2047487312449307. Epub 2012 May 15.

PMID:
22588087
14.

Performance of the QRISK cardiovascular risk prediction algorithm in an independent UK sample of patients from general practice: a validation study.

Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, Brindle P.

Heart. 2008 Jan;94(1):34-9. Epub 2007 Oct 4.

PMID:
17916661
15.

Are traditional risk factors valid for assessing cardiovascular risk in end-stage renal failure patients?

Shah DS, Polkinghorne KR, Pellicano R, Kerr PG.

Nephrology (Carlton). 2008 Dec;13(8):667-71. doi: 10.1111/j.1440-1797.2008.00982.x. Epub 2008 Aug 28.

PMID:
18761627
16.
17.

Peripheral neuropathy and the risk of cardiovascular events in type 2 diabetes mellitus.

Brownrigg JR, de Lusignan S, McGovern A, Hughes C, Thompson MM, Ray KK, Hinchliffe RJ.

Heart. 2014 Dec;100(23):1837-43. doi: 10.1136/heartjnl-2014-305657. Epub 2014 Aug 5.

PMID:
25095826
18.

Assessment of diet quality improves the classification ability of cardiovascular risk score in predicting future events: The 10-year follow-up of the ATTICA study (2002-2012).

Georgousopoulou EN, Panagiotakos DB, Pitsavos C, Stefanadis C; ATTICA study group.

Eur J Prev Cardiol. 2015 Nov;22(11):1488-98. doi: 10.1177/2047487314555095. Epub 2014 Oct 14.

PMID:
25316412
19.

Personalized prediction of lifetime benefits with statin therapy for asymptomatic individuals: a modeling study.

Ferket BS, van Kempen BJ, Heeringa J, Spronk S, Fleischmann KE, Nijhuis RL, Hofman A, Steyerberg EW, Hunink MG.

PLoS Med. 2012;9(12):e1001361. doi: 10.1371/journal.pmed.1001361. Epub 2012 Dec 27.

20.

Predictive value of brachial flow-mediated dilation for incident cardiovascular events in a population-based study: the multi-ethnic study of atherosclerosis.

Yeboah J, Folsom AR, Burke GL, Johnson C, Polak JF, Post W, Lima JA, Crouse JR, Herrington DM.

Circulation. 2009 Aug 11;120(6):502-9. doi: 10.1161/CIRCULATIONAHA.109.864801. Epub 2009 Jul 27.

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