Skip to main content

Advertisement

SpringerLink
Log in

Cardiovascular Disease Risk Assessment: a Review of Risk Factor-based Algorithms and Assessments of Vascular Health

  • Novel + Emerging Risk Factors (K Nasir, Section Editor)
  • Published:
Current Cardiovascular Risk Reports Aims and scope Submit manuscript

Abstract

Risk factors related to the development of subclinical atherosclerosis and subsequent cardiovascular disease (CVD) events are well established. Starting with the Framingham Risk Score (FRS), several algorithms have been developed using these risk factors to predict the development of CVD, and the recent ACC/AHA guidelines recommend the use of new pooled cohort equations to predict 10-year risk for a first hard atherosclerotic cardiovascular disease (ASCVD) event in subjects 40–79 years of age. However, the limitations of these risk factor-based algorithms have been well documented and include decreased calibration when applied to populations outside of the initial cohort, inability to account for lifetime risk in younger populations, and the heterogeneity that exists between the presence of risk factors and actual atherosclerotic disease burden. As such, recent strategies have attempted to incorporate measurements of both vascular health, including carotid interna media thickness (CIMT), microalbuminuria, flow-mediated dilation (FMD) and aortic stiffness, and subclinical atherosclerosis through coronary artery calcium (CAC) screening into traditional risk assessment. Here, we review these different strategies for risk assessment and examine how these strategies can be combined to improve discrimination and reliability.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Laslett LJ, Alagona Jr P, Clark 3rd BA, Drozda Jr JP, Saldivar F, Wilson SR, et al. The worldwide environment of cardiovascular disease: prevalence, diagnosis, therapy, and policy issues: a report from the American College of Cardiology. J Am Coll Cardiol. 2012;60(25 Suppl):S1–S49.

    Article  PubMed  Google Scholar 

  2. Yusuf S, Hawken S, Ounpuu S, Dans T, Avezum A, Lanas F, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet. 2004;364(9438):937–52.

    Article  PubMed  Google Scholar 

  3. National Cholesterol Education Program Expert Panel on Detection E. Treatment of high blood cholesterol in A. Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation. 2002;106(25):3143–421.

    Google Scholar 

  4. Cook NR, Paynter NP, Eaton CB, Manson JE, Martin LW, Robinson JG, et al. Comparison of the Framingham and Reynolds risk scores for global cardiovascular risk prediction in the multiethnic Women’s Health Initiative. Circulation. 2012;125(14):1748.

    Article  PubMed Central  PubMed  Google Scholar 

  5. Bitton A, Gaziano TA. The Framingham Heart Study’s impact on global risk assessment. Prog Cardiovasc Dis. 2010;53(1):68–78.

    Article  PubMed Central  PubMed  Google Scholar 

  6. Emerging Risk Factors C, Kaptoge S, Di Angelantonio E, Pennells L, Wood AM, White IR, et al. C-reactive protein, fibrinogen, and cardiovascular disease prediction. N Engl J Med. 2012;367(14):1310–20.

    Article  PubMed  Google Scholar 

  7. Siontis GC, Tzoulaki I, Siontis KC, Ioannidis JP. Comparisons of established risk prediction models for cardiovascular disease: systematic review. BMJ. 2012;344:e3318.

    Article  PubMed  Google Scholar 

  8. Ridker PM, Buring JE, Rifai N, Cook NR. Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. JAMA J Am Med Assoc. 2007;297(6):611–9.

    Article  CAS  Google Scholar 

  9. 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. BMJ. 2008;336(7659):1475–82.

    Article  PubMed Central  PubMed  Google Scholar 

  10. Conroy RM, Pyorala K, Fitzgerald AP, Sans S, Menotti A, De Backer G, et al. Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J. 2003;24(11):987–1003.

    Article  PubMed  CAS  Google Scholar 

  11. D’Agostino Sr RB, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation. 2008;117(6):743–53.

    Article  PubMed  Google Scholar 

  12. Goff DC, Jr., Lloyd-Jones DM, Bennett G, Coady S, D’Agostino RB, Sr., Gibbons R, et al. 2013 ACC/AHA Guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation. 2013 Nov 12. This article describes the most recent guidelines on cardiovascular risk assessment from the ACC/AHA.

  13. Folsom AR, Kronmal RA, Detrano RC, O’Leary DH, Bild DE, Bluemke DA, et al. Coronary artery calcification compared with carotid intima-media thickness in the prediction of cardiovascular disease incidence: the Multi-Ethnic Study of Atherosclerosis (MESA). Arch Intern Med. 2008;168(12):1333–9.

    Article  PubMed Central  PubMed  Google Scholar 

  14. Kitagawa K, Hougaku H, Yamagami H, Hashimoto H, Itoh T, Shimizu Y, et al. Carotid intima-media thickness and risk of cardiovascular events in high-risk patients. Results of the Osaka Follow-Up Study for Carotid Atherosclerosis 2 (OSACA2 Study). Cerebrovasc Dis. 2007;24(1):35–42.

    Article  PubMed  Google Scholar 

  15. Yeboah J, McClelland RL, Polonsky TS, Burke GL, Sibley CT, O’Leary D, et al. Comparison of novel risk markers for improvement in cardiovascular risk assessment in intermediate-risk individuals. JAMA J Am Med Assoc. 2012;308(8):788–95.

    Article  CAS  Google Scholar 

  16. Peters SA, den Ruijter HM, Bots ML, Moons KG. Improvements in risk stratification for the occurrence of cardiovascular disease by imaging subclinical atherosclerosis: a systematic review. Heart. 2012;98(3):177–84.

    Article  PubMed  Google Scholar 

  17. Yuyun MF, Khaw KT, Luben R, Welch A, Bingham S, Day NE, et al. A prospective study of microalbuminuria and incident coronary heart disease and its prognostic significance in a British population: the EPIC-Norfolk study. Am J Epidemiol. 2004;159(3):284–93.

    Article  PubMed  Google Scholar 

  18. Yuyun MF, Adler AI, Wareham NJ. What is the evidence that microalbuminuria is a predictor of cardiovascular disease events? Curr Opin Nephrol Hypertens. 2005;14(3):271–6.

    Article  PubMed  CAS  Google Scholar 

  19. Jambrik Z, Venneri L, Varga A, Rigo F, Borges A, Picano E. Peripheral vascular endothelial function testing for the diagnosis of coronary artery disease. Am Heart J. 2004;148(4):684–9.

    Article  PubMed  Google Scholar 

  20. Huang PH, Chen JW, Lu TM, Yu-An Ding P, Lin SJ. Combined use of endothelial function assessed by brachial ultrasound and high-sensitive C-reactive protein in predicting cardiovascular events. Clin Cardiol. 2007;30(3):135–40.

    Article  PubMed  Google Scholar 

  21. Vlachopoulos C, Aznaouridis K, Stefanadis C. Prediction of cardiovascular events and all-cause mortality with arterial stiffness: a systematic review and meta-analysis. J Am Coll Cardiol. 2010;55(13):1318–27.

    Article  PubMed  Google Scholar 

  22. Budoff MJ, Young R, Lopez VA, Kronmal RA, Nasir K, Blumenthal RS, et al. Progression of coronary calcium and incident coronary heart disease events: MESA (Multi-Ethnic Study of Atherosclerosis). J Am Coll Cardiol. 2013;61(12):1231–9.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  23. Detrano R, Guerci AD, Carr JJ, Bild DE, Burke G, Folsom AR, et al. Coronary calcium as a predictor of coronary events in four racial or ethnic groups. N Engl J Med. 2008;358(13):1336–45.

    Article  PubMed  CAS  Google Scholar 

  24. DeFilippis AP, Blaha MJ, Ndumele CE, Budoff MJ, Lloyd-Jones DM, McClelland RL, et al. The association of Framingham and Reynolds risk scores with incidence and progression of coronary artery calcification in MESA (Multi-Ethnic Study of Atherosclerosis). J Am Coll Cardiol. 2011;58(20):2076–83.

    Article  PubMed Central  PubMed  Google Scholar 

  25. Okwuosa TM, Greenland P, Burke GL, Eng J, Cushman M, Michos ED, et al. Prediction of coronary artery calcium progression in individuals with low Framingham Risk Score: the Multi-Ethnic Study of Atherosclerosis. J Am Coll Cardiol Img. 2012;5(2):144–53.

    Article  Google Scholar 

  26. Michos ED, Nasir K, Braunstein JB, Rumberger JA, Budoff MJ, Post WS, et al. Framingham risk equation underestimates subclinical atherosclerosis risk in asymptomatic women. Atherosclerosis. 2006;184(1):201–6.

    Article  PubMed  CAS  Google Scholar 

  27. Guijarro C. High-sensitivity C-reactive protein: potential adjunct for global risk assessment in the primary prevention of cardiovascular disease. Circulation. 2001;104(22):E127.

    PubMed  CAS  Google Scholar 

  28. Ridker PM, Hennekens CH, Buring JE, Rifai N. C-reactive protein and other markers of inflammation in the prediction of cardiovascular disease in women. N Engl J Med. 2000;342(12):836–43.

    Article  PubMed  CAS  Google Scholar 

  29. Ridker PM, Cushman M, Stampfer MJ, Tracy RP, Hennekens CH. Plasma concentration of C-reactive protein and risk of developing peripheral vascular disease. Circulation. 1998;97(5):425–8.

    Article  PubMed  CAS  Google Scholar 

  30. Gaskins AJ, Wilchesky M, Mumford SL, Whitcomb BW, Browne RW, Wactawski-Wende J, et al. Endogenous reproductive hormones and C-reactive protein across the menstrual cycle: the BioCycle Study. Am J Epidemiol. 2012;175(5):423–31.

    Article  PubMed Central  PubMed  Google Scholar 

  31. Bower JK, Lazo M, Juraschek SP, Selvin E. Within-person variability in high-sensitivity C-reactive protein. Arch Intern Med. 2012;172(19):1519–21.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  32. Shah T, Newcombe P, Smeeth L, Addo J, Casas JP, Whittaker J, et al. Ancestry as a determinant of mean population C-reactive protein values: implications for cardiovascular risk prediction. Circ Cardiovasc Genet. 2010;3(5):436–44.

    Article  PubMed  CAS  Google Scholar 

  33. Cushman M, Legault C, Barrett-Connor E, Stefanick ML, Kessler C, Judd HL, et al. Effect of postmenopausal hormones on inflammation-sensitive proteins: the Postmenopausal Estrogen/Progestin Interventions (PEPI) Study. Circulation. 1999;100(7):717–22.

    Article  PubMed  CAS  Google Scholar 

  34. Miller M, Zhan M, Havas S. High attributable risk of elevated C-reactive protein level to conventional coronary heart disease risk factors: the Third National Health and Nutrition Examination Survey. Arch Intern Med. 2005;165(18):2063–8.

    Article  PubMed  CAS  Google Scholar 

  35. Folsom AR, Chambless LE, Ballantyne CM, Coresh J, Heiss G, Wu KK, et al. An assessment of incremental coronary risk prediction using C-reactive protein and other novel risk markers: the atherosclerosis risk in communities study. Arch Intern Med. 2006;166(13):1368–73.

    Article  PubMed  CAS  Google Scholar 

  36. Pandey AK, Pandey S, Blaha MJ, Agatston A, Feldman T, Ozner M, et al. Family history of coronary heart disease and markers of subclinical cardiovascular disease: where do we stand? Atherosclerosis. 2013;228(2):285–94.

    Article  PubMed  CAS  Google Scholar 

  37. Rissanen AM. Familial occurrence of coronary heart disease: effect of age at diagnosis. Am J Cardiol. 1979;44(1):60–6.

    Article  PubMed  CAS  Google Scholar 

  38. Wang TJ, Nam BH, D’Agostino RB, Wolf PA, Lloyd-Jones DM, MacRae CA, et al. Carotid intima-media thickness is associated with premature parental coronary heart disease: the Framingham Heart Study. Circulation. 2003;108(5):572–6.

    Article  PubMed  Google Scholar 

  39. Nasir K, Michos ED, Rumberger JA, Braunstein JB, Post WS, Budoff MJ, et al. Coronary artery calcification and family history of premature coronary heart disease: sibling history is more strongly associated than parental history. Circulation. 2004;110(15):2150–6.

    Article  PubMed  Google Scholar 

  40. Ridker PM, Paynter NP, Rifai N, Gaziano JM, Cook NR. C-reactive protein and parental history improve global cardiovascular risk prediction: the Reynolds Risk Score for men. Circulation. 2008;118(22):2243–51.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  41. Zethelius B, Berglund L, Sundstrom J, Ingelsson E, Basu S, Larsson A, et al. Use of multiple biomarkers to improve the prediction of death from cardiovascular causes. N Engl J Med. 2008;358(20):2107–16.

    Article  PubMed  CAS  Google Scholar 

  42. Cooney MT, Dudina AL, Graham IM. Value and limitations of existing scores for the assessment of cardiovascular risk: a review for clinicians. J Am Coll Cardiol. 2009;54(14):1209–27.

    Article  PubMed  Google Scholar 

  43. Brindle P, Emberson J, Lampe F, Walker M, Whincup P, Fahey T, et al. Predictive accuracy of the Framingham coronary risk score in British men: prospective cohort study. BMJ. 2003;327(7426):1267.

    Article  PubMed Central  PubMed  Google Scholar 

  44. Brindle PM, McConnachie A, Upton MN, Hart CL, Davey Smith G, Watt GC. The accuracy of the Framingham risk-score in different socioeconomic groups: a prospective study. Br J Gen pract J R Coll Gen Pract. 2005;55(520):838–45.

    Google Scholar 

  45. Liu J, Hong Y, D’Agostino Sr RB, Wu Z, Wang W, Sun J, et al. Predictive value for the Chinese population of the Framingham CHD risk assessment tool compared with the Chinese Multi-Provincial Cohort Study. JAMA J Am Med Assoc. 2004;291(21):2591–9.

    Article  CAS  Google Scholar 

  46. D’Agostino Sr RB, Grundy S, Sullivan LM, Wilson P, Group CHDRP. Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. JAMA J Am Med Assoc. 2001;286(2):180–7.

    Article  Google Scholar 

  47. Cavanaugh-Hussey MW, Berry JD, Lloyd-Jones DM. Who exceeds ATP-III risk thresholds? Systematic examination of the effect of varying age and risk factor levels in the ATP-III risk assessment tool. Prev Med. 2008;47(6):619–23.

    Article  PubMed Central  PubMed  Google Scholar 

  48. Berry JD, Lloyd-Jones DM, Garside DB, Greenland P. Framingham risk score and prediction of coronary heart disease death in young men. Am Heart J. 2007;154(1):80–6.

    Article  PubMed Central  PubMed  Google Scholar 

  49. Lloyd-Jones DM, Larson MG, Beiser A, Levy D. Lifetime risk of developing coronary heart disease. Lancet. 1999;353(9147):89–92.

    Article  PubMed  CAS  Google Scholar 

  50. Grover SA, Lowensteyn I, Joseph L, Kaouache M, Marchand S, Coupal L, et al. Patient knowledge of coronary risk profile improves the effectiveness of dyslipidemia therapy: the CHECK-UP study: a randomized controlled trial. Arch Intern Med. 2007;167(21):2296–303.

    Article  PubMed  CAS  Google Scholar 

  51. Blaha MJ, Silverman MG, Budoff MJ. Is there a role for coronary artery calcium scoring for management of asymptomatic patients at risk for coronary artery disease?: clinical risk scores are not sufficient to define primary prevention treatment strategies among asymptomatic patients. Circ Cardiovasc Imaging. 2014;7(2):398–408.

    Article  PubMed  Google Scholar 

  52. de Ruijter W, Westendorp RG, Assendelft WJ, den Elzen WP, de Craen AJ, le Cessie S, et al. Use of Framingham risk score and new biomarkers to predict cardiovascular mortality in older people: population based observational cohort study. BMJ. 2009;338:a3083.

    Article  PubMed Central  PubMed  Google Scholar 

  53. Anand SS, Islam S, Rosengren A, Franzosi MG, Steyn K, Yusufali AH, et al. Risk factors for myocardial infarction in women and men: insights from the INTERHEART study. Eur Heart J. 2008;29(7):932–40.

    Article  PubMed  Google Scholar 

  54. Ridker PM, Cook NR. Statins: new American guidelines for prevention of cardiovascular disease. Lancet. 2013;382(9907):1762–5.

    Article  PubMed  Google Scholar 

  55. Kavousi M, Leening MJ, Nanchen D, Greenland P, Graham IM, Steyerberg EW, et al. Comparison of application of the ACC/AHA guidelines, Adult Treatment Panel III guidelines, and European Society of Cardiology guidelines for cardiovascular disease prevention in a European cohort. JAMA J Am Med Assoc. 2014;311(14):1416–23. This article shows the limitations of applying current treatment guidelines to cohorts of different ethnicities and demonstrates the potential for overtreatment with current ACC/AHA guidelines.

    Article  CAS  Google Scholar 

  56. Polak JF, Szklo M, Kronmal RA, Burke GL, Shea S, Zavodni AE, et al. The value of carotid artery plaque and intima-media thickness for incident cardiovascular disease: the multi-ethnic study of atherosclerosis. J Am Heart Assoc. 2013;2(2):e000087.

    Article  PubMed Central  PubMed  Google Scholar 

  57. Plantinga Y, Dogan S, Grobbee DE, Bots ML. Carotid intima-media thickness measurement in cardiovascular screening programmes. Eur j cardiovasc Prev Rehabil Off J Eur Soc Cardiol Work Groups Epidemiol Prev Card Rehabil Exerc Physiol. 2009;16(6):639–44.

    Google Scholar 

  58. Lorenz MW, Markus HS, Bots ML, Rosvall M, Sitzer M. Prediction of clinical cardiovascular events with carotid intima-media thickness: a systematic review and meta-analysis. Circulation. 2007;115(4):459–67.

    Article  PubMed  Google Scholar 

  59. Newman AB, Naydeck BL, Sutton-Tyrrell K, Edmundowicz D, O’Leary D, Kronmal R, et al. Relationship between coronary artery calcification and other measures of subclinical cardiovascular disease in older adults. Arterioscler Thromb Vasc Biol. 2002;22(10):1674–9.

    Article  PubMed  CAS  Google Scholar 

  60. Touboul PJ, Vicaut E, Labreuche J, Belliard JP, Cohen S, Kownator S, et al. Correlation between the Framingham risk score and intima media thickness: the Paroi Arterielle et Risque Cardio-vasculaire (PARC) study. Atherosclerosis. 2007;192(2):363–9.

    Article  PubMed  CAS  Google Scholar 

  61. Greenland P, Alpert JS, Beller GA, Benjamin EJ, Budoff MJ, Fayad ZA, et al. ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults: executive summary: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. Circulation. 2010;122(25):2748–64.

    Article  PubMed  Google Scholar 

  62. Naghavi M, Falk E, Hecht HS, Jamieson MJ, Kaul S, Berman D, et al. From vulnerable plaque to vulnerable patient—part III: executive summary of the Screening for Heart Attack Prevention and Education (SHAPE) Task Force report. Am J Cardiol. 2006;98(2A):2H–15H.

    Article  PubMed  Google Scholar 

  63. Bogojevic Z, Bakris GL. Albuminuria and cardiovascular risk. Heart Fail Clin. 2006;2(1):53–9.

    Article  PubMed  Google Scholar 

  64. Arnlov J, Evans JC, Meigs JB, Wang TJ, Fox CS, Levy D, et al. Low-grade albuminuria and incidence of cardiovascular disease events in nonhypertensive and nondiabetic individuals: the Framingham Heart Study. Circulation. 2005;112(7):969–75.

    Article  PubMed  Google Scholar 

  65. Asselbergs FW, Hillege HL, van Gilst WH. Framingham score and microalbuminuria: combined future targets for primary prevention? Kidney Int Suppl. 2004;92:S111–4.

    Article  PubMed  CAS  Google Scholar 

  66. Smink PA, Lambers Heerspink HJ, Gansevoort RT, de Jong PE, Hillege HL, Bakker SJ, et al. Albuminuria, estimated GFR, traditional risk factors, and incident cardiovascular disease: the PREVEND (Prevention of Renal and Vascular Endstage Disease) study. Am J Kidney Dis Off J Natl Kidney Found. 2012;60(5):804–11.

    Article  Google Scholar 

  67. Asmar R, Rudnichi A, Blacher J, London GM, Safar ME. Pulse pressure and aortic pulse wave are markers of cardiovascular risk in hypertensive populations. Am J Hypertens. 2001;14(2):91–7.

    Article  PubMed  CAS  Google Scholar 

  68. Blacher J, Asmar R, Djane S, London GM, Safar ME. Aortic pulse wave velocity as a marker of cardiovascular risk in hypertensive patients. Hypertension. 1999;33(5):1111–7.

    Article  PubMed  CAS  Google Scholar 

  69. Ikonomidis I, Lekakis J, Papadopoulos C, Triantafyllidi H, Paraskevaidis I, Georgoula G, et al. Incremental value of pulse wave velocity in the determination of coronary microcirculatory dysfunction in never-treated patients with essential hypertension. Am J Hypertens. 2008;21(7):806–13.

    Article  PubMed  Google Scholar 

  70. Tzortzis S, Ikonomidis I, Lekakis J, Papadopoulos C, Triantafyllidi H, Parissis J, et al. Incremental predictive value of carotid intima-media thickness to arterial stiffness for impaired coronary flow reserve in untreated hypertensives. Hypertens Res Off J Jpn Soc Hypertens. 2010;33(4):367–73.

    Article  Google Scholar 

  71. Shechter M, Issachar A, Marai I, Koren-Morag N, Freinark D, Shahar Y, et al. Long-term association of brachial artery flow-mediated vasodilation and cardiovascular events in middle-aged subjects with no apparent heart disease. Int J Cardiol. 2009;134(1):52–8.

    Article  PubMed  Google Scholar 

  72. Oei HH, Vliegenthart R, Hofman A, Oudkerk M, Witteman JC. Risk factors for coronary calcification in older subjects. The Rotterdam Coronary Calcification Study. Eur Heart J. 2004;25(1):48–55.

    Article  PubMed  Google Scholar 

  73. Taylor AJ, Bindeman J, Feuerstein I, Cao F, Brazaitis M, O’Malley PG. Coronary calcium independently predicts incident premature coronary heart disease over measured cardiovascular risk factors: mean three-year outcomes in the Prospective Army Coronary Calcium (PACC) project. J Am Coll Cardiol. 2005;46(5):807–14.

    Article  PubMed  CAS  Google Scholar 

  74. LaMonte MJ, FitzGerald SJ, Church TS, Barlow CE, Radford NB, Levine BD, et al. Coronary artery calcium score and coronary heart disease events in a large cohort of asymptomatic men and women. Am J Epidemiol. 2005;162(5):421–9.

    Article  PubMed  Google Scholar 

  75. Budoff MJ, McClelland RL, Nasir K, Greenland P, Kronmal RA, Kondos GT, et al. Cardiovascular events with absent or minimal coronary calcification: the Multi-Ethnic Study of Atherosclerosis (MESA). Am Heart J. 2009;158(4):554–61.

    Article  PubMed Central  PubMed  Google Scholar 

  76. Raggi P, Callister TQ, Shaw LJ. Progression of coronary artery calcium and risk of first myocardial infarction in patients receiving cholesterol-lowering therapy. Arterioscler Thromb Vasc Biol. 2004;24(7):1272–7.

    Article  PubMed  CAS  Google Scholar 

  77. Lakoski SG, Greenland P, Wong ND, Schreiner PJ, Herrington DM, Kronmal RA, et al. Coronary artery calcium scores and risk for cardiovascular events in women classified as “low risk” based on Framingham risk score: the multi-ethnic study of atherosclerosis (MESA). Arch Intern Med. 2007;167(22):2437–42.

    Article  PubMed  Google Scholar 

  78. Blaha MJ, Budoff MJ, DeFilippis AP, Blankstein R, Rivera JJ, Agatston A, et al. Associations between C-reactive protein, coronary artery calcium, and cardiovascular events: implications for the JUPITER population from MESA, a population-based cohort study. Lancet. 2011;378(9792):684–92. This study shows the benefit of CAC screening in the assessment of cardiovascular risk in comparison to traditional risk factor based scoring.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  79. Park R, Detrano R, Xiang M, Fu P, Ibrahim Y, LaBree L, et al. Combined use of computed tomography coronary calcium scores and C-reactive protein levels in predicting cardiovascular events in nondiabetic individuals. Circulation. 2002;106(16):2073–7.

    Article  PubMed  CAS  Google Scholar 

  80. Mohlenkamp S, Lehmann N, Moebus S, Schmermund A, Dragano N, Stang A, et al. Quantification of coronary atherosclerosis and inflammation to predict coronary events and all-cause mortality. J Am Coll Cardiol. 2011;57(13):1455–64.

    Article  PubMed  Google Scholar 

  81. DeFilippis AP, Kramer HJ, Katz R, Wong ND, Bertoni AG, Carr J, et al. Association between coronary artery calcification progression and microalbuminuria: the MESA study. J Am Coll Cardiol Img. 2010;3(6):595–604.

    Article  Google Scholar 

  82. Rana JS, Gransar H, Wong ND, Shaw L, Pencina M, Nasir K, et al. Comparative value of coronary artery calcium and multiple blood biomarkers for prognostication of cardiovascular events. Am J Cardiol. 2012;109(10):1449–53.

    Article  PubMed  CAS  Google Scholar 

  83. Greenland P, Alpert JS, Beller GA, Benjamin EJ, Budoff MJ, Fayad ZA, et al. 2010 ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2010;56(25):e50–e103.

    Article  PubMed  Google Scholar 

  84. Mettler Jr FA, Huda W, Yoshizumi TT, Mahesh M. Effective doses in radiology and diagnostic nuclear medicine: a catalog. Radiology. 2008;248(1):254–63.

    Article  PubMed  Google Scholar 

  85. Kim KP, Einstein AJ, Berrington de Gonzalez A. Coronary artery calcification screening: estimated radiation dose and cancer risk. Arch Intern Med. 2009;169(13):1188–94.

    Article  PubMed Central  PubMed  Google Scholar 

  86. Berrington de Gonzalez A, Kim KP, Smith-Bindman R, McAreavey D. Myocardial perfusion scans: projected population cancer risks from current levels of use in the United States. Circulation. 2010;122(23):2403–10.

    Article  PubMed  Google Scholar 

  87. Okwuosa TM, Greenland P, Ning H, Liu K, Lloyd-Jones DM. Yield of screening for coronary artery calcium in early middle-age adults based on the 10-year Framingham Risk Score: the CARDIA study. J Am Coll Cardiol Img. 2012;5(9):923–30.

    Article  Google Scholar 

  88. Nasir K, Michos ED, Blumenthal RS, Raggi P. Detection of high-risk young adults and women by coronary calcium and National Cholesterol Education Program Panel III guidelines. J Am Coll Cardiol. 2005;46(10):1931–6.

    Article  PubMed  Google Scholar 

  89. Goff Jr DC, Bertoni AG, Kramer H, Bonds D, Blumenthal RS, Tsai MY, et al. Dyslipidemia prevalence, treatment, and control in the Multi-Ethnic Study of Atherosclerosis (MESA): gender, ethnicity, and coronary artery calcium. Circulation. 2006;113(5):647–56.

    Article  PubMed  CAS  Google Scholar 

  90. Erbel R, Mohlenkamp S, Moebus S, Schmermund A, Lehmann N, Stang A, et al. Coronary risk stratification, discrimination, and reclassification improvement based on quantification of subclinical coronary atherosclerosis: the Heinz Nixdorf Recall study. J Am Coll Cardiol. 2010;56(17):1397–406.

    Article  PubMed  Google Scholar 

  91. Nasir K, Vasamreddy C, Blumenthal RS, Rumberger JA. Comprehensive coronary risk determination in primary prevention: an imaging and clinical based definition combining computed tomographic coronary artery calcium score and national cholesterol education program risk score. Int J Cardiol. 2006;110(2):129–36.

    Article  PubMed  Google Scholar 

  92. Wilson PW, D’Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97(18):1837–47.

    Article  PubMed  CAS  Google Scholar 

Download references

Compliance with Ethics Guidelines

Conflict of Interest

Andrew Paul DeFilippis, Christopher Joseph Carrubba, Michael J Blaha, and Khurram Nasir declare that they have no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Author information

Authors and Affiliations

  1. University of Colorado, Aurora, CO, USA

    Christopher Joseph Carrubba

  2. Johns Hopkins Ciccarone Center for The Prevention of Heart Disease, Baltimore, MD, USA

    Michael J. Blaha

  3. Center for Prevention and Wellness Research, Baptist Health Medical Group, Johns Hopkins Ciccarone, Center for the Prevention of Heart Disease, Department Epidemiology, Robert Stempel College of Public Health, Department of Medicine, Herbert Wertheim College of Medicine, Florida International University, Miami, FL, USA

    Khurram Nasir

  4. Ciccarone Center for the Prevention of Heart Disease, Johns Hopkins, Baltimore, MD, USA

    Andrew Paul DeFilippis

  5. Division of Cardiovascular Medicine, University of Louisville, 550 South Jackson Street, Louisville, KY, 40202, USA

    Andrew Paul DeFilippis

Authors
  1. Christopher Joseph Carrubba
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael J. Blaha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Khurram Nasir
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andrew Paul DeFilippis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrew Paul DeFilippis.

Additional information

This article is part of the Topical Collection on Novel and Emerging Risk Factors

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Carrubba, C.J., Blaha, M.J., Nasir, K. et al. Cardiovascular Disease Risk Assessment: a Review of Risk Factor-based Algorithms and Assessments of Vascular Health. Curr Cardiovasc Risk Rep 8, 419 (2014). https://doi.org/10.1007/s12170-014-0419-0

Download citation

  • Published:

  • DOI: https://doi.org/10.1007/s12170-014-0419-0

Keywords

Search

Navigation