Framingham – Wilson
| Background info: Framingham-Wilson |
|
1) End-points measured
- 10 year risk of total coronary heart disease (CHD)
- Total CHD is defined to include:
- Angina pectoris
- Myocardial infarction
- Coronary death
- Note: “hard CHD” includes total CHD without angina pectoris
2) Profile of original population at baseline
- Framingham, USA
- Age: 30 to 74 (mean age 49)
- Baseline free of overt CHD
- Participant number: 2489 men and 2856 women
- Population selected from:
- 11th examination of the original Framingham cohort AND
- the initial examination of the Framingham Offspring Study
3) Validation (the following prospective studies excludes cohort with history of CVD and diabetes at baseline)
| Author | Year | Ethnicity | Age group | Follow up | Calibration^ | Discrimination^^ | Conclusion |
|---|---|---|---|---|---|---|---|
| Asia pacific (1) | 2007 | Asia, Chinese, US | 8 years | 2.76 (men), 1.02 (women) | 0.75 | Overestimate in Chinese population, recalibration required | |
| Barroso (2) | 2010 | Spain | 40-65 | 10 years | 1.64 | 0.7 | Over-predict risk by 64% |
| Berry (3) | 2007 | US (male) | 18-39 | 30 years | Neither method classified individuals under 30 yrs as high risk despite substantial risk factor burden | ||
| Brunner (4) | 2010 | UK (Whitehall II) | 40-63 | 11 years | 0.7 | Including a random blood glucose in Framingham risk profile improve risk prediction for CHD | |
| Buitrago (5) | 2011 | Spain | 35-74 | 10 years | 1.73 | 0.71 | Overestimate coronary risk by 73% |
| Comin (6) | 2007 | Spain | 35-74 | 5 years | More than half of coronary events occur in patients not classified as high risk | ||
| Empana (7) | 2003 | Belfast, France (PRIME) | 50-59 | 5 years | 2.35 (France), 1.34 (Belfast) | 0.61-0.68 | Function should not be used to estimate absolute CHD risk in Belfast |
| Greenland (8) | 2004 | US | median 7 years | Risk assessment by Framingham can be improved by coronary artery calcium scoring (CACS) | |||
| Jimenez-Corona (9) | 2009 | Mexican | 35-64 | median 6.2 years | 1.84 (men), 1.55 (women) | Overestimate incident MI and CHD death risk in men | |
| Mora (10) | 2005 | US | <60 | 8.7 years | High BMI contributed significantly to CHD, most notable for obese siblings with a high Framingham score | ||
| Orford (11) | 2002 | Normative ageing study | 30-74 | 10 years | <1 (low risk), >1 (high risk) | 0.6 | Underestimated absolute risk in low-risk group and overestimate risk in high risk group |
| Thomsen (12) | 2002 | Danish (Glostrup population study) | 49-70 | 10 years | 0.75-0.77 | Using this Framingham risk-score on a Danish population will lead to a significant overestimation of coronary risk | |
| Rodondi (13) | 2014 | US (Health ABC study) | 70-79 | 8 years | Underestimate by 51% (women) and 8% (men) | 0.577 (women), 0.583 (men) | Framingham Risk Score underestimates CHD risk in older adults, particularly in women. |
| Suka (14) | 2001 | Japanese (male) | 30-59 | 5-7 years | 0.71 | Updated Framingham risk score could provide a reasonable rank ordering of CHD risk | |
| Suka (15) | 2002 | Japanese (male) | 30-59 | 5-7 years | 0.67 | Incidence of CHD increased with increase in estimated risk | |
| Uthoff (16) | 2010 | Switzerland ( with clinically overt atherosclerosis) | 6 years | 0.56 | Not useful in secondary prevention setting | ||
| Van der heijden (17) | 2009 | Dutch Caucasian | 50-75 | 10 years | 0.73 | SCORE was the best predictor of fatal CHD events | |
| Van Kempen (18) | 2014 | Dutch (Rotterdam Study) | 68 (median) | Max 17 years | Low to intermediate risk: 17.5% observed vs. 16.6% expected | 0.66 | Framingham CVD risk predictions perform well in the low- to intermediate risk categories in the Rotterdam Study. |
Note:
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4) Length of follow up
- 12 years
5) Risk factors involved
- Non-modifiable risk factors
- Age
- Modifiable risk factors
- Diabetes
- Smoking
- JNC-V blood pressure categories (JNC: Joint National Committee)
- NCEP total cholesterol categories (NCEP: National Cholesterol Education Programme)
- LDL cholesterol categories
References
Primary publication:
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):1837-1847. http://circ.ahajournals.org/content/97/18/1837.full
1. Asia Pacific Cohort Studies C, Barzi F, Patel A, et al. Cardiovascular risk prediction tools for populations in Asia. Journal of Epidemiology & Community Health. 2007;61(2):115-121.
2. Barroso LC, Muro EC, Herrera ND, Ochoa GF, Hueros JI, Buitrago F. Performance of the Framingham and SCORE cardiovascular risk prediction functions in a non-diabetic population of a Spanish health care centre: a validation study. Scandinavian Journal of Primary Health Care. Dec 2010;28(4):242-248.
3. Berry JD, Lloyd-Jones DM, Garside DB, Greenland P. Framingham risk score and prediction of coronary heart disease death in young men. American Heart Journal. 2007;154(1):80-86.
4. Brunner EJ, Shipley MJ, Marmot MG, Kivimaki M, Witte DR. Do the Joint British Society (JBS2) guidelines on prevention of cardiovascular disease with respect to plasma glucose improve risk stratification in the general population? Prospective cohort study. Diabetic Medicine. May 2010;27(5):550-555.
5. Buitrago F, Calvo-Hueros JI, Canon-Barroso L, et al. Original and REGICOR Framingham functions in a nondiabetic population of a Spanish health care center: a validation study. Annals of Family Medicine. 2011;9(5):431-438.
6. Comin E, Solanas P, Cabezas C, et al. Estimating cardiovascular risk in Spain using different algorithms. Revista Espanola de Cardiologia. 2007;60(7):693-702.
7. Empana JP, Ducimetiere P, Arveiler D, et al. Are the Framingham and PROCAM coronary heart disease risk functions applicable to different European populations? The PRIME Study. European Heart Journal. November 2003;24(21):1903-1911.
8. Greenland P, LaBree L, Azen SP, Doherty TM, Detrano RC. Coronary artery calcium score combined with Framingham score for risk prediction in asymptomatic individuals. JAMA. 2004;291(2):210-215.
9. Jimenez-Corona A, Lopez-Ridaura R, Williams K, Gonzalez-Villalpando ME, Simon J, Gonzalez-Villalpando C. Applicability of Framingham risk equations for studying a low-income Mexican population. Salud Publica de Mexico. 2009;51(4):298-305.
10. Mora S, Yanek LR, Moy TF, Fallin MD, Becker LC, Becker DM. Interaction of body mass index and framingham risk score in predicting incident coronary disease in families. Circulation. 2005;111(15):1871-1876.
11. Orford JL, Sesso HD, Stedman M, Gagnon D, Vokonas P, Gaziano JM. A comparison of the Framingham and European Society of Cardiology coronary heart disease risk prediction models in the normative aging study. American Heart Journal. 2002;144(1):95-100.
12. Rodondi N, Locatelli I, Aujesky D, et al. Framingham Risk Score and alternatives for prediction of coronary heart disease in older adults. PLoS ONE. 2014;7(3):e34287.
13. Thomsen TF, McGee D, Davidsen M, Jorgensen T. A cross-validation of risk-scores for coronary heart disease mortality based on data from the Glostrup Population Studies and Framingham Heart Study. International Journal of Epidemiology. 2002;31(4):817-822.
14. Suka M, Sugimori H, Yoshida K. Application of the updated Framingham risk score to Japanese men. Hypertension Research – Clinical & Experimental. 2001;24(6):685-689.
15. Suka M, Sugimori H, Yoshida K. Validity of the Framingham Risk Model applied to Japanese men. Methods of Information in Medicine. 2002;41(3):213-215.
16. Uthoff H, Staub D, Socrates T, et al. PROCAM-, FRAMINGHAM-, SCORE- and SMART-risk score for predicting cardiovascular morbidity and mortality in patients with overt atherosclerosis. Vasa. 2010;39(4):325-333.
17. van der Heijden AAWA, Ortegon MM, Niessen LW, Nijpels G, Dekker JM. Prediction of coronary heart disease risk in a general, pre-diabetic, and diabetic population during 10 years of follow-up: accuracy of the Framingham, SCORE, and UKPDS risk functions: The Hoorn Study. Diabetes Care. 2009;32(11):2094-2098.
18. van Kempen BJH, Ferket BS, Kavousi M, et al. 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. International Journal of Cardiology. 2014;171(3):413-418.