Terrence Steyer, M.D.
Chair, Department of Family Medicine
Kannel WB, McGee D, and Gordon T (1976)
The Framingham Coronary Heart Disease (CHD) Prediction Score was originally developed to assess the relative importance of CHD risk factors and to quantify the absolute level of CHD risk for individuals without a history of cardiovascular disease. Several risk factors proved to be strong, largely independent predictors of cardiovascular disease (CVD). The original development of the CHD prediction score from the Framingham data (Kannel et al. 1976) included the factors of age, gender, cigarette smoking, systolic blood pressure, total serum cholesterol, electrocardiographic measure of left ventricular hypertrophy (ECG-LVH), and diabetes (as measured by glucose intolerance test).
A newer risk profile score developed by Anderson et al. (1991) included participants from the Framingham Offspring Study so extended the age range to 30-74. Equations using either systolic or diastolic blood pressure were developed. In addition, the ratio of total cholesterol to HDL-cholesterol was added to the functions. The definition of diabetes was amended to include the receipt of treatment or a fasting glucose measurment of 140 mg/dl or higher.
In 1998 Wilson et al. developed an algorithm replacing the continuous measurements of blood pressure, total cholesterol, HDL, and LDL with categorical variables based upon the National Cholesterol Education Program (NCEP) CHD risk categories. They also chose to remove ECG-LVH from their model due to the high association with hypertension and a lack of standard universally accepted ECG criteria. They found that CHD prediction was comparable with earlier models that used these factors as continuous variables.
A Risk Assessment Calculator using this algorithm is available on the web site hosted by the National Cholesterol Education Program.
A report by Grundy et al. (2001), derived from a workshop on cardiovascular risk assessment sponsored by the National Heart, Lung, and Blood Institute, addressed whether risk equations developed in the Framingham Heart Study (FHS) for predicting new-onset CHD apply to diverse population groups. Preparation for the workshop included a reanalysis and comparison of prospective studies in several different populations in which risk factors were related to cardiovascular outcomes. Some studies included fatal and nonfatal CHD end points, whereas others contained only CHD mortality. Extensive collaboration provided as much uniformity as possible with respect to both risk factors and CHD end points.
More recent modelling using the Framingham population has centered on the development of models using updated data (D'Agostino et al. 1991, Karp et al. 2004). These risk assessment models calculate CHD risk based on continually updated information rather than simply relying on baseline information that may have been collected as long as 30 years prior. Follow-up periods between 2 and 30 years have been investigated with results providing better prediction than models using only baseline information.
In the Grundy et al. (2001) report both diabetes and hypertension remain strong predictors of CHD in older persons, but elevated serum cholesterol declines in relative risk. Attributable risk accompanying high serum cholesterol increases with advancing age, but it is difficult to differentiate higher and lower risk in patients >65 years of age on the basis of serum cholesterol levels alone. In data from the Cardiovascular Health Study, FHS equations were poor predictors of risk after 65 years of age. In contrast, noninvasive assessments of coronary plaque burden, such as carotid IMT, assumed increasing power to predict risk.
Congruence of FHS predictions for hard CHD between white populations of the FHS and ARIC must be considered a major conclusion of the workshop (Grundy et al. 2001). FHS equations seemingly can be applied broadly to white populations in the United States. This conclusion is strengthened by the similarities between multivariate relative risk and population baseline absolute risk in FHS men and the PHS.
In broad terms, FHS equations for hard CHD apply similarly in white and black populations in the United States. The influence of blood pressure is an exception. FHS equations underpredicted blood pressure–associated risk in ARIC black men and women. Use of FHS equations in blacks probably should give extra weight to blood pressure. In the Women’s Pooling Project and NHANES, equations for the white populations were not highly predictive of deaths resulting from CHD/CVD in the black populations. Thus, factors operating subsequent to the onset of CHD may affect CVD mortality in black persons.
Workshop comparisons confirmed previous observations that absolute baseline risk differs among populations. Variation in population baseline risk can be distinguished from differences in the population-attributable fraction for the major risk factors. Differences in population baseline risk may extend to various ethnic groups and will require adjustment of absolute risk estimates based on ethnicity. Ethnic differences in CVD risk could be explained by variability in underlying or emerging risk factors not included in FHS equations. In Asian men from Honolulu and Hispanics in Puerto Rico, FHS coefficients overpredicted risk for CHD. Simple calibration adjustment to the FHS functions to adjust for baseline average CHD incidence rates greatly improved the performance of the FHS functions in these populations. No data were available on the predictive power of FHS equations in Americans of South Asian origin, a population known to have a high baseline risk.
D'Agostino et al. (2001) reported on their assessment of the Framingham coronary heart disease prediction scores in minority populations. They used data from six other studies and applied the Framingham scores to those ethnically diverse cohorts.
Anderson KM, Wilson PWF, Odell PM, Kannel WB (1991) An updated coronary risk profile. A statement for health professionals. Circulation 83: 356-362.
D'Agostino RB, Russell MW, Huse DM, et al. (2000) Primary and subsequent coronary risk appraisal: new results from the Framingham Study. Am Heart J 139: 272-281.
D'Agostino RB, Grundy S, Sullivan LM, WIlson P (2001) Validation of the Framingham coronary heart disease prediction scores. Results of a multiple ethnic groups investigation. JAMA 286: 180-187.
Grundy SM, D'Agostino RB, Mosca L, Burke GL, Wilson PW, Rader DJ, Cleeman JI, Roccella EJ, Cutler JA, Friedman LM. Cardiovascular risk assessment based on US cohort studies: findings from a National Heart, Lung, and Blood institute workshop. [Congresses] Circulation 104(4):491-6.
Kannel WB, McGee D, and Gordon T (1976) A general cardiovascular risk profile: the Framingham Study. Am J Cardiol 38: 46-51.
Karp I, Abrahamowica M, Bartlett G, Pilote L (2004) Updated risk factor values and the ability of the multivariable risk score to predict coronary heart disease. Am J Epidemiol 160: 707-716.
Wilson, PWF, D'Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB (1998) Prediction of coronary heart disease using risk factor categories. Circulation 97: 1837-1847.