Coronary heart disease prediction and prevention

Abstract

Prediction of coronary heart disease (CHD) is based on multivariable risk equations developed from population-based observational studies in which people without clinical CHD at the initiation of study were examined and followed until their first CHD events. The risk equations from the Framingham Heart Study have been widely used in our clinical practice1-3 and research. 4,5 The recent report of the third National Cholesterol Education Program-Adult Treatment Panel (NCEP-ATP) incorporated the Framingham risk equations to predict ten-year absolute CHD risk and to identify certain patients who are at high risk and more likely to benefit from primary prevention with aggressive lipid-lowering treatment.1 In addition to CHD prediction, population-based observational studies also provide the clue to understand how much of CHD can be prevented by modifying major cardiovascular risk factors such as serum cholesterol level, blood pressure level, and current smoking. 4,6,7 In this narrative review, we described how CHD prediction works and how it can be improved by including nontraditional cardiovascular risk factors. We also discussed about how likely it is to prevent the majority of CHD. How Do We Predict CHD?The Framingham Heart Study has developed mathematical functions to assess the relative importance of CHD risk factors and to quantify absolute CHD risk for individual patients. 8-10Detailed methods of derivation of CHD risk equations were described elsewhere.8 Briefly, sex-specific CHD risk equations were derived from a population-based sample of 2489 men and 2856 women, 30 to 74 years of age, who were free of overt cardiovascular disease at the time of their 11th examination of the original Framingham Cohort or the initial examination of the Framingham Offspring Study in 1971 to 1974. CHD risk factors were routinely and systematically measured during these examinations and twelve-year follow-up was obtained for the development of "hard" CHD events, defined as myocardial infarction and CHD death. Sex-specific Cox proportional hazards regression was performed to calculate the relative importance of CHD risk factors using age, current smoking, presence of diabetes, the fifth Joint National Committee on Hypertension blood pressure categories, and the second NCEP-ATP cholesterol categories as covariates. (Table 1) In addition, score sheets were developed from the beta-coefficients of Cox proportional hazards models to provide ten-year absolute CHD risk and to make it easy to implement as part of a screening program. They were adopted by the NCEP-ATP III guideline. 1 (Figure 1) The predictive capability of the model using a continuous variable or a categorical variable for cholesterol level was almost identical. 8The equation is particularly useful when there are multiple mild abnormalities that increase CHD risk synergistically.In the Framingham risk equation, several candidate risk factors such as family history of CHD, elevated fibrinogen levels, left ventricular hypertrophy on the electrocardiogram, postmenopausal e...