Despite the epidemiological insights from the Framingham Study 1 in the early 1950s and the resulting significant advances in the diagnosis and management of coronary heart disease (CHD), it remains the leading cause of death in the United States. In part, this is because sudden cardiac death is the first presentation of CHD in 50% of men and 64% of women 2,3 and, therefore, the only available strategy for reducing mortality in these patients is primary prevention. This is the target population for atherosclerosis imaging, which has been proposed as a strategy for the earlier and more accurate identification of individuals at risk for CHD so that lifesaving preventive strategies can be more optimally targeted in those at risk.
Limitations of Current Primary Risk Assessment StrategiesCurrent guidelines for primary prevention recommend initial assessment and risk stratification based on traditional risk factors (eg, the Framingham Risk Score [FRS] in the United States and the Systemic Coronary Risk Evaluation in Europe), followed by goal-directed therapy as necessary to modify those risk factors. 4 However, these traditional prevention strategies can be inadequate, as cardiovascular events do occur in patients without known risk or in low and intermediate risk groups in whom an aggressive treatment strategy would not be indicated. This is highlighted by a study of 222 young adults (men Յ55 years and women Յ65 years) without known prior CHD, hospitalized for acute myocardial infarction, of whom 70% were in a low-risk category with a 10-year risk of CHDϽ10% based on their FRS. 5 Furthermore, when the 10-year risk of these patients was stratified by number of risk factors and low-density lipoprotein cholesterol level, three quarters did not meet National Cholesterol Education Program III criteria 6 to be identified as at sufficient risk to qualify for cholesterol lowering therapy. Part of the reason why FRS fails to detect risk may be its development in an almost entirely white population such that the risk prediction algorithm may not fit other populations as well. 7 Furthermore, the model ignores several major risk factors including diabetes, family history sedentary lifestyle and obesity.