CDC/AHA Workshop on Markers of Inflammation and Cardiovascular Disease - Application to Clinical and Public Health Practice - Ability of inflammatory markers to predict disease in asymptomatic patients - A background paper

There is great interest in moving beyond established risk factors to consider markers of inflammation for the prediction of initial cardiovascular disease events. Inflammatory markers such as leukocyte count, serum amyloid A, C-reactive protein, and vascular outcomes in individuals free of cardiovascular disease at baseline are the key markers that have been investigated in the population setting. A meta-analysis of 11 prospective studies in asymptomatic individuals compared people in the bottom third of the C-reactive protein distribution with those in the top tertile. The authors reported an odds ratio of 2.0 (95% CI 1.6 to 2.5) for coronary heart disease among people in the top tertile. These results are among the strongest assembled thus far to recommend incorporating newer biomarkers into coronary heart disease risk estimation algorithms. A variety of issues should be considered and conditions satisfied before vascular disease risk factors are adopted into regular use. The type of vascular event and the follow-up interval are important features because results for short-, intermediate-, and long-term intervals may yield different results. The factors under consideration should have been standardized, and characteristics such as the variability of the measurements, correlations with established factors, evidence from observational studies and clinical trials, type of effect ( linear, nonlinear, dichotomous), improvement in overall prediction ( discrimination), generalization of results ( calibration), and cost can affect the utility. Each of these issues needs to be considered and the effects on relative, absolute, and population-attributable risks described. In particular, we need to ( 1) develop sound strategies for implementing new testing and ( 2) demonstrate the benefit of testing by using the current foundation of prior probabilities developed from already-published risk factor assessments based on large population studies.