OBJECTIVES: Currently, only symptomatic patients are screened for CAD, leaving those asymptomatic but at risk patients unscreened and therefore unaware of their disease burden. Improved asymptomatic screening is an area of unmet medical need, given that 50% of CAD patients present with a myocardial infarction as their first symptom. Our objective was to develop an epidemiological and economic model to describe the cost-effectiveness of screening for coronary artery disease in at-risk populations. METHODS: A Markov microsimulation model was developed to compare CAD screening strategies for asymptomatic patients. The screening algorithm is defined based on its ability to risk stratify the population as measured by the area under the receiver operating characteristic curve. The structure of the economic model links three main hypotheses: 1) screening for CAD provides improved risk stratification; 2); which leads to initiation of effective and cost-effective interventions; and 3) effective interventions reduce the burden of CAD. Outcomes are measured as incidence of major adverse cardiovascular events (MACE). The population in the model is defined according to age, sex, diabetes status and ethnicity. The economic model can be calculated for a population with any combination of these risk factors. RESULTS: On the basis of United States CAD registry data, the model identified segments of the population with the highest incidence of CAD and therefore with the greatest capacity to benefit from CAD screening. These segments are defined according to observable risk factors meaning they can be used to identify a population eligible for CAD screening. CONCLUSIONS: Screening for CAD has the potential to reduce disease burden and save lives. The model may be a useful tool to 1) improve risk stratification techniques for asymptomatic screening; 2) identify populations where screening may be cost-effective; and 3) identify areas where clinical data is needed to support model validation.