Quality assurance in coronary artery bypass grafting (CABG) surgery requires a comparison of operative mortality against an accepted standard of care. Raw mortality statistics are unacceptable in this context, and risk factor analysis is essential. However, this principle has not been adequately demonstrated in previous reports. Our goal in this study was to develop a risk model of accepted CABG mortality and illustrate its proper use in coronary artery surgery. The model was derived from a Bayesian analysis of 6,630 patients undergoing CABG in the Coronary Artery Surgery Study (CASS) registry. Age, sex, ventricular function, previous myocardial infarction, extent of coronary artery disease, unstable angina, and surgical priority were used by the model to sort patients into risk categories. From January 1984 through December 1987, 840 patients underwent isolated CABG at our hospital. With raw mortality data, the 3.9% (33/840) mortality of our patients was significantly different from the 2.3% (153/6,630) CASS mortality (p less than 0.001). When our patients were entered into the CASS model for risk stratification, however, our CABG mortality conformed to the CASS experience. These results illustrate the fallacy of using raw mortality statistics for interinstitutional comparisons. This type of risk model is a fundamental element of CABG quality assurance.