The risk prediction model was constructed by using 22 clinical variables identified a priori by an expert panel: age, sex, race, surgical priority (ie, elective, urgent, emergent, or salvage), previous heart surgery, left ventricular ejection fraction, extent of left main coronary artery disease, extent of overall coronary artery disease, recent myocardial infarction, percutaneous coronary intervention on the same admission, ventricular arrhythmia, congestive heart failure (ie, New York Heart Association classification), history of angina, severity of angina (ie, Canadian Cardiovascular Society classification), mitral regurgitation, serum creatinine level, hypertension, diabetes mellitus, cerebrovascular disease, peripheral vascular disease, chronic obstructive pulmonary disease, and renal failure. The model was fitted by means of multivariate logistic regression. Initially, the entire data set was divided randomly into 2 sets: a “training set” for model development and a “test set” for assessment of model fit and discrimination. The Hosmer-Lemeshow goodness-of-fit test for the model was nonsignificant (P = .10), and the C-index for the model was 0.80, suggesting that the model had good overall predictive discrimination. A full description of the risk prediction model has been published15 and is available online at http://www.oshpd.cahwnet.gov/HQAD/Outcomes/Clinical.htm#CCMRP.