Development and validation of a simple model to predict severe coronary artery disease after myocardial infarction: Potential impact on cardiac catheterization use in the United States and Canada

Background improved patient selection may optimize the efficiency of cardiac catheterization in both high- and low-rate regions. The purpose of this study was to develop and validate a clinical model for predicting high-risk coronary artery disease (CAD) after myocardial infarction (MI) and to examine the model's potential impact on the use rate of both US and Canadian catheterization practices. Methods and Results By the use of baseline clinical variables from 1122 patients in the angiographic substudy of the Global Use of Strategies to Open Occluded Arteries in Acute Coronary Syndromes (GUSTO-1) trial, we developed a model that was predictive of severe CAD (left main or triple-vessel disease). The final model, which included prior MI, age, sex, hyperlipidemia, and decreased left ventricular ejection fraction (C-index = 0.70), was externally validated in 781 patients in the GUSTO IIb trial. Although the probability of severe CAD predicted 5-year survival, the frequency,of catheterization in both Canada and the United States bore no relationship to severe CAD risk in the GUSTO-1 trial. By use of the model, we estimated that as much as 15% of US catheterizations from both GUSTO-1 and. GUSTO IIb might have been avoided, without significantly compromising the number of patients with severe CAD who were identified (sensitivity = 0.94). By applying the model to Canadian practices, an additional 30 cases of severe CAD might have been identified per every 1000 catheterizations performed, without increasing the catheterization rate. Conclusions The likelihood of severe CAD after ST-elevation MI may be predicted from simple baseline clinical variables. The use of a severe CAD predictive model to guide patient selection might enhance the cost-effectiveness of both aggressive and conservative catheterization practices.