Comparison of Mayo Clinic risk score and American College of Cardiology/American Heart Association lesion classification in the prediction of adverse cardiovascular outcome following percutaneous coronary interventions

Abstract

ObjectivesWe compared American College of Cardiology/American Heart Association (ACC/AHA) lesion classification with the recently proposed Mayo Clinic risk score to predict complications following percutaneous coronary intervention (PCI). BackgroundThe ability of the ACC/AHA classification system to predict complications following PCI has been modest. With the inclusion of patient demographics, acuity of presentation, and measure of left ventricular function, models with better discriminatory accuracy are presently available. MethodsThe Mayo Clinic risk score is constructed by adding integer scores for the presence of eight variables. We mapped the lesion-specific risk levels to a patient level by counting the number of lesions in each class (A, B1, B2, C, and unknown). ResultsIn 5,064 PCIs, 183 patients (4%) had the primary end point (death, Q-wave myocardial infarction, stroke, emergency coronary artery bypass graft). Of the 7,632 treated lesions, 891 (12%) were unsuccessfully treated with PCI (residual stenosis >20%). The discriminatory ability of the Mayo Clinic risk score model for prediction of the primary end point, as measured by the c-statistic, was 0.78 (95% confidence interval [CI] 0.74 to 0.81). The Mayo Clinic risk score offered significantly better risk stratification than the ACC/AHA lesion classification counts (95% CI for c-statistic difference: 0.05 to 0.15). Regarding angiographic success, the ACC/AHA lesion classification was a better system (95% CI for c-statistic difference: −0.08 to −0.03 favoring ACC/AHA classification), although its absolute ability was modest (c = 0.58). ConclusionsMayo Clinic risk score offers significantly better prediction for cardiovascular complications than the ACC/AHA classification. However, lesion classification by ACC/AHA classification is a better predictor for angiographic success.