RESUMEN
OBJECTIVES: We sought to validate recently proposed risk adjustment models for in-hospital percutaneous transluminal coronary angioplasty (PTCA) mortality on an independent data set of high risk patients undergoing PTCA. BACKGROUND: Risk adjustment models for PTCA mortality have recently been reported, but external validation on independent data sets and on high risk patient groups is lacking. METHODS: Between July 1, 1994 and June 1, 1996, 1,476 consecutive procedures were performed on a high risk patient group characterized by a high incidence of cardiogenic shock (3.3%) and acute myocardial infarction (14.3%). Predictors of in-hospital mortality were identified using multivariate logistic regression analysis. Two external models of in-hospital mortality, one developed by the Northern New England Cardiovascular Disease Study Group (model NNE) and the other by the Cleveland Clinic (model CC), were compared using receiver operating characteristic (ROC) curve analysis. RESULTS: In this patient group, an overall in-hospital mortality rate of 3.4% was observed. Multivariate regression analysis identified risk factors for death in the hospital that were similar to the risk factors identified by the two external models. When fitted to the data set, both external models had an area under the ROC curve >0.85, indicating overall excellent model discrimination, and both models were accurate in predicting mortality in different patient subgroups. There was a trend toward a greater ability to predict mortality for model NNE as compared with model CC, but the difference was not significant. CONCLUSIONS: Predictive models for PTCA mortality yield comparable results when applied to patient groups other than the one on which the original model was developed. The accuracy of the two models tested in adjusting for the relatively high mortality rate observed in this patient group supports their application in quality assessment or quality improvement efforts.