DEVELOPMENT AND VALIDATION OF A SIMPLIFIED PREDICTIVE INDEX FOR MAJOR COMPLICATIONS IN CONTEMPORARY PERCUTANEOUS TRANSLUMINAL CORONARY ANGIOPLASTY PRACTICE

Objectives. This study was designed to determine the preprocedural risk factors for major complications (emergent coronary bypass surgery, myocardial infarction or death) of coronary angioplasty and to derive and validate a simplified index that predicts patients' a priori risk of complications. Background. Previous studies of risk factors for complications after coronary angioplasty may not be generalizable to current, broad-based angioplasty practice. Furthermore, to our knowledge a clinically useful predictive index has not been derived and independently validated. Methods. From data collected prospectively for the Registry of the Society for Cardiac Angiography and Interventions for 1992, multivariable logistic regression was used to determine which variables were independently associated,vith complications in 10,622 first angioplasty procedures. Stepwise regression and receiver operating characteristic curves then were used in this registry to develop a predictive index for complications that was validated using 5,250 first angioplasty procedures in the 1993 registry. Results. Predictors of major complications were multivessel disease, unstable angina, recent myocardial infarction, type C lesion or left main angioplasty, shock, age, geographic region and absence of previous coronary bypass surgery. The derived predictive index consisted of the first six of these variables plus aortic valve disease and classified patients into four risk groups: low (1.3% complications), moderate (2.8%), high (12.7%) and very high (29.7%) risk. This index demonstrated consistent reliability and discriminatory ability when applied to the 1993 data. Conclusions. Predictors of major complications identified in selected populations also apply currently in broad-based practice. From these variables, a predictive index can stratify patients into risk groups before angioplasty, thus aiding in risk assessment, resource allocation and risk adjustment.