Multivariate prediction of in-hospital mortality after percutaneous coronary interventions in 1994-1996

OBJECTIVES Using recent data, we sought to identify risk factors associated with in-hospital mortality among patients undergoing percutaneous coronary interventions. BACKGROUND The ability to accurately predict the risk of an adverse outcome is important in clinical decision making and for risk adjustment when asse ssing quality of care. Most clinical prediction rules for percutaneous coro nary intervention (PCI) were developed using data collected before the broa der use of new interventional devices. METHODS Data were collected on 15,331 consecutive hospital admissions by si x clinical centers. Logistic regression analysis was used to predict the ri sk of in-hospital mortality. RESULTS Variables associated with an increased risk of in-hospital mortalit y included older age, congestive heart failure, peripheral or cerebrovascul ar disease, increased creatinine levels, lowered ejection fraction, treatme nt of cardiogenic shock, treatment of an acute myocardial infarction, urgen t priority, emergent priority, preprocedure insertion of an intraaortic bal loon pump and PCI of a type C lesion. The receiver operating characteristic area for the predicted probability of death was 0.88, indicating a good ab ility to discriminate. The rule was well calibrated, predicting accurately at all levels of risk. Bootstrapping demonstrated that the estimate was sta ble and performed well among different patient subsets. CONCLUSIONS In the current era of interventional cardiology, accurate calcu lation of the risk of in-hospital mortality after a percutaneous coronary i ntervention is feasible and may be useful for patient counseling and for qu ality improvement purposes. (J Am Coll Cardiol 1999;34:681-91) (C) 1999 by the American College of Cardiology.