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 assessing quality of care. Most clinical prediction rules for percutaneous coronary intervention (PCI) were developed using data collected before the broader use of new interventional devices.
Methods: Data were collected on 15,331 consecutive hospital admissions by six clinical centers. Logistic regression analysis was used to predict the risk of in-hospital mortality.
Results: Variables associated with an increased risk of in-hospital mortality included older age, congestive heart failure, peripheral or cerebrovascular disease, increased creatinine levels, lowered ejection fraction, treatment of cardiogenic shock, treatment of an acute myocardial infarction, urgent priority, emergent priority, preprocedure insertion of an intraaortic balloon 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 ability to discriminate. The rule was well calibrated, predicting accurately at all levels of risk. Bootstrapping demonstrated that the estimate was stable and performed well among different patient subsets.
Conclusions: In the current era of interventional cardiology, accurate calculation of the risk of in-hospital mortality after a percutaneous coronary intervention is feasible and may be useful for patient counseling and for quality improvement purposes.