Simplified risk score models accurately predict the risk of major in-hospital complications following percutaneous coronary intervention

The objectives of this analysis were to develop and validate simplified ris k score models for predicting the risk of major in-hospital complications a fter percutaneous coronary intervention (PCI) in the era of widespread sten ting and use of glycoprotein Ilb/IIIa antagonists. We then sought to compar e the performance of these simplified models with those of full logistic re gression and neural network models. From January 1, 1997 to December 31, 19 99, data were collected on 4,264 consecutive interventional procedures at a single center. Risk score models were derived from multiple logistic regre ssion models using the first 2,804 cases and then validated on the final 1, 460 cases. The area under the receiver operating characteristic (ROC) curve for the risk score model that predicted death was 0.86 compared with 0.85 for the multiple logistic model and 0.83 for the neural network model (vali dation set). For the combined end points of death, myocardial infarction, o r bypass surgery, the corresponding areas under the ROC curves were 0,74, 0 .78, and 0,81, respectively. Previously identified risk factors were confir med in this analysis. The use of stents was associated with a decreased ris k of in-hospital complications. Thus, risk score models can accurately pred ict the risk of major in-hospital complications after PCI, Their discrimina tory power is comparable to those of logistic models and neural network mod els. Accurate bedside risk stratification may be achieved with these simple models. (C) 2001 by Excerpta Medico, Inc.