In the modern era of percutaneous coronary revascularization, major adverse events are declining and are best predicted by patient clinical characteristics rather than just lesion morphology.
Traditionally, procedural risks associated with conventional balloon coronary angioplasty have been largely attributed to unfavorable lesion morphology. However, factors predicting adverse events in the current practice of percutaneous coronary revascularization are unclear. The present study was undertaken to determine factors predicting major adverse events (death or Q-wave myocardial infarction or emergency bypass surgery) in 3,335 consecutive patients undergoing percutaneous coronary revascularization in the current practice of percutaneous coronary revascularization. During the period of observation, the rate of lesions treated successfully increased from 91% to 95% (P < 0.0001), whereas the rate of major adverse events (MACE) decreased from 3.6% to 1.6% (odds ratio OR, 0.70 per year). Using multiple stepwise logistic regression analysis, cardiogenic shock (OR, 8.59; confidence interval CI, 4.27-17.27), renal disease (OR, 3.33; CI, 1.95-5.69), evolving myocardial infarction (OR, 2.80; CI, 1.47-5.31), congestive heart failure (OR, 2.18; CI, 1.23-3.86), total number of lesions treated (OR, 1.28; CI, 1.03-1.59), age (OR, 1.03; CI, 1.01-1.06), and history of prior coronary intervention (OR, 0.51; CI 0.26-0.99) were identified as independent predictors of MACE. In addition, vascular disease (OR, 2. 48; CI 1.37-4.50) and unstable angina pectoris (OR, 0.44; CI 0.25-0. 79) were related to adverse events when patients in cardiogenic shock were excluded from the model. With the exception of most unfavorable lesion morphology (AHA/ACC lesion type C; OR, 2.05; CI, 1.19-3.52), anatomic parameters added no further information. In the present era of device technology, success rates of percutaneous coronary revascularization procedures have increased and remain to be determined by lesion morphology. In contrast, the rate of MACE is declining and best predicted by easily identified patient characteristics.
Harrell et al. (Mon,) studied this question.
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