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Risk predictors in patients scheduled for percutaneous coronary revascularization
L Harrell1, H Schunkert, I F Palacios
1Cardiac Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.
Insights
Patient characteristics, not lesion morphology, now predict major adverse cardiac events after percutaneous coronary revascularization. This shift highlights evolving risk factors in modern interventional cardiology practices.
Area of Science:
- Cardiovascular Medicine
- Interventional Cardiology
- Clinical Research
Background:
- Procedural risks in conventional balloon coronary angioplasty were historically linked to unfavorable lesion morphology.
- Predictors of adverse events in current percutaneous coronary revascularization (PCR) practices remain unclear.
Purpose of the Study:
- To identify factors predicting major adverse events (death, Q-wave myocardial infarction, emergency bypass surgery) in contemporary PCR.
- To evaluate the influence of patient characteristics versus lesion morphology on PCR outcomes.
Main Methods:
- Analysis of 3,335 consecutive patients undergoing PCR.
- Utilized multiple stepwise logistic regression to identify independent predictors of major adverse cardiac events (MACE).
- Compared outcomes based on lesion morphology (AHA/ACC lesion type C) and patient factors.
Main Results:
- Successful PCR rates increased from 91% to 95%, while MACE rates decreased from 3.6% to 1.6% annually.
- Independent predictors of MACE included cardiogenic shock, renal disease, evolving myocardial infarction, congestive heart failure, number of lesions treated, age, and prior coronary intervention history.
- Unfavorable lesion morphology (AHA/ACC type C) was a predictor, but patient characteristics were more significant in the current era.
Conclusions:
- In the current era of advanced device technology, percutaneous coronary revascularization success is less dependent on lesion morphology.
- Major adverse cardiac events are declining and are primarily predicted by easily identifiable patient characteristics.
- Risk stratification for PCR should focus on patient comorbidities and clinical status rather than solely on angiographic lesion characteristics.
Abstract:
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.