Predictors of outcome in patients with acute coronary syndromes without persistent ST-segment elevation. Results from an international trial of 9461 patients. The PURSUIT Investigators.

BACKGROUND: Appropriate treatment policies should include an accurate estimate of a patient's baseline risk. Risk modeling to date has been underutilized in patients with acute coronary syndromes without persistent ST-segment elevation. METHODS AND RESULTS: We analyzed the relation between baseline characteristics and the 30-day incidence of death and the composite of death or myocardial (re)infarction in 9461 patients with acute coronary syndromes without persistent ST-segment elevation enrolled in the PURSUIT trial [Platelet glycoprotein IIb/IIIa in Unstable angina: Receptor Suppression Using Integrilin (eptifibatide) Therapy]. Variables examined included demographics, history, hemodynamic condition, and symptom duration. Risk models were created with multivariable logistic regression and validated by bootstrapping techniques. There was a 3.6% mortality rate and 11.4% infarction rate by 30 days. More than 20 significant predictors for mortality and for the composite end point were identified. The most important baseline determinants of death were age (adjusted chi(2)=95), heart rate (chi(2)=32), systolic blood pressure (chi(2)=20), ST-segment depression (chi(2)=20), signs of heart failure (chi(2)=18), and cardiac enzymes (chi(2)=15). Determinants of mortality were generally also predictive of death or myocardial (re)infarction. Differences were observed, however, in the relative prognostic importance of predictive variables for mortality alone or the composite end point; for example, sex was a more important determinant of the composite end point (chi(2)=21) than of death alone (chi(2)=10). The accuracy of the prediction of the composite end point was less than that of mortality (C-index 0.67 versus 0.81). CONCLUSIONS: The occurrence of adverse events after presentation with acute coronary syndromes is affected by multiple factors. These factors should be considered in the clinical decision-making process.

Duke Scholars

Author Kerry L. Lee Biostatistics & Bioinformatics, Division of Biostatistics

Author Paul Wayne Armstrong Medicine, Cardiology