Clinical predictors of 30-day cardiac events in patients with acute coronary syndrome at a community hospital.

OBJECTIVE

We sought to determine predictors of coronary events (cardiac death, acute myocardial infarction, and urgent revascularization) within 30 days after admission.

METHODS

We prospectively collected data on 400 patients admitted through our emergency room for unstable angina and acute coronary syndromes. Patients with ST-segment elevation myocardial infarction and those who required thrombolysis were excluded.

RESULTS

Of 383 patients who were eligible, 120 patients had coronary events within 30 days. Statistically significant variables associated with coronary events were advanced age, male sex, family history of premature coronary artery disease (CAD), diabetes mellitus, tobacco abuse, prior congestive heart failure, prior myocardial infarction, and history of CAD. Symptoms at presentation associated with cardiac events were typical angina and shortness of breath. Objective measures of ischemia associated with cardiac events were elevated troponin T, elevated creatine kinase MB, and ischemic electrocardiographic changes. Using forward stepwise regression analysis, we generated a model to predict 30-day major adverse cardiac events. The strongest predicting variable was serum troponin T (accounting for 33% of predicting r2, P < 0.001) followed by typical angina (r2 increasing to 37%), ischemic electrocardiographic changes (40%), prior CAD (42%), family history of premature CAD (44%), shortness of breath (46%), and positive creatine kinase MB (48%). The positive predictive power of the complete model was r2 = 48%, P < 0.001.

CONCLUSION

Our model incorporating elements from the patient's demographic, medical history, presentation, and ischemic assessment identified 48% of patients presenting with unstable angina and acute coronary syndromes who will suffer a major adverse cardiac event within 30 days of admission. Although the strongest predictor was identified as serum troponin T, other clinical criteria offered improvement in our predictive abilities. Therefore, good initial clinical evaluation in addition to simple tests such as serum cardiac markers and electrocardiography are valuable in risk stratification of patients presenting with acute coronary syndromes and cardiac chest pain. Additional testing may be necessary to improve the positive predictive value of the model. Cardiac enzymes and electrocardiographic changes have the highest negative predictive value for occurrence of major adverse cardiac events. Identification of high-risk patients is essential to direct resources toward these patients and to avoid unnecessary costs and risk to the low-risk population.