Background:
Early identification and diagnosis are critical in the management of patients with acute coronary syndrome (ACS) since time-dependent therapies reduce patient mortality and morbidity.
Objective:
The aims of this study were to describe differences in presenting symptoms by individual ACS diagnoses and determine the prognostic value of both signs (electrocardiographic evidence of ischemia) and symptoms for an ACS diagnosis.
Method:
Patients > 21 years old, with any ECG ischemic changes (ST-elevation, ST-depression, T-wave inversion), elevated serum troponin, and ACS symptoms presenting to one of five emergency departments (ED) were eligible for the study. Patients completed the ACS Symptom Checklist, a validated 13-item instrument that measures cardiac symptoms (typical and atypical). Pearson Chi-square tests were used for bivariate analyses and logistic regression was used for multivariate modeling.
Results:
A total of 1,031 patients (mean age 60
+
14, 62% male, 70% White) were enrolled; 450 (43.7%) were diagnosed with ACS. One hundred eleven (11%) had ST-elevation myocardial infarction (STEMI), 236 (23%) had non-ST elevation myocardial infarction (NSTEMI), 103 (10%) had unstable angina (UA), and 581 (56%) were ruled-out for ACS. Patients with STEMI were more likely to report chest pain, diaphoresis, and higher symptom distress (p<0.05) at presentation than those without. Patients with NSTEMI were more likely to report arm pain and patients with UA were more likely to report lightheadedness (p<0.05). The presence of any chest symptoms (OR 2.24; 95% CI 1.27-3.97), higher symptom distress (OR 1.07; 95% CI 1.0-1.15), and a lower number of symptoms (OR 0.92; 95% CI 0.86-0.98) were independent predictors of an ACS diagnosis (p<0.05). The strongest predictor of an ACS diagnosis was the presence of ECG ischemic changes (OR 4.51, 95% CI 3.20-6.36) adjusting for symptoms, age, gender, heart rate, arrhythmia, and troponin levels (p<0.001).
Conclusion:
ECG signs of ischemia combined with specific symptom characteristics may enhance timely triage and detection of ACS in the ED. Predictive models that incorporate presenting signs and symptoms should be explored for this vulnerable population.