The Influence of Gender on Symptom Characteristics During Acute Coronary Syndrome

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Abstract More than 1.4 million Americans are expected to experience an episode of acute coronary syndromes (ACS) in 2010. Evaluation of the symptoms of ACS is important since symptoms determine when, or if, patients seek care. Recent studies have identified gender differences in symptoms of ACS, however retrospective designs, abstraction of symptoms from medical records, and variations in assessment forms make it difficult to determine the clinical significance of gender differences or if gender specific health messages are warranted. Therefore, the aims of this prospective study are to: 1) evaluate for gender differences in the occurrence, severity, and distress of 13 ACS symptoms after adjusting for age, diagnosis, comorbidities, and functional status, 2) establish the sensitivity, specificity, and predictive value of symptoms as an indicator of ACS for women and men, 3) explore symptom clusters including characteristics of gender, age, diagnosis, comorbidities, and functional status, and 4) determine if there are gender differences in the longitudinal (30 days and 6 months) outcomes of functional status, major cardiovascular events, hospital admissions, emergency department visits, and mortality. An exploratory aim is to determine if a relationship exists between selected biomarkers and symptoms of ACS for women and men. A novel approach, in which symptoms are recorded in the emergency department as they are occurring, will be used. The sample will be comprised of 630 patients admitted to the emergency department with symptoms suggestive of ACS: 522 patients with confirmed ACS and 108 patients ruled out for ACS. Participants will be recruited from four medical centers in Illinois and Oregon. The 13-item ACS Symptom Checklist will be completed on presentation to the emergency department. Comorbidities, activity status, and patient variables will be measured when the patient is stable. Pilot data indicate that the procedures are feasible and safe. Analysis of covariance will be used to test the hypothesis that there are gender differences in symptoms. Symptom clusters will be examined using agglomerative cluster analysis, and patient outcomes will be evaluated using linear mixed methods. Findings will aid in establishing the clinical significance of ACS symptoms.