Abstract 108: Differences in Cardiologists’ Interpretation and Management of Chest Pain Symptoms and Abnormal Stress Tests in Women and Men

Background: Gender differences in evaluation of coronary artery disease (CAD) have been suggested in observational cohorts. Whether gender differences in care exist under standardized conditions is not well known. Methods: We developed a web-based survey that randomly varied a case vignette by patient gender alone. The first part described a patient presenting with symptoms suggestive of CAD and the second described the results of an abnormal ETT (exercise treadmill test) in that patient. For both parts, the scenario was designed to represent an intermediate likelihood of obstructive CAD regardless of patient gender. Providers were asked to rate the likelihood of obstructive CAD and to choose the best treatment option. The survey was emailed to all physician members of a state cardiology association. A total of 3 reminder emails were sent. Results: Of the 317 cardiologists with valid addresses, 71 (22.4%) completed the survey. Participants were mostly men (82.4%) and non-invasive cardiologists (63%). For both parts of the vignette, cardiologists tended to estimate the likelihood of obstructive CAD as “high” more often when the patient was a man (Table). Respondents were equally likely to recommend cardiac testing based on symptoms suggestive of CAD regardless of patient gender; however, after an abnormal ETT, cardiologists were more likely to recommend angiography for male patients and more likely to recommend a second stress test with imaging for female patients (Table). Conclusions: Using a standardized case patient at intermediate risk for CAD, cardiologists tended to estimate the likelihood of CAD as higher and were more likely to recommend cardiac angiography after an abnormal ETT in male patients and more likely to recommend stress testing with imaging for women. Our findings suggest differences in how cardiologists evaluate symptoms and manage abnormal stress test results according to patient gender. ![][1] [1]: /embed/graphic-1.gif