Abstract 1581: Longitudinal 2-dimensional Strain Of Left Ventricle At Rest Predicts High Risk Coronary Artery Disease Without Regional Wall Motion Abnormality
Objectives: Left main (LMD) and 3-vessel disease (3VD) are well-known risk factors for diagnostic stress test for coronary artery disease (CAD). However regional wall motions in those patients are frequently normal at resting status. We aimed to evaluate the segmental and global peak systolic longitudinal strain (PSLS) of left ventricle (LV) by 2D speckle tracking method and its usefulness as a screening method for high risk CAD.
Methods: A total of 100 patients who underwent echocardiography and coronary angiography (CAG) were evaluated. Patients with regional wall motion abnormalities (RWMA), LV ejection fraction (EF) <50%, valvular heart disease, atrial fibrillation or poor images were excluded. Echocardiographic examination including 2D strain analysis was performed and patients were grouped according to the results of CAG as following; LMD or 3VD as high risk group, 1- or 2-vessel disease as low risk group, and normal group. The global and segmental PSLS of basal, mid and apical LV were analyzed offline.
Results: The PSLS of every segment of LV could be obtained successfully with good tracking quality in 88 (88%) patients. There were no significant differences in age, sex, blood pressure, and LV EF (See Table⇓). The global and segmental PSLS of LV was significantly lower in high risk group compared with normal group. The mean PSLS of basal and mid LV was significantly reduced in high risk group compared with other groups. The receiver operational characteristic (ROC) curve demonstrated that the mid and basal PSLS could effectively predict high risk group (area under ROC curve=0.84, 95%CI=0.76~0.92). According to the ROC curve, −17.9% may be an optimal operational cut-off level to discriminate high risk group from others (specificity 79%, sensitivity 83%).
Conclusions: Longitudinal 2D strain of LV at rest is significantly reduced in high risk CAD without RWMA and it might be useful for prediction of high risk before stress test.