Calcified plaques of extracranial carotid arteries and left ventricular geometry as predictors of coronary artery disease

Patients and Methods: 113 patients underwent cardiac catheterization with selective coronary angiography, ultrasound examination of carotid arteries, and echo cardiography. Coronary angiograms were analyzed for disease severity and extent (number of main vessels with > 50% stenosis), carotid ultrasound for number and distribution of calcified plaques among the carotid arteries. Left ventricular diameter (LVEDD), interventricular septal thickness (IVS), and posterior wall thickness (LVPW) in end-diastole were measured echocardiographically. Left ventricular mass divided by body surface area (Q, normal < 150 g/m(2)) and left ventricular relative wall thickness (RWT = [IVS + LVPW]/LVEDD, normal < 0.44) were calculated. A normal left ventricular mass/body surface area with increased relative wall thickness was regarded as left ventricular concentric remodeling, while a hypertrophied left ventricle was denoted eccentric if the relative wall thickness was normal and concentric if the relative wall thickness was increased. Besides the traditional vascular risk factors hypertension, diabetes, smoking and hypercholesterolemia as well as body mass index, age and sex were analyzed. Results: Calcified plaques of carotid arteries were significantly correlated (r = 0.432, p < 0.001) with coronary artery stenoses as well as hypercholesterolemia (r = 0,434, p < 0.001), increasing age (r = 0.389, p < 0.001), diabetes (r = 0.273, p = 0.002), hypertension (r = 0.203, p = 0.015), and left ventricular hypertrophy (r = 0. 188, p = 0.023) in contrast to smoking status, body mass index, and male sex. The number of calcified plaques was also significantly correlated (r 0.504, p < 0.001) with severity and extent of coronary artery disease. Multiple stepwise regression analysis showed higher predictive values including calcified carotid plaques. Conclusion: Thus, determination of calcified carotid plaques is useful to improve the predictive value of risk factor-based multivariate models.