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Medical Science Monitor Basic Research
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eISSN: 1643-3750

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A novel echocardiographic method for the prediction of coronary artery disease

Yilmaz Gunes, Mustafa Tuncer, Mustafa Yildirim, Unal Guntekin, Hasan Ali Gumrukcuoglu, Musa Sahin

Med Sci Monit 2008; 14(9): MT42-46

ID: 867949


Background: Coronary artery disease (CAD) is a leading cause of death and prevention and early detection of CAD are major goals of healthcare. Although ultrasound methods allow valid and repeatable detection of structure and function of cardiovascular system, a rapid ultrasonographic method for the prediction of CAD has not yet been defined.
Material and Method: Ninety-one patients with newly diagnosed significant CAD on coronary angiography and 36 patients with normal coronary arteries were included in the study. Aortic strain, aortic distensibility, aortofemoral pulse-wave propagation velocity (PWPV), and color M-mode propagation velocity of the descending aorta (AVP) were measured.
Results: Male sex and smoking were significantly more frequent in the CAD group. Mean values of the left ventricular ejection fraction and AVP were significantly lower with higher PWPV in patients with CAD than controls. Multivariate regression analysis including age, hypertension, LDL cholesterol, diabetes, smoking habit, body mass index, left ventricular ejection fraction, AVP, aortic strain, aortic distensibility, and PWPV revealed that AVP (beta=0.850, p<0.001) and PWPV (beta=0.166, p=0.008) were the only significant predictors of CAD. However, when AVP was extracted from the regression model, the adjusted R2 decreased from 0.652 to 0.099. An AVP value of < or =41 cm/s, determined by receiver operating curve analysis, predicted CAD with 82.4% sensitivity and 97.2% specificity (positive predictive value: 98.7% and negative predictive value: 68.2%).
Conclusions: Bedside risk stratification for CAD is feasible by echocardiographic determination of AVP. This novel parameter may be particularly useful in identifying patients who will benefit from further diagnostic strategies for CAD.

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