Do echocardiographic stress, strain, and elastic modulus parameters accurately distinguish patients with significant coronary artery disease from those with non-significant disease?
Regional circumferential elastic modulus derived from 2D speckle tracking echocardiography and a thick-walled ellipsoidal model can non-invasively identify patients with significant coronary artery disease requiring intervention.
Abstract BackgroundIn this study, physical parameters of the strain of left ventricle (LV), wall stress with a thick-walled ellipsoidal model, and elastic modulus of LV were extracted for distinguishing patients who were stent candidates.Materials and MethodsEighty-eight patients with suspected coronary artery disease (CAD) underwent echocardiography and angiography. Based on angiography results, the patients were divided into two groups (64 patients candidate for PCI (percutaneous coronary intervention) and 24 cases in the control group). Long-axis and short-axis echocardiographic views were acquired. Radial, longitudinal, and circumferential stress were estimated by the thick-walled ellipsoidal model. Circumferential strain (CS) and longitudinal strain (Ls) were obtained for 18 segments in the endocardium layer of LV, and then GLS (global longitudinal strain) and GCS (global circumferential strain) were calculated.ResultAnterior and inferoseptal circumferential wall stresses in end-systole (ES) were statistically significant (P0.05). Peak systolic GCS and GLS were lower in the PCI group (-18.13±3.03 and -16.57±2.88%) compared to the control group (-21.97±3.97 and 19.14±2.17%), respectively (p<0.05). The receiver operator characteristic curve (ROC) analysis revealed that GLS and GCS had the highest area under the ROC curve with a sensitivity of 83% and specificity of 63% for GLS and sensitivity of 71% and specificity of 59% for GCS.ConclusionStress and strain parameters are suggested as non-invasive and quantitative tools for measuring left ventricular function before angiography. Regional elastic modulus was a powerful predictor that can be distinguishing significant CAD patients.
Ahmadi et al. (Thu,) studied this question.