Predictive Value of Global Longitudinal Strain Imaging in Detecting Significant Coronary Artery Disease in Patients with Non-ST-segment Elevation Myocardial Infarction

Abstract

Background: Two-dimensional speckle-tracking echocardiography (2D-STE) is a newer method for assessing left ventricular (LV) systolic function. The aim of this study was to assess the predictive value of 2D global longitudinal strain (GLS) in the detection of longitudinal LV systolic dysfunction and the identification of significant coronary artery disease (CAD) in non-ST-segment elevation myocardial infarction (NSTEMI) patients without wall motion abnormalities. Materials and Methods: We enrolled 100 patients who were suspected cases of NSTEMI with echocardiography showing LV ejection fraction >50% and no regional wall motion abnormality. LV myocardial function was assessed using conventional echocardiographic measurements and myocardial deformation analysis with 2D-STE. The patients found to have significant CAD on coronary angiography were included in the cases group and those without significant CAD were considered as controls. Results: Mean age of the patients was 58.36 ± 11.21 years versus 56.54 ± 8.10 years in controls. Male preponderance was found in both case and control groups, with 66% (n = 33) and 56% (n = 28) males, respectively. The patients had significantly lower GLS as compared to the controls (-16.31 ± 1.34% vs -19.17 ± 1.63%, P < 0.001). The patients with positive troponin assay had a lower mean GLS −16.49 ± 1.5% as compared to those with negative troponin assay (−19.32 ± 1.54%, P < 0.001). Multivariate analyses indicated that GLS was an independent predictor of CAD in those with NSTEMI (GLS odds ratio = 0.024, P < 0.001). Conclusions: The present study demonstrates that 2D-STE is superior to conventional echocardiography in excluding significant CAD. It is a promising, easy to perform, bedside tool for diagnosis and prognostication in patients with NSTEMI. GLS has a potential to be used in conjugation with other risk stratification strategies to identify patients at high risk.