0308: Longitudinal 2D strain predicts severe coronary artery disease in patients with NSTEMI, normal left ventricular ejection fraction and no wall motion abnormality

Background In the era of high-sensitivity troponin, numerous patients are characterized with NSTEMI. Noninvasive tools are needed for early risk stratification. Longitudinal strain provided by speckle tracking echocardiography (2D-STE) has been proved to be very sensitive for diagnosing sub-clinical myocardial injuries, especially in the setting of ischemia. The aim of this study was to study speckle tracking longitudinal strain for early risk stratification in NSTEMI with no sign of myocardial dysfunction (left ventricular ejection fraction (LVEF) >50% and no abnormality of regional wall motion). Methods and results 40 patients were prospectively examined by echocardiography immediately prior to coronary angiography after a first hospitalisation for NSTEMI. Global longitudinal strain (GLS) was provided semi automatically and we calculated territorial longitudinal strains (TLS) on the basis of the perfusion areas of the 3 major coronary arteries, by averaging all segmental peaks systolic strain values within each territory. The subjects were classified into three groups depending on the extent of CAD: no significant CAD, one-vessel and two-vessel CAD (excluding left main [LM] or proximal left anterior descending [LAD] arteries), three-vessel CAD or LM and proximal LAD arteries involvement (qualified as severe CAD). A significant worsening of GLS depending on the extent of CAD was found (-21±1.6% [no significant CAD] vs. -19.9±1.8% [1 or 2 -vessel disease] vs. -7.2±2.2% [severe CAD] p= 0.002). Multivariate analyses have confirmed that only GLS (OR: 5.62; 95%CI: 1.40 to 22.60 p=0.015) prognosticates severe CAD. Receiver operating characteristic (ROC) curves analyses of GLS showed a cut-off value of - 18%, to identify patients with severe CAD. Conclusion This study confirms that in NSTEMI with no sign of myocardial dysfunction, GLS enables rapid risk stratification and predicts severe CAD with excellent accuracy.