Global Myocardial Work Combined with Treadmill Exercise Stress to Detect Significant Coronary Artery Disease

•MW combined with exercise stress can help to identify significant CAD. •GWE is superior to GLS at peak exercise in detecting significant CAD. •GWW at the recovery period can identify significant CAD. •A new model comprising peak GWE and recovery GWW accurately detects significant CAD. Background Myocardial work (MW) derived from the left ventricular pressure-strain loop is a novel and noninvasive method for assessing left ventricular function that accounts for loading conditions. We aimed to explore whether global MW combined with treadmill exercise stress could detect significant coronary artery disease (CAD) in patients with angina pectoris. Methods Eighty-five patients with angina pectoris and no prior CAD history were included. All patients underwent treadmill exercise stress echocardiography and coronary angiography. Global MW was constructed from speckle-tracking echocardiography indexed to the brachial systolic blood pressure. The association between MW parameters and the presence of significant CAD was assessed with logistic regression. The discriminative power of MW parameters to detect CAD was assessed with receiver operative characteristic curve, net reclassification improvement, and integrated discrimination improvement analysis. Results Twenty-five patients had a positive exercise echocardiogram, while significant coronary artery stenosis (≥70% in one or more major epicardial vessels or ≥50% in the left main coronary artery) was observed in 41 patients. The global wasted work (GWW) and global work efficiency (GWE) were significantly higher or lower, respectively, in patients with significant CAD compared with those of nonsignificant CAD at the peak exercise and during recovery periods (P < .05 for all). Multivariate logistic regression analysis demonstrated that peak GWE and recovery GWW could predict significant CAD. Peak GWE had the highest area under the receiver operating characteristic curve (AUC) among all global MW parameters (AUC = 0.836). Furthermore, a model comprising peak GWE and recovery GWW performed better for the identification of significant CAD than peak GWE alone (AUC = 0.856). Conclusions Peak GWE could detect significant CAD. The new model, incorporating peak GWE and recovery GWW, not only identified but also provided additional value for estimating the probability of significant CAD. Global MW parameters combined with exercise stress perform as an accurate noninvasive screening before the invasive diagnostic technique. Myocardial work (MW) derived from the left ventricular pressure-strain loop is a novel and noninvasive method for assessing left ventricular function that accounts for loading conditions. We aimed to explore whether global MW combined with treadmill exercise stress could detect significant coronary artery disease (CAD) in patients with angina pectoris. Eighty-five patients with angina pectoris and no prior CAD history were included. All patients underwent treadmill exercise stress echocardiography and coronary angiography. Global MW was constructed from speckle-tracking echocardiography indexed to the brachial systolic blood pressure. The association between MW parameters and the presence of significant CAD was assessed with logistic regression. The discriminative power of MW parameters to detect CAD was assessed with receiver operative characteristic curve, net reclassification improvement, and integrated discrimination improvement analysis. Twenty-five patients had a positive exercise echocardiogram, while significant coronary artery stenosis (≥70% in one or more major epicardial vessels or ≥50% in the left main coronary artery) was observed in 41 patients. The global wasted work (GWW) and global work efficiency (GWE) were significantly higher or lower, respectively, in patients with significant CAD compared with those of nonsignificant CAD at the peak exercise and during recovery periods (P < .05 for all). Multivariate logistic regression analysis demonstrated that peak GWE and recovery GWW could predict significant CAD. Peak GWE had the highest area under the receiver operating characteristic curve (AUC) among all global MW parameters (AUC = 0.836). Furthermore, a model comprising peak GWE and recovery GWW performed better for the identification of significant CAD than peak GWE alone (AUC = 0.856). Peak GWE could detect significant CAD. The new model, incorporating peak GWE and recovery GWW, not only identified but also provided additional value for estimating the probability of significant CAD. Global MW parameters combined with exercise stress perform as an accurate noninvasive screening before the invasive diagnostic technique.