Abstract
Aim: To evaluate the diagnostic value of analyzing the regional longitudinal strain by 2D speckle tracking modality during dobutamine stress echocardiography (DSE) in detecting the culprit disease in either coronary circulation.
Methodology: Prospectively 101 symptomatic patients at intermediate pretest probability for CAD were studied within one month by DSE, followed by coronary angiography. The 3 standard apical views were acquired both at rest and peak stress at an average frame rate of 70/sec. The following indices: longitudinal peak systolic strain (LS), longitudinal peak systolic strain rate (LSRs), and longitudinal early diastolic strain rate (LSRd) were obtained from each segment of the 17 segment model. Regional strain was calculated as the mean of strain parameters of all 10 segments representing the anterior coronary circulation and of the 7 segments representing the posterior coronary circulation separately. Coronary stenosis ≥ 50% was considered significant.
Results: 52 patients had normal or mild CAD , the 49 patients with significant CAD were classified into ACC group n-21, and PCC group n=22. Those with mixed territory disease (n=17) were excluded from final analysis.
At rest, LS & LSRs were significantly lower in PCC than ACC group (-19.462.6 vs -21.762.5, p: 0.007 and -1.2360.17 vs -1.4960.33, p: 0.015 respectively).
At peak stress, all strain parameters were significantly different between the two groups regardless of the involved territory. In Receiver Operator Characteristics analysis, peak stress LS in either ACC and PCC territories showed the largest area under the curve: 0.9 (p<0.001) and 0.88 (p<0.001) respectively. The mean LS at cutoff value of -21% & -20% had 90.5% sensitivity and 88.5% specificity for detecting significant ACC disease, and 81.8% sensitivity and 79% specificity for detecting significant PCC disease respectively
Conclusion: Baseline regional strain analysis has the potential to detect significant
CAD in PCC territory. At peak stress the longitudinal systolic strain can detect significant disease in either coronary territory with high sensitivity and specificity.
