Why the study?
Does velocity vector imaging detect diastolic abnormalities during stress echocardiography in patients with coronary ischemia compared to those without CAD?
Does velocity vector imaging detect diastolic abnormalities during stress echocardiography in patients with coronary ischemia compared to those without CAD?
Velocity vector imaging can detect significant diastolic abnormalities in the poststress recovery period in patients with coronary ischemia.
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May support VVI for detecting poststress diastolic changes in CAD; hypothesis-generating and requires prospective validation.
BACKGROUND: The ischemic cascade has long been known to begin with diastolic dysfunction before detectable systolic abnormalities. The advent of speckle-tracking imaging and velocity vector imaging (VVI) has provided accurate and reproducible interpretation of systolic abnormalities in numerous disease processes; however, this imaging tool has been only recently been proposed for detecting diastolic abnormalities. METHODS: We analyzed pre and poststress echocardiography images of ten patients using VVI. We calculated normalized strain time (NST) as the duration strain was at least 90% of the measured peak and subtracted pre and poststress NST to calculate prolongation of NST as a sign of diastolic dysfunction. These intervals were measured from left ventricular longitudinal cine images obtained from two and 4-chamber in five patients not only with a positive stress echocardiographic response but also anatomy confirmed by coronary angiography. They were then compared to five patients without coronary artery disease (CAD). RESULTS: Differences in pre and poststress NST measured in the apical 4-chamber view were greater in CAD patients than without (40 ± 16 vs. 12 ± 19; P = 0.04). CONCLUSIONS: Significant diastolic abnormalities were detected using a semi-automated VVI analysis in the poststress recovery period. A prospective study is now required in a larger number of patients to correlate the development of diastolic strain abnormalities with extent and location of CAD.
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Miller et al. (2016) studied this question.
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