|Year : 2016 | Volume
| Issue : 1 | Page : 1-6
Diastolic abnormalities detected by velocity vector imaging in the presence of coronary ischemia: A pilot stress echocardiographic study
Brian Edward Miller1, Angel Lopez-Candales2
1 Department of Internal Medicine, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
2 Department of 1Cardiovascular Health and Disease, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
|Date of Web Publication||18-May-2016|
Brian Edward Miller
Department of Internal Medicine, 231 Albert Sabin Way, Academic Health Center, Cincinnati, OH 45267-0542
Source of Support: None, Conflict of Interest: None
| Abstract|| |
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.
Keywords: Diastolic dysfunction, echocardiography, ischemic memory, left ventricular function, strain
|How to cite this article:|
Miller BE, Lopez-Candales A. Diastolic abnormalities detected by velocity vector imaging in the presence of coronary ischemia: A pilot stress echocardiographic study. Heart Views 2016;17:1-6
|How to cite this URL:|
Miller BE, Lopez-Candales A. Diastolic abnormalities detected by velocity vector imaging in the presence of coronary ischemia: A pilot stress echocardiographic study. Heart Views [serial online] 2016 [cited 2019 Feb 17];17:1-6. Available from: http://www.heartviews.org/text.asp?2016/17/1/1/182647
| Introduction|| |
Diagnosis of coronary artery disease (CAD) as the cause of chest pain requires the use of a careful clinical history as well as an additional investigation. Coronary angiography is considered the “gold standard” of diagnostic tests, but known limitations such as being invasive, costly, and not yielding physiologic information makes it inappropriate as the initial study in most patients., Thus, routine evaluation of patients presenting with chest pain suspected to be due to CAD often includes some form of noninvasive stress testing. Several tests that vary in sensitivity, specificity, and cost are available, and the optimal testing strategy differs according to the patient population and specific patient characteristics. Furthermore, the clinical applicability and ultimate utility of any particular stress testing modality such as exercise electrocardiographic testing, stress echocardiography, and stress radionuclide myocardial perfusion imaging have been previously reviewed.,,, The high specificity of stress echocardiography compared to other modalities contributes to its utility as a cost-effective diagnostic method.
Myocardial ischemia is known to progress in a “cascade” of events in which the various markers are hierarchically ranked in a well-defined time sequence., Current stress testing modalities are limited in their ability to detect functional myocardial changes prior to the manifestation of systolic abnormalities. There have been recent developments in magnetic resonance imaging  and gated single-photon emission computed tomography/positron emission tomography , to attempt to identify abnormalities in diastolic dysfunction in patients with ischemia.
Echocardiography assessment has generally been limited to the identification of ischemia as a stress-induced worsening of myocardial contractility when compared to baseline. This is presumed due to a transient regional imbalance between oxygen demand and supply that ultimately compromises myocardial calcium handing and hence contractility.
The introduction of tissue Doppler imaging (TDI) was first used to assess changes in diastolic velocities after ischemia/reperfusion in a dog model  and was subsequently utilized during dobutamine stress echocardiographic (DSE) examination. In addition, relaxation time as measured by TDI after dobutamine stress has been proposed as a novel marker for ischemia. Unfortunately, the overall utility of TDI is limited by angle dependency, an artifact from cardiac movement, and small areas of myocardial interrogation.
The advent of speckle tracking strain imaging  and its use in velocity vector imaging (VVI) allows measurements to be made along the entire myocardium using offline analysis of standard B-mode cine images without the limitations of TDI. Speckle tracking strain analysis has been previously used to demonstrate diastolic abnormalities at rest  as well as exercise-induced diastolic stunning, and VVI has been used to detect postexercise diastolic stunning, an easy to use and automated imaging approach to assess diastole has not been developed. Consequently, we devised a pilot study to test a new normalized strain algorithm to objectively assess diastolic properties of the left ventricular (LV) myocardium in patients with and without documented occlusive CAD.
| Methods|| |
In this retrospective analysis, we queried our stress echocardiographic laboratory database for patients who had a stress echocardiogram and coronary angiogram. Inclusion criteria was as follows: Patients with a stress echocardiogram with good endocardial border resolution in the 2 and 4-chamber apical views and coronary angiography demonstrating either nonocclusive disease, occlusive left anterior descending artery (LAD), or right coronary artery (RCA). Patients were excluded from analysis for the following: Resting wall motion abnormalities, conduction abnormalities that affect anteroseptal wall motion interpretation, and the presence of ectopy.
For this pilot study, in order to assess the feasibility of the proposed strain algorithm, five patients were identified that had a cardiac catheterization without obstructive disease near the time of stress echocardiogram, forming our control group. Five additional patients whose cardiac catheterization showed obstructive disease in at least the LAD or RCA formed our subject group.
Our Institutional Review Board (IRB # 12012604) approved the study (6/29/2014).
Baseline and stress echocardiographic images were obtained according to published guidelines by the American Society Echocardiography  and studies were performed using commercially available systems (Vivid 7 and 9; GE Medical Systems, Milwaukee, WI, USA).
Strain pattern analysis
Echocardiographic images were loaded into our strain analysis software, two-dimensional cardiac performance analysis (TomTec Imaging Systems). Both two-ventricle and four-ventricle cine views, pre and poststress, were analyzed. This began with a tracing of the endocardial border with manual readjustment of epicardial border outline based on varying myocardial thickness [Figure 1]. In some cases, the epicardial border was not clearly visualized, as it fell beyond the field of view of the ultrasound. The application then performed its speckle tracking analysis and output strain curves and vector images, which were inspected for consistency. If the application failed to adequately pick up the endocardial border, the myocardial tracing was repeated until the quality was assured.
|Figure 1: Example myocardial tracing: Representative endo and epicardial recognition after initial tracing and manual adjustment, shown as a still frame from cine loop|
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The matrix output from this analysis was loaded into RStudio Version 0.98.501 (RStudio, Inc.). The raw output contained 49 locations along myocardium, of which the most lateral, near the mitral annulus, was removed, as this was a common location for error in the computer generated endocardial tracing. The first cardiac cycle from each was then normalized to 60 bpm in a linear fashion. A measurement was taken of the total duration of time that the strain was at least 90% of the peak systolic strain, which we will refer to as the normalized strain time (NST), as shown in [Figure 2]. This measurement was made along 48 consecutive segments along the endocardial border from the septum to the lateral mitral annulus in both the 2 and 4-chamber views and summated for each view. Then for view separately, the NST in the prestress strain matrix was subtracted from the NST in the poststress strain matrix.
|Figure 2: Rest and recovery single vector strain pattern, in the case of a single peaking strain pattern, normalized strain time = A (at rest). With a double peaking strain pattern, normalized strain time = B + C. This would follow for subsequent patterns, as the normalized strain time is always the duration of time the strain magnitude is at least 90% of the peak|
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All continuous data are presented as mean ± standard deviation and groups compared by two-tailed unpaired t-test assuming unequal variances. Dichotomous variables are displayed as counts and groups compared using Chi-squared test. Sample sizes were inadequate for multivariate analysis. Reliability of NST as a new echocardiographic measure was assessed using the intra-class correlation coefficient. In order to compare reproducibility of a single reader, two patients were randomly selected from each group, and NST in two and 4-chamber views at rest and stress were re-measured after a 6 months span. For comparison among different readers, two patients were randomly chosen from each group, and a trained and blinded reader measured variables of interest. NST measurements were compared on a pair-wise basis and grouped by either 2 or 4-chamber view. Intraclass correlation coefficients were calculated using one- and two-way agreement models for comparison between single and multiple readers, respectively. All statistics were calculated in RStudio Version 0.98.501 (RStudio, Inc.).
| Results|| |
Based on the prespecified criteria listed under the methods section, two groups of patients were used for analysis. There were five patients found to meet criteria for each of the two groups, labeled subjects and controls. There was no attempt to match them in terms of baseline characteristics; however, of the baseline characteristics collected, only hypertension was statistically different between groups. Other characteristics that neared significance were gender and diabetes. No patients had symptoms of, or being treated for, clinical heart failure. Complete demographics of the studied population are shown in [Table 1]. Given the small sample size of this feasibility study, we have outlined some characteristics of the stress test and cardiac catheterizations in [Table 2]. As can be seen, there were seven patients who underwent dobutamine stress tests and three exercise stress tests. Overall, there were nearly an equal number of positive stress tests between those with and without occlusive CAD.
By measuring the NST pre and poststress, as described above, we found even though the NST measured from both 2 and 4-chamber views poststress was higher in both groups; the increased NST was more significant in CAD patients. The difference in NST pre and poststress in the apical 4-chamber view was greater between patients with (40 ± 16) and without (12 ± 19) CAD (P = 0.04) [Table 3]. Patients with multi versus single-vessel disease had a trend toward increased difference in pre and poststress NST (49.5 ± 9.4 vs. 25.4 ± 20.6, P = 0.09). Most importantly, the pre and poststress difference in NST when measured from the 4-chamber view as shown in [Table 3] not only was significant but also the only statistically different variable. Representative pre and poststress NST curves in a patient with and without CAD are shown in [Figure 3].
|Figure 3: Comparison of normalized strain time: Representative pre and poststress normalized strain time curves in a patient with (a) and without (b) coronary artery disease showing the prolongation of the recovery normalized strain time with coronary artery disease|
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Finally, we assessed the reliability of this new measurement of NST for both 2-chamber and 4-chamber views using the intra-class correlation coefficient. Consistency of the primary reader over time was fair for 2-chamber view and good for 4-chamber view. Agreement between the primary and a secondary reader was good for both views [Table 4].
| Discussion|| |
Even though, stress echocardiography is a recognized cost-effective and highly specific imaging modality test; its diagnostic prowess has been traditionally limited to the identification of stress-induced regional myocardial systolic dysfunction., However, the ischemic response is a well-known cascade of events known to begin with diastolic dysfunction., Diastole is also an energy-dependent process, involving the reuptake of calcium ions in the sarcoplasmic reticulum, and it has been shown that this active process is hindered in acutely ischemic  as well as stunned cardiomyocytes.
Our study provides evidence that stress echocardiography might be useful in identifying diastolic abnormalities in patients with significant CAD. Furthermore, our approach is unique as it offers several distinct advantages compared with prior studies that attempted to characterize early abnormalities in diastolic function during stress. Unlike previous studies,, our algorithm not only offers a more robust double peaking pattern of abnormalities but also our technique does not involve any manual selection or manipulation of vectors. In addition, our study utilized longitudinal strain in the horizontal long axis view, whereas these prior studies used the short axis view. We feel the additional length of the myocardium, as well as the inclusion of more distal vascular territories, provided us with a more accurate measure of true cardiac function.
The ultimate goal of this new algorithm is to improve the diagnostic capabilities of stress echocardiography by incorporating VVI analysis for evaluation of myocardial diastolic function to identify ischemia even if the threshold to reach the development of regional systolic abnormalities is not attained. To highlight this potential, patient six in [Table 2] had an elevated coronary calcium score as part of his preoperative evaluation prompting a DSE that showed normal LV ejection fraction both at baseline and peak stress with no evidence for stress-induced ischemia. However, given the high pretest probability for CAD coronary angiography was obtained showing triple vessel disease and the patient was subsequently referred for coronary artery bypass surgery. Even though, this patient had no evidence of regional systolic wall motion abnormalities; the NST pre and poststress difference measured from the 4-chamber view was 51.3 ms, the second highest among all ten patients, which would have identified this patient with CAD based on our algorithm.
A number of limitations regarding this study deserve mention. First, this was a retrospective study consisting of a small number of patients. However, this pilot study was intended to assess the feasibility of performing the proposed measurements. Depending on institutional preferences, either stress echocardiography or myocardial perfusion imaging are used in the identification of patients at risk of CAD. Since stress echocardiography is often utilized in low-risk patients, most test results are negative for ischemia and hence do not proceed to coronary angiography. It was also challenging to find patients with a negative stress echocardiogram that subsequently underwent a coronary angiogram. Additionally, to the small number of patients it is impossible to make any distinction in the ability of VVI tracking to identify ischemia depending on the nature of the test; that is between treadmill and dobutamine infusions. Finally, although all durations were normalized to heart rate in a linear method, there is likely an unpredictable and confounding effect of heart rate on durations measured.
Generally, dobutamine will shorten relaxation time in the healthy myocardium as compared to natural sympathetic drive at peak heart rate. However, this effect is significantly reduced in ischemic myocardium. When dobutamine and exercise stressing techniques are directly compared in patients with CAD, there has not been shown to be a significant difference in the degree of diastolic dysfunction, though the dysfunction tends to persist longer in the case of dobutamine after peak stress. This was not observed in our study groups; however, our results are confounded by the need to use dobutamine after a failure to achieve target heart rate or and significant intolerance to or inability to exercise.
| Conclusions|| |
Our data seems to suggest a unique method of detecting the presence of CAD, presumably at the earliest stages the ischemic cascade, at the time of development of diastolic dysfunction. While similar studies have demonstrated similar findings in the past, this study is unique in that utilizes an automated algorithm to analyze longitudinal strain patterns. This technique has the potential to increase the sensitivity and specificity of stress echocardiography. This has the potential to increase the sensitivity of stress echocardiography to the degree of nuclear imaging. Prospective studies are now necessary to validate this technique in this likely under-recognized and undertreated population.
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[Figure 1], [Figure 2], [Figure 3]
[Table 1], [Table 2], [Table 3], [Table 4]