Speckle-tracking strain echocardiography for detecting cardiac dyssynchrony in a canine model of dyssynchrony and heart failure

Echocardiographic parameters for cardiac dyssynchrony Arita T et al.

1

Speckle-tracking strain echocardiography for detecting cardiac dyssynchrony in a

canine model of dyssynchrony and heart failure

Takeshi Arita MD1, George P. Sorescu MD1,3, Brian T. Schuler MD1, Laura S. Schmarkey

BS3, John D. Merlino MD, FACC1,3, Jakob Vinten-Johansen PhD, FAHA2,3, Angel R.Leon

MD, FACC 1,3, Randolph P. Martin MD, FACC, FASE1, Dan Sorescu MD, FACC 1

1 Division of Cardiology, Department of Medicine 2 Division of Cardiothoracic Surgery,

Department of Surgery, 3Carlyle Fraser Heart Center, Emory University School of Medicine,

Emory University, Atlanta, GA 30322

Address correspondence to:

Dan Sorescu, M.D, FACC

Emory University School of Medicine

Division of Cardiology

1639 Pierce Drive, WMB Room 319

Atlanta, GA 30322

Telephone: (404) 727-3364

Fax: (404) 727-3585

Email: [email protected]

Running title: Echocardiographic parameters for dyssynchrony

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Copyright Information

Articles in PresS. Am J Physiol Heart Circ Physiol (April 20, 2007). doi:10.1152/ajpheart.00168.2007

Copyright © 2007 by the American Physiological Society.

mailto:[email protected]


2

Total word count: 5477

Authors have nothing to disclose pertinent to this study.

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Abstract

Background: Multiple echocardiographic criteria have been proposed to diagnose

mechanical dyssynchrony in patients with heart failure without being validated against a

model of cardiac dyssynchrony with heart failure. This study examines which of these

methods can detect dyssynchrony in a canine model.

Methods: Adult mongrel dogs underwent His bundle ablation and right ventricular pacing for

4 weeks at either 110 bpm to induce dyssynchrony without heart failure (D group, n=12) or

170 bpm to induce dyssynchrony with heart failure (DHF group, n=9). To induce heart

failure with narrow QRS, atria were paced at 190 bpm for 4 weeks (HF group, n=8). Tissue

Doppler imaging (TDI) and 2D echocardiography were performed at baseline and end of the

study. Standard deviation of time to peak systolic velocity (color coded TDI), time to peak S

wave on pulse wave TDI, time to peak radial and circumferential strain by speckle tracking

analysis (Err, Ecc respectively) as well as septal to posterior wall motion delay on M-mode

were obtained.

Results: In D group, only Err and Ecc were increased by dyssynchrony. In contrast, all the

echocardiographic parameters of dyssynchrony appeared significantly augmented in the DHF

group. Receiver operator curve analysis showed good sensitivity of Err (90%) and Ecc to

(100%) detected dyssynchrony without heart failure and excellent sensitivity and specificity

of Err and Ecc to detect dyssynchrony with heart failure

Conclusion: Radial strain by speckle tracking is more accurate than TDI velocity to detect

cardiac dyssynchrony in a canine model of dyssynchrony with or without heart failure.

Keywords: echocardiography, heart failure, dyssynchrony, tissue Doppler, strain

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Introduction

Cardiac resynchronization therapy (CRT) improves clinical status and promotes

reverse remodeling in patients with advanced heart failure(5). CRT corrects the mechanical

dyssynchrony that worsens systolic(18, 21) and diastolic dysfunction(19, 29) and creates

functional mitral regurgitation(11). However, those same clinical trial results suggest that

30-35% of patients with systolic dysfunction and ventricular conduction delay with QRS

exceeding 120 milliseconds (msec) fail to respond to CRT. Absence of dyssynchrony in the

presence of wide QRS may explain the observed failure to respond. Conversely, detecting

dyssynchrony independent of QRS duration may identify other potential responders.

Therefore, optimizing patient selection for CRT depends on accurate detection of cardiac

dyssynchrony. Methods to detect mechanical dyssynchrony include magnetic resonance

imaging(26), radionuclear scintigraphy(12), invasive conductance catheter recordings(23)

and echocardiography. Preliminary clinical studies suggest echocardiography with tissue

Doppler imaging (TDI) as an effective and practical modality to assess mechanical

dyssynchrony by analyzing time to peak systolic velocity(4, 6, 22, 28). However, the physical

properties of Doppler imaging limit assessment of motion and deformation to the longitudinal

axis lying parallel to the transducer, whereas ventricular motion occurs in three dimensions.

Recent developments in speckle-tracking(13) allow assessment of radial and circumferential

strain on B-mode echocardiography similar to tagged magnetic resonance imaging(1, 7).

Whether conventional echocardiography, TDI, or novel speckle-tracking accurately detect

mechanical dyssynchrony remains undetermined; the various clinically utilized

echocardiography-Doppler techniques have not undergone extensive testing in an

experimental model of dyssynchrony. The present study tests the accuracy of detecting

dyssynchrony of multiple echocardiography parameters in an experimental canine model of

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dyssynchrony with or without heart failure, with specific attention to a comparison between

novel speckle-tracking-derived radial and circumferential strain and widely-adopted

TDI-derived longitudinal velocity methods.

Methods

Animal models

The experimental procedures complied with the “Guiding Principles in the Use and Care of

Animals” published by the National Institutes of Health (NIH publication No. 85-23, revised

1996) and were approved by The Institutional Animal Care and Use Committee of Emory

University. (see online supplemental data)

Adult mongrel dogs underwent atrioventricular junction ablation followed by

implantation of a dual chamber pacing system. Atrioventricular pacing (atrioventricular

interval of 150 msec) at 110 beats per minute induced dyssynchrony and wide QRS without

heart failure (D: n=12) and right ventricular pacing at 170 bpm induced dyssynchrony and

wide QRS with heart failure (DHF group: n=9). A third group underwent implantation of a

dual chamber pacing system without junctional ablation and received atrial pacing at 190

bpm for 4 weeks to induce heart failure with narrow QRS (HF group: n=8). Intracardiac

recordings confirmed the absence of intrinsic atrioventricular conduction in the animals that

underwent junctional ablation. We measured QRS duration on surface lead II.

Echocardiography

We performed tissue Doppler and two-dimensional echocardiography (Vivid-7,

General Electric, Vingmed, Norway) at baseline and four weeks after pacing with animals

under general anesthesia (closed chest). All the echocardiographic images were obtained at

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intrinsic normal sinus rhythm both at baseline and four weeks after. One cardiologist (D.S.)

obtained the images, and a second investigator (T.A.) analyzed them offline. We derived the

ventricular volumes and ejection fractions using the Simpson biplane method. Color-coded

tissue Doppler image were obtained on apical two-, three-, four-chamber views with a frame

exceeding 150 frames per second (see supplemental video). Pulse wave tissue Doppler

images were obtained on apical two-, three- and four- chamber views. We excluded

foreshortened apical image view for measurement. TDI involved sampling a region of interest

in the basal six and mid six segments. The speckle-tracking analysis utilized two-dimensional

images obtained in the mid-level short axis with a frame rate of 70-110 per second. We

generated the mid-short axis level images at the level of both papillary muscles perpendicular

to the long axis of the ventricle. We recorded and stored at least three clips of 3-5

consecutive cardiac cycles of each apical and short axis image for off-line digital analysis. All

the measured parameters except speckle-tracking analysis were calculated as the average of

three consecutive beats.

Assessment of dyssynchrony

Color-coded tissue Doppler (Figure 1A). We measured longitudinal dyssynchrony as the

standard deviation of time from onset of QRS to peak systolic velocity (TPVs-SD) within the

ejection phase across 12 (6 basal and 6 mid) segments according to a previously described

method (28).

Pulse wave tissue Doppler image (Figure 1B). We also used an alternative method to assess

longitudinal dyssynchrony by calculating the standard deviation of time from onset of QRS to

peak S wave (pkS-SD) using pulse wave tissue Doppler image in the 12 segments(6).

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M-mode (Figure 1C). We defined the septal-to-posterior wall motion delay (SPWMD) as

the time difference between the peak inward excursions of the interventricular septum and

left posterior wall on M-mode images obtained from the mid-short axis view(17).

Speckle-tracking of short axis B-mode image (Figure 1D,E) Analysis of mid-level short

axis images used dedicated software (EchoPac PC, BTO5.0.1, GE Vingmed, Norway) to

determine radial and circumferential dyssynchrony. After tracing the endocardial border at

the end systole, we set the region of interest from the endocardium to the epicardial edge.

Automatic speckle-tracking analyzed each short axis image to track motion of speckles

contained within the region of interest on a frame to frame basis. The software automatically

computed distances between adjacent speckles to obtain the radial and circumferential strain

curves as a function of time. Assessing radial and circumferential dyssynchrony involved

exporting the strain data for automatic processing on a customized program (Matlab,

MathWorks; Natick , Massachusetts) to calculate the standard deviation of time from onset of

QRS to peak radial and circumferential strain (Err, Ecc respectively) across the six mid-level

segments.

Reproducibility of echocardiographic analysis

We used the Bland-Altman method and standard error of the mean to assess inter-

and intra-observer variability.

Intraobserver variability: Bland-Altman analysis computed the limits of agreement for time

to peak systolic velocity, time to peak S wave, SPWMD, time to peak radial strain and time to

peak circumferential strain of 5.2±20.9 msec, -4.3±14.4 msec, -2.6±28.9 msec, -1.4±15.8

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msec and 11.6±76.4 msec respectively. The variability for each parameter measured 7.0%,

6.8%, 14.4%, 2.3% and 10.4% respectively. Inter-observer variability: To determine the

inter-observer variability, two independent observers (TA and GPS) performed independent

analysis of the same echocardiographic images. Time to peak systolic velocity, time to

peak S wave, SPWMD, time to peak radial strain and time to peak circumferential strain

showed an average difference of -1.3±19.4 msec, 0.25±24.5 msec, -6.2±23.0 msec, -6.2±33.4

msec and -4.7±81.0 msec respectively. The variability for each parameter measured 9.8%,

7.9%, 17.3%, 6.3% and 15.4% respectively.

Statistical analysis

All the data appear as mean ± SD. For statistical analysis, we used the

Wilcoxon-t-test or Mann-Whitney-U-test to analyze differences in the means. Receiver

operating curve (ROC) analysis was performed to obtain the cut-off point for determining

intraventricular mechanical dyssynchrony with or without heart failure. Area under the curve

(AUC) was computed for each significant parameter. In ROC analysis, the DHF and D served

as separate positive controls with the combined baseline group serving as a negative control.

The relationship between dyssynchrony parameters and basic echocardiographic parameters

was analyzed using linear regression and is expressed as a Pearson’s correlation coefficient

followed by stepwise multivariate regression analysis. Statistical analysis was performed with

commercial software (SPSS 13.0, SPSS Inc, IL, USA). We defined the two-sided p value of <

0.05 as statistically significant.

Results

Baseline characteristics

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Figure 2 and Table 1 contain the results of the ablation and pacing intervention in the

three groups of animals. RV pacing after His-bundle ablation increased the QRS duration

from 91.2±12.9 msec to 166.7±14.6 msec but did not affect ejection fraction (D group).

Rapid atrial pacing caused systolic dysfunction (LVEF reduced from 58.7±6.0% to

22.2±4.7 %), but produced no significant change in QRS duration (HF group). Rapid RV

pacing and His-bundle ablation worsened left ventricular ejection fraction (58.4 ± 9.4 to 21.4

± 7.6 %) and prolonged QRS duration (93.7±6. to 166.1±17.5 msec ) to produce

dyssynchrony and heart failure (group DHF).

Echocardiographic parameters of dyssynchrony

a. Color-coded and pulse wave tissue Doppler analysis of longitudinal dyssynchrony.

Junctional ablation or rapid pacing alone did not change the color tissue Doppler-derived

TPVs-SD in the D and HF groups. The combination of ablation and rapid RV pacing

increased TPVs-SD in the DHF group (Table 2 and Figure 3A). Rapid atrial pacing-induced

heart failure (HF with narrow QRS) and the ablation/rapid RV pacing combination (DHF,

heart failure with wide QRS) increased the pulse wave tissue Doppler-derived time to peak

velocity (pkS-SD); dyssynchrony alone (D) did not affect the pkS-SD (Table 2 and Figure

3B). Applying multiple published color-coded and pulse wave tissue Doppler methods

found no statistical significant difference in time to peak longitudinal velocity in the D group;

ablation and rapid RV pacing (DHF group) did affect some parameters (see online

supplemental table 1)

b. M mode measurement of dyssynchrony.

The M mode-derived SPWMD did not change as a result of either dyssynchrony or heart

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failure alone but did increase with the combination of dyssynchrony and heart failure (Table 2

and Figure 3C).

c. Radial and circumferential strain analysis from short axis.

The Err increased significantly in all three groups (Table 2 and Figure 3D). The DHF group

demonstrated the greatest change in the Err (>5-fold increase in mean SDV, p=0.001), with

less change in the HF and D groups (2- fold increase, p=0.019 and >3- fold, p=0.018,

increase over baseline respectively). The DHF group showed significantly greater Err than

the D and HF groups (both p<0.001). The results suggest that Err detects dyssynchrony

irrespective of heart failure and may also reflect the degree of dyssynchrony.

The circumferential short axis strain increased in all three groups (Table 2 and Figure 3E).

Circumferential dyssynchrony increased in the D and DHF groups (p=0.03 and p<0.001

respectively). However, the lack of difference in the changes detected in the HF and DHF

groups (p=0.20) suggests that compared to radial strain analysis, circumferential strain

analysis has sensitivity but lacks specificity in detecting dyssynchrony.

Receiver-operator curves (ROC) for echocardiographic parameters of dyssynchrony

Using the D as the positive control for dyssynchrony shows that Err (AUC=0.819)

and Ecc (AUC=0.874) effectively detect dyssynchrony, without heart failure (Table 3A).

Using DHF group as a positive control shows that radial and circumferential dyssynchrony

detect dyssynchrony in heart failure (DHF) better (AUC=1) than tissue Doppler and M-mode

derived parameters (Table 3B). Tissue Doppler and M-mode parameters appear insensitive

while radial and circumferential dyssynchrony appear more sensitive and specific for

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detecting dyssynchrony independent of QRS duration. All parameters detect dyssynchrony

with adequate sensitivity and specificity in heart failure associated with prolonged QRS but

radial dyssynchrony had the best overall sensitivity and specificity.

Detecting dyssynchrony in heart failure with narrow QRS

Adopting cut-off values from the ROC analysis (using DHF as positive control)

yields the following results: With cut-off point for Err >63.94 msec, 37.5% (3 of 8) of heart

failure animals demonstrated radial dyssynchrony; with a cut off value for Ecc of >107.69

msec, 50% (4 of 8) showed circumferential dyssynchrony but was less specific.

Relationship between echocardiographic dyssynchrony parameters and global cardiac

function

The longitudinal dyssynchrony parameters (i.e. TPVs-SD and pkS-SD) correlated

with EF, EDV and ESV, but not with QRS duration during univariate analysis. The radial

strain-derived dyssynchrony parameter correlated with EF, ESV, EDV and QRS. The

circumferential strain-derived dyssynchrony parameter and SPWMD correlated with EF, ESV

and QRS, but not EDV (Table 4A). Multivariate analysis showed that TPVs-SD and pkS-SD

independently correlated with EF (p<0.001) but not with QRS duration. In contrast,

SPWMD and Err correlated both with EF and QRS duration (p<0.01) and Ecc correlated with

EF, EDV and QRS duration (p<0.01) (Table 4B).

Discussion

Differences among various parameters to assess dyssynchrony

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Preliminary clinical reports demonstrate the value of echocardiography for assessing

cardiac dyssynchrony and predicting response to CRT(2, 17, 28). However, these clinical

parameters to measure mechanical dyssynchrony lack testing in an animal model for cardiac

dyssynchrony. Because of large variability of clinical variables in human, to better test the

sensitivity and specificity of widely used echocardiography measures of dyssynchrony we

designed an experimental model of dyssynchrony and heart failure using the same

echocardiographic technology used in human. We used three canine groups: HF with

narrow QRS (fast atrial pacing with intact AV node), dyssynchrony without HF (wide QRS,

RV pacing only without heart failure), and the combination of both (RV pacing with heart

failure- wide QRS and heart failure). We chose a model of RV apical pacing after

His-bundle ablation for its similarity with dyssynchrony encountered in humans with

spontaneous LBBB (3). Although this may not reflect the whole spectrum of human disease,

nonetheless induces dyssynchronous mechanical activation of the heart and is therefore

useful as positive control for cardiac dyssynchrony. RV pacing induced mechanical

dyssynchrony independent of ventricular size and function(20). Rapid RV pacing after

atrioventricular block induced both dyssynchrony and heart failure. Rapid atrial pacing

produced cardiomyopathy without ECG criteria of dyssynchrony. We used the model to test

various conventional echocardiographic and Doppler parameters and a novel speckle-tracking

method to determine their accuracy in detecting dyssynchrony.

Our results suggest that radial or circumferential strain measured by the novel

speckle-tracking in short axis images detects dyssynchrony with or without heart failure

better than the tissue Doppler-derived echocardiographic parameters (time to peak systolic

velocity). Radial and circumferential strain analysis may be superior to measures of

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longitudinal velocity for several reasons. First, the contraction of the ventricle occurs in a

more circumferential rather than longitudinal direction due to the helical architecture of

myocardial fibers(14)-(10). Second, Waldman(25) showed that the amplitude of

longitudinal strain did not change significantly during epicardial pacing while circumferential

and radial strain did change. Third, strain data reflect cardiac deformation, while velocity

data reflect cardiac motion(8); tethering, translational motion or rotation of the heart may

affect time to peak velocity in a given myocardial segment. The peaks of the velocity and

strain curves do not coincide. Peak systolic velocity occurs in early to mid phase of

contraction; however, peak strain for each myocardial segment occurs at the end of segmental

contraction.

Pulse wave tissue Doppler detected dyssynchrony, but not in the absence of heart

failure. Pulse wave tissue Doppler has excellent temporal resolution compared to color-coded

tissue Doppler(16). The multivariate regression analysis demonstrated that both

color-coded and the pulse wave tissue Doppler indices correlated better with ejection fraction

(systolic dysfunction) and less with QRS duration. Yu reported that TPVs-SD correlated with

left ventricular end systolic volume in heart failure patients with narrow and wide QRS

duration during multivariate regression analysis(27). His results and ours suggest that data

from global ventricular size and function, and not the timing of the altered sequence of

contraction affect longitudinal velocity.

The SPWMD has also been proposed to quantitatively assesses dyssynchrony in

patients with heart failure(17). Since the acquisition measures the timing of excursion points,

the angle of acquisition and translation can affect the measurement and distort displacement.

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Factors that independently affect septal movement such as right ventricular pressure overload

or prior cardiac surgery could impact radial septal motion and decrease the reliability of this

measurement for the detection of dyssynchrony. Also, although relatively easy to obtain,

the low reproducibility of the SPWMD, as shown in our study and also by Marcus et al(15),

limits its value in clinical use.

Radial or circumferential strain from speckle-tracking analysis

Speckle-tracking analysis provides angle-independent information on motion and

deformation in any direction of a cardiac coordinate system. Several studies demonstrated its

validity against sonomicrometry(1) and tagged MRI(7). Speckle-tracking echocardiography

offers higher temporal resolution than MRI making it suitable for assessing the timing of

cardiac motion. Speckle-tracking offers easier access, less cost, and consumes less time

compared to tagged MRI. In our study, the processing and analysis of echocardiographic data

was automatically performed by a software and therefore independent of operator. The

limitations of speckle-tracking include its dependence on the angle of acquisition in the short

axis. In the present study, this limitation impacted more the measurement of circumferential

(especially posterior wall) rather than radial strain, and might explain the observed large

variability in circumferential strain determination even in the assumingly normal ventricles

(baseline dog). With less variability and similar accuracy in detecting dyssynchrony the

radial strain derived from short axis views was superior to circumferential strain. Our

observations agree with Suffoletto who reported that speckle tracking derived-radial

dyssynchrony has high sensitivity and specificity in predicting response in CRT in humans

(24). That analysis used the difference in time to peak radial strain between two segments

(anteroseptal and posterior wall) to measure dyssynchrony. We used the standard deviation

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in time to peak radial strain across six segments in the mid-short axis view to achieve greater

accuracy. We observed that the difference in time to peak radial strain between the

anteroseptal and posterior wall segments increased in D (p=0.047, AUC=0.776) and also in

DHF (p=0.0019, AUC=0.977). However, differences were lower than those calculated

using the SD in the six segment model (Table 3A and 3B).

Mechanical dyssynchrony with narrow QRS

The atrial pacing cardiomyopathy model in this study produced a valid

representation of heart failure with normal QRS duration (fast atrial pacing with normal AV

node conduction). In our study, 30% of the animals with HF and normal QRS duration

displayed radial or circumferential dyssynchrony according to the criteria derived from

analysis of the DHF group. However, although this method is very sensitive to detect

mechanical dyssynchrony per se, it does not identify the threshold value which defines the

response to cardiac resynchronization therapy in subjects with heart failure and normal QRS.

Whether echocardiography and tissue Doppler imaging can detect dyssynchrony accurately

enough to predict CRT response remains to be determined and further studies are required to

test this notion. Our results suggest that longitudinal velocity-derived parameters do not

detect dyssynchrony well; therefore the role of these methods in expanded clinical application

of CRT in the narrow QRS population remains unproven.

Finally, our data suggest that systolic dysfunction may beget intraventricular

mechanical dyssynchrony. Severe systolic dysfunction caused radial dyssynchrony of

similar magnitude to that observed in D group and this was further enhanced in DHF group.

The pattern of mechanical dyssynchrony caused by systolic dysfunction likely differs from

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that associated with ECG-defined dyssynchrony (such as in left bundle branch block)(9).

Whether CRT corrects heart failure-related dyssynchrony with normal QRS duration as

effectively as it improves dyssynchrony in heart failure associated with left bundle branch

block needs further testing in prospective human clinical studies.

Limitations of our study

The current study suggests that speckled-tracking derived strain analysis is more

sensitive and specific than velocity-based echocardiographic methods to detect cardiac

dyssynchrony in a canine model of cardiomyopathy with dyssynchrony. The purpose of our

study was to generate a relative homogenous model of dyssynchrony (electrical

dyssynchrony with left ventricular systolic dysfunction) so we can compare various

echocardiographic methods that have been proposed in the human studies to detect

dysynchrony. Therefore to induce dyssynchrony we chose a model of right ventricular

pacing with non-ischemic cardimyopathy. Obviously, our current results cannot be

automatically extrapolated to all various subgroups of cardiac dyssynchrony encountered in

human heart failure (for example ischemic cardiomyopathy). One limitation of this model is

that we were unable to analyze speckle-tracking derived longitudinal strain because the

quality of our echocardiographic images from apical views in dogs was inadequate to

perform this analysis. In humans, with the current technology this can be performed reliably

and reproducibly. Furthermore, the specific cutoffs for various dyssynchrony parameters

need to be validated against response to cardiac resynchronization therapy in humans.

Nonetheless our study suggests that speckle-tracking strain analysis is superior to

longitudinal velocity data and provides the scientific basis to design further studies in which

radial, circumferential or longitudinal strain are compared to longitudinal velocity to test

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response from cardiac resynchronization therapy, especially in patients with narrow QRS and

heart failure.

Conclusion

We demonstrated that radial and circumferential strains derived by speckle-tracking in short

axis images accurately identify mechanical dyssynchrony in the presence or absence of left

ventricular dysfunction. The majority of the animals with heart failure and narrow QRS

showed mechanical dyssynchrony during speckle-tracking analysis. Prospective randomized

clinical studies will need to determine whether speckle-tracking radial or circumferential

strain detects dyssynchrony in humans with heart failure accurately enough to identify

appropriate candidates for CRT.

Acknowledgements

The Carlyle Fraser Heart Center provided the research grant to support this study. The authors

would also like to express our gratitude to Maria Alexandra Pernetz RDCS and Jing-Ping Sun

MD for their critical reading of the manuscript.

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Figure legends

Figure 1:

(A) Color-coded TDI: time from onset of QRS to peak systolic velocity (TPVs) was

measured (apical four chamber view).

(B) Pulse wave TDI: The time from onset of QRS to peak systolic velocity was measured.

(C) Septal-to-posterior wall motion delay (SPWMD) obtained as timing difference between

most inward excursion point of interventricular septum and posterior wall in M mode

(D) Radial strain curve by speckle tracking analysis; the time to peak radial strain (tErr) was

obtained from two-dimensional image of short axis. Upper panel shows radial strain

waveform obtained from control animal (at baseline), lower panel shows radial strain

waveform obtained from an animal with dyssynchrony and heart failure.

(E) Circumferential strain curve by speckle tracking analysis; the time to peak circumferential

strain (tEcc) was obtained from two-dimensional image of short axis. Upper panel shows

circumferential strain waveform obtained from control animal (at baseline), lower panel

shows circumferential strain waveform obtained from an animal with dyssynchrony and heart

failure

Figure 2: Shown are the results of left ventricular ejection fraction and QRS duration for

each group before implant (pre) and 4 weeks after (post) . (A) Ejection fraction (B) QRS

duration on surface EKG for the same groups. (* P<0.05 post vs pre) (D=dyssynchrony

without heart failure, HF= heart failure with narrow QRS, DHF=dyssynchrony with heart

failure)

Figure 3: Parameters of dyssynchrony by echocardiography for each group. (A) Standard

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deviation of (SD) of time to peak systolic velocity by color-coded tissue Doppler image (B)

Standard deviation of time to peak S wave by pulse wave (PW) tissue Doppler image (C)

Septal-to-posterior wall motion delay (SPWMD) on M-mode tracings (D) Standard deviation

of time to peak radial strain by speckle-tracking analysis of B-mode mid short axis image. (E)

Standard deviation of time to peak circumferential strain by speckle-tracking analysis of

B-mode mid short axis image. (* P<0.05 post vs pre) (D=dyssynchrony without heart failure,

HF= heart failure with narrow QRS, DHF=dyssynchrony with heart failure)

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Table 1. Basic characteristics (* P<0.05 vs pre, † P<0.05 HF or DHF vs D, ‡ P<0.05 DHF

vs HF )

D (n=12) HF (n=8) DHF (n=9)

pre post pre post pre post

EF(%) 59.7±5.8 60.1±9.9 58.7±6.0 22.2±4.7*† 58.4±9.4 21.4±7.6*†

EDV(ml) 46.8±13.0 41.8±12.8 48.5±12.1 62.7±19.3† 49.6±10.0 71.3±18.3*†

ESV(ml) 19.2±7.2 17.2±9.2 20.5±7.4 48.7±15.9*† 20.5±6.1 56.2±16.3*†

QRS(msec) 91.2±12.9 166.7±14.6* 93.0±9.2 93.4±15.3† 93.7±6.7 166.1±17.5*‡

PQ (msec) 126.5±14.6 122.6±33.3 148.9±18.6 131.4±35.2 126.8±29.9 130.3±33.5

HR (bpm) 82.0±12.0 86.8±17.0 72.1±7.8 101.0±16.9* 80.0±19.8 113.2±15.8*†

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Table 2. Dyssynchrony parameters analysis by echocardiography (* P<0.05 vs pre, †

P<0.05 HF or DHF vs D, ‡ P<0.05 DHF vs HF )

D HF DHF

pre post pre post pre post

TPVs

(msec) 17.7±5.2 22.4±10.9 24.9±11.5 35.5±8.3† 21.8±5.4 42.0±13.4*†

pkS

(msec) 16.2±10.3 21.8±9.2 19.7±11.6 36.3±10.2*† 19.3±4.7 35.4±10.1*†

SPWMD

(msec) 74.4±34.0 139.2±110.2 85.5±37.8 127.8±122.7 75.4±40.8 253.2±118.7*†

Err

(msec) 16.6.6±12.0 53.1±32.2* 24.5±22.5 52.0±26.6* 27.3±16.5

137.5±39.7*

†‡

Ecc

(msec) 48.9±23.0 95.9±40.7* 60.1±35.7 116.4±40.3* 31.8±26.0 139.0±22.7*†

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Table 3A. Receiver operator curve analysis (D as positive control)

Variables AUC p value 95%CI Cut-off(msec) Sensitivity Specificity

TPVs 0.514 0.886 0.295-0.734

pkS 0.576 0.477 0.375-0.776

SPWMD 0.690 0.069 0.511-0.869

Err 0.819 0.003 0.661-0.976 26.6 90.0 70.4

Ecc 0.874 0.001 0.755-0.993 43.8 100 59.3

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Table 3B. Receiver operator curve analysis (DHF as positive control)

Variables AUC p value 95%CI Cut-off(msec) Sensitivity Specificity

TPVs 0.904 <0.001 0.787-1.021 28.20 88.9 86.2

pkS 0.931 0.001 0.838-1.023 23.03 100.0 83.3

SPWMD 0.877 0.001 0.685-1.068 190.00 77.8 100.0

Err 1.000 <0.001 1.000-1.000 63.94 100.0 100.0

Ecc 1.000 <0.001 1.000-1.000 107.69 100.0 100.0

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Table 4A. Univariate analysis for relationship between dyssynchrony parameters and

basic characteristics

TPVs pkS SPWMD Err Ecc

r p r p r p r p r p

EF -0.688 <0.001 -0.666 <0.001 -0.427 0.001 -0.657 <0.001 -0.610 <0.001

EDV 0.505 <0.001 0.501 <0.001 0.196 0.153 0.345 0.011 0.103 0.465

ESV 0.665 <0.001 0.655 <0.001 0.349 0.009 0.565 <0.001 0.399 0.003

QRS .0.286 0.030 0.188 0.195 0.462 <0.001 0.612 <0.001 0.470 <0.001

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Table 4B. Multivariate stepwise regression analysis for relationship between

dyssynchrony parameters and basic characteristics.

TPVs pkS SPWMD Err Ecc

beta p beta p beta p beta p beta p

EF -0.684 <0.001 -0.666 <0.001 -0.357 0.003 -0.569 <0.001 -0.798 <0.001

EDV 0.153 0.217 0.158 0.25 -0.121 0.399 -0.100 0.311 -0.426 <0.001

ESV 0.268 0.196 0.313 0.146 -0.223 0.354 -0.167 0.309 0.022 0.968

QRS 0.162 0.106 0.145 0.187 0.4 0.001 0.516 <0.001 0.394 <0.001

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Figure 1B

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Figure 1C

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Figure 1D

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Figure 1E

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Figure 2A

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Figure 2B

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Figure 3A

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Figure 3B

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Figure 3C

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Figure 3D

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Figure 3E

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Speckle-tracking strain echocardiography for detecting cardiac dyssynchrony in a canine model of dyssynchrony and heart failure