Cardiac magnetic resonance assessment of dyssynchrony and myocardial scar predicts function class improvement following cardiac resynchronization therapy

Abstract

Objectives: We tested a circumferential mechanical dyssynchrony index (circumferential uniformity ratio estimate [CURE]; 0 to 1, 1 = synchrony) derived from magnetic resonance-myocardial tagging (MR-MT) for predicting clinical function class improvement following cardiac resynchronization therapy (CRT).

Background: There remains a significant nonresponse rate to CRT. MR-MT provides high quality mechanical activation data throughout the heart, and delayed enhancement cardiac magnetic resonance (DE-CMR) offers precise characterization of myocardial scar.

Methods: MR-MT was performed in 2 cohorts of heart failure patients with: 1) a CRT heart failure cohort (n = 20; left ventricular ejection fraction of 0.23 +/- 0.057) to evaluate the role of MR-MT and DE-CMR prior to CRT; and 2) a multimodality cohort (n = 27; ejection fraction of 0.20 +/- 0.066) to compare MR-MT and tissue Doppler imaging septal-lateral delay for assessment of mechanical dyssynchrony. MR-MT was also performed in 9 healthy control subjects.

Results: MR-MT showed that control subjects had highly synchronous contraction (CURE 0.96 +/- 0.01), but tissue Doppler imaging indicated dyssynchrony in 44%. Using a cutoff of <0.75 for CURE based on receiver-operator characteristic analysis (area under the curve: 0.889), 56% of patients tested positive for mechanical dyssynchrony, and the MR-MT CURE predicted improved function class with 90% accuracy (positive and predictive values: 87%, 100%); adding DE-CMR (% total scar <15%) data improved accuracy further to 95% (positive and negative predictive values: 93%, 100%). The correlation between CURE and QRS duration was modest in all cardiomyopathy subjects (r = 0.58, p < 0.001). The multimodality cohort showed a 30% discordance rate between CURE and tissue Doppler imaging septal-lateral delay.

Conclusions: The MR-MT assessment of circumferential mechanical dyssynchrony predicts improvement in function class after CRT. The addition of scar imaging by DE-CMR further improves this predictive value.

Figure 1. Quantification of circumferential mechanical dyssynchrony from MR-MT strain map

Calculation of the CURE for mechanical dyssynchrony using Fourier analysis with extreme examples of spatial distribution of strain for synchrony (straight line) versus dyssynchrony (sine wave pattern). S₀ is the zero-order or constant term of the Fourier transform, while S₁ is the first-order term, representing low frequency changes at a given time. CURE for a given short-axis slice is generated first by determining instantaneous circumferential strains at 24 equally spaced segments in a short-axis slice at each time point, then subjecting this strain v. segment data to Fourier analysis with determination of the ratio of first to zero order power.

Figure 2. MR-MT temporal and spatial circumferential strain maps for a normal subject and a subject with cardiomyopathy and dyssynchrony

In the normal subject, the progression of the strain versus time (negative strain represents systole) is uniform in each of the 24 segments in each slice (A), and there is synchronous negative strain for each segment along the circumference of the left ventricle (B). In the subject with dyssynchrony and cardiomyopathy, the strain versus time maps show variable timing of contraction (blue arrows, negative strain) and stretch (red arrows, positive strain) in septal versus lateral segments (C). In this subject, some segments have positive strain (stretch) and others have negative strain (contraction) during systole (D).

Figure 3. Correlations for MR-MT circumferential dyssynchrony (CURE), TDI septal-lateral delay, and QRSd

The modest correlation between CURE and QRSd is shown (r=−0.58, p<0.001) for all 43 subjects in the CRT-HF and multimodality cohorts (A). For the multimodality cohort only (B), CURE and the TDI septal-lateral delay are shown versus QRS duration. As expected (based on prior published data), there is no correlation between TDI and QRSd (r=0.04, p=0.83), but there is a significant correlation between CURE and QRSd (r=−0.60, p=0.001) (B). The CURE-QRSd correlation is also shown (C) for the CRT-HF cohort only (r=−0.40, p=0.08).

Figure 4. Clinical response to CRT based on CMR findings

These plots show data based on individual improvement in function class for patients in the CRT-HF cohort based on baseline characteristics such as QRSd (A), CURE (B), and percent left ventricular scar volume by DEMRI (C). Those with function class improvement are shown in the left column of each panel, while those without improvement are shown in the right panel. QRSd (A) has no association with improvement in function class. MR-MT (B) has better accuracy (90%) than DEMRI (78%) and (C) MR-MT and DEMRI combined have superior accuracy (95%). This highlights the superior accuracy of CURE for predicting function class improvement.

Figure 5. CRT nonresponse due to extensive scar

Scar imaging from a subject with significant circumferential dyssynchrony but CRT nonresponse shows extensive left ventricular scar on long axis (A) and short axis (B) images. This subject had a nonischemic cardiomyopathy (normal coronary angiography) despite the apparent regional transmural scar. This underscores the importance of CMR relative to echocardiography in the assessment of CRT candidates. Not only does MR-MT provide a very accurate measure of dyssynchrony, but the scar imaging data provides important information regarding the myocardial substrate such as extent and distribution of scar.