It's Time for a Paradigm Shift in the Quantitative Evaluation of Left Ventricular Dyssynchrony

Abstract

The acute adverse effects of left ventricular (LV) dyssynchrony on cardiac performance were first described in 1925 by Carl Wiggers 1 . In recent years, the accurate diagnosis of LV dyssynchrony has become the focus of a myriad of publications, driven by the advent of cardiac resynchronization therapy (CRT) to treat heart failure due to severe LV dysfunction in the setting of marked prolongation of the QRS interval 2-4 . In the initial large clinical trials of CRT, QRS duration was used as a measure of dyssynchrony to select patients for treatment 2, 3 . However, sensitivity 5 and specificity 6 of QRS duration to predict response to CRT were less than optimal. Subsequently, numerous "time-to-peak" parameters based directly on the motion of the LV walls were developed to diagnose LV mechanical dyssynchrony with echocardiography in an attempt to improve CRT selection criteria 7 .Echocardiographic mechanical dyssynchrony parameters initially showed promise in predicting response to CRT in single-center studies 8-12 . However, the multicenter Predictors of Response to CRT (PROSPECT) study recently reported that no echocardiographic dyssynchrony parameter could be recommended to improve patient selection for CRT beyond current guidelines 13 . In addition, the Resynchronization Therapy in Narrow QRS (RETHINQ) trial recently reported that patients with narrow QRS and evidence of mechanical dyssynchrony do not benefit from CRT 14 .So where do we go from here? Should selection of patients for CRT based on mechanical dyssynchrony be abandoned in the wake of the negative results from PROSPECT and RETHINQ? We believe that techniques to quantify LV mechanical dyssynchrony need to be refined, not forgotten, and will still play a role in improving CRT selection criteria in the future. This refinement of dyssynchrony quantification requires a paradigm shift. First, time-to-peak methods for quantifying dyssynchrony utilize only a single time point on the velocity or strain curves and should be replaced with more quantitatively sophisticated methods. Utilizing more data reduces variability and increases accuracy. Second, "response to CRT" should no longer

Constrained manifold learning for the characterization of pathological deviations from normality

“…In particular, we would like to apply these considerations to cardiac resynchronization therapy (CRT) studies, supported by the following paradigm: CRT targets the correction of the dyssynchrony in the motion of the cardiac chambers, leading to improvements in the cardiac function, the patient condition, and ventricular size (StJohnSutton et al, 2003); however, CRT patient selection still discards mechanical dyssynchrony as a selection criteria (Dickstein et al, 2010). There are several reasons for this: there is currently no consensus regarding the accurate characterization of mechanical dyssynchrony, its link with CRT outcome, and the way to include it within the patient selection process (Fornwalt et al, 2009;Fornwalt, 2011;Delgado and Bax, 2011;Sung and Foster, 2011).…”

Characterization of myocardial motion patterns by unsupervised multiple kernel learning

“…Indeed, they all seek for simple key markers of the disease, such as peak measurements or specific timings, while induced changes tend to be more complex, both spatially and temporally. A more complete analysis should therefore target the study of cardiac function at the pattern level, as was previously recommended to improve the prediction of response to CRT ( Fornwalt et al, 2009 ) and to reach a deeper understanding of myocardial mechanics and physiology ( Bijnens et al, 2012 ). Such an analysis should also allow jointly analyzing multiple variables, eventually at different hierarchical levels.…”

Temporal diffeomorphic free-form deformation: Application to motion and strain estimation from 3D echocardiography

“…Its improved robustness and accuracy compared to other methods makes it an excellent approach to improve the quantification of mechanical dyssynchrony, currently more based on myocardial velocity timings or in 2D analysis (Fornwalt et al (2009)). The potential of analyzing the real 3D nature of myocardial mechanics provides an excellent tool to understand and recognize patterns of dyssynchrony that could be eventually corrected with CRT.…”