Cardiac dyssynchrony deteriorates cardiac function and often cannot be treated effectively. The goal of this proposal is to develop and provide a new analysis technique for understanding the complex cardiac motion patterns (in time and space) of patients with cardiac dyssynchrony, with the hope of improving its treatment outcomes, specifically with respect to cardiac resynchronization therapy (CRT). CRT, the most effective treatment for dyssynchrony with worsening heart failure, significantly improves outcome in only ~66% of heart failure patients selected for the treatment, which is based on ECG criteria. Selection criteria based on imaging are essential to improving the success rate. Unfortunately, response rates are not higher with selection criteria based on echocardiography, the most popular cardiac imaging technique. Cine cardiovascular magnetic resonance (CMR) has the potential to better characterize dyssynchrony, as it shows cardiac mechanics and intramural wall motion with much higher spatial resolution than echocardiography. However, quantitative assessments of CMR have been mostly limited to global volumetric measures, which ignore most of the motion information captured by the images. For example, studies of a number of distinctive motion features of dyssynchrony (such as septal flash and apical rocking) have been confined to qualitative assessment, limiting inference of their potential utility for improving CRT treatment. To accurately quantify cardiac function through CMR, we have developed biomechanical models for describing cardiac function and machine learning technology for identifying morphological and functional patterns atypical for healthy hearts. We propose to combine these two technologies to accurately quantify cardiac dyssynchrony within the Left Ventricle (LV). Specifically, our methods will extract a rich description of LV motion and strain from the CMRs of a set of retrospectively selected subjects with synchronous or dyssynchronous LV motion. We will then use machine learning methods to identify local and global motion patterns specific to dyssynchrony. Finally, we will correlate these patterns to already existing clinical scores to find potentially predictive markers with respect to CRT outcome. We hypothesize that these markers will have a higher correlation to CRT outcome than current clinical markers alone. Identifying these markers will have the potential to further stratify the disease with respect to the expected outcome of CRT, which then can be used to derive new selection criteria that lead to higher success rates. The project will also disseminate our novel, data-driven methodology for quantifying that motion. Other research groups can apply our tools to specifically study dyssynchrony, as well as other cardiac diseases impacting LV motion in general.
A significant proportion of patients with heart failure and cardiac dyssynchrony fail to positively respond to cardiac resynchronization therapy, as the current clinical selection criteria are too broad. Our goal is to more accurately characterize the complex shape and motion patterns of cardiac dyssynchrony, through new machine learning technology applied to dynamic cardiovascular magnetic resonance images. We envision that our findings would further improve both our understanding of cardiac dyssynchrony in relation to heart failure and the ability to predict its response to resynchronization therapy, potentially leading to new clinical guidelines and improved clinical outcomes.
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