UC San Diego

Dyssynchronous heart failure (DHF) is a severe form of heart failure where conduction block in the left bundle branch causes delayed left ventricular electrical activation and discoordinated systolic contraction, dramatically reducing cardiac output. Cardiac resynchronization therapy (CRT) is a cost effective pacing treatment that has been shown to improve symptoms and survival, especially due to left ventricular reverse remodeling. However, approximately 50% of patients do not show objective evidence of reverse remodeling even after 6 months of CRT. A deeper understanding of the physiological mechanisms leading to positive long-term outcomes and identification of patients who are most likely to benefit are needed to maximize quality of care and minimize health risks and economic costs. The ability to predict the outcome and personalize CRT application for an individual patient from clinical measurements alone is challenging given the wide inter-patient variability of clinical features and pathophysiological complexity of DHF. In this work, we seek to answer questions regarding physiological mechanisms that are implicated in CRT response, baseline physiological features that are predictive of response, and personalized CRT application for an individual patient. For this purpose, we construct patient-specific computational models of DHF which integrate anatomical, electrophysiological, biomechanical, and hemodynamic clinical and empirical data to quantitatively characterize baseline and CRT physiology to understand how patients differ in response. The primary aims of this thesis will be to : 1) construct patient-specific computational models of DHF incorporating clinical and empirical measurements to test whether the models can recapitulate characteristics of DHF and predict measured acute effects of CRT; 2) test the hypothesis that CRT response physiologically depends on the severity of baseline heterogeneity of mechanical loading caused by electrical dyssynchrony and ventricular dilation; 3) test the hypothesis that CRT response can be predicted from novel model-derived biomarkers of electrical dyssynchrony. Through quantification and prediction of patient-specific cardiovascular physiology in disease and therapy, computational models have great potential to enhance the quality of medical care by providing novel diagnostic value to support clinical decisions regarding the best personalized approach to treat the individual patient