Precision Medicine to Determine Electromechanical Determinants of Myocardial Recovery

Heart failure (HF) is an epidemic, resulting in >1 million hospitalizations costing 24 billion annually. HF not due to coronary artery disease, termed non-ischemic cardiomyopathy (NICM) accounts for a significant proportion of HF. NICM is deemed idiopathic in most cases, and predictors of disease prognosis remain unknown, resulting in 1) imprecise patient care and 2) inefficient resource allocation. While some patients progress to end-stage disease, a significant subset will experience heart muscle (myocardial) recovery which yields a better prognosis. However, factors determining myocardial recovery remain unclear. Defining these factors represents a major unmet need in cardiovascular medicine. Heart electromechanical coupling, which can be measured using sophisticated electrocardiographic (ECG) and echocardiographic techniques that our group has developed, may provide further mechanistic insight into myocardial recovery. Biologic systems like HF are complex, and like the weather, they are influenced by many factors and difficult to predict. By utilizing novel statistical methods like machine-learning, we plan to investigate electromechanical coupling within the complex interplay of other patient-specific data of patients with HF. Better understanding of the factors that determine myocardial recovery will lead to more personalized care of HF patients, improved resource allocation, and provide insight into novel therapeutics for HF.