Computer-assisted optimizationof therapies for heart failure

There are nearly 6 million Americans with heart failure (HF), and roughly 550,000 new cases are diagnosed each year. Despite advances in treatment strategies, the 5 year survival rate remains less than 50%. Current standards for diagnosis include the use of magnetic resonance imaging (MRI) in order to measure wall strain and ejection fraction. However, this does not identify the underlying cause, or elucidate what is happening at the cell level. Since therapies for HF induce transmurally dependent changes at the cell level, it is essential to have an understanding at this scale. If cellular level function can be tied to performance at the global level, this would create a very powerful tool for developing therapies to improve pump function and reduce risk to patients. Deciding which therapy is best for the patient is nontrivial. The primary goal of the proposed work is to develop a technique that can take patient specific data and build a computational model that can predict which therapies will provide the best improvement in pump function. The aims of the study are: (1) To investigate regional ventricular function at the cell and global level using experimental measurements in rats, then use this data to develop and validate animal specific models which include new contractile capabilities, and (2) To investigate how drug therapies affect different transmural regions at the cell level, then use this data in the computational model to predict changes in global function and compare to MRI measurements of treated animals.