PROJECT SUMMARY: Disorganized electrical waves that produce complex arrhythmias in the heart are a major health issue. According to the Center for Disease Control, an estimated 400,000-460,000 unexpected sudden cardiac deaths occur every year in the US, in most cases due to ventricular fibrillation (VF). Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia; it contributes to 160,000 deaths annually, and its prevalence is projected to increase from 5.2 million in 2010 to 12.1 million by 2030. The primary defibrillation therapy for termination of VF and AF has significant side effects, including severe pain and anxiety disorders from inappropriate firings along with myocardial damage from repeated shocks, which can also lead to increased mortality. While there is no other reliable current cure for VF, AF can be treated with radiofrequency ablation; however, its success rate for paroxysmal AF is only about 60%, and for persistent AF it is even less effective at 30%. Thus, it is clear that there is a need for more effective approaches to treat AF and VF. The ultimate goal of these studies is to develop a successful low-energy defibrillation protocol that minimizes the energy requirements for suppression of VF and AF, to the point where such an approach could be implemented reliably via an implantable device in ambulatory patients. Toward this end, the project will utilize live explanted hearts from heart failure patients who receive new donor hearts, using appropriate approvals. In this project, first the detailed dynamics of AF and VF in the explanted hearts will be quantified, and advanced computational tools to simulate these arrhythmias in anatomically accurate three-dimensional heart structures will be developed. Mathematical models of cardiac cells will be fitted to experimental data to accurately reproduce the complex spatiotemporal dynamics of AF and VF in the human heart. These models will be used in advanced computational tools to systematically study and optimize the many parameters for low-energy defibrillation in simulations using machine learning algorithms. Then, the defibrillation protocol will be tested and further optimized experimentally in the explanted hearts and then with in vivo animal (swine) studies to investigate clinical translation. Throughout, a state-of-the-art optical-mapping methodology will be used to visualize and record electrical activity to characterize fibrillation and inform the mathematical models; similarly, the computational results will inform low-energy defibrillation experiments in the explanted hearts. The use of explanted hearts not only reduces the use of animals but sidesteps translational problems associated with studies in non-human species, making the defibrillation studies proposed in this study as applicable to clinical settings as possible. In the end, this project not only will lead to better cardioversion and defibrillation therapies with greater reductions in pain, but als...