Predicting mechanisms of arrhythmia in heart failure using high performance computing.

摘要

Differences in electrophysiological properties of cardiac cells produce repolarization gradients across the transmural wall. Under pathological conditions, such as Heart Failure (HF) and Long QT Syndrome (LQTS), these gradients are amplified and can form the substrate for re-entry, arrhythmia and possible sudden cardiac death which remains a leading cause of death in the western world. It is therefore important to work towards a more complete understanding of the mechanisms of arrhythmia so that more effective diagnostic and therapeutic procedures can be developed. The work presented in this study describes computational approaches for developing cardiac models which can be used in conjunction with experiments to test hypotheses by which arrhythmias may arise in the heart. Particular emphasis is placed on the role of repolarization abnormalities in the generation of arrhythmias under conditions of HF.; There are two main components of our model: (a) a description of the detailed anatomical structure of the tissue; and (b) experimentally-derived electrophysiological models of ion currents. The computational domain consists of millions of mesh points each representing a cardiac myocyte. Current fluxes within each myocyte were modeled using coupled non-linear ordinary differential equations (ODEs). We adopted a stiff symmetric operator-splitting scheme to eliminate the need to use extremely small integration time steps, which would otherwise be required to satisfy the stiffness of the ODE system. In the scheme we adopted, the reaction term was solved using a stiff integration method, and the diffusion term was solved with a second order Runge-Kutta method. This scheme increased computational speed by almost ten fold compared to a simple numerical method such as the forward Euler method. High Performance Computing (HPC) was employed to cut down simulation runtime. The parallel algorithm developed was highly scalable to the problem size as well as the number of processors, and could be ported to other parallel machines.; The models were used to examine HF-related functional changes in electrical conduction and action potential duration (APD) gradients. Modeling results predict that the interplay between HF-induced electrophysiological remodeling of cell properties and reduced conductivity significantly increases repolarization gradients and APD dispersion (APDD) both of which have been shown to increase the risk of arrhythmias. These models can serve as computational tools to study the mechanisms underlying cardiac arrhythmias.