Abstract
Computer studies are often used to study mechanisms of cardiac arrhythmias, including atrial fibrillation (AF). A crucial component in these studies is the electrophysiological model that describes the membrane potential of myocytes. The models vary from detailed, describing numerous ion channels, to simplified, grouping ionic channels into a minimal set of variables. The parameters of these models, however, are determined across different experiments in varied species. Furthermore, a single set of parameters may not describe variations across patients, and models have rarely been shown to recapitulate critical features of AF in a given patient. In this study we develop physiologically accurate computational human atrial models by fitting parameters of a detailed and of a simplified model to clinical data for five patients undergoing ablation therapy. Parameters were simultaneously fitted to action potential (AP) morphology, action potential duration (APD) restitution and conduction velocity (CV) restitution curves in these patients. For both models, our fitting procedure generated parameter sets that accurately reproduced clinical data, but differed markedly from published sets and between patients, emphasizing the need for patient-specific adjustment. Both models produced two-dimensional spiral wave dynamics for that were similar for each patient. These results show that simplified, computationally efficient models are an attractive choice for simulations of human atrial electrophysiology in spatially extended domains. This study motivates the development and validation of patient-specific model-based mechanistic studies to target therapy.
Highlights
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is associated with increased morbidity and mortality from stroke and heart failure [1]
Simulations generated by computers are often an effective way to study the dynamics of cardiac cells
Because of difficulties in studying AF mechanisms in humans, and since animal models of AF may differ from human AF, mechanistic studies of arrhythmias are increasingly turning to computational modeling to bridge the gap between clinical unmet needs and cellular studies
Summary
Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and is associated with increased morbidity and mortality from stroke and heart failure [1]. Because of difficulties in studying AF mechanisms in humans, and since animal models of AF may differ from human AF, mechanistic studies of arrhythmias are increasingly turning to computational modeling to bridge the gap between clinical unmet needs and cellular studies Essential in these computational studies is the choice of the electrophysiological model which simulates the membrane potential through a set of parameterized equations that describe the ion channels. This model can range in complexity from detailed [4,5,6,7,8], which describe as many channels as possible, to simplified [9,10,11], which capture only essential dynamical features of cardiac tissue. These computational models are rarely validated in humans, and their parameters are typically based on imprecise, incomplete, or animal data
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