Modeling of whole-heart electrophysiology and mechanics: Toward patient-specific simulations

The practice of cardiovascular care has seen significant advances in the past 40 years with dramatic reduction of mortality from heart diseases. Nevertheless, cardiac diseases remain the leading cause of morbidity and mortality in the developed world and are on the rise in developing countries [37]. It is well recognized that the conventional clinical practice of using population-based metrics to prescribe one size fits all treatment methods does not provide optimal health care for many patients because of the individual variability in pathophysiology. Moreover, in many situations, physicians do not have a way of predicting patient responses to various therapeutic interventions, and therefore have to rely on trial and error to identify the treatment-response relationship. An emerging paradigm that addresses these challenges is the so-called personalized medicine, which seeks to develop diagnosis and treatment methods that can be tailored by the physician a priori according to the specific needs of an individual patient [25, 44, 52]. Application of such personalized approach to cardiac care can dramatically improve the treatment of heart diseases. To fully utilize the quality and diversity of clinically available data for personalized cardiac care, it is necessary to integrate structural and functional data at molecular, cellular, tissue, and organ level into a consistent framework which can be used to predict the outcomes of therapeutic interventions. Computational modeling provides a powerful tool to perform this data integration [29, 32].