Modeling cardiac growth:an alternative approach

Models of cardiac growth might assist in clinical decision\u3cbr/\u3emaking, in particular for long-term prognosis of the effect of interventions.\u3cbr/\u3eMost growth models strictly enforce the amount and direction\u3cbr/\u3eof volume change and prevent runaway growth by limiting maximum\u3cbr/\u3egrowth. These assumptions have been questioned. We propose an alternative\u3cbr/\u3emodel for cardiac growth, in which the actual volume change of\u3cbr/\u3ea tissue element is determined by the desired volume change in that\u3cbr/\u3eelement and the degree to which this change is resisted by the surrounding\u3cbr/\u3etissue. The model was evaluated on its ability to reproduce a stable\u3cbr/\u3ehealthy left ventricular configuration under normal hemodynamic load.\u3cbr/\u3eA homeostatic equilibrium state could not be obtained, which might be\u3cbr/\u3edue to limitations in the mechanics model or an inadequate stimuluseffect\u3cbr/\u3erelation in the growth model. Still, the basic idea underlying the\u3cbr/\u3emodel could be an interesting alternative to current growth models

Computational models in cardiology

The treatment of individual patients in cardiology practice increasingly relies on advanced imaging, genetic screening and devices. As the amount of imaging and other diagnostic data increases, paralleled by the greater capacity to personalize treatment, the difficulty of using the full array of measurements of a patient to determine an optimal treatment seems also to be paradoxically increasing. Computational models are progressively addressing this issue by providing a common framework for integrating multiple data sets from individual patients. These models, which are based on physiology and physics rather than on population statistics, enable computational simulations to reveal diagnostic information that would have otherwise remained concealed and to predict treatment outcomes for individual patients. The inherent need for patient-specific models in cardiology is clear and is driving the rapid development of tools and techniques for creating personalized methods to guide pharmaceutical therapy, deployment of devices and surgical interventions