A simple multi-scale model to evaluate left ventricular growth laws

Cardiac growth is the natural capability of the heart of\u3cbr/\u3eadapting to changes in blood flow demands. Cardiac diseases can trigger\u3cbr/\u3ethe same process leading to an abnormal type of growth. Although\u3cbr/\u3eseveral models have been published, details on this process remain still\u3cbr/\u3eunclear. This study offers an analysis on the driving force of cardiac\u3cbr/\u3egrowth along with an evaluation on the final grown state. Through a\u3cbr/\u3ezero dimensional model of the left ventricle we evaluate cardiac growth\u3cbr/\u3ein response to three valve diseases, aortic and mitral regurgitation along\u3cbr/\u3ewith aortic stenosis. We investigate how different combinations of stress\u3cbr/\u3eand strain based stimuli affect growth in terms of cavity volume and\u3cbr/\u3ewall volume. All of our simulations are able to reach a converged state\u3cbr/\u3ewithout any growth constraint. The simulated grown state corresponded\u3cbr/\u3eto the experimentally observed state for all valve disease cases, except\u3cbr/\u3efor aortic regurgitation simulated with a mix of stress and strain stimuli.\u3cbr/\u3eThus we demonstrate how a simple model of left ventricular mechanics\u3cbr/\u3ecan be used to have a first evaluation of a designed growth law

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