The heart is a dynamic organ that continuously adapts to the needs of the body. The adaptation is normally useful and necessary, for example when physical exercise causes the heart to grow larger and pump more powerfully, to
increase blood flow and oxygen supply to the body. But in some chronic heart diseases, the heart's adaptability can work against its purpose, and contribute to aggravate the function rather than improve it. For instance, the heart
wall may grow thicker, which can make the heart pump more forcefully and supply more blood to the body, but at the same time makes the heart stiffer. A stiffer heart may not be properly filled for each heartbeat, and reduced filling
gives less blood to eject and therefore reduced performance. The heart can try to compensate for this by growing even bigger and thicker, which worsens the problem and may lead to a vicious cycle that over time leads to heart failure.
To give optimal treatment for chronic heart patients it is important to understand the physiological processes that drive the heart's adaptation, and what makes these vital mechanisms turn harmful in some situations. In the DynaComp
project we will use mathematical models and computer simulations to compute the mechanical forces in the heart muscle during a heartbeat, both for healthy hearts and in disease. In addition, we will create models for how these forces make the heart grow and adapt over time, and how the dynamics of this process is altered during disease. The results of the project can provide a better understanding of the physiology of the heart, and lead to better diagnosis and treatment of chronic heart disease.
Heart failure (HF) is a chronic disease where the heart progressively loses its the ability to adequately pump blood. This loss of function results from the dynamic nature of the heart, and how it grows and remodels in response to mechanical loads and stimuli. While treatment options exist that can stop and even reverse this pathological remodeling, many patients still do not benefit, and there are large gaps in knowledge about the fundamental mechanisms that drive the remodeling. These mechanisms have been difficult to elucidate using experimental methods, and new tools are needed both for fundamental research and for clinical diagnosis and stratification of HF patients. We hypothesise that computational methods are well suited to supplement and augment experimental and clinical research, but there are currently no robust and efficient computational tools for predicting heart dynamics over weeks and months. In the DynaComp project we aim to develop these tools, and to explore their capabilities and limitations for fundamental HF research as well as direct clinical use. Previously developed methods for data driven computational heart mechanics will be extended to predict growth and remodeling on a time scale of weeks and months, and validated the models against a comprehensive set of experimental data. The computational framework will also be integrated with tools for uncertainty quantification and sensitivity analysis, and we will make it freely available to the research community as open source software. We will apply the modeling framework in a detailed study of existing and novel biomarkers used in HF diagnosis, to improve their theoretical foundation and potentially give more accurate HF diagnosis. The project will expand the ability of computer science to give insight into cardiac function, advance our understanding of how mechanical factors drive pathological remodeling and HF, and narrow the translational gap between experimental and clinical HF research.