Numerical modelling of patient specific cardiovascular risk indicators in human arteries for clinical analysis and risk assessment

Cardiovascular diseases are responsible for approximately 23% of deaths and therefore for the highest number of total deaths. They pose major challenges for societies and their health systems now and in the future. Cardiovascular diseases cost e.g. the EU economy around 282€ billion with 46% of these costs were due to health care caused by demographic trends in western societies, specifically a declining population with higher life expectancy at the same time. 

To meet these challenges, there is a need for preventive technologies, which help to avoid the most serious manifestations of cardiovascular diseases. For example, atherosclerosis in the carotid artery is an inflammatory response of main artery vessel walls to lipid metabolism disturbances and endothelial stress, which leads to the formation of multi-focal plaques and thus to the narrowing and hardening of the arteries and consequently to an insufficient supply of oxygen to the brain. This is responsible for an estimated 18-25% of thrombo-embolic strokes and therefore playing a fundamental role in the occurrence of ischemic strokes. Less prevalent than atherosclerosis in the carotid artery, but at least  as deadly when it happens (up to 50% death rate), is an aortic dissection. If the inner wall layer tears e.g. after an aortic aneurysm, blood runs through the tear and dissects the inner and the middle wall layer of the aorta and forms a false lumen. The outer layer of the weakened wall can now disrupt und lead to life-threatening conditions. 

The underlying risk indicators which are very well known to influence these degenerating events are highly individual and often the impact on specific patients is hard to measure. This applies, for example, to all factors related to a person’s lifestyle, such as smoking, eating, exercise, etc., all known for their overall relevance in the progression of cardiovascular diseases. These factors are not quite handy for analyzing the acute risk in arteries. A more practical and objectifiable approach for clinical risk assessment for vessel degeneration on the long run is the analysis of blood induced wall shear (endothelial) stress (WSS) in patient specific morphologies with methods of Computational Fluid Dynamics (CFD). 

The scientific discussion of WSS as a risk factor can look back on a long history. The correlation between low and oscillatory WSS and plaque location in the carotid artery was already reported by Ku et al. in 1995, trough comparison of flow dynamics in plexiglas models and plaque location in carotids obtained from autopsy. To this time numerical but also in vivo models often were strongly simplified under the assumption of rigid walls, no backflow of the blood and thus far from real hemodynamic behavior. Since then, much happened in acquiring real boundary conditions. Pulsatile flow became standard, the modeling of Fluid-Structure Interaction (FSI) got more attention, and much research takes place to get adequate outflow conditions and to represent the non-Newtonian behavior of blood. Further state of the art research resulted in new models predicting vessel degeneration and hemodynamic risks by taking mechanical as well as chemical processes into account, resulting in plaque growth models, structural stent modeling, hemodynamic modeling of red blood cells as new techniques for patient specific in  vivo models to mention some of them. 

To benefit from the models for clinical application, it is from uttermost importance to develop practical tools for clinical usage, practitioners and researchers. Increasing efforts are therefore being made to link the numerical modelling of cardiovascular risk indicators with parallel clinical observations, but further efforts need to be made. 

Based on the state of research, the objective of my PhD project is to develop validated numerical CFD models and refine them for patient specific arteries that shall supports physician in the risk assessment of vessel degeneration, link them with the clinical observation e.g. by testing clinical hypothesis that are made by practitioners and supporting the development of handy tools for practitioners and researchers by generation of simulated hemodynamical data.