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Mechanisms of aortic dissection: From pathological changes to experimental and in silico models
Malte Rolf-Pissarczyk1, Richard Schussnig2,3, Thomas-Peter Fries3
1Institute of Biomechanics, Graz University of Technology, Austria.
Insights
Innovative models, including experimental and in silico approaches, are crucial for understanding aortic dissection. These advanced methods improve our knowledge of disease progression and aid in developing better treatments and medical implants.
Area of Science:
- Biomedical Engineering
- Computational Mechanics
- Cardiovascular Research
Background:
- Aortic dissection causes significant morbidity and mortality.
- Understanding disease initiation and progression is vital.
- Existing models require enhancement for accurate pathological characterization.
Purpose of the Study:
- To review medical data on pathological alterations in aortic dissection.
- To assess experimental, multiscale material, and in silico models.
- To discuss future perspectives in disease modeling and clinical applications.
Main Methods:
- Review of existing literature on experimental and in silico models.
- Analysis of multiscale material models incorporating patient data.
- Synthesis of data on hemodynamic changes and wall microstructure.
Main Results:
- Experimental models reveal hemodynamic and microstructural changes.
- In silico models integrate patient data for correlation analysis.
- Multiscale models study stress, damage, and failure in dissected aortas.
Conclusions:
- Advanced models are essential for understanding aortic dissection.
- Patient-derived data integration enhances in silico model utility.
- Future research should focus on disease modeling, numerical challenges, and clinical translation.
Abstract:
Aortic dissection continues to be responsible for significant morbidity and mortality, although recent advances in medical data assimilation and in experimental and in silico models have improved our understanding of the initiation and progression of the accumulation of blood within the aortic wall. Hence, there remains a pressing necessity for innovative and enhanced models to more accurately characterize the associated pathological changes. Early on, experimental models were employed to uncover mechanisms in aortic dissection, such as hemodynamic changes and alterations in wall microstructure, and to assess the efficacy of medical implants. While experimental models were once the only option available, more recently they are also being used to validate in silico models. Based on an improved understanding of the deteriorated microstructure of the aortic wall, numerous multiscale material models have been proposed in recent decades to study the state of stress in dissected aortas, including the changes associated with damage and failure. Furthermore, when integrated with accessible patient-derived medical data, in silico models prove to be an invaluable tool for identifying correlations between hemodynamics, wall stresses, or thrombus formation in the deteriorated aortic wall. They are also advantageous for model-guided design of medical implants with the aim of evaluating the deployment and migration of implants in patients. Nonetheless, the utility of in silico models depends largely on patient-derived medical data, such as chosen boundary conditions or tissue properties. In this review article, our objective is to provide a thorough summary of medical data elucidating the pathological alterations associated with this disease. Concurrently, we aim to assess experimental models, as well as multiscale material and patient data-informed in silico models, that investigate various aspects of aortic dissection. In conclusion, we present a discourse on future perspectives, encompassing aspects of disease modeling, numerical challenges, and clinical applications, with a particular focus on aortic dissection. The aspiration is to inspire future studies, deepen our comprehension of the disease, and ultimately shape clinical care and treatment decisions.
