Predicting false lumen thrombosis in patient-specific models of aortic dissection

Area of Science:

• Biomedical Engineering
• Computational Fluid Dynamics
• Cardiovascular Research

Background:

• Aortic dissection involves splitting of aortic wall layers, creating a false lumen (FL).
• False lumen patency or thrombosis significantly impacts patient outcomes in type B aortic dissection.
• Current methods lack the ability to predict the likelihood of FL thrombosis.

Purpose of the Study:

• To develop and validate a computational model for predicting thrombus formation and growth within the false lumen of aortic dissections.
• To assess the influence of thrombus on blood flow dynamics under physiological conditions.
• To evaluate the model's potential for clinical application in patient-specific risk stratification.

Main Methods:

• Development of a computational model based on fluid shear rate, residence time, and platelet distribution.
• Utilizing convection-diffusion-reaction transport equations to simulate thrombus dynamics.
• Application of the model to a patient-specific type B aortic dissection with available follow-up imaging data.

Main Results:

• The model accurately predicted thrombus formation and growth patterns in qualitative agreement with clinical data.
• Results indicate that aortic dissection geometry significantly influences the extent and location of thrombosis.
• The computational model demonstrated high efficiency, suggesting feasibility for clinical use.

Conclusions:

• The developed computational model shows promise for predicting false lumen thrombosis in individual aortic dissection patients.
• This tool can potentially serve as a clinical decision-making aid for assessing the need for early endovascular intervention.
• Understanding thrombosis dynamics is crucial for improving patient outcomes in aortic dissection management.