Mechanisms Driving Thoracic Aortic Aneurysm Stability.

Area of Science:

• Biomedical Engineering
• Computational Biology
• Cardiovascular Research

Background:

• Thoracic aortic aneurysms (TAAs) involve complex biological and mechanical factors.
• Current risk stratification for TAAs relies on size and expansion rate, which are insufficient for predicting clinical behavior.
• Existing methods struggle to capture the diverse natural history of aneurysm progression.

Purpose of the Study:

• To develop a computational biomechanical model for simulating thoracic aortic aneurysm (TAA) progression.
• To investigate the role of rate-dependent parameters and vessel properties in TAA natural history.
• To establish a foundation for patient-specific risk prediction in TAAs.

Main Methods:

• Utilized a constrained mixture theory of growth and remodeling to simulate TAA lesion progression.
• Incorporated rate-dependent parameters influencing material structure and mechanosensitivity.
• Co-varied parameters to model diverse aneurysm behaviors and outcomes.

Main Results:

• The computational framework successfully simulated clinically relevant TAA phenotypes.
• The model captured divergence in lesion behavior despite similar initial dilatation or expansion rates.
• Demonstrated the ability to simulate a spectrum of TAA outcomes.

Conclusions:

• The developed computational model provides insights into TAA mechanisms.
• This framework supports more accurate, patient-specific risk prediction for thoracic aortic aneurysms.
• Paves the way for integrating machine learning tools for clinical translation in TAA management.