Cardiovascular disease management: the need for better diagnostics

Area of Science:

• Biomedical Engineering
• Computational Fluid Dynamics
• Vascular Medicine

Background:

• Current cardiovascular diagnostics rely on physiological or anatomic measurements, often leading to treatment decisions based on estimates.
• Existing methods for carotid, coronary, and abdominal aortic aneurysm (AAA) interventions do not fully account for plaque character, lesion anatomy, or arterial wall composition.
• There is a critical need for diagnostic tools that reflect the advanced understanding of vascular disease.

Purpose of the Study:

• To introduce and review the concept and application of "patient-specific diagnostics" in vascular disease.
• To highlight the potential of computer modeling to predict clinical outcomes by integrating multiple factors.
• To explore the applicability of this approach to carotid, coronary, aneurismal disease, and aortic dissection.

Main Methods:

• Reconstructing patient-specific computer models from clinical imaging modalities.
• Extracting material properties from experimental measurements of vascular specimens.
• Incorporating advanced material models (nonlinear anisotropic) and performing dynamic simulations using fluid-structure interaction (FSI).

Main Results:

• Patient-specific diagnostics offer a sophisticated approach to evaluating vascular pathology.
• This multimodal strategy has the potential to predict clinical outcomes based on arterial wall composition, surface anatomy, and hemodynamic forces.
• The approach promises to improve the effectiveness of interventions for various cardiovascular conditions.

Conclusions:

• Patient-specific diagnostics represent a significant advancement over current diagnostic methods.
• This approach enables better patient selection and more targeted interventions.
• Modeling the results of different therapies can further optimize treatment strategies for vascular diseases.