Contact pressure explains half of the abdominal aortic aneurysms wall thickness inter-study variability

Area of Science:

• Biomedical Engineering
• Medical Imaging
• Computational Biology

Background:

• Stochastic rupture risk assessment for abdominal aortic aneurysms (AAA) requires large datasets for accurate distribution estimation.
• Existing AAA datasets often contain fewer than 100 samples, insufficient for reliable tail distribution analysis of wall thickness.
• Accurate AAA wall thickness distribution is crucial for predicting biomechanical integrity and rupture risk.

Purpose of the Study:

• To develop a stochastic Bayesian model for merging abdominal aortic aneurysm (AAA) wall thickness data from diverse sources.
• To enable accurate distribution estimation and improve stochastic rupture risk assessment using limited and varied data.
• To predict undeformed AAA wall thickness by integrating data from summary statistics and direct measurements.

Main Methods:

• A stochastic Bayesian model was formulated to merge thickness data from literature and an additional 81 patients.
• Wall thickness was measured at two contact pressures for 34 cases to estimate radial stiffness.
• The model accommodates data presented as summary statistics and accounts for variations in contact pressure.

Main Results:

• Merging data and accounting for contact pressure reduced median differences between groups by 45%.
• Combined AAA wall thickness data fit a lognormal distribution with parameters μ = 0.85 and σ = 0.32.
• The proposed model successfully predicts undeformed wall thickness and handles heterogeneous data sources.

Conclusions:

• The developed stochastic Bayesian model effectively integrates disparate AAA wall thickness datasets, enhancing data size and quality.
• This approach improves the reliability of distribution estimation for AAA wall thickness, crucial for risk assessment.
• The model provides a robust method for utilizing summary statistics and direct measurements in AAA biomechanical analysis.