A robust approach for exploring hemodynamics and thrombus growth associations in abdominal aortic aneurysms.

Longitudinal studies of vascular diseases often need to establish correspondence between follow-up images, as the diseased regions may change shape over time. In addition, spatial data structures should be taken into account in the statistical analyses to avoid inferential errors. This study investigates the association between hemodynamics and thrombus growth in abdominal aortic aneurysms (AAAs) while emphasizing on the abovementioned methodological issues. Six AAA surfaces and their follow-ups were three-dimensionally reconstructed from computed-tomography images. AAA surfaces were mapped onto a rectangular grid which allowed identification of corresponding regions between follow-ups. Local thrombus thickness was measured at initial and follow-up surfaces and computational fluid dynamic simulations provided time-average wall shear stress (TAWSS), oscillatory shear index (OSI), and relative residence time. Six Bayesian regression models, which account for spatially correlated measurements, were employed to explore associations between hemodynamics and thrombus growth. Results suggest that spatial regression models based on TAWSS and OSI offer superior predictive performance for thrombus growth relative to alternative specifications. Ignoring the spatial data structure may lead to improper assessment with regard to predictor significance.