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

Abstract

Aortic dissection causes splitting of the aortic wall layers, allowing blood to enter a ‘false lumen’ (FL). For type B dissection, a significant predictor of patient outcomes is patency or thrombosis of the FL. Yet, no methods are currently available to assess the chances of FL thrombosis. In this study, we present a new computational model that is capable of predicting thrombus formation, growth and its effects on blood flow under physiological conditions. Predictions of thrombus formation and growth are based on fluid shear rate, residence time and platelet distribution, which are evaluated through convection–diffusion–reaction transport equations. The model is applied to a patient-specific type B dissection for which multiple follow-up scans are available. The predicted thrombus formation and growth patterns are in good qualitative agreement with clinical data, demonstrating the potential applicability of the model in predicting FL thrombosis for individual patients. Our results show that the extent and location of thrombosis are strongly influenced by aortic dissection geometry that may change over time. The high computational efficiency of our model makes it feasible for clinical applications. By predicting which aortic dissection patient is more likely to develop FL thrombosis, the model has great potential to be used as part of a clinical decision-making tool to assess the need for early endovascular intervention for individual dissection patients.

4-D Flow MRI-Based Computational Analysis of Blood Flow in Patient-Specific Aortic Dissection

“…Aortic dissection can cause serious complications, such as aortic aneurysm and rupture (with internal bleeding) and organ malperfusion. Computational methods have been employed to study the complex phenomena involved in dissection development [27][28][29], thrombosis [14,15] and progression [19]. However, the limited availability of patient-specific flow data led to the common use of idealized boundary conditions (BCs), such as flat or Womersley velocity profiles based on representative aortic flow waveforms at the inlet, and constant pressure or mass flow division at outlets.…”

“…The aberrant flow patterns result in large spatial variations in wall shear stress (WSS), with high values near the tears and low shear stress in the proximal section of the FL [11]. Computational models have also been developed to predict FL thrombosis and the effectiveness of endovascular and surgical treatments [12][13][14][15].…”

“…WSS is a key driver of aortic remodeling [16], which can strongly influence the formation and advancement of aortic dissections. High WSS has been associated with tear initiation [8] and the occurrence of retrograde type A aortic dissection [17], whilst low WSS is a key factor in FL thrombosis [14,15,17]. On the other hand, pressure is an important predictor of disease outcomes [18], as a high pressure imbalance between true and false lumen could cause true lumen collapse or aortic wall rupture.…”

Data-driven Modeling of Thrombus Size and Shape in Aortic Dissections: Role of Hemodynamics

“…Numerical simulations of thrombus formation in patient-specific aortic dissections are relatively rare. In a recent study, Menichini et al 17 proposed a thrombosis model for predicting the false lumen in patient-specific type B aortic dissections. The model is based on continuum fields of activated and resting platelets as well as most relevant coagulation enzymes.…”

“…(9) for platelets can be used to estimate platelet residence times in a similar fashion. Evidence suggests that regions with higher residence times are more prone to initiate thrombus and further growth 17,22 . Having accurate spatio-temporal measurements of clot volume fraction φ c is essential for long-term growth and remodeling simulations.…”