Mathematical modelling of thrombus formation in Type B aortic dissection
File(s)
Author(s)
Menichini, Claudia
Type
Thesis
Abstract
Type B aortic dissection is a critical clinical emergency caused by the rupture of the inner wall of the aorta. The formation of a tear allows blood to flow into the aortic wall layers, leading to the formation of a second blood-filled region known as the "false lumen". This disease compromises blood supplies to organs in the abdomen and can lead to complications such as aortic aneurysm, rupture or malperfusion syndromes, resulting in a high mortality risk.
Although Type B aortic dissections are generally associated with high in-hospital survival, post-discharge prognosis is poor and highly variable. Significant predictors for long term outcomes include the formation and extent of thrombosis in the false lumen. Partial false lumen thrombosis has been identified as a significant predictor for late complications due to increased false lumen pressure and hence a high risk for aneurysm growth and rupture. On the other hand, improved outcomes are associated with complete false lumen thrombosis, which can be achieved through endovascular repair. If it was possible to predict which patients would develop false lumen thrombosis and to what extent and how to improve the chances for complete thrombosis, treatments could be significantly improved.
The main purpose of this thesis is to develop a computationally efficient approach to predict thrombus formation in patients affected by Type B aortic dissection. A hemodynamics-based computational model has been developed, which employs shear rates, fluid residence time, and platelet distribution to evaluate the likelihood for thrombosis and to simulate the growth of thrombus and its effects on blood flow over time. The computational approach has been applied to idealised and patient-specific geometries to (i) assess the accuracy of model predictions, and (ii) identify geometric features that favour false lumen thrombosis. Finally, data acquired as part of the ADSORB trial have been used to further validate the model and to identify potential predictors of true and false lumen volume changes. It has been demonstrated that the model is capable of predicting which patients are most likely to develop thrombosis of the false lumen and to what extent, for both medically treated patients and those following TEVAR. This information can potentially help clinicians to select the most appropriate treatment or surgical strategy for individual patients.
Although Type B aortic dissections are generally associated with high in-hospital survival, post-discharge prognosis is poor and highly variable. Significant predictors for long term outcomes include the formation and extent of thrombosis in the false lumen. Partial false lumen thrombosis has been identified as a significant predictor for late complications due to increased false lumen pressure and hence a high risk for aneurysm growth and rupture. On the other hand, improved outcomes are associated with complete false lumen thrombosis, which can be achieved through endovascular repair. If it was possible to predict which patients would develop false lumen thrombosis and to what extent and how to improve the chances for complete thrombosis, treatments could be significantly improved.
The main purpose of this thesis is to develop a computationally efficient approach to predict thrombus formation in patients affected by Type B aortic dissection. A hemodynamics-based computational model has been developed, which employs shear rates, fluid residence time, and platelet distribution to evaluate the likelihood for thrombosis and to simulate the growth of thrombus and its effects on blood flow over time. The computational approach has been applied to idealised and patient-specific geometries to (i) assess the accuracy of model predictions, and (ii) identify geometric features that favour false lumen thrombosis. Finally, data acquired as part of the ADSORB trial have been used to further validate the model and to identify potential predictors of true and false lumen volume changes. It has been demonstrated that the model is capable of predicting which patients are most likely to develop thrombosis of the false lumen and to what extent, for both medically treated patients and those following TEVAR. This information can potentially help clinicians to select the most appropriate treatment or surgical strategy for individual patients.
Version
Open Access
Date Issued
2017-12
Date Awarded
2018-03
Copyright Statement
Attribution NoDerivatives 4.0 International Licence (CC BY-ND)
License URL
Advisor
Xu, Xiao Yun
Publisher Department
Chemical Engineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)