REDUCED ORDER MODELLING AND BIOCHEMICAL PATIENT-SPECIFICITY IN A COMPUTATIONAL MODEL OF CEREBRAL ANEURYSM THROMBOSIS: TOWARDS CLINICAL APPLICABILITY

The files contain data on thrombin and fibrin concentration, velocity and clot size for haemophilliac, healthy and thrombotic patients. 

Computational fluid dynamics (CFD) models of cerebral aneurysm thrombosis are patient-specific insofar as geometry and haemodynamics are concerned. The biochemical reactions that result in clotting require considerable resources to fit all parameters on a per patient or population basis. Furthermore, translation of these CFD models to clinical contexts is limited by model complexity and computational cost. In this study, we present a model that couples results from a calibrated automated thrombogram (CAT), an in vitro test that is used to determine clotting function on a per patient basis, with CFD. The CAT data was fitted into population-specific biochemical profiles of haemophiliac, healthy and thrombotic patients, and applied to the aneurysmal wall of a 2D idealized geometry. There was faster clot growth in the thrombotic case, followed by the normal and haemophiliac cases, respectively. Complex vorticial structures formed as the different clots evolved. The patterns in clot growth, distribution of thrombin and fibrin, and velocity profile showed that there is a strong link between haemodynamics and biochemistry. The model was verified by comparing it to experimental results, and it successfully captured the qualitative features of in vitro clotting.

Polynomial and logistic regression machine learning algorithms were used to develop a reduced order model from CFD results. This model is relatively simple but would have far greater utility in a clinical context as it does not require solution of numerical methods or specialized CFD training.