Biomechanics and Biotribology of the Haemophilic Ankle - A Computational Study

Haemophilia is a genetic disorder, caused by a deficiency in coagulation factors, in which recurring bleeds occur; eighty percent of these bleeds occur in the musculoskeletal system. Of these musculoskeletal bleeds, a high number of incidences occurs in the ankle. This is hypothesised to be due to the high stresses on the ankle in everyday activities, starting as young as 24 months when the haemophiliac becomes mobile.
Clinically, haemophilia patients present with early onset of osteoarthritis. However the pathogenesis and morphological changes in the ankle as the disease progresses are little studied.
This project aims to develop patient-specific finite element models of the haemophilic ankle in order to explore how changes in the morphology of the ankle joint relate to a change in the biotribology as the disease progresses.

The complexity of the ankle joint leads to challenging computational approaches including non-linear contact mechanics on non-uniform surfaces. This project will identify changes in bone morphology based on clinical images and define key clinical features and how they develop in time in a patient-specific approach. There are further engineering challenges in developing patient-specific models from clinical-grade longitudinal MR image data due to the necessity to capture both the specificity of each patient and the large variation from patient to patient, with abstraction of the difference in imaging protocols. This project will investigate means to capture the specific morphological changes between patients while keeping a computational model which is solvable for the large variation of morphology between patients, for example by studying which key clinical features have the most impact on biotribology changes. Methods to characterise the longitudinal variance of the joint biotribology as the disease progresses will also be developed so that the pattern of different patient groups can be robustly compared to clinical outcomes.

The methods developed in this project will provide a new suite of robust computational tools that can be used to develop patient cohorts approaches to examine the likelihood of early onset of osteoarthritis. The characterisation methods will also provide new information on which clinical features may be good candidates as image markers of osteoarthritis in the challenging and at-risk cohort of haemophilic patients. This will provide tools and methods for more clinical work in targeting prevention methods.