Predictive models of advanced coronary plaque formation, progression and rupture risk

Coronary heart disease is the leading cause of death globally. Identification of coronary plaques at risk of causing future acute coronary syndromes (ACS) in appropriately selected high risk patients remains a major unmet clinical challenge. The current studentship will complement our current translational interdisciplinary research project which will develop the first comprehensive fluid structure interaction (FSI) finite element model of the biomechanical determinants of advanced coronary atherosclerotic plaque formation and rupture risk in a novel model of advanced coronary plaque in hyperlipidaemic transgenic minipigs that we have developed. FSI modelling utilises intracoronary imaging and flow data that are routinely performed in patients, as source data. The outputs of this finite element model are spatially co-registered in 3D with histology.

In addition, we will perform measurements of local release of mechanistically relevant biomarkers from plaque using a novel intracoronary sampling catheter. These data provide a rich data set from which to develop a predictive model of advanced coronary plaque formation and rupture risk based on measures of plaque biomechanics and the local haemodynamic environment. We will construct this model using machine and deep learning approaches which have been developed by our group. The predictive model which we will derive from this learning data set will be validated using data from a patient cohort which we will acquire in parallel. This approach has a high chance of successful clinical translation and commercialisation. The availability of such a predictive model may enable early identification of high risk patients, in whom treatment can be personalised either through intensification of conventional therapies, or consideration of novel systemic or locally delivered therapies, with the aim of preventing future ACS, which will enable considerable health and economic gains.