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

冠心病是全球主要的死亡原因。在适当选择的高危患者中识别有可能导致未来急性冠脉综合征(ACS)的冠状动脉斑块仍然是一个重大的临床挑战。目前的学生将补充我们目前的跨学科翻译研究项目，该项目将开发第一个全面的流体结构相互作用(FSI)有限元模型，用于我们开发的高脂血症转基因小型猪的高级冠状动脉斑块形成和破裂风险的新模型中的高级冠状动脉斑块形成和破裂的生物力学决定因素。FSI模型利用患者常规进行的冠脉内成像和血流数据作为源数据。该有限元模型的输出在3D空间上与组织学共同配准。此外，我们将使用一种新型的冠状动脉内采样导管来测量斑块中与机械相关的生物标记物的局部释放。这些数据提供了丰富的数据集，可以根据斑块生物力学和局部血液动力学环境的测量结果，开发高级冠状动脉斑块形成和破裂风险的预测模型。我们将使用我们团队开发的机器和深度学习方法来构建这个模型。我们将从这个学习数据集中得到的预测模型将使用我们将并行获得的患者队列的数据进行验证。这种方法很有可能成功地进行临床翻译和商业化。这种预测模型的可获得性可以使早期识别高危患者，在这些患者中，可以通过强化传统治疗或考虑新的全身或局部治疗来个体化治疗，目的是预防未来的急性冠脉综合征，这将使健康和经济获得可观的收益。

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.