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

冠心病是全球死亡的主要原因。在适当选择的高风险患者中，识别有导致未来急性冠脉综合征（ACS）风险的冠状动脉斑块仍然是一个主要的未满足的临床挑战。目前的学生将补充我们目前的跨学科转化研究项目，该项目将开发第一个综合流体结构相互作用（FSI）有限元模型，用于我们开发的高脂血症转基因迷你猪晚期冠状动脉粥样硬化斑块形成和破裂风险的生物力学决定因素。FSI建模利用患者常规进行的冠状动脉内成像和血流数据作为源数据。该有限元模型的输出在空间上与组织学在3D中共同注册。此外，我们将使用新型冠状动脉内取样导管测量斑块中机械相关生物标志物的局部释放。这些数据为基于斑块生物力学和局部血流动力学环境的晚期冠状动脉斑块形成和破裂风险的预测模型提供了丰富的数据集。我们将使用我们小组开发的机器和深度学习方法构建这个模型。我们将从这个学习数据集中得出的预测模型将使用我们将并行获得的患者队列数据进行验证。这种方法有很高的机会成功的临床转化和商业化。这种预测模型的可用性可能使高风险患者的早期识别成为可能，对这些患者的治疗可以通过加强常规治疗或考虑新的全身或局部治疗来个性化，目的是预防未来的ACS，这将带来可观的健康和经济收益。

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.