A whole-heart model of multiscale soft tissue mechanics and fluid structure interaction for clinical applications (Whole-Heart-FSI)

心脏病是英国和世界范围内导致残疾和死亡的主要原因，导致巨大的医疗费用。以患者个体为基础的风险预测是不完善的。先进的医学发展已经挽救了许多人的生命，特别是在收缩性心力衰竭方面。然而，目前尚无舒张性心力衰竭(射血分数保留)的治疗选择，因为它的多种机制和联合模式的复杂性。结构性心脏病，如心肌梗塞(MI-俗称心脏病发作)和二尖瓣返流(MR，收缩时血液通过二尖瓣泄漏到左心房)，这些生物力学因素是至关重要的，通常是心力衰竭的先兆。尽管心肌梗死后立即进行治疗，心肌梗死最终仍可导致扩张性心力衰竭。MR可导致肺动脉高压和水肿，随后导致右心负荷过重和心力衰竭。对于这些情况，最大的挑战是心脏不能像目前的大多数研究那样被建模为孤立的左心室；流动-结构相互作用(FSI)、心脏-瓣膜相互作用、多尺度软组织力学以及组织生长和重塑(G&R)在结构性疾病的进展中都发挥着重要作用。这个项目旨在通过提供一个多尺度计算框架来包括全心FSI和G&R来应对这一挑战。利用SofTMech开发的新的数学工具(本构定律、G&R、升标和统计推断)，我将建立一个现实的四腔心脏模型，其中包括心脏瓣膜、腔室-腔室、心脏-血液和心脏-循环相互作用，它将足够强大，以模拟MI、MR及其病理后果。这项工作将与我的临床、工业和学术合作者密切合作。该模型将量化哪些因素导致不利的G&R，以及随着疾病的进展预计会发生哪些变化。我们还将确定重要的生物力学标记(例如，本构参数、能量指数、应力/应变演化)。这些生物力学参数的预测值将与其他已建立的不利重构的预测指标进行评估，如缺血持续时间、初次经皮冠状动脉介入治疗后的最终冠状动脉血流分级以及MRI显示的微血管阻塞。因此，该项目将产生新的可检验的假说，并将是朝着为临床医生提供更一致的决策支持迈出的重要一步，因为医学进步的速度和复杂性越来越超过个别临床医生的处理能力。由于该项目内置了统计仿真和不确定性量化，模型预测将快速和量化，并对替代治疗的结果进行误差限制。因此，我们还将解决说服临床医生从模拟获得的信息将是正确的和与他们的日常实践相关的关键方面。这项拟议的研究恰好在医疗技术“优化治疗”和“开发未来疗法”的优先领域内，并瞄准了“数学科学的新联系”和数学科学的“统计和应用概率”。

Heart disease is the leading cause of disability and death in the UK and worldwide, resulting in enormous health care costs. Risk prediction on an individual patient basis is imperfect. Advanced medical development has already saved many lives, particularly in systolic heart failure. However, there is currently no treatment option for diastolic heart failure (with preserved ejection fraction) due to its complexity of multiple mechanisms and co-modality. Structural heart diseases, such as myocardial infarction (MI- commonly known as heart attack) and mitral regurgitation (MR, a leakage of blood through the mitral valve to left atrium in systole), where biomechanical factors are crucial, are often precursors to heart failure. MI can eventually lead to dilated heart failure despite immediate treatments post-MI. MR can induce pulmonary hypertension and oedema and subsequently, right heart overload and heart failure. The grand challenge is for these situations the heart simply cannot be modelled as an isolated left ventricle (as in most of the current studies); flow-structure interaction (FSI), heart-valve interaction, multiscale soft tissue mechanics, and tissue growth and remodelling (G&R) all play important roles in the progression of the structural diseases. This project is set up to meet this challenge by delivering a multiscale computational framework to include Whole-Heart FSI with G&R. Making use of the novel mathematical tools (constitutive laws, G&R, upscaling and statistical inference) developed by SofTMech, I will build a realistic four-chamber heart model that include heart-valve, chamber-chamber, heart-blood, and heart-circulation interactions, which will be powerful enough to model MI, MR and their pathological consequences. This work will be in close collaboration with my clinical, industrial and academic collaborators. The model will quantify which factors lead to adverse G&R and what variations are to be expected as the disease progresses. We will also identify significant biomechanical markers (e.g. constitutive parameters, energy indices, stress/strain evolution). The predictive values of these biomechanical parameters will be assessed against other established predictors of adverse remodellings, such as duration of ischaemia, final coronary flow grade after a primary percutaneous coronary intervention, and microvascular obstruction revealed by MRI. Thus, this project will generate new testable hypotheses and will be a significant step up towards more consistent decision-support for clinicians, since increasingly the pace and complexity of medical advances outstrip the ability of individual clinicians to cope with. Due to the statistical emulation and uncertainty quantification built into the project, the model predictions will be fast and quantified with error bounds on the outcome of alternative treatments. Consequently, we will also address the critical aspect of convincing clinicians that information obtained from simulations will be correct and relevant to their daily practice. The proposed research is right within the Healthcare Technologies "Optimising Treatment" and "Developing Future Therapies" priority areas, as well as targeting "New Connections from Mathematical Sciences", and "Statistics and Applied Probability" of Mathematical Sciences.