Computer to Clinic: Personalised Fluid-Mechanical Models Applied to Heart Failure

心力衰竭（HF）的定义是由于细胞收缩能力下降、解剖结构扩大和冠状动脉微血管阻力增加而导致心脏泵血能力下降。这种泵功能的丧失是西方社会死亡率和发病率显著增加的原因。还有英国。随着老年人口的增加，心衰正迅速成为一种流行病。目前，HF的终生风险为1 / 5，与急性和长期住院治疗相关的费用正在增加。这种疾病的重要性促使应用最先进的临床成像技术来辅助诊断和临床计划。目前，心壁运动、心室血流模式和冠状动脉灌注的测量为心衰患者的特征提供了高分辨率的数据集。然而，临床实践中使用基于人群的指标，这些指标来源于不同的图像集，由于病理生理学的个体差异，常常表明相互矛盾的治疗计划。因此，尽管影像学进步，确定心衰患者的最佳治疗策略仍然存在问题。为了充分利用成像技术的价值，以及它们产生的综合信息内容，需要将多种类型的功能数据集成到一个一致的框架中。这反过来将支持从预先确定治疗方案的临床指标转向基于个人生理的真正个性化护理的范式转变。支持这种转变的一个令人兴奋和非常有前途的策略是将多个图像集同化到个性化和生物物理一致的数学模型中。这些模型的发展提供了捕捉与潜在病理生理机制相关的多因素因果关系的能力。此外，利用生物物理基础提供了独特的机会，通过推导无法成像但可能在HF中发挥关键机制作用的数量来辅助治疗决策，例如组织应力和泵效率。在成像技术进步的同时，该方法还得到了互补技术持续发展的支持，包括改进的数值方法和提高的单位计算成本性能。这一计算进步加速了多物理场功能对一系列器官模型的补充，这些模型最近被组织成国际倡议，如IUPS赞助的生理组和VPH项目。在这些项目中，心脏可以说是目前最先进的综合器官模型的范例。因此，它代表了一个有希望的第一个候选者，用于关注一种重要的人类疾病。在此期间，我的目标将是专注于个性化和应用这些模型在临床和工业环境中治疗心衰患者。模型模拟将集中于量化诊断，帮助患者选择，并指导由伦敦国王学院（KCL）心血管成像组的主要临床医生进行的特定治疗的介入计划。除了该模型的直接临床应用外，研究还将重点放在左心室辅助装置（lvad）的调整上，该装置通常与HF患者的心脏相连，通过将左心室的血液直接泵入主动脉来减少机械负荷。通过这些应用，我的目标是提高我们对这种重大心血管疾病的理解，并展示生物物理模型改善人类医疗保健的潜力。

Heart Failure (HF) is defined by the heart's reduced ability to pump blood due to a drop in cellular contractility, enlarged anatomy and increased coronary micro-vascular resistance. This loss of pump function accounts for a significant increase in both mortality and morbidity in western society. With the U.K.'s elderly population expanding, HF is rapidly becoming an epidemic. There is currently a 1 in 5 life-time risk of HF and costs associated with acute and long term hospital treatments are accelerating. The significance of the disease has motivated the application of state of the art clinical imaging techniques to aid diagnosis and clinical planning. Measurements of cardiac wall motion, chamber flow patterns and coronary perfusion currently provide high resolution data sets for characterising HF patients. However, the clinical practice of using population-based metrics derived from separate image sets often indicates contradictory treatments plans due to inter-individual variability in pathophysiology. Thus, despite imaging advances, determining optimal treatment strategies for HF patients remains problematic. To exploit the full value of imaging technologies, and the combined information content they produce, requires the ability to integrate multiple types of functional data into a consistent framework. This in turn will support a paradigm shift away from predefined clinical indices determining treatment options and a move towards true personalisation of care based on an individual's physiology.An exciting and highly promising strategy for underpinning this shift is the assimilation of multiple image sets into personalised and biophysically consistent mathematical models. The development of such models provides the ability to capture the multi-factorial cause and effect relationships which link the underlying pathophysiological mechanisms. Furthermore, using a biophysical basis presents unique opportunities to assist with treatment decisions through the derivation of quantities that cannot be imaged but are likely to play a key mechanistic role in HF e.g. tissue stress and pump efficiency.In parallel with imaging advances the approach is also underpinned by the ongoing development of complementary technologies, including improved numerical methods and increased performance per unit cost of computing. This computational progress has accelerated the addition of multi-physics functionality to a range of organ models which have recently been organized into international initiatives such as the IUPS sponsored Physiome and VPH projects. Within these programmes the heart is arguably the most advanced current exemplar of an integrated organ model. As such it represents a promising first candidate with which to focus on an important human disease.My goal during this fellowship will be to focus on personalising and applying these models in clinical and industrial settings for treating HF patients. Model simulations will be focused on quantifying diagnosis, aiding patient selection and guiding interventional planning for specific treatments carried out by leading clinicians based in the cardio-vascular imaging group at Kings College London (KCL). In addition to this direct clinical application of the model, the research will also be focused on the tuning of Left Ventricular Assist Devices (LVADs) which are often connected to the heart in HF to reduce mechanical load by pumping blood from the left ventricle directly into the aorta. Through these applications my aim is to both improve our understanding of this significant cardiovascular disease and demonstrate the potential of biophysical models for improving human healthcare.