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降低。这种泵功能的丧失导致了西方社会中死亡率和发病率的显著增加。与英国随着老年人口的增加，HF正在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。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。心壁运动、腔室流动模式和冠状动脉灌注的测量目前提供了用于表征HF患者的高分辨率数据集。然而，由于病理生理学的个体间变异性，使用来自单独图像集的基于人群的指标的临床实践通常表明相互矛盾的治疗计划。因此，尽管成像技术取得了进步，但确定HF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及其产生的综合信息内容，需要能够将多种类型的功能数据集成到一个一致的框架中。这反过来又将支持一种范式转变，从预定义的临床指标决定治疗方案，并转向基于个人生理学的真正个性化护理。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为个性化和生物医学一致的数学模型。这种模型的发展提供了捕获多因素因果关系的能力，这些因果关系将潜在的病理生理机制联系起来。此外，使用生物物理学基础提供了独特的机会，通过导出无法成像但可能在HF中发挥关键机制作用的量（例如组织应力和泵效率）来帮助治疗决策。与成像技术的进步平行，该方法也得到了互补技术的持续发展的支持，包括改进的数值方法和增加的每单位计算成本的性能。这一计算进展加速了多物理功能的添加到一系列器官模型中，这些模型最近被组织成国际倡议，如IUPS赞助的Physiome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范例。因此，它代表了一个有希望的第一个候选人，专注于一个重要的人类疾病。我在这个奖学金期间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HF患者。模型模拟将侧重于量化诊断，帮助患者选择和指导介入计划的具体治疗进行的主要临床医生在国王学院伦敦（KCL）的心血管成像组。除了该模型的直接临床应用外，该研究还将重点关注左心室辅助装置（LVAD）的调整，LVAD通常连接到HF中的心脏，通过将血液从左心室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要心血管疾病的了解，并展示生物物理模型在改善人类医疗保健方面的潜力。

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.