Scaling Cardiac Biomechanics Digital Twins for Personalised Medicine

扩展心脏生物力学数字孪生以实现个性化医疗

• 批准号: EP/X012603/1
• 负责人: Steven Niederer
• 金额: $ 190.35万
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX012603%2F1
• 关键词: Scaling; Cardiac; Biomechanics; Digital; Twins

Modelling and simulation play important roles in designing everything from planes to cars to bridges. However, advances in connectivity and computing now enable models to be linked directly to a specific object or system, creating a "digital twin". Digital twins represent a computational surrogate for a particular object and are updated through time as more information becomes available. However, digital twins are not limited to manufactured objects alone. This project aims to develop digital twins of patients, where a model will track a patient through time. We focus on making digital twins of patients' hearts using detailed imaging data sets over the period of a clinical trial. This is the first step towards models that are updated in real-time, track the patient throughout their life and directly feed back into informing patient care. The digital twin approach builds on patient-specific computer models of the heart that are currently being evaluated to guide procedures in the UK at King's College London and in the US. These models are designed to optimise treatments for a specific patient's pathophysiology but only simulate a small number of heartbeats. Digital twins, which track a patient through time, will forecast disease progression and response to therapy. This represents the next step in simulation guided therapy, where the optimal treatment and, importantly, when to deliver it, will be predicted. This project will address the technical challenges in calibrating computer models of large numbers of patients, how to efficiently update these models through time as more data becomes available, how to analyse images of the heart recorded over the duration of a clinical trial and how to predict complex changes in shape and function of the heart. The approaches will be applied to study three patient groups in three studies. First, we will test if multi-scale cardiac biomechanics models can identify common causes of pump dysfunction in heart failure patients. Second, we will test if digital twins can predict which patients who have recovered from heart failure can stop their heart failure mediation. Thirdly, we will test if digital twin forecasts can be used to predict recovery and pre-empt the need for advanced heart failure therapy in newly diagnosed heart failure patients. This will provide the first demonstration of cardiac biomechanics digital twins using real clinical data to answer important clinical questions.

建模和仿真在设计从飞机到汽车再到桥梁的一切事物中发挥着重要作用。然而，连通性和计算方面的进步现在使模型能够直接与特定对象或系统联系起来，创造了一个“数字孪生”。数字双胞胎代表了特定对象的计算代理，并随着时间的推移而更新，因为有更多的信息可用。然而，数字双胞胎不仅限于制造对象。该项目旨在开发患者的数字双胞胎，其中模型将通过时间跟踪患者。我们专注于在临床试验期间使用详细的成像数据集制作患者心脏的数字双胞胎。这是迈向实时更新模型的第一步，可以在患者的整个生命周期中跟踪患者，并直接反馈给患者护理。数字孪生方法建立在患者特定的心脏计算机模型基础上，目前正在评估该模型，以指导英国伦敦国王学院和美国的手术。这些模型旨在优化针对特定患者病理生理学的治疗，但仅模拟少量心跳。数字双胞胎，通过时间跟踪病人，将预测疾病的进展和对治疗的反应。这代表了模拟引导治疗的下一步，其中最佳治疗以及重要的是，何时提供它将被预测。该项目将解决校准大量患者计算机模型的技术挑战，如何随着时间的推移有效地更新这些模型，如何分析临床试验期间记录的心脏图像以及如何预测心脏形状和功能的复杂变化。这些方法将应用于三项研究中的三个患者组。首先，我们将测试多尺度心脏生物力学模型是否可以识别心力衰竭患者泵功能障碍的常见原因。其次，我们将测试数字双胞胎是否可以预测哪些从心力衰竭中恢复的患者可以停止他们的心力衰竭调解。第三，我们将测试数字孪生预测是否可用于预测康复并预先满足新诊断心力衰竭患者对高级心力衰竭治疗的需求。这将提供心脏生物力学数字双胞胎使用真实的临床数据来回答重要的临床问题的第一个演示。