Personalised thermal-fluid models for planning catheter ablation therapy for atrial arrhythmia

用于规划房性心律失常导管消融治疗的个性化热流体模型

• 批准号: EP/P013228/1
• 负责人: Adelaide De Vecchi
• 金额: $ 11.9万
• 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: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FP013228%2F1
• 关键词: Personalised; thermal; fluid; models; planning

In recent years, significant advances in clinical imaging have provided a wealth of detailed information on the internal function and anatomy of the human body. Fundamental questions on the progression of diseases and on their treatment can be addressed using this information. A highly promising avenue for the exploitation of this potential is the creation of biophysical computer models of the organs based on these state-of-the-art data. These models combine the available clinical information in a consistent mathematical framework that can be tailored to the specific characteristics of the individual patient. They can be used to predict the complex internal dynamics of a working organ, as well as its diseases' mechanisms, providing a powerful tool to personalise treatment. This project will create a toolbox to generate personalised models of flow and temperature in the heart. Specifically, this platform will be designed to simulate atrial fibrillation, a disease that commonly affects one chamber of the heart (the left atrium), and its treatment, known as catheter ablation. When the left atrium is in fibrillation, the wall stops contracting and starts quivering. This behavior is triggered by abnormal electrical impulses at a specific site, called the driver site. As a result, the motion of the blood flow in the atrium is weakened. This abnormal behavior can reduce the supply of blood to the body. Catheter ablation consists in burning the driver site in the atrial wall by applying heat via a catheter, in order to suppress the abnormal electrical impulses and restore contraction. However, patient outcomes are suboptimal and reoccurrence of fibrillation after a single procedure is high. This is due to the strong dependence of this treatment effectiveness on patient-specific factors that are difficult to quantify from the imaging data alone.The proposed research will focus on improving outcomes using personalised computer models. Previous pilot work has proven the potential of this type of approach in predicting the size of the lesion caused by ablation. The overall goal of this project is to create physiologically accurate, personalised models to inform the choice of the ablation parameters such as the catheter voltage and the ablation time. This modelling toolbox will then be applied to a cohort of patients with the aim of increasing significantly the clinical impact of the approach. The research will be undertaken at St Thomas' Hospital, one of the biggest UK referral centres for atrial fibrillation. Given the multi-disciplinary aspect of the project, internal collaborations have been set up with groups within different areas of expertise. Clinical guidance and patient datasets will be provided by Dr M. O'Neill and his team, the Cardiac Arrhythmia Research Group, while Dr O. Aslanidi and Dr D. Nordsletten will provide expertise in scar and blood flow modelling, respectively. This work will therefore generate impact in different areas, from clinical research to mathematical modelling. Ultimately, however, the beneficiaries of the proposed project are the patients themselves, who will benefit from a personalised and more efficient approach to catheter ablation.

近年来，临床影像学的显著进步为人体的内部功能和解剖结构提供了丰富的详细信息。利用这些信息，可以解决有关疾病发展及其治疗的基本问题。开发这一潜力的一个非常有希望的途径是根据这些最先进的数据创建器官的生物物理计算机模型。这些模型将可用的临床信息结合在一个一致的数学框架中，该框架可以根据个别患者的具体特征进行定制。它们可以用来预测工作器官的复杂内部动力学及其疾病的机制，为个性化治疗提供了一个强大的工具。该项目将创建一个工具箱，以生成个性化的心脏流动和温度模型。具体地说，这个平台将被设计成模拟心房颤动，一种通常影响心脏单腔(左心房)的疾病，及其治疗，称为导管消融。当左心房处于颤动状态时，心壁停止收缩并开始颤动。这种行为是由特定地点的异常电脉冲触发的，该地点称为司机地点。结果，中庭内的血流运动减弱。这种不正常的行为会减少人体的血液供应。导管消融是通过导管加热来烧毁房壁上的起搏部位，以抑制异常电脉冲，恢复收缩。然而，患者的结果并不理想，单次手术后纤颤的复发率很高。这是因为这种治疗效果强烈依赖于患者特有的因素，这些因素很难仅从成像数据中量化。拟议的研究将侧重于使用个性化的计算机模型来改善结果。以前的试点工作已经证明了这种类型的方法在预测消融造成的病变大小方面的潜力。该项目的总体目标是创建生理上准确的个性化模型，以便为消融参数的选择提供信息，如导管电压和消融时间。这个建模工具箱随后将应用于一组患者，目的是显著增加该方法的临床影响。这项研究将在圣托马斯医院进行，该医院是英国最大的房颤转诊中心之一。鉴于该项目涉及多学科，已与不同专业领域的小组建立了内部协作。临床指导和患者数据集将由M.O‘Neill博士和他的团队--心律失常研究小组提供，而O.Aslanidi博士和D.Nordsletten博士将分别提供疤痕和血流建模方面的专业知识。因此，这项工作将在不同领域产生影响，从临床研究到数学建模。然而，最终，拟议项目的受益者是患者本身，他们将受益于个性化和更有效的导管消融方法。

项目成果

Mechanistic modelling of Virchows triad to assess thrombogenicity and stroke risk in atrial fibrillation patients

• DOI: 10.1093/ehjdh/ztac076.2788
• 发表时间: 2022-12-22
• 期刊: European Heart Journal. Digital Health

Investigating Strain as a Biomarker for Atrial Fibrosis Quantified by Patient Cine MRI Data

研究应变作为心房纤维化的生物标志物，通过患者电影 MRI 数据进行量化

• DOI: 10.22489/cinc.2020.212
• 发表时间: 2020
• 作者: Qureshi A

Imaging and biophysical modelling of thrombogenic mechanisms in atrial fibrillation and stroke.

• DOI: 10.3389/fcvm.2022.1074562
• 发表时间: 2022
• 期刊: Frontiers in cardiovascular medicine
• 影响因子: 3.6

Left Atrial Appendage Morphology Impacts Thrombus Formation Risks in Multi-Physics Atrial Models

左心耳形态影响多物理心房模型中的血栓形成风险

• DOI: 10.23919/cinc53138.2021.9662901
• 发表时间: 2021
• 作者: Qureshi A

Modelling Virchow's Triad to Improve Stroke Risk Assessment in Atrial Fibrillation Patients

建立 Virchow 三联征模型以改善心房颤动患者中风风险评估

• DOI: 10.22489/cinc.2022.378
• 发表时间: 2022
• 作者: Qureshi "