Development of a novel computational framework to support therapeutic-planning in selecting the optimal thromboembolic prevention treatment for AF

开发一种新型计算框架，支持选择最佳房颤血栓栓塞预防治疗的治疗计划

• 批准号: 2719105
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2719105
• 关键词: Development; novel; computational; framework; support

1) Brief description of the context of the research including potential impactAtrial fibrillation (AF), the most prevalent cardiac arrhythmia, is characterised by rapid and disorganised heartbeats. It affects 2-3% of the population, with prevalence rising to 9% for individuals > 65 years. It is the leading cause of thromboembolic events (i.e. stroke and vascular dementia), and its projected incidence is more than double by 2035.It is estimated that > 90% of thrombi responsible for thromboembolic events under AF originate in the left atrial appendage (LAA), a complex-shaped protrusion of 2-4 cm, which departs from the left atrium (LA).The most common therapy to prevent thrombi in AF patients is oral anticoagulation; this is however contraindicated in up to 44% of AF patients, due to associated lifetime haemorrhagic risk, cost and several food/drug interactions needing frequent laboratory monitoring. Alternatively, surgical LAA exclusion can be performed applying an external suture ligation - e.g. LARIAT snare device (SentreHEART) - or a self-closing clamping device - e.g. AtriClip LAA Exclusion System (AtriCure). Percutaneous LAA occlusion devices overcome the drawbacks of invasive surgery, based on self-expanding nitinol frames supporting polyester occlusion patches. The two most common solutions are the Watchman FLX (Boston Scientific) and the Amplatzer Amulet (Abbott). However, these can develop peri-device leaks, requiring long-term anticoagulants treatment, thus inherently defeating the main procedure purpose.As all these options present major drawbacks, it is crucial to develop a novel approach to identify the patient groups for which the risk of thromboembolic events justifies a specific therapeutic approach, and the safest and most effective therapy for each subject.2) Aims and ObjectivesThe overall project goal is developing a novel computational framework to support clinical decision-making in selecting the optimal thromboembolic prevention therapy for each AF patient, identifying fluid-dynamic parameters that are impossible to measure in-vivo. 1. Analyse and segment in-vivo medical images of AF patients to obtain LAA's 3D morphological/dynamic characteristics.2. Develop Fluids Structure Interaction (FSI) computational models for the AF population, including LAA/LA anatomical features, investigating the relations between different LAA morphologies and hemodynamics, as well as the capturing of the ones not available from clinical diagnostics.3. Develop models of the main thromboembolic prevention treatments (i.e. anticoagulants and LAA excluder and occluding devices), allowing the evaluation of anatomy-treatment interaction and fluid-dynamic conditions.4. Validate the computational models with retrospective clinical data.3) Novelty of Research MethodologyDevelopment of a validated FSI computational framework, here specifically applied to AF patients, to select the most suitable therapeutic strategy to prevent thromboembolic events among those currently available: anticoagulation drugs, surgical/percutaneous occlusion of the LAA.4) Alignment to EPSRC's strategies and research areasThe project aims at developing a novel computational framework to support therapeutic-planning in the selection of the optimal thromboembolic prevention treatment for AF patients. This will be based on fluid dynamic parameters impossible to measure in-vivo, and will require the synergy between recent developments in medical imaging and computational engineering. It aligns ideally with the EPSRC's strategic priorities of patient-specific illness prediction, accurate diagnosis, and also transforming health and healthcare.5) Any companies or collaborators involvedProfessor Pier Lambiase, Co-Director of Cardiovascular Research Barts NHS Trust/UCL Institute of Cardiovascular Science (ICS)Dr Oliver Segal from Cardiac Units at UCLH and the Barts Health NHS TrustDr Claire Martin

1)研究背景的简要描述，包括潜在的影响房颤（AF），最常见的心律失常，其特征是快速和无序的心跳。它影响2-3%的人口，对于> 65岁的个体，患病率上升至9%。它是血栓栓塞事件的主要原因（即中风和血管性痴呆），并且其预计的发病率到2035年将超过两倍。据估计，在AF下引起血栓栓塞事件的血栓中> 90%起源于左心耳（LAA），其为2- 4cm的复杂形状的突起，预防房颤患者血栓形成最常用的治疗方法是口服抗凝剂;然而，这在高达44%的AF患者中是禁忌的，由于相关的终生出血风险，成本和几种需要频繁实验室监测的食品/药物相互作用。或者，可以使用外部缝合结扎（例如LARIAT圈套器（SentreHEART））或自闭合夹持器械（例如AtriClip LAA封堵系统（AtriCure））进行手术LAA封堵。经皮左心耳封堵器克服了侵入性手术的缺点，其基于支持聚酯封堵补片的自膨式镍钛合金框架。两种最常见的解决方案是Watchman FLX（Boston Scientific）和Amplatzer Amulet（Abbott）。然而，这些可能会发生器械周围渗漏，需要长期抗凝治疗，因此本质上违背了主要手术目的。由于所有这些选择都存在重大缺陷，因此开发一种新方法来识别血栓栓塞事件风险证明特定治疗方法合理的患者组至关重要，以及对每个受试者最安全和最有效的治疗方法。2）目的和目标项目的总体目标是开发一种新的计算框架来支持临床决策-在为每个AF患者选择最佳血栓栓塞预防治疗时，识别不可能在体内测量的流体动力学参数。1.对房颤患者的活体医学图像进行分析和分割，获得左心耳的三维形态/动态特征.为AF人群开发流体结构相互作用（FSI）计算模型，包括LAA/LA解剖特征，研究不同LAA形态与血流动力学之间的关系，以及捕获临床诊断中不可用的参数。开发主要血栓栓塞预防治疗（即抗凝剂和LAA封堵器和封堵器）的模型，允许评价解剖结构-治疗相互作用和流体动力学条件。3）研究方法的新奇开发了一个经过验证的FSI计算框架，这里专门应用于AF患者，以在目前可用的治疗策略中选择最合适的治疗策略来预防血栓栓塞事件：抗凝药物，LAA的手术/经皮闭塞。4）与EPSRC的策略和研究领域保持一致该项目旨在开发一种新的计算框架，以支持治疗-为房颤患者选择最佳血栓栓塞预防治疗的计划。这将基于不可能在体内测量的流体动力学参数，并且将需要医学成像和计算工程的最新发展之间的协同作用。它与EPSRC的患者特异性疾病预测、准确诊断以及改变健康和医疗保健的战略优先事项完美结合。5）参与的任何公司或合作者Pier Lambiase教授，Barts NHS Trust/UCL心血管科学研究所（ICS）的心血管研究联合主任UCLH心脏单位和Barts Health NHS Trust的奥利弗西格尔博士克莱尔马丁博士