Imaging and biophysical modelling of thrombogenic mechanisms in atrial fibrillation and stroke.
Imaging and biophysical modelling of thrombogenic mechanisms in atrial fibrillation and stroke.
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DOI：
10.3389/fcvm.2022.1074562
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发表时间：
2022
影响因子：
3.6
通讯作者：
中科院分区：
文献类型：
--
作者：
心房颤动（AF）几乎是所有缺血性中风的三分之一，左心房附件（LAA）被确定为主要的血栓栓塞来源。目前的卒中风险分层方法，如CHA2DS2-VASc评分，主要依赖于临床合并症，而不是血栓形成机制，如血瘀、高凝性和内皮功能障碍，即众所周知的Virchow三联征。虽然使用现有的心脏成像技术（如经食管超声心动图）可以检测心房颤动相关的血栓，但在血栓形成之前可靠地评估心房颤动患者的血栓形成性的需求越来越大。在过去的十年中，心脏成像和基于图像的生物物理建模已经成为再现血栓形成机制的有力工具。临床成像技术，如心脏计算机断层扫描、磁共振和超声心动图技术可以测量血流速度和识别LA纤维化（内皮功能障碍的一种指标），但成像在评估凝血动力学方面的能力仍然有限。计算机心脏建模工具，如血流的计算流体动力学，模拟凝血级联的反应-扩散-对流方程，以及与内皮损伤相关的替代血流指标，在血栓形成的流行和先进的机制理解中不断增长。然而，单独使用这两种技术都不能完全阐明房颤的血栓形成性。未来，将心脏成像与计算机建模相结合，并整合机器学习方法，直接从成像数据中获得快速结果，将需要在严格的验证和临床验证框架下发展，但可能为在不断增长的房颤患者群体中增强个性化卒中风险分层铺平道路。本审查将集中讨论这些领域的重大进展。
Atrial fibrillation (AF) underlies almost one third of all ischaemic strokes, with the left atrial appendage (LAA) identified as the primary thromboembolic source. Current stroke risk stratification approaches, such as the CHA2DS2-VASc score, rely mostly on clinical comorbidities, rather than thrombogenic mechanisms such as blood stasis, hypercoagulability and endothelial dysfunction—known as Virchow’s triad. While detection of AF-related thrombi is possible using established cardiac imaging techniques, such as transoesophageal echocardiography, there is a growing need to reliably assess AF-patient thrombogenicity prior to thrombus formation. Over the past decade, cardiac imaging and image-based biophysical modelling have emerged as powerful tools for reproducing the mechanisms of thrombogenesis. Clinical imaging modalities such as cardiac computed tomography, magnetic resonance and echocardiographic techniques can measure blood flow velocities and identify LA fibrosis (an indicator of endothelial dysfunction), but imaging remains limited in its ability to assess blood coagulation dynamics. In-silico cardiac modelling tools—such as computational fluid dynamics for blood flow, reaction-diffusion-convection equations to mimic the coagulation cascade, and surrogate flow metrics associated with endothelial damage—have grown in prevalence and advanced mechanistic understanding of thrombogenesis. However, neither technique alone can fully elucidate thrombogenicity in AF. In future, combining cardiac imaging with in-silico modelling and integrating machine learning approaches for rapid results directly from imaging data will require development under a rigorous framework of verification and clinical validation, but may pave the way towards enhanced personalised stroke risk stratification in the growing population of AF patients. This Review will focus on the significant progress in these fields.
影响因子：
2.9
作者：
Kang SS;Choi JK;Kim IS;Yoon YJ;Shin KM
通讯作者：
Shin KM