主动脉瓣狭窄,高血压,左心室肥厚在老年人中经常共存,在心脏和脉管系统之间的耦合中造成有害的不匹配,称为心室-血管(VA)耦合。左VA联轴器受损,衰老和疾病中心血管功能障碍的一个关键方面,对最佳心血管性能提出了重大挑战。本系统综述旨在评估通过计算模型模拟和研究这种耦合的影响。通过对从WebofScience等受尊敬的数据库中获得的34篇相关文章进行全面分析,Scopus,和PubMed直到2022年7月14日,我们探索了各种建模技术和模拟方法,以解开这种损害背后的复杂机制。我们的评论强调了计算模型在提供超出临床观察的详细见解方面的重要作用,能够更深入地了解心血管系统。通过阐明现有的心脏模型(3D,2D,and0D),心脏瓣膜,和血管(3D,1D,and0D),以及讨论机械边界条件,模型参数化和验证,耦合方法,计算机资源和多样化的应用,我们建立了一个全面的领域概述。心里对不同维度选择的描述以及利弊,阀门,并提供流通。至关重要的是,我们强调了在病理条件下评估心脏-血管相互作用的重要性,并提出了未来的研究方向,例如开发完全耦合的个性化多维模型,深度学习技术的整合,以及对生物标志物混杂效应的综合评估。
Aortic stenosis, hypertension, and left ventricular hypertrophy often coexist in the elderly, causing a detrimental mismatch in coupling between the heart and vasculature known as ventricular-vascular (VA) coupling. Impaired left VA coupling, a critical aspect of cardiovascular dysfunction in aging and disease, poses significant challenges for optimal cardiovascular performance. This systematic
review aims to assess the impact of simulating and studying this coupling through computational models. By conducting a comprehensive analysis of 34 relevant articles obtained from esteemed databases such as Web of Science, Scopus, and PubMed until July 14, 2022, we explore various modeling techniques and simulation approaches employed to unravel the complex mechanisms underlying this impairment. Our
review highlights the essential role of computational models in providing detailed insights beyond clinical observations, enabling a deeper understanding of the cardiovascular system. By elucidating the existing models of the heart (3D, 2D, and 0D), cardiac valves, and blood vessels (3D, 1D, and 0D), as well as discussing mechanical boundary conditions, model parameterization and validation, coupling approaches, computer resources and diverse applications, we establish a comprehensive overview of the field. The descriptions as well as the pros and cons on the choices of different dimensionality in heart, valve, and circulation are provided. Crucially, we emphasize the significance of evaluating heart-vessel interaction in pathological conditions and propose future research directions, such as the development of fully coupled personalized multidimensional models, integration of deep learning techniques, and comprehensive assessment of confounding effects on biomarkers.