Morris Paul D, Narracott Andrew, von Tengg-Kobligk Hendrik, Silva Soto Daniel Alejandro, Hsiao Sarah, Lungu Angela, Evans Paul, Bressloff Neil W, Lawford Patricia V, Hose D Rodney, Gunn Julian P
Department of Cardiovascular Science, University of Sheffield, Sheffield, UK Insigneo Institute for In Silico Medicine, Sheffield, UK Department of Cardiology, Sheffield Teaching Hospitals NHS Trust, Sheffield, UK.
Department of Cardiovascular Science, University of Sheffield, Sheffield, UK Insigneo Institute for In Silico Medicine, Sheffield, UK.
Heart. 2016 Jan;102(1):18-28. doi: 10.1136/heartjnl-2015-308044. Epub 2015 Oct 28.
This paper reviews the methods, benefits and challenges associated with the adoption and translation of computational fluid dynamics (CFD) modelling within cardiovascular medicine. CFD, a specialist area of mathematics and a branch of fluid mechanics, is used routinely in a diverse range of safety-critical engineering systems, which increasingly is being applied to the cardiovascular system. By facilitating rapid, economical, low-risk prototyping, CFD modelling has already revolutionised research and development of devices such as stents, valve prostheses, and ventricular assist devices. Combined with cardiovascular imaging, CFD simulation enables detailed characterisation of complex physiological pressure and flow fields and the computation of metrics which cannot be directly measured, for example, wall shear stress. CFD models are now being translated into clinical tools for physicians to use across the spectrum of coronary, valvular, congenital, myocardial and peripheral vascular diseases. CFD modelling is apposite for minimally-invasive patient assessment. Patient-specific (incorporating data unique to the individual) and multi-scale (combining models of different length- and time-scales) modelling enables individualised risk prediction and virtual treatment planning. This represents a significant departure from traditional dependence upon registry-based, population-averaged data. Model integration is progressively moving towards 'digital patient' or 'virtual physiological human' representations. When combined with population-scale numerical models, these models have the potential to reduce the cost, time and risk associated with clinical trials. The adoption of CFD modelling signals a new era in cardiovascular medicine. While potentially highly beneficial, a number of academic and commercial groups are addressing the associated methodological, regulatory, education- and service-related challenges.
本文综述了心血管医学中采用和翻译计算流体动力学(CFD)建模的方法、益处及挑战。CFD作为数学的一个专业领域和流体力学的一个分支,常用于各种对安全至关重要的工程系统,如今越来越多地应用于心血管系统。通过促进快速、经济、低风险的原型制作,CFD建模已经彻底改变了诸如支架、人工瓣膜和心室辅助装置等设备的研发。与心血管成像相结合,CFD模拟能够详细表征复杂的生理压力和流场,并计算无法直接测量的指标,例如壁面剪应力。CFD模型目前正在转化为临床工具,供医生用于各类冠状动脉、瓣膜、先天性、心肌和外周血管疾病。CFD建模适用于微创患者评估。针对个体的(纳入个体独特数据)和多尺度的(结合不同长度和时间尺度的模型)建模能够实现个性化风险预测和虚拟治疗规划。这与传统上依赖基于登记处的、人群平均数据有很大不同。模型整合正逐步朝着“数字患者”或“虚拟生理人”的方向发展。当与人群尺度的数值模型相结合时,这些模型有可能降低与临床试验相关的成本、时间和风险。采用CFD建模标志着心血管医学进入了一个新时代。尽管可能带来巨大益处,但一些学术和商业团体正在应对相关的方法学、监管、教育和服务方面的挑战。