Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:4395-4398. doi: 10.1109/EMBC46164.2021.9629890.
Computation of Fractional Flow Reserve (FFR) through computational fluid dynamics (CFD) is used to guide intervention and often uses a number of clinically-derived metrics, but these patient-specific data could be costly and difficult to obtain. Understanding which parameters can be approximated from population averages and which parameters need to be patient-specific is important and remains largely unexplored. In this study, we performed a global sensitivity study on two 1D models of FFR to identify the most influential patient parameters. Our results indicated that vessel compliance, cardiac cycle period, flow rate, density, viscosity, and elastic modulus contributed minimally to the variance in FFR and may be approximated from population averages. On the other hand, outlet resistance (i.e., microvascular resistance), stenosis degree, and percent stenosis length contributed the most to FFR computation and needed to be tuned to the patient of interest. Selective measuring of patient-specific parameters may significantly reduce costs and streamline the simulation pipeline without reducing accuracy.
通过计算流体动力学(CFD)计算分数血流量储备(FFR)用于指导介入治疗,通常使用一些临床衍生的指标,但这些患者特异性数据可能昂贵且难以获得。了解哪些参数可以从人群平均值近似得到,哪些参数需要是患者特异性的非常重要,但这方面的研究仍在探索中。在这项研究中,我们对两种 FFR 的一维模型进行了全局敏感性研究,以确定最有影响的患者参数。我们的结果表明,血管顺应性、心动周期、血流量、密度、粘度和弹性模量对 FFR 的变化贡献最小,可能可以从人群平均值近似得到。另一方面,出口阻力(即微血管阻力)、狭窄程度和狭窄长度百分比对 FFR 的计算贡献最大,需要针对感兴趣的患者进行调整。选择性测量患者特异性参数可能会显著降低成本并简化模拟流程,而不会降低准确性。