Penn State Heart and Vascular Institute, Pennsylvania State University College of Medicine Hershey PA.
Methodist DeBakey Heart and Vascular Center Houston Methodist Hospital Houston TX.
J Am Heart Assoc. 2023 Feb 7;12(3):e027649. doi: 10.1161/JAHA.122.027649. Epub 2023 Jan 23.
Background Computational fluid dynamics has shown good agreement with contrast-enhanced magnetic resonance imaging measurements in cardiovascular disease applications. We have developed a biomechanical model of microvascular perfusion using contrast-enhanced magnetic resonance imaging signal intensities derived from skeletal calf muscles to study peripheral artery disease (PAD). Methods and Results The computational microvascular model was used to study skeletal calf muscle perfusion in 56 individuals (36 patients with PAD, 20 matched controls). The recruited participants underwent contrast-enhanced magnetic resonance imaging and ankle-brachial index testing at rest and after 6-minute treadmill walking. We have determined associations of microvascular model parameters including the transfer rate constant, a measure of vascular leakiness; the interstitial permeability to fluid flow which reflects the permeability of the microvasculature; porosity, a measure of the fraction of the extracellular space; the outflow filtration coefficient; and the microvascular pressure with known markers of patients with PAD. Transfer rate constant, interstitial permeability to fluid flow, and microvascular pressure were higher, whereas porosity and outflow filtration coefficient were lower in patients with PAD than those in matched controls (all values ≤0.014). In pooled analyses of all participants, the model parameters (transfer rate constant, interstitial permeability to fluid flow, porosity, outflow filtration coefficient, microvascular pressure) were significantly associated with the resting and exercise ankle-brachial indexes, claudication onset time, and peak walking time (all values ≤0.013). Among patients with PAD, interstitial permeability to fluid flow, and microvascular pressure were higher, while porosity and outflow filtration coefficient were lower in treadmill noncompleters compared with treadmill completers (all values ≤0.001). Conclusions Computational microvascular model parameters differed significantly between patients with PAD and matched controls. Thus, computational microvascular modeling could be of interest in studying lower extremity ischemia.
背景 计算流体动力学在心血管疾病应用中已显示出与对比增强磁共振成像测量结果的良好一致性。我们已经开发了一种基于对比增强磁共振成像信号强度的微血管灌注生物力学模型，用于研究外周动脉疾病（PAD）。
方法和结果 该计算微血管模型用于研究 56 名个体（36 名 PAD 患者，20 名匹配对照者）的骨骼肌灌注。招募的参与者在休息时和 6 分钟跑步机行走后接受对比增强磁共振成像和踝臂指数测试。我们已经确定了微血管模型参数（包括转移率常数，血管通透性的度量；反映微血管通透性的间质对流体流动的渗透性；孔隙度，细胞外空间分数的度量；流出过滤系数；以及微血管压力）与已知的 PAD 患者标志物之间的关联。转移率常数、间质对流体流动的渗透性和微血管压力在 PAD 患者中高于匹配对照组（所有 值≤0.014）。在所有参与者的汇总分析中，模型参数（转移率常数、间质对流体流动的渗透性、孔隙度、流出过滤系数、微血管压力）与静息和运动踝臂指数、跛行发作时间和峰值行走时间显著相关（所有 值≤0.013）。在 PAD 患者中，与跑步机完成者相比，跑步机未完成者的间质对流体流动的渗透性和微血管压力较高，而孔隙度和流出过滤系数较低（所有 值≤0.001）。
结论 PAD 患者和匹配对照组之间的计算微血管模型参数有显著差异。因此，计算微血管建模可能对外周动脉疾病的研究感兴趣。
