| Autor: |
Gimnich OA; Penn State Heart and Vascular Institute, Pennsylvania State University College of Medicine Hershey PA., Belousova T; Methodist DeBakey Heart and Vascular Center Houston Methodist Hospital Houston TX., Short CM; Section of Cardiovascular Research, Department of Medicine Baylor College of Medicine Houston TX., Taylor AA; Section of Cardiovascular Research, Department of Medicine Baylor College of Medicine Houston TX.; Michael E DeBakey VA Medical Center Houston TX., Nambi V; Section of Cardiovascular Research, Department of Medicine Baylor College of Medicine Houston TX.; Department of Medicine, Section of Cardiology Baylor College of Medicine Houston TX.; Michael E DeBakey VA Medical Center Houston TX., Morrisett JD; Section of Cardiovascular Research, Department of Medicine Baylor College of Medicine Houston TX., Ballantyne CM; Section of Cardiovascular Research, Department of Medicine Baylor College of Medicine Houston TX.; Department of Medicine, Section of Cardiology Baylor College of Medicine Houston TX., Bismuth J; Division of Vascular and Endovascular Surgery Louisiana State University Health Sciences Center New Orleans LA., Shah DJ; Methodist DeBakey Heart and Vascular Center Houston Methodist Hospital Houston TX., Brunner G; Penn State Heart and Vascular Institute, Pennsylvania State University College of Medicine Hershey PA.; Section of Cardiovascular Research, Department of Medicine Baylor College of Medicine Houston TX. |
| Jazyk: |
angličtina |
| Zdroj: |
Journal of the American Heart Association [J Am Heart Assoc] 2023 Feb 07; Vol. 12 (3), pp. e027649. Date of Electronic Publication: 2023 Jan 23. |
| DOI: |
10.1161/JAHA.122.027649 |
| Abstrakt: |
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 P 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 P 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 P 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. |
| Databáze: |
MEDLINE |
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