Computational study to assess arterial function recovery in PAD patients with micro-vascular insufficiency and integration of smart screening technology using skin patch biosensor to predict the severity of peripheral artery disease
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Peripheral arterial disease (PAD) is characterized by atherosclerotic blockages of the arteries supplying blood to the lower extremities, which cause a progressive accumulation of ischemic injury. Despite revascularization treatment intervention some PAD patients require follow up secondary treatment due to a continued decline in limb function, quality of life and walking parameters. Standard revascularization surgical procedures restore blood flow in the main arteries via bypass surgical grafting. However, nutrient transport and oxygen transfer take place at the level of the microvasculature and capillaries. Nevertheless, an assessment of the microvascular circulation is lacking. Multi-physics simulation software was used to model the phenomena to assess the effectiveness of the standard lower limb revascularization treatment in PAD patients who may have microvascular dysfunction. It was observed that there was 71.73 % decrease in the oxygen transferred to the surrounding tissues when there was blockage at the microvascular level. This model identifies the need to measure the microvascular circulation in the compromised limbs of PAD patients to optimize diagnosis and treatment strategies that reflect the underlying pathophysiology. Also the study suggests for early detection of PAD through screening methods. Current screening methods require trained personnel, special equipment and less accurate. In this study, we present a new sophisticated smart skin technology that could be used as a point-of-care continuous monitoring system for PAD screening. The smart skin biosensor was attached to a human arm phantom to detect blood blow changes. As a result the biosensor was able to detect blood flow in arm phantom and was also be able to record pulse volume changes in the blood flow. The result was then validated using ultrasound technique and found that the biosensor had 94% accuracy with the ultrasound measure.
Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Industrial and Manufacturing Engineering