Computational Network Model Prediction of Hemodynamic Alterations Due to Arteriolar Rarefaction and Estimation of Skeletal Muscle Perfusion in Peripheral Arterial Disease.

OBJECTIVE: To estimate the relative influence of input pressure and arteriole rarefaction on gastrocnemius muscle perfusion in patients with PAD after exercise and/or percutaneous interventions.
METHODS: A computational network model of the gastrocnemius muscle microcirculation was adapted to reflect rarefaction based on arteriolar density measurements from PAD patients, with and without exercise. A normalized input pressure was applied at the feeder artery to simulate both reduced and restored ABI in the PAD condition.
RESULTS: In simulations of arteriolar rarefaction, resistance increased non-linearly with rarefaction, leading to a disproportionally large drop in perfusion. In addition, perfusion was less sensitive to changes in input pressure as the degree of rarefaction increased. Reduced arteriolar density was observed in PAD patients and improved 33.8% after three months of exercise. In model simulations of PAD, ABI restoration yielded perfusion recovery to only 66% of baseline. When exercise training was simulated by reducing rarefaction, ABI restoration increased perfusion to 80% of baseline.
CONCLUSION: Microvascular resistance increases non-linearly with increasing arteriole rarefaction. Therefore, muscle perfusion becomes disproportionally less sensitive to ABI restoration as arteriole rarefaction increases. These results highlight the importance of restoring both microvascular structure and upstream input pressure in PAD therapy.