Evaluation of Ultrasonic Neuroma Size With Residual Limb Pain in Above-Knee Amputees

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Background: The formation of neuromas, a fusiform swelling of a nerve or nerve ending, is a well documented response to limb amputation. Likewise, Residual Limb Pain (RLP), pain felt from the remaining portion of the amputated limb, is common among amputees. Neuromas are found in more than 90% of lower extremity amputations, of which 30-50% are pain-generating for the patient . And while surgical techniques reveal the commonly held belief that neuroma formation is one of the causal drivers behind RLP, there has been no study to demonstrate that the two events - namely the magnitude of neuroma formation and the magnitude of pain experienced by amputees - are correlated. If this correlation, or lack thereof, were elucidated with the use of ultrasonography, this would provide the preliminary data which could lead to further studies in neuroma minimization and/or proliferation. Objective: This study seeks to use ultrasonography (US) to quantify the degree to which neuroma size and the nature of surrounding tissue correlate with the experience of RLP in trans-femoral amputees. Setting and Subjects: This study will enroll 30 trans-femoral amputees, male and female, who are over the age of 18. The study will take place in the OHSU Orthopaedic Outpatient Clinic, and OHSU Imaging Department. Intervention: None. This is an observational clinical study in which we will characterize the sciatic nerve/neuroma and surrounding tissue using ultrasound. Measurements: We will use US to measure the cross sectional area of the neuroma at its widest point and compare this to the cross section of the same neuron at the lesser trochanter. Furthermore, we will describe the morphology of the neuroma. To quantify the subject's pain experience we will utilize the Questionnaire for Persons with Transfemoral Amputation (Q-TFA), Trinity Amputation \& Prosthetic Experience Scale (TAPES), Visual Analog Scale (VAS), and the Short Form 36 (SF-36). Analysis: The statistical analysis will employ a Pearson correlation coefficient and linear regression analysis.

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