Prediction of Skeletal Medial-Lateral for transfemoral ischial containment sockets.

Q Medicine
Michael P Dillon, Richard G D Fernandez, Bircan Erbas, Chris Briggs, Matthew Quigley
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引用次数: 1

Abstract

Accurate measurement of the pelvis is critical for well-fitting and comfortable ischial containment sockets. The "Skeletal Medial-Lateral (ML)" is intrusive and unreliable to measure in vivo. This study aimed to determine how accurately the Skeletal ML could be predicted and to identify which measurements were significant predictors. Computed tomography scans were randomly sampled from a cadaveric database (n = 200). Inclusion criteria were age > 20 yr; lower-limb alignment that replicated the anatomical position; and no evidence of osteological trauma, implants, or bony growths. Multivariate linear regression models were developed to predict the Skeletal ML based on a suite of independent variables, including sex, body mass, and distance between pelvic landmarks. The regression model explained 76% of the variance in the Skeletal ML (p < 0.001). Variables that contributed significantly to the prediction of the Skeletal ML (p < 0.05) included body mass, sex, inter-greater trochanter distance, pelvic depth, and age. Significant predictors of the Skeletal ML dimension characterize variation in subcutaneous adipose tissue thickness and pelvic morphology. The Skeletal ML could be predicted with relatively small errors (standard error of the estimate = 7 mm) that could be easily and reliably adjusted during socket fitting. Further research is needed to test the predictive tool in a real-world setting.

经股骨坐骨包容窝的内外侧骨预测。
骨盆的精确测量对于合适和舒适的坐骨收容窝是至关重要的。“骨骼内外侧(ML)”是侵入性的,在体内测量是不可靠的。本研究旨在确定预测骨骼ML的准确性,并确定哪些测量值是重要的预测因子。计算机断层扫描从尸体数据库中随机取样(n = 200)。入选标准:年龄> 20岁;复制解剖位置的下肢对齐;也没有骨外伤,植入物或骨生长的证据。建立多元线性回归模型,基于一套独立变量,包括性别、体重和骨盆标志之间的距离来预测骨骼ML。回归模型解释了76%的骨骼ML方差(p < 0.001)。对骨骼ML预测有显著贡献的变量包括体重、性别、大转子间距离、骨盆深度和年龄(p < 0.05)。骨骼ML尺寸的重要预测因子表征了皮下脂肪组织厚度和骨盆形态的变化。骨骼ML可以预测相对较小的误差(估计的标准误差= 7 mm),可以在套接过程中轻松可靠地调整。需要进一步的研究来在现实环境中测试预测工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
1.64
自引率
0.00%
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0
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