Torsional stiffness and strength of the proximal tibia are better predicted by finite element models than DXA or QCT.
Torsional stiffness and strength of the proximal tibia are better predicted by finite element models than DXA or QCT.
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DOI：
10.1016/j.jbiomech.2013.04.016
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发表时间：
2013-06-21
影响因子：
2.4
通讯作者：
Troy KL
中科院分区：
文献类型：
--
作者：
Edwards WB;Schnitzer TJ;Troy KL
脊髓损伤的个体在神经损伤处的骨矿物质会迅速流失。这种骨质流失的临床后果是膝关节周围骨折的高发生率。预测该部位骨骼力学能力的能力可作为评估脊髓损伤人群骨折风险的重要临床工具。本研究的目的是发展和统计比较非侵入性方法来预测胫骨近端扭转刚度(K)和强度（Tult）。22个人类胫骨被分配到一个“训练集”或一个“测试集”（每个11个样本），并机械加载到失效。该训练集用于建立特定受试者的有限元（FE）模型，以及基于双能x射线吸收仪（DXA）和定量计算机断层扫描（QCT）的统计模型，以预测K和Tult；该测试集用于交叉验证。力学试验在所有标本中均产生临床相关的螺旋形骨折。所有方法都是准确可靠的K预测因子（交叉验证r2≥0.91，误差≤13%），然而，FE模型解释了测量Tult的额外15%的方差，并且比DXA和QCT模型的误差小12-16%。考虑到测量值与FE预测K（交叉验证r2= 0.95，误差= 10%）和Tult（交叉验证r2= 0.91，误差= 9%）之间的强相关性，我们认为FE建模过程已经达到了回答临床相关问题所需的准确性水平。
Individuals with spinal cord injury experience a rapid loss of bone mineral below the neurological lesion. The clinical consequence of this bone loss is a high rate of fracture around regions of the knee. The ability to predict the mechanical competence of bones at this location may serve as an important clinical tool to assess fracture risk in the spinal cord injury population. The purpose of this study was to develop, and statistically compare, non-invasive methods to predict torsional stiffness (K) and strength (Tult) of the proximal tibia. Twenty-two human tibiae were assigned to either a “training set” or a “test set” (11 specimens each) and mechanically loaded to failure. The training set was used to develop subject-specific finite element (FE) models, and statistical models based on dual energy x-ray absorptiometry (DXA) and quantitative computed tomography (QCT), to predict K and Tult; the test set was used for cross-validation. Mechanical testing produced clinically relevant spiral fractures in all specimens. All methods were accurate and reliable predictors of K (cross-validation r2 ≥ 0.91; error ≤ 13%), however FE models explained an additional 15% of the variance in measured Tult and illustrated 12–16% less error than DXA and QCT models. Given the strong correlations between measured and FE predicted K (cross-validation r2= 0.95; error = 10%) and Tult (cross-validation r2= 0.91; error = 9%), we believe the FE modeling procedure has reached a level of accuracy necessary to answer clinically relevant questions.