Computational simulation of bone fracture healing under inverse dynamisation

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• 作者：Cameron J. Wilson ; Michael A. Schütz…
• 关键词：Fracture healing ; Fracture fixation ; Models ; Biological ; Dynamisation
• 刊名：Biomechanics and Modeling in Mechanobiology
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：16
• 期：1
• 页码：5-14
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Theoretical and Applied Mechanics; Biomedical Engineering; Biological and Medical Physics, Biophysics;
• 出版者：Springer Berlin Heidelberg
• ISSN：1617-7940
• 卷排序：16

文摘

Adaptive finite element models have allowed researchers to test hypothetical relationships between the local mechanical environment and the healing of bone fractures. However, their predictive power has not yet been demonstrated by testing hypotheses ahead of experimental testing. In this study, an established mechano-biological scheme was used in an iterative finite element simulation of sheep tibial osteotomy healing under a hypothetical fixation regime, “inverse dynamisation”. Tissue distributions, interfragmentary movement and stiffness across the fracture site were compared between stiff and flexible fixation conditions and scenarios in which fixation stiffness was increased at a discrete time-point. The modelling work was conducted blind to the experimental study to be published subsequently. The simulations predicted the fastest and most direct healing under constant stiff fixation, and the slowest healing under flexible fixation. Although low fixation stiffness promoted more callus formation prior to bridging, this conferred little additional stiffness to the fracture in the first 5 weeks. Thus, while switching to stiffer fixation facilitated rapid subsequent bridging of the fracture, no advantage of inverse dynamisation could be demonstrated. In vivo data remains necessary to conclusively test this treatment protocol and this will, in turn, provide an evaluation of the model’s performance. The publication of both hypotheses and their computational simulation, prior to experimental testing, offers an appealing means to test the predictive power of mechano-biological models.