颈椎退行性病变的有限元分析:当前研究结果和设计方法的范围审查,包括材料特性选择建议。

Benjamin Davies, Samuel Schaefer, Amir Rafati Fard, Virginia Newcombe, Michael Sutcliffe
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引用次数: 0

摘要

背景:颈椎退行性脊髓病(DCM)是一种慢动作脊髓损伤,由脊髓退行性病变造成的慢性机械负荷引起。可发生一系列不同的退行性病变。有限元分析(FEA)可以预测脊髓上机械应力和应变的分布,帮助了解任何机械负荷的影响。有限元分析的关键假设之一是每个解剖元素在负荷下的行为(即其材料特性):本范围审查旨在采用结构化流程来选择最适合用于 DCM 有限元分析的材料特性。在此过程中,它还概述了脊髓疾病的现有建模方法以及对 DCM 的临床见解:我们采用定性综述的方法进行了范围界定综述。讨论脊髓在健康或疾病(包括 DCM)中使用有限元分析模型的观察性研究均可纳入综述。我们遵循了 PRISMA-ScR(系统综述和荟萃分析的首选报告项目扩展至范围综述)指南。我们对 MEDLINE 和 Embase 数据库进行了检索,检索期至 2021 年 9 月 1 日。此外,还进行了引文检索,以检索用于定义材料特性的文献。对重复的标题和摘要进行筛选并提取数据。使用我们开发的质量评估工具(改编自纽卡斯尔-渥太华量表)对证据质量进行评估,并针对 DCM 材料特性进行筛选,最后提出建议。根据无元分析综合报告指南,对文献进行了定性综合:结果:共收录了 60 篇论文:41篇(68%)为 "有限元分析文章",19篇(32%)为 "资料来源文章"。大多数有限元分析文章(33/41,80%)分别对灰质和白质进行了建模,模型通常基于表格数据或较少见的高弹性奥格登变体或线性弹性函数。在 19 篇来源文章中,有 14 篇(74%)被确定为描述脊髓的材料特性,其中 3 篇(21%)被认为与 DCM 最为相关。在 41 篇有限元分析文章中,15 篇(37%)侧重于 DCM,其中 9 篇(60%)侧重于后纵韧带骨化。我们对 DCM FEA 的汇总结果表明,脊髓负荷受退行性病变模式的影响,单纯减压(如椎板切除术)足以解决这一问题,而减压结合其他手术(如椎板切除术和融合术)则无法解决这一问题:结论:FEA 是一种很有前途的技术,可用于探索 DCM 的病理生物学并为临床治疗提供依据。本综述介绍了一种结构化方法,可帮助未来的研究人员将 FEA 用于 DCM。然而,这些建议还存在局限性和更广泛的不确定性。要将 FEA 应用于 DCM 的临床治疗,很可能需要克服这些限制因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Finite Element Analysis for Degenerative Cervical Myelopathy: Scoping Review of the Current Findings and Design Approaches, Including Recommendations on the Choice of Material Properties.

Background: Degenerative cervical myelopathy (DCM) is a slow-motion spinal cord injury caused via chronic mechanical loading by spinal degenerative changes. A range of different degenerative changes can occur. Finite element analysis (FEA) can predict the distribution of mechanical stress and strain on the spinal cord to help understand the implications of any mechanical loading. One of the critical assumptions for FEA is the behavior of each anatomical element under loading (ie, its material properties).

Objective: This scoping review aims to undertake a structured process to select the most appropriate material properties for use in DCM FEA. In doing so, it also provides an overview of existing modeling approaches in spinal cord disease and clinical insights into DCM.

Methods: We conducted a scoping review using qualitative synthesis. Observational studies that discussed the use of FEA models involving the spinal cord in either health or disease (including DCM) were eligible for inclusion in the review. We followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The MEDLINE and Embase databases were searched to September 1, 2021. This was supplemented with citation searching to retrieve the literature used to define material properties. Duplicate title and abstract screening and data extraction were performed. The quality of evidence was appraised using the quality assessment tool we developed, adapted from the Newcastle-Ottawa Scale, and shortlisted with respect to DCM material properties, with a final recommendation provided. A qualitative synthesis of the literature is presented according to the Synthesis Without Meta-Analysis reporting guidelines.

Results: A total of 60 papers were included: 41 (68%) "FEA articles" and 19 (32%) "source articles." Most FEA articles (33/41, 80%) modeled the gray matter and white matter separately, with models typically based on tabulated data or, less frequently, a hyperelastic Ogden variant or linear elastic function. Of the 19 source articles, 14 (74%) were identified as describing the material properties of the spinal cord, of which 3 (21%) were considered most relevant to DCM. Of the 41 FEA articles, 15 (37%) focused on DCM, of which 9 (60%) focused on ossification of the posterior longitudinal ligament. Our aggregated results of DCM FEA indicate that spinal cord loading is influenced by the pattern of degenerative changes, with decompression alone (eg, laminectomy) sufficient to address this as opposed to decompression combined with other procedures (eg, laminectomy and fusion).

Conclusions: FEA is a promising technique for exploring the pathobiology of DCM and informing clinical care. This review describes a structured approach to help future investigators deploy FEA for DCM. However, there are limitations to these recommendations and wider uncertainties. It is likely that these will need to be overcome to support the clinical translation of FEA to DCM.

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