[更新2025:全膝关节置换术(TKA)后的生物力学和运动学]。

IF 0.5
Markus Heinecke, Leandra Bauer, Benjamin Jacob, Julia Kirschberg, Arnd Steinbrück, Georg Matziolis, Matthias Woiczinski
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引用次数: 0

摘要

背景:为了优化原发性全膝关节置换术(TKA)后的临床效果,研究重新聚焦于膝关节的生物力学特征。除了植入物的设计和对齐理念之外,自然关节运动学、功能运动范围和稳定性的恢复关键取决于患者特定的解剖条件。生物力学:仪器TKA系统已经证明了冠状排列和中外侧负荷分布的重要性。此外,髌股关节对齐作为术后成功的决定因素已引起关注。虽然静态放射学评估仍然是金标准,但它们可以通过仪器步态分析进行有意义的补充,以捕获动态腿部对齐并量化膝关节外内收力矩的影响。此外,计算模拟有助于更精确地分析植入物特定的加载条件和运动学行为。新方法:结合实验方法,如体外运动学测试,这些工具有助于详细评估复杂的运动和负荷情况,从而支持个性化治疗和康复策略的发展。此外,运动表型的新分类的发展对患者的系统分类和干预的个性化具有巨大的潜力,目的是提高功能结果和满意度。使用特定对象的肌肉骨骼模型和有限元分析(FEA)可以模拟个体解剖约束下的关节力学,从而有助于优化植入物定位和减少生物力学负荷。未来,将人工智能和机器学习整合到术前规划中,有望完善针对患者的治疗算法。前景:这些生物力学见解的临床转化最终需要在更大的患者群体中进行验证,以证实其疗效和长期益处。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
[Update 2025: Biomechanics and kinematics after total knee arthroplasty (TKA)].

Background: In order to optimise clinical outcomes after primary total knee arthroplasty (TKA), research has refocused on the knee joint's biomechanical characteristics. Beyond implant design and alignment philosophy, the restoration of natural joint kinematics, functional range of motion, and stability critically depends on patient-specific anatomical conditions.

Biomechanics: Instrumented TKA systems have demonstrated the significance of coronal alignment and mediolateral load distribution. Furthermore, patellofemoral joint alignment has gained attention as a determinant of postoperative success. While static radiographic assessments remain the gold standard, they can be meaningfully complemented by instrumented gait analysis to capture dynamic leg alignment and quantify the influence of the external knee adduction moment. Furthermore, computational simulations facilitate a more precise analysis of implant-specific loading conditions and kinematic behaviour.

New methods: In combination with experimental approaches, such as in vitro kinematic testing, these tools facilitate a detailed evaluation of complex movement and load scenarios, thereby supporting the development of personalised therapeutic and rehabilitative strategies. Furthermore, the development of novel classifications of kinematic phenotypes holds great potential for the systematic categorisation of patients and the personalisation of interventions, with the aim of enhancing functional outcomes and satisfaction. The use of subject-specific musculoskeletal models and finite element analysis (FEA) permits the simulation of joint mechanics under individual anatomical constraints, thus contributing to the optimisation of implant positioning and the reduction of biomechanical load. In the future, the integration of artificial intelligence and machine learning into preoperative planning is expected to refine patient-specific treatment algorithms.

Prospect: The clinical translation of these biomechanical insights will ultimately require validation in larger patient cohorts in order to substantiate their efficacy and long-term benefit.

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