Unicompartmental Knee Arthroplasty: Statistical modelling for the assessment of surgical technique, implant performance and patient selection

膝关节炎等疾病的传统治疗方法包括物理治疗和药物治疗。然而，当病情对患者来说变得非常痛苦时，就会采取手术干预。自然膝关节的运动涉及股骨底部与胫骨顶部的关节连接。这些骨骼的表面被关节软骨覆盖，关节软骨允许关节平稳、无痛地运动。股骨底部和胫骨顶端有两个表面或‘髁状突’；在严重的情况下，软骨从两个髁状突上磨损，它们必须通过全膝关节置换术(TKA)来替代。在某些情况下，只有一个髁突受到关节炎的影响，但在全膝关节置换术中，两个髁突都被替换了。单髁膝关节置换术是一种替代全膝关节置换术的方法，由于其功能改善、长期临床效果良好以及微创手术技术的优点，它是一种替代TKA的方法。特别是，对于更活跃的单间隔性膝关节疾病患者，UKA提供了比TKA更有效的解决方案，因为膝关节的力学得到了更好的保存，更多的功能解剖得到了维护。UKA还具有康复快、住院时间短、手术快、恢复快等优点。有证据表明，将全膝关节置换术翻修为全膝关节置换术的效果类似于主要的全膝关节置换术，而且据报道比典型的全膝关节置换术的翻修术更容易操作。然而，尽管如此，作为TKA的替代方案，UKA仍未得到充分利用。这部分与感知问题有关，部分与技术不当导致的历史上较高的失败率有关。因此，有必要提高对手术技术如何影响UKA性能和失败风险的理解，以便为采用最佳手术技术的UKA的临床决策提供信息。大多数通过计算评估关节置换性能的尝试都涉及到一种确定的方法，即在单个骨骼中模拟单个植入物，并施加单个载荷。这只代表了一种可能的情况，即可能存在数千种情况。最近，出现了一种用统计方法取代确定性方法的做法，这种方法试图考虑到系统中的所有变异性来源。例如，可以分析植入一系列骨骼的植入物在不同载荷下的性能。在本项目中，将应用统计方法来分析UKA的性能。这项研究将利用基于大型骨骼CT扫描库的“统计膝关节”。这个统计上的膝关节代表了一个广泛的患者群体，其中将植入Uncondyls植入物。手术技术的变化将通过改变手术伤口的性质和周围软组织结构的位置来解释。通过这种方式，可以确定外科技术如何影响植入物的性能，即它磨损的速度有多快，松动的可能性有多大。这些知识将被用来开发一种工具，可以用来指导外科医生在计划手术时需要仔细考虑他们的外科技术的哪些方面，以实现更好的患者结果。作为植入物设计过程的一部分，工业也可以从该工具中受益。新的和现有的植入物的性能可以在设计阶段快速可靠地评估，所需的物理测试次数可以大大减少。此外，预计性能不佳的设计可以在早期阶段被淘汰，从而为设计过程带来可观的成本和时间效益。该工具的相应好处将是更坚固的植入物和更长的寿命，使患者和医疗保健提供者都受益。

Traditional methods of treatment for conditions such as arthritis of the knee involve physiotherapy and medication. However, when the condition becomes excessively painful for the patient, surgical intervention is undertaken. Movement of the natural knee joint involves the base of the femur bone articulating against the top of the tibia bone. The surfaces of these bones are covered by articular cartilage which allows smooth, pain free movement at the joint. The base of the femur and the top of the tibia have two surfaces or 'condyles'; in severe cases, the cartilage is worn away from both condyles, and they have to be replaced by a total knee arthroplasty (TKA). In some cases only one of the condyles is affected by arthritis, and yet both condyles are replaced in a TKA procedure. Unicondylar Knee Arthroplasty (UKA), which resurfaces only the affected side, is an alternative to TKA which is becoming an increasingly popular because of its improved functional outcome, favourable long term clinical results and the benefits of minimally invasive surgical techniques. In particular, UKA offers a more effective solution than TKA for more active patients with single compartment knee disease, because the mechanics of the knee are better preserved, and more functional anatomy is maintained. UKA also has advantage of rapid rehabilitation, short hospital stay, quicker operation and quicker recovery. Evidence suggests that revision of a UKA to a TKA results in performance similar to a primary TKA and has been reported to be an easier procedure than the typical revision TKA. However, despite this, UKA is still under-exploited as an alternative to TKA. This is partly related to perception issues, and partly to historically higher failure rates due to improper technique. Therefore, it is desirable to improve the understanding of how surgical technique impacts UKA performance and failure risks, to inform clinical decision-making for UKA with best-practice surgical technique. Most attempts to assess the performance of a joint replacement computationally have involved a 'deterministic' approach, that is, a single implant is modelled in a single bone and a single load is applied. This represents only one possible situation, when potentially many thousands could exist. Recently, there has been a move to replace deterministic approaches with statistical approaches, which attempt to take into account all sources of variability in the system. For example, the performance of an implant in a series of bones under varying loads can be analysed. In this project, statistical approaches will be applied to analyse the performance of UKA. The research will utilise a 'statistical knee joint' based on a large library of bone CT scans. This statistical knee joint represents a wide population of patients into which the unicondylar implant will be implanted. Variations in surgical technique will be accounted for by altering the nature of the surgical cuts and positions of the surrounding soft tissue structures. In this way, a knowledge of how the surgical technique can affect implant performance, in how quickly it wears and how likely it is to loosen, can be ascertained. This knowledge will be used to develop a tool that can be used to guide surgeons on what aspects of their surgical technique need careful consideration when planning their surgery in order to achieve improved patient outcomes. Industry can also benefit from the tool as part of the implant design process. The performance of new and existing implants can be robustly evaluated rapidly at the design stage, and the number of physical tests required can be reduced dramatically. In addition, designs that are predicted to perform poorly can be eliminated at an early stage, leading to substantial cost and time benefits for the design process. The commensurate benefit of this tool will be more robust implants with a longer lifespan, benefiting both the patient and the healthcare provider.