Multi-platform pipeline for engineering human knee joint function

骨关节炎是一种严重的关节疾病。它是全球残疾的主要原因，随着人口老龄化，它的负担越来越大。在英国，每年有10万名患者需要全膝关节置换术来治疗他们的骨性关节炎，四分之一的患者等待治疗的医学定义是生活在比死亡更糟糕的状态。尽管全球普遍存在全膝关节置换术，但不幸的是，五分之一的患者在手术后感到不满意。这些不满意的患者在日常生活活动中的膝关节功能恶化了30%，例如，膝关节不稳定导致残疾，并可能导致摔倒。患者报告说，在移动时感觉不安全，特别是在楼梯上。这影响了他们的信心、独立性、活跃性、幸福感和死亡率，并增加了NHS/社会成本。我们患者和公众参与小组中的患者还描述了膝关节僵硬的负担，这与机械不稳定相反，突出了穿袜子/鞋子的困难，或者无法在地板上与孙子孙女玩耍。受影响最大的患者需要翻修手术，五分之一的翻修手术是由关节功能不佳引起的(约1500名英国患者/年)。在处理功能不佳方面取得的进展有限，导致2020年修订的比例与2012年相同。此外，传统上，全膝关节置换术的“成功”是由注册机构根据患者原位膝关节植入物的存活率进行评估的，这种方法越来越过时，因为接受全膝关节置换手术的患者更年轻，工作更多，对功能的要求也更高。必须通过增加我们对患者膝关节的运动、负荷和稳定性的了解来解决功能不佳的问题，无论是在手术前，还是在全膝关节置换术后，通过增加对患者膝关节的运动、负荷和稳定性的了解，以了解患者对其OA的反应，以模拟和预测患者对手术的反应。需要研究揭示手术如何影响功能，使所有患者都能从中受益。为了了解膝关节骨性关节炎及相关干预措施的影响，传统上，工程师与临床医生联系，开发工具和方法，帮助他们了解膝关节功能，改进植入物设计，并帮助临床决策。然而，目前的能力限制了该领域量化和模拟真实关节功能的能力，导致治疗对五分之一的患者无效，因此研究人员必须汇集他们的专业知识和研究设施来提高他们的水平。在我们的项目中，我们将结合最先进的方法，从先进的计算机建模(在硅胶中)，到植入膝盖的机器人驱动测试(体外)，再到移动患者的三维X射线成像(体内)与机器学习驱动的分析，以提供能够推动2030年后外科创新的膝关节分析管道。我们将建立开放获取的数据、模型库和输出，以便在临床和研究领域广泛采用，以造福于超出我们项目范围的学术和临床创新。通过整合和发展硅胶、体外和体内方法，我们和更广泛的研究领域将有能力了解膝关节功能和功能障碍，以便所有患者从他们的膝盖治疗和手术中受益，这将在正确的时间针对正确的患者。我们的项目将通过应用我们的管道来解决膝关节置换术后因不稳定而导致的残疾问题，从而达到短期效果。长期而言，这条管道将支持对关节功能的临床前和临床后分析，使植入物创新能够改善结果；个性化药物的患者分层；关节保护的早期干预；运动损伤和软组织创伤的新型干预；以及加速恢复活动和工作的手术程序和康复路径。

Osteoarthritis (OA) is a serious disease of the joints. It is the leading cause of disability globally, with increasing burden with the aging population. In the UK, 100,000 patients/year require total knee replacement to treat their OA with one-in-four awaiting treatment being medically defined as living in a state worse than death. Despite the prevalence of total knee replacement globally, unfortunately, one-in-five patients are dissatisfied after their surgery.Knee function during activities of daily living is 30% worse for these dissatisfied patients, for example, knee instability leads to disability and can lead to falls. Patients report feeling unsafe while moving, especially on stairs. This impacts on their confidence, independence, activity, wellbeing, and mortality with added NHS/societal cost. Patients in our Patient and Public Involvement Group also describe the burden of knee stiffness, the opposite of mechanical instability, highlighting difficulty putting on their socks/shoes, or inability to play with grandchildren on the floor. Those most affected require revision surgery, with a fifth of all revision procedures caused by poor joint function (~1,500 UK patients/year). Progress in tackling poor function has been limited, leading to the same proportion of revisions in 2020 as in 2012. Moreover, total knee replacement "success" has traditionally been evaluated by registries in relation to survival of the patients' knee implants in-situ, an approach that is increasingly outdated as the patients undergoing total knee replacement surgery are younger, in work, and more functionally demanding. Poor function must be addressed by increasing our understanding of movement, loading and stability of patients' knees, both prior to surgery, to understand individual patients' response to their OA, and following total knee replacement to model and predict how individuals will respond to their surgery. Research is needed to reveal how surgery affects function so that all patients can benefit from it.To understand the impact of knee OA and associated interventions, traditionally, engineers link with clinicians to develop tools and methods that can inform their understanding of knee function, enhance implant design and aid in clinical decision making. However, current capability is limiting the field's ability to quantify and simulate real joint function, leading to treatments that are ineffective for one-in-five patients and therefore researchers must pool their expertise and research facilities to raise their game. For our project, we will combine state of the art methods, ranging from advanced computer modelling (in silico), through robot driven testing of implanted knees (in vitro), to 3-dimensional X-ray imaging of moving patients (in vivo) with Machine Learning driven analysis, to deliver a knee joint analysis pipeline capable of driving surgical innovation beyond 2030. We will establish open access data, model libraries and outputs as for wide adoption across the clinical and research field for the benefit of academic and clinical innovations beyond the scope of our project. By integrating and advancing in silico, in vitro and in vivo methods, we and the wider research field will be empowered to understand knee function and dysfunction so that all patients benefit from their knee treatments and surgery, which will be targeted to the right patients at the right time. Our project will achieve short-term impact through applying our pipeline to tackle the disability after knee arthroplasty caused by instability. Longer-term the pipeline will underpin pre- and post-clinical analyses of joint function, enabling implant innovation for improved outcomes; patient stratification for personalised medicine; earlier interventions for joint preservation; novel interventions for sports injuries and soft-tissue trauma; and surgical procedures and rehabilitation pathways that accelerate return to activity and work.