Multi-platform pipeline for engineering human knee joint function

用于工程人体膝关节功能的多平台管道

• 批准号: EP/X039870/1
• 负责人: Catherine Holt
• 金额: $ 130.01万
• 依托单位: CARDIFF UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX039870%2F1
• 关键词: Multi; platform; pipeline; engineering; human

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by registries in relation to survival of the patients' knee implants in-situ, an approach that is increasingly outdated as the patients undergoing total knee replacement surgery are younger, in work, and more functionally demanding.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.

骨关节炎（OA）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，负担日益加重。在英国，每年有 100,000 名患者需要全膝关节置换术来治疗他们的 OA，其中四分之一的等待治疗在医学上被定义为生活在比死亡更糟糕的状态。尽管全膝关节置换术在全球范围内盛行，但不幸的是，五分之一的患者在手术后不满意。这些不满意的患者在日常生活活动中的膝关节功能会恶化 30%，例如，膝关节不稳定会导致残疾，并可能导致跌倒。患者报告在移动时感到不安全，尤其是在楼梯上。这会影响他们的信心、独立性、活动、福祉和死亡率，并增加 NHS/社会成本。我们的患者和公众参与小组中的患者也描述了膝盖僵硬的负担，这与机械不稳定相反，强调穿袜子/鞋子有困难，或者无法与孙子们在地板上玩耍。受影响最严重的患者需要进行翻修手术，其中五分之一的翻修手术是由关节功能不良引起的（英国每年约 1,500 名患者）。解决功能不良问题的进展有限，导致 2020 年的翻修比例与 2012 年相同。此外，传统上，登记处根据患者膝关节植入物的原位存活情况来评估全膝关节置换术的“成功”，随着接受全膝关节置换手术的患者更年轻、有工作、功能要求更高，这种方法越来越过时。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良的问题，在手术前了解患者个体对其 OA 的反应，并在全膝关节置换术后建模并预测个体对其膝关节的反应。外科手术。需要进行研究来揭示手术如何影响功能，以便所有患者都能从中受益。为了了解膝关节骨关节炎和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，增强膝关节功能。植入物设计和辅助临床决策。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。对于我们的项目，我们将结合最先进的方法，从先进的计算机建模（计算机模拟）到机器人驱动的植入膝盖测试（体外），再到移动患者的 3 维 X 射线成像（体内）通过机器学习驱动的分析，提供能够在 2030 年以后推动外科创新的膝关节分析管道。我们将建立开放访问数据、模型库和输出，以便在临床和研究领域广泛采用，从而造福于学术和临床创新超出了我们项目的范围。通过整合和推进计算机、体外和体内方法，我们和更广泛的研究领域将能够了解膝关节功能和功能障碍，以便所有患者从膝关节治疗和手术中受益，这些治疗和手术将针对合适的患者正确的时间。我们的项目将通过应用我们的产品线来解决膝关节置换术后不稳定引起的残疾问题，从而实现短期影响。从长远来看，该管道将支持关节功能的临床前和临床后分析，从而实现植入物创新以改善结果；个性化医疗的患者分层；早期干预以保护关节；针对运动损伤和软组织创伤的新颖干预措施；以及加速恢复活动和工作的外科手术和康复途径。