Multi-platform pipeline for engineering human knee joint function
Lead Research Organisation:
CARDIFF UNIVERSITY
Department Name: Sch of Engineering
Abstract
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by registries in relation to survival of the patients' knee implants in-situ, an approach that is increasingly outdated as the patients undergoing total knee replacement surgery are younger, in work, and more functionally demanding.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.
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.
