FP-00032

Section 1 - Basic information about you and your application:

Title of research project
Comparative Cost-Effectiveness Analysis of Traditional Implants and 3D Printed Implants in Arthroplasty

Grant Type
The ORUK Inspiration Fund

Research area
Treatment

Duration
12

Start date
February 1, 2024

Have you previously received funding from ORUK?
No.

Profession
Academic scientist

Your current job title/position
Lecturer in Engineering & Entrepreneurship

Are you an early-career researcher (ECR)? (definition of ECR)
no

Section 2 - Lay summary

Lay summary:

An increase in sports injuries and global ageing has led to a surge in annual arthroplasties, with a parallel rise in 3D printed customised implants usage. These implants, aligning more closely to original joint performance, are a leap in orthopaedic surgery. However, gaps exist in knowledge concerning their optimal demographic utilisation, cost-effectiveness and patient experience. To investigate these, we propose a research employing historical data from the National Joint Registry (NJR) and Hospital Episode Statistics (HES), applying advanced modelling, statistical analysis and machine learning algorithms to elucidate the areas of concern. Moreover, a patient participatory research will be conducted ensuring their perspectives playing a central role in the study design and outcomes.

The National Joint Registry (NJR) has documented approximately four hundred instances of 3D printed implants being used in joint replacement [1]. Although the global 3D printed implant market size is expected to grow to $5.36 billion in 2027 at a CAGR of 19.0% [2], no data is available on the global prevalence of 3D printed implants in arthroplasty. The ambition of this patient-centred research is to understand the prevalence, cost-effectiveness, and patient experience of 3D printed implants in arthroplasty.

By quantifying the adoption rate of 3D printed implants in arthroplasty, the research could contribute to planning for future demand and might identify regions or populations where these procedures are under-utilised. If 3D printed implants prove effective, they could drive a shift towards more personalised medicine in orthopaedics, where treatments are tailored to the individual patient’s anatomy and needs, potentially improving outcomes and patient satisfaction.

By evaluating the cost-effectiveness of 3D printed implants from statistical analyses of NJR and HES data, the research could influence health policy decisions. For instance, if 3D printed implants prove to be cost-effective in the long term, health policy makers or national health services (NHS) may be more likely to make them accessible to more patients.

This research also actively involve patients through participatory research methods, which will include surveys and semi-structured interviews. Through these interactions, valuable first-hand information about patients’ experiences, encompassing aspects like pain levels, recovery time, mobility, and overall satisfaction, will be gathered. By directly engaging patients in the research process, the goal is to ensure that their interests are fully represented and understood, leading to improved design and application of 3D printed implants.

Section 3 - Purpose of research

Purpose of research:

The objectives of the cost-effectiveness analyses by comparing traditional and 3D printed orthopaedic implants cover a broad spectrum from assessing adoption rates, revision rates, costs, and cost-effectiveness to patient preferences and implications for health care policies. This evidence based research aims to guide decisions of various stakeholders, including patients, surgeons, the NHS, healthcare providers, and implant manufacturers.

Conduct a systematic review on published studies to compare outcomes between traditional and 3D printed orthopaedic implants used in arthroplasties, which could provide additional evidence beyond what is in the National Joint Registry (NJR).

Comprehensively assess crucial factors impacting adoption and revision rates. This process includes summarising data from the NJR on various aspects such as implant types, brands, disease categories, patient demographics, triggers for revision, and the performance of components. Additionally, valuable insights will be obtained from surveys and interviews with patients and surgeons to understand their experiences and preferences better.

Apply suitable statistical methodologies to evaluate collected data, yielding dependable conclusions about implant type, brand, patient group, and disease type’s influence on cost-effectiveness. Moreover, suggest optimal implant types for patients, considering post-operative expectations of pain relief, mobility enhancement, overall health improvement, financial implications, social life alterations, and general quality of life improvement.

Construct predictive models to forecast cost and cost-effectiveness using statistical and machine learning techniques. This can enable predictions of outcomes such as the post-operative EQ-5D score for a patient based on implant type, age, sex, and ASA classification.

Based on the cost-effectiveness analysis findings, it could be found how future trends in health care policies being impacted concerning orthopaedic implants.

Overall, this research’s insights will hold pivotal value for decision-making processes across healthcare systems and industries.

Section 4 - Background to investigation

Background to investigation:

With an increasing aging population, a rise in the incidence of sports-related injuries, and a growing number of individuals affected by joint disease, arthroplasty has seen extensive worldwide use as a standard treatment option. Reports from the National Joint Registry (NJR) of England, Wales, Northern Ireland, and the Isle of Man indicate that over 200,000 such operations are carried out annually [1]. In the USA, American Joint Replacement Registry reported over 2.2 million primary and revision hip and knee arthroplasties have been performed between 2012 and 2020 [3]. While these procedures have enhanced patient quality of life, they have also correspondingly amplified the cost of care. UK studies have demonstrated a maximum net monetary gain of approximately £150,000 for total hip replacement patients [4], while in the US, the total cost for the same procedure can range between $30,000 and $112,000 [5].

In conventional arthroplasty, pre-operative planning can present significant challenges for surgeons tasked with addressing complex structural areas and severe deformities. This complexity can raise the risk of sub-optimal adaptation and functionality in some patients. However, the advent of 3D printing technology has opened the doors for the creation of bespoke implants that align with individual patient anatomy. These implants can be designed using patient-specific CT or MRI scans and can be manufactured with a variety of materials, including metals, ceramics, and polymers.

Though 3D printed implants have the potential to elevate clinical outcomes, there are reservations regarding their cost-effectiveness. Custom implants necessitate additional time and resources for design, manufacture, and testing, potentially driving up costs when compared to traditional implants. There are unanswered questions about the long-term performance and durability of 3D printed implants, as well as potential adverse events associated with the use of novel materials and manufacturing processes.

Currently, there is a limited number of studies on comparing the cost-effectiveness of traditional, mass-produced joint replacement implants with 3D printed, customised ones. Despite the advantages of 3D printed customised implants in terms of improving surgical success rates, reducing the risk of post-operative complications, enhancing patient quality of life, and consequently decreasing healthcare and societal costs, the high costs from manufacturing, equipment, and learning add extra burdens for healthcare providers and patients. A recent systematic review revealed that the cost-effectiveness of 3D printed implants can vary based on the specific clinical scenario. However, the evidence base remains sparse and inconclusive overall.

Choosing the right implants for arthroplasty remains a complex issue, given the inherent differences between individual patients. Different implant types may yield varying cost-effectiveness results across different patient groups. Therefore, in order to achieve precision medicine in arthroplasty, a thorough comparison of the cost-effectiveness of traditional and 3D printed customized implants is crucial. This will enable the selection of the best treatment solutions according to the specific needs and backgrounds of different patient groups.

The PI has extensive research experience in orthopaedic engineering, who remains an active contributor to the translational healthcare technology field, committed to furthering excellence in research, industrial and enterprising applications. The multi-disciplinary research was developed through collaborations with consultants/surgeons, academics and industrial partners, involving developing laboratory prototypes and taking them through commercialisation, clinical validation and human testing to make a real-world impact.

As a musculoskeletal research scientist, the Principal Investigator (PI) possesses a wealth of research experience in the field of orthopaedic engineering and continues to make significant contributions to the domain of translational healthcare technology. The PI is committed to promoting excellence in research and its applications in industry and entrepreneurship.

An area of particular focus in the PI’s research is the emerging technology of 3D printing, which has immense potential for applications within the orthopaedic domain. The PI’s work explores how this innovative technology can revolutionize the field, from the customization of implants to improvements in surgical procedures, potentially improving patient outcomes and the overall cost-effectiveness of treatments.

Co-Investigator 1 is a medical statistician with a wealth of experience in applying classical statistical approaches such as sample size estimation, sample randomisation, regression modelling, and hypothesis statistical testing for clinical endpoint assessment in clinical trials. Co-1 also incorporated a variety of widely-used machine learning algorithms addressing various aspects of data analysis, which included dimensionality reduction, clustering, classification and model cross-validation. By applying these machine learning techniques, we are able to handle large, complex datasets of NJR, identify patterns and relationships within the data, create predictive models and validate models to ensure their robustness and reliability. In addition to their statistical expertise, Co-I 1 had extensive wet lab based experiences and previous closely collaboration with clinicians, which could help the data analysis have prioritised considerations in the feasibility of clinical application and the direct benefit of patients. Co-I 1 will be leading the WP 2 on cost-effectiveness analyses by creating statistical models and machine learning algorithms.

The patient participatory researcher (Co-I 2) is an accomplished researcher specializing in the social studies of medicine. With a wide range of expertise, Co-I 2 excels in both qualitative (survey and interview) and quantitative (statistics and modeling) research methods within the emerging healthcare technology and health inequity field. One notable research project completed by Co-I 2 focused on developing a cost model for 3D printed pharmaceuticals. Through this study, Co-I 2 explored the economic implications of implementing this innovative technology in the pharmaceutical industry. The areas of expertise Co-2 can contribute to this project encompass patient participatory research, economic model of 3D printing, and policy engagement.

Our team has been fortunate to form a collaborative relationship with the National Joint Registry (NJR), who could grant us accessing to their patient data. We proposed a research project on access to the data, received positive feedback from the NJR regarding our proposed research, and attended interview in July 2023. They could officially grant us permission to utilise their data in August 2023. This partnership will be instrumental in enabling our project to move forward.

Section 5 - Plan of investigation

Plan of investigation:

Work Package (WP) 1 – Tracking adoption rates of 3D printing technology for orthopaedic implants over time (Researchers: Musculoskeletal Research Scientist (PI) & PhD student)

A significant objective of our research is to chronicle the trajectory of 3D printing technology’s adoption for orthopaedic implants over time.

WP 1.1: Comprehensive literature review

A thorough literature review will be undertaken to gain insights into the adoption trends and benchmarks in the industry. A systematic review of studies comparing the outcomes between 3D printed implants and traditional ones will be done. This is expected to supplement and extend the data available in the National Joint Registry (NJR).

WP 1.2: Engagement through surveys and interviews

To further understand and quantify the use and adoption of 3D printed implants, surveys and interviews with practicing orthopaedic surgeons will be conducted. These engagements will focus on gauging the level of familiarity surgeons have with the different implant types and their perceived procedural difficulty. To access a broad range of professionals, these interviews will be conducted at conferences hosted by the British Orthopaedic Association and the British Orthopaedic Research Society.

Through monitoring adoption rates and understanding their trends, we can identify potential barriers and accelerators to the uptake of 3D printed implants. This information can guide policy decisions and help forecast future tendencies in this area. As 3D printing techniques, materials, costs, and outcomes continue to evolve, monitoring these changes can further clarify how the value proposition for this technology is changing.

 

WP 2 – Evaluating cost-effectiveness of traditional implants and 3D printed implants in Arthroplasty (Researchers: Medical Statistician (Co-I 1) & PhD student)

The goal of this work package is to investigate the cost-effectiveness of conventional implants versus 3D printed custom implants in arthroplasty using classic statistical approaches and machine learning algorithms applied to the National Joint Registry (NJR) data.

WP 2.1: Survival time analysis for various implant types

We will apply Markov model (Figure 1) [6] and Cox Regression model (Figure 2) [7] for the survival time analysis. Two events will be defined: first revision and second revision. Survival rates will be estimated over time for each type of implant separately. Patients will be split into training and validation sets for model validation. The training set will be used to train the model, and the validation set will evaluate the model’s performance. Cross-validation will be conducted, followed by Receiver Operating Characteristic Curve (ROC) plotting to depict sensitivity and specificity for different thresholds. If the Area Under Curve (AUC) exceeds 0.83, the model will be deemed suitable for estimating survival time (Figure 3).

WP 2.2: Analyses of NHS cost and cost-effectiveness for arthroplasty

WP 2.2.1: Cost prediction

We will construct a cost prediction model for patients using predictors such as initial surgery cost, first revision cost, second revision cost, and lifelong maintenance cost. Estimated survival time of revisions for hypothesised implant types will be derived from WP 2.1. To predict NHS costs for a patient population over a given future period, the costs of individual patients will be combined.

WP 2.2.2: Cost-effectiveness analysis

We will calculate the cost-effectiveness for each implant type as the ratio between the average net cost for arthroplasty and the average increase in Quality-Adjusted Life Years (QALYs) for patients. NHS-wide cost-effectiveness will incorporate data from individual patients.

WP 2.3: Identification of optimal implants type for patients

WP 2.3.1: Classic statistic – Linear regression model

We will create a linear regression model to recommend the optimal implant, with the implant types as predictor variables, and the differences between pre-operative and post-operative values for metrics such as pain score, mobility measure, EQ-5D, IMD, socio-economic deprivation index, QOL index or a self-defined variable COMBINED individually as response variables. Potential confounders (e.g., age group, surgery location) will be adjusted by their inclusion as covariates in the regression model. Significant differences among implant types will be considered if the coefficient is significantly greater or less than 0, visualised using a violin plot.

WP 2.3.2: Machine learning algorithm — Decision tree

We will build a decision tree (Figure 4) [8] to classify patients, with condition nodes as differences of pre-operative and post-operative scores (pain, mobility, EQ-5D, IMD, social-economic deprivation index, QOL, individually), and leaves as the different implant types. To validate post-operative EQ-5D prediction model and the decision tree model, we will use the same approach of cross validation and ROC AUC analysis as described in WP2.1.

 

WP 3 – Examining patient experience variances between traditional and 3D printed implants (Researchers: Patient Participatory Researcher (Co-I 2) & PhD student)

To fully understand the 3D implants on patients, it is essential to explore their first hand experiences through a comprehensive approach that includes online surveys, interviews, and workshops. By combining quantitative and qualitative data collection methods, we aim to gain valuable insights into the patient perspective, ultimately informing and improving future implant designs and patient-centred care.

WP 3.1: Online survey

An online survey will be designed to collect quantitative data from a diverse pool of patients who have undergone 3D implant procedures. The survey will encompass questions related to demographics, implant details, pre-operative expectations, post-operative experiences, and overall satisfaction. The use of a standardized survey will allow for the systematic analysis of responses, enabling valuable statistical insights.

WP 3.2: Semi-structured interview

In-depth semi-structured interviews will be conducted with a smaller subset of patients to gain a deeper understanding of their experiences. Through open-ended questions, patients will be encouraged to share their perspectives, emotions, and any unforeseen challenges they encountered during the entire process. Interviews will be recorded and transcribed to ensure accurate representation of the participants’ narratives.

WP 3.3: Patient-centred workshops

Patient-centred workshops will be organized, bringing together patients, healthcare professionals, researchers, and providers. These interactive sessions will facilitate a collaborative environment where participants can openly discuss their experiences with 3D implants. The workshops will encourage creative brainstorming, idea sharing, and exploring potential improvements to the implant process from a patient’s point of view.

Section 6 - Research environment and resources

Research environment and resources:

Our team has been fortunate to establish a collaborative partnership with the National Joint Registry (NJR), potentially allowing us to access their extensive patient data. We proposed a research project to access this data and, after a promising initial response and an interview held in July 2023, we expect to gain official permission to utilise their data in August 2023. This partnership will serve as a crucial enabler for the advancement of our project.

Our team comprises a musculoskeletal research scientist who serves as the Principal Investigator (PI), a medical statistician (Co-I 1), and a patient participatory researcher (Co-I 2). Additionally, a PhD student, who started working on the project in September 2022, will also be contributing to our research.

The PI brings a wealth of experience from a prestigious biomechanics laboratory that boasts a 50-year history of conducting orthopaedic research. Having collaborated with some of the top orthopaedic surgeons in Britain, the PI has forged strong professional relationships that will prove invaluable, particularly when conducting clinician-focused interviews.

Currently, the PI’s group offers a robust infrastructure for researchers requiring access to 3D printers or software support, further augmenting our research capabilities.

Section 7: Research impact

Who will benefit from this research?

Patients could access more effective, cost-efficient solutions, informed by clear data on implant options. Healthcare providers could gain insights on implant effectiveness, potentially revolutionising orthopaedic protocols if 3D printed options prove better. Findings could guide policy makers and NHS in informed decision-making regarding funding and health spending, particularly if 3D printed implants are cost-effective. The research could stimulate innovation among implant manufacturers, prompting adaptation if 3D printed versions are more cost-effective. Finally, the academic and research community would benefit from this study’s valuable contribution to orthopaedics and health economics, potentially inspiring further research. In summary, the project’s potential to impact healthcare decision-making across various levels, from patients to manufacturers, could lead to significant societal benefits.

How can your research be translated in real-life?

The research findings can guide patients in their choices regarding implant type for their surgeries. If 3D printed implants are found to be more cost-effective or provide better outcomes, patients can opt for this technology with confidence.

Surgeons and hospitals can utilise the data to guide their decisions regarding implant types in surgeries. This can influence clinical practice, potentially making 3D printed implants the preferred choice.

The project’s results can be used by policy-makers to shape healthcare policies and guidelines. It could lead to changes in healthcare policies, making these implants more accessible to a wider patient population.

Moreover, it could drive innovation in the orthopaedic implant manufacturing sector, with more investment in 3D printing technology and its applications in healthcare.

In conclusion, the results of this project have the potential to be applied directly in the real world, affecting patient decisions, clinical practice, healthcare policy, and manufacturing innovation.

How will your research be beneficial for ORUK and its purpose?

This project aligns with the fundamental principle of Orthopaedic Research UK (ORUK), which is to enhance the quality of life for a population suffering from musculoskeletal problems that is expected to grow over time. By comparing the cost-effectiveness of traditional and 3D printed implants in arthroplasty, we aim to generate a robust evidence base that will guide effective interventions. The insights derived from this research could enable us to identify the most efficient, beneficial and sustainable treatment options for joint replacement. These findings, in turn, will support ORUK’s mission to advance patient care through improved treatments, informed by rigorous research and economic considerations. The ultimate goal is to enhance the quality of life for patients, while also ensuring the sustainability of healthcare resources for the growing needs of the population. It is a real research with patients at its heart.

Section 8: Outreach and engagement

The insights derived from this study will be disseminated through co-authored papers and conference presentations, ensuring wide accessibility to the research outcomes. Moreover, stakeholders will have access to a patient outcome report, further encouraging the adoption and enhancement of patient-centred healthcare approaches.

The primary outlet for reporting our work is scientific and clinical journals; in the case of the proposed work, Journal of Bone and Joint Surgery and BMJ will be particularly important outlets for our research. These are the clinical journals most likely to influence practising surgeons and thus allow surgeons to explain the reasoning for their treatment choice to patients. We will present our results and findings at international and national conferences such as ISTA 2024, BORS, AAOM and etc.

We will collaborate with our university’s media department to craft press releases and articles that effectively communicate our findings. These will be shared with local and national news outlets.

Upon completion of the project and publication of the results, the anonymised and aggregated data will
be made publicly available through a reputable open-access data repository. This will allow other
researchers and interested parties to access, analyse, and build upon the findings of the project.

All data management and sharing activities will be conducted in compliance with applicable ethical
guidelines, data protection regulations, and intellectual property rights.

We aims to maximise the value of the generated data and findings, foster collaboration and encourage knowledge transfer to public.

Section 9: Research budget

Requested funding from ORUK

University fees (if any)
£0

Salary
£33230

Consumables
£3560

Publications
£3150

Conference attendance
£9800

Other items
£

Total 'requested fund'
£49740



Other items




Other secured funds

Internal funding
£6600

Partner (University)
£120000

Partner (Commercial)
£0

Partner (Charity)
£0

Other sources
£0

Total 'other funds)
£126600

Section 10: Intellectual property and testing on animal

Is there an IP linked to this research?
No

Who owns and maintains this patent?


Does your research include procedures to be carried out on animals in the UK under the Animals (Scientific Procedures) Act?
No

If yes, have the following necessary approvals been given by:

The Home office(in relation to personal, project and establishment licences)?


Animal Welfare and Ethical Review Body?


Does your research involve the use of animals or animal tissue outside the UK?
No

Does the proposed research involve a protected species? (If yes, state which)



Does the proposed research involve genetically modified animals?


Include details of sample size calculations and statistical advice sought. Please use the ARRIVE guidelines when designing and describing your experiments.


There should be sufficient information to allow for a robust review of any applications involving animals. Further guidance is available from the National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs), including an online experimental design assistant to guide researchers through the design of animal experiments.

Please provide details of any moderate or severe procedures


Why is animal use necessary, are there any other possible approaches?


Why is the species/model to be used the most appropriate?