Establishing the feasibility of real world surveillance of orthopaedic devices using routinely collected data.

Implants used in bone and joint surgery make up 65% of all devices used in the NHS. Information is collected for some implants, like hip and knee replacements, but not the majority used in other operations. Doctors are putting thousands of medical implants into people every year, but unfortunately most of these implants are difficult to monitor for their safety. This has led to some people being harmed. A government led enquiry in the UK was asked to focus on medications and a medical device that have been found to have harmful side effects. Many patients contributed to the enquiry, but unfortunately because routinely collected big data is currently not fully utilised, no one actually knows how many patients were harmed.

This project will test if it is possible to collect information about bone and joint implants, so that future surgeries are safer for patients and better value for money for the NHS. The project will also focus on how best to inform patients, if an issue is found with a device they have had implanted, and how they would like their data to be used to help them get the best care.

The main aims and objectives of the research study.

This study uses routine data from one large NHS Trust mapped to a common data model (CDM) used in an international data science community called OHDSI (Observational Health Data Sciences and Informatics). The research is establishing the feasibility of orthopaedic implant safety surveillance, using longitudinal data linked between the feasibility of orthopaedic implant safety surveillance. This uses longitudinal data linked between primary and secondary care, alongside mortality data to identify complications associated with device use. This fellowship will establish the feasibility of data-driven decision identification of devices at risk of causing harm. It will also develop infrastructure for a UK centre which is leading international collaborative research in big data and devices.

The objectives of this fellowship are to:

1. Establish the feasibility of surveillance of orthopaedic devices using routine NHS data.
2. Identify if orthopaedic device surveillance is possible within an international common data model, enabling multi-centre replication.
3. Undertake surgical and device research in underrepresented patient groups.
4. Build an infrastructure upon which to lead future work in national and international post-market surveillance of orthopaedic devices, externally validating results internationally to address biases.
5. Establish the patient perspective upon how best to involve them in care pathways should a device appear to have higher levels of complications.
6. If sufficient time, or international replication does not appear feasible, to determine if this data -driven approach can be used to identify comparable cost effectiveness of orthopaedic devices.

How this research is going to help address MSK health.

This project is a direct response to the national enquiry into drug and implant safety, designing a method for quickly identifying patients who have been treated with implants that may be causing harm. It aims to offer solutions to better monitor patients who are at risk, and to streamline the work needed in the NHS to monitor devices. Alongside this, the research will include patients and members of the public from the beginning to ask them how they would best want to see their data used, and how best they would like to have information about implant safety given to them.

The main research methods, or datasets being used.

This project is establishing the feasibility of undertaking epidemiological studies, investigating adverse events associated with orthopaedic device use, and the temporal and geographic trends in patient demographics and treatment undertaken. Studies will be developed in UK data, undertaken in anonymised routinely collected data mapped to the OMOP (Observational Medical Outcomes Partnership) CDM.

The research is using 1 NHS dataset of routine healthcare records covering primary, secondary care, intensive care and mortality data. 2.4 million patients are currently included in the host mapped dataset over a ten-year life course; data are refreshed monthly. Mapping of the host institution to the CDM is already complete for routine fields due to a successful grant awarded by the EU. This fellowship works with collaborative partners to map surgical procedure and device data collected at the time of surgery, including unique device identifier, into the CDM. The researcher will undertake mapping of this data into the CDM within an iterative process with data scientists, with the aim of also developing methods converting reproducible analytical pipelines for drug utilisation already used in the OHDSI community for device utilisation analyses.

All analysis plans will be set a priori in a published protocol. In descriptive epidemiology studies, the number of patients in each subgroup will be reported, along with chosen covariates of interest. Standardised mean differences will be reported and plotted to compare subgroups. In comparative cohort studies, multivariable regression modelling and large-scale data driven propensity score analysis will be undertaken to identify associations with outcome using R software.

Research results generated.

Currently beginning feasibility work within NHS dataset, considering which device identifiers are best to use and most available in the data

Impact of this Orthopaedic Research UK fellowship on your research and career

“As part of undertaking this work, I was able to lead our group to apply for a UKRI fellowship alongside UK OHDSI node partners- this was successful in seed funding (£60k) and we are currently applying for stage 2.”

Engagement with research users, special interest groups and the general public to inform them about the research.

Currently leading UK research into surgical devices within the UK OHDSI node of partners with data converted to the CDM.

Researcher: Miss Jennifer Lane.

Supervisor: Prof Xavier Griffin.

University or Trust: Queen Mary University of London.

Award stream: Early Career Research Fellowship.

Award duration: 1 year.

Amount rewarded: £50,000.

Collaborations/ partners:

• Mrs Kristin Kostka and Prof Dani Prieto-Alhambra.
• Established collaborators in Oxford, Spain and the USA.
• Administrative claims data from private and national insurance providers: IBM MarketScan and Pharmetrics.
• Observational Health Data Sciences and Informatics (OHDSI) data science community