PhDs in Focus: Enhancing opioid safety – Predicting adverse outcomes in musculoskeletal and post-surgery patients
Welcome to our PhDs in Focus blog series, where our PhD students are showcasing their pioneering research projects at the National Institute for Health and Care Research (NIHR) Manchester Biomedical Research Centre (BRC).
In this blog, Manchester BRC PhD student Christina Diomatari outlines how their PhD project aims to improve the safety of opioid use for patients with musculoskeletal conditions and those recovering from orthopaedic surgery, as part of the Rheumatic and Musculoskeletal Diseases theme.
Predicting and preventing adverse outcomes in opioid use
Opioids, such as codeine and morphine, are a class of drugs originally derived from the poppy plant, known for their pain-relieving properties. They can be helpful in managing pain especially in the acute setting and are also commonly used in people with musculoskeletal conditions such as low back pain, fibromyalgia, rheumatoid arthritis, and osteoarthritis, as well as in those undergoing orthopaedic surgeries, such as joint and knee replacement.
However, the prolonged use of opioids can lead to tolerance, dependency, and a range of other adverse events such as overdose, respiratory depression, and even premature death. In the face of these risks, it is crucial to develop robust methods to predict and prevent adverse outcomes for patients associated with opioid use.
Utilising clinical prediction models to reduce opioid-related risks
My PhD research project funded by the National Institute for Health and Care Research (NIHR) Manchester Biomedical Research Centre (BRC) aims to utilise clinical prediction models to address this issue, by predicting which of these patients are at highest risk of specific side effects. This predictive capability can enable healthcare providers to tailor treatment plans more effectively, optimise pain management strategies, and ultimately reduce the incidence of opioid-related complications in this vulnerable population.
Academic research: A collaborative approach
My interdisciplinary supervisory research team with the Rheumatic and Musculoskeletal Diseases theme of the NIHR Manchester BRC combines expertise in rheumatology, epidemiology (Dr Meghna Jani), data analysis, statistics and prediction modelling (Drs Glen Martin and David Jenkins) to ensure a comprehensive approach to clinical prediction modelling.
UK healthcare data
By using large scale representative UK population healthcare data from primary and secondary care, such as patient medical, diagnostic records, and hospital admission electronic health records and integrating this clinical information with advanced data analysis, we seek to pave the way for clinical prediction models applicable for the general UK population. This could lead to more personalised approaches to opioid prescribing and monitoring, ultimately enhancing the quality of patient safety and care.
Advancements in predictive modelling
Key to our approach is the use of multi-outcome prediction models, which not only consider the prediction of a specific adverse event occurring but also multiple potential adverse events happening simultaneously, along with their interactions.
This approach enhances the accuracy of our models by predicting which patients are at higher risk of developing multiple adverse events, reflecting a more realistic clinical scenario where patients may experience multiple interacting adverse outcomes at the same time.
By predicting these interactions, we aim to provide a more holistic and effective risk assessment, which could potentially lead to better-informed clinical decisions and safer opioid prescribing practices.
The road ahead
Looking forward, our research will continue to evolve as we refine our predictive models. As part of a larger programme of work we aim to implement such models in practice, with the purpose of improving the lives of patients with musculoskeletal conditions and those recovering from orthopaedic surgery.