Opioid use including prescription opioids (such as codeine, tramadol, oxycodone) for non-cancer pain and related harmful effects have reached epidemic proportions in the US and Canada, with rising trends in the UK. Opioid users may be particularly vulnerable to related harms due to factors including older age, existing health-conditions and drug-interactions. However, there are limited alternative options for pain-relief medicines. Therefore, depriving everyone of opioids is not a solution. A better understanding of patient and prescribing characteristics where harms outweigh the benefits is imperative for informed decision-making and safe prescribing. Currently it is unclear which patients are at the highest risk of opioid-related harms and who could be safely prescribed them. This project is timely because advances in methods are now available to allow important questions to be addressed that was not possible previously. Two main aims are addressed: (i) Within the class of opioids, are there certain drugs with a higher risk of death and serious side effects? Specifically, how do these change with dose, strength and when taken with other medications? (ii) Individual prediction: Given everything known about a patient, if they start taking an opioid at a particular dose what their risk of developing a serious side-effect? The results will be directly relevant to clinicians, patients, public health, policy makers to drive improvements in outcomes and future prescribing.
Opioid use for non-cancer pain has been increasing over the last 15-20 years in North America and Europe and has emerged as a major public health concern in the last decade. Opioids are associated with multiple non-serious and serious harms that can lead to hospitalisation. Much of the literature focuses on opioid safety as a class on a population level, however important pharmacokinetic and pharmacodynamic differences between opioids can affect their safety and potential for dependence. Currently a ‘one size fits all’ prescribing approach is employed, as it is not clear which patients may develop opioid-related harms and what treatment regimens predispose to worse outcomes. Clinical prediction models (CPMs) are statistical tools/algorithms that use patient information to predict the risk of outcomes of interest. However, CPMs for prediction of opioid-associated harms are scarce, and where they exist are developed only for one safety outcome and not used in clinical practice. This approach fails to capture multi-safety outcome patterns evolving over time.
Aims of this project are: (1). To evaluate the comparative safety of opioids for serious and non-serious adverse events, considering the dose, potency and duration (2). To develop prediction models for key opioid-associated safety outcomes. CPRD Gold and Aurum will be linked to Hospital Episode Statistics, deprivation indices and Office of National Statistics data. To develop prediction models for key opioid-associated safety outcomes. Cox-proportional hazard models will be used, with outcome specific methods to adjust for confounding factors. Novel methods for developing multi-outcome prediction models will be used alongside traditional predictive modelling for individual risk prediction. This work will unpick some of the complexities of opioid-associated adverse event moving from risk estimation towards personalised risk. The results will be directly relevant to clinicians, patients, public health, policy makers to drive improvements in outcomes and future prescribing.
Adverse events (AEs) associated with prescription opioids: serious infection, cognitive adverse events including dementia and delirium, fractures and gastrointestinal outcomes (constipation, bowel obstruction), long-term opioid use, non-fatal opioid overdose, opioid-dependence and deaths
Meghna Jani - Chief Investigator - University of Manchester
Meghna Jani - Corresponding Applicant - University of Manchester
Carlos Raul Ramirez Medina - Collaborator - University of Manchester
Glen Martin - Collaborator - University of Manchester
Ramiro Bravo - Collaborator - University of Manchester
William Dixon - Collaborator - University of Manchester
CHESS (Hospitalisation in England Surveillance System);HES Accident and Emergency;HES Admitted Patient Care;HES Outpatient;ONS Death Registration Data;Patient Level Index of Multiple Deprivation;COVID-19 Linkages