Opioids are strong painkillers commonly prescribed for severe pain. Increasing evidence from the United States (US) and Canada has shown that many patients use opioids long-term after surgery, despite never using them before. Long-term opioid use can result in dependency and addiction and affect several body systems causing adverse effects such as respiratory depression, bone fractures, and drug-related deaths.
In England, we know that the number of prescriptions for strong opioids is increasing. However, we presently lack knowledge about the characteristics of patients at risk of remaining on opioids long-term and the risk of the associated adverse events in patients after surgery.
We will examine the care records of patients living in England who persistently use opioids after surgery by using data from two large national datasets. By analysing these data, we can understand how many patients are on opioids long-term after surgery and what factors are associated with receiving extended periods of opioid prescribing. We also want to find out what adverse consequences are associated with the long-term use of opioids and whether other factors may influence the risk of those adverse consequences.
By conducting this study, we hope to classify patient groups at risk of long-term use of opioids and, hence, develop strategies to reduce the harm associated with these drugs.
Opioids remain the mainstay of post-surgical pain management. However, long-term use of opioids does not provide any additional analgesic benefit. Despite the vast number of surgical procedures conducted in the UK, an epidemiological study to examine the persistent utilisation and safety of opioids for patients after surgery is lacking. This study examines the risk of persistent opioid use and its associated severe adverse events in patients undergoing major surgery in England.
A retrospective cohort study will use Clinical Practice Research Datalink (CPRD) with linkage to Hospital Episode Statistics (HES) and the Office of National Statistics registry from January 2000 to March 2021. The study cohort will include patients with a record of major surgical procedures in HES from January 2000 to March 2020 and at least one prescription of opioids in CPRD within 30 days of hospital discharge. Patients will be followed from the date of receiving at least one opioid prescription in primary care to the date of transferring out of the GP practice, the date of death or the end of study (31 March 2021), whichever happens first to identify opioid prescription patterns and severe adverse events (bone fracture, drug-related death, suicide).
The prevalence of persistent opioid use measured in the first patient-year will be associated with covariates using multi-level regression. The incidence of severe adverse events and the time to the first event will be described using absolute risks and potentially competing risks comparing persistent and non-persistent opioid users. A Cox proportional hazards model will estimate the risk of all-cause mortality and opioid-related adverse events associated with persistent opioid utilisation. Time-varying exposure and confounders will be measured every three months and the average weighted hazards ratios of the first incident will be estimated by a marginal structural model with an inverse probability weighting on propensity scores.
1.1. The incidence of persistent long term opioid users following surgery
2. The incidence of severe adverse events, i.e. bone fractures, suicides and drug-related deaths in persistent long term opioid users following surgery.
3. The prescription patterns, including opioid prescriptions (type, formulation and amount) and other concomitant medication (benzodiazepines, gabapentinoids, antidepressants, Z-drugs and antipsychotics) associated with the prevalence of severe adverse events in persistent long term opioid users after surgery.
Neetu Bansal - Chief Investigator - University of Manchester
Li-Chia Chen - Corresponding Applicant - University of Manchester
Alison Wright - Collaborator - University of Manchester
Benjamin Brown - Collaborator - University of Manchester
Christopher Armitage - Collaborator - University of Manchester
Darren Ashcroft - Collaborator - University of Manchester
Matthew Carr - Collaborator - University of Manchester
Roger Knaggs - Collaborator - University of Nottingham
Sujesh Bansal - Collaborator - University of Manchester
Teng-Chou Chen - Collaborator - University of Manchester
HES Admitted Patient Care;ONS Death Registration Data;Patient Level Index of Multiple Deprivation;Practice Level Index of Multiple Deprivation