POS0937 FACTORS ASSOCIATED WITH HOSPITAL ADMISSIONS DUE TO OPIOID-RELATED HARMS IN PATIENTS WITH RHEUMATIC AND MUSCULOSKELETAL DISEASES

Background Hospital admissions due to opioid-related toxicity have doubled in the United Kingdom over the last decade. Rheumatic and musculoskeletal diseases (RMDs) are some of the most common indications for prescribing opioids in primary care. Little is known about what individual factors are associated with serious opioid-related harms in this population. A better understanding of these risks is imperative for safe prescribing of opioids in patients with RMDs. Objectives To assess patient factors associated with opioid-related hospitalisations in new opioid users with the following RMDs: rheumatoid arthritis (RA), ankylosing spondylitis (AS), psoriatic arthritis (PsA), systemic lupus erythematosus (SLE), osteoarthritis (OA), and fibromyalgia. Methods This retrospective cohort study evaluated new adult opioid users without cancer between 01-Jan-2006 and 31-Aug-2021 using data from the Clinical Practice Research Datalink Aurum diagnosed with one or more of the six RMDs. Patient-level data were linked to Hospital Episodes Statistics (HES). The main outcome of opioid-related hospitalisations within five years of first opioid prescription, were defined using ICD-10 codes from HES. Logistic regression and random forest classification were used to assess patient characteristics associated with opioid-related hospitalisations. To identify the most relevant variables we used “Boruta” feature selection, a wrapper algorithm built around the random forest classifier that compares the importance of the real predictor variables with those of permuted copies of the original features using statistical testing and several iterations of random forests. Feature importance is ranked by the Boruta algorithm using mean Z-scores (the number of standard deviations from the mean a data point is). The higher the Boruta importance score, the stronger the impact the particular input variable has on the outcome variable. Results The cohort comprised 1,329,698 new opioid users (801,533 women [60.3%]; 992,542 White patients [88.2%]), with a mean age of 60 years [SD 17]. The proportion patients with different RMDs in order of frequency were OA: 1,246,574 (93.7%); RA, 50,000 (3.8%)], fibromyalgia [47,708, 3.6%], PsA [11,181 (0.8%)], SLE [6,757 (0.5%)] and AS [6,560 (0.5%). Of our study population, 4,016 individuals (0.3%) experienced a hospitalization for opioid-related harms within our follow-up period of five years after first prescription date. Logistic regression and random forest models showed consistent results when ranking the most important variables associated to opioid-related hospital admissions. The main risk factor identified consistently across both methods was history of alcohol excess, with an odds ratio (OR) of 10.7, 95% confidence interval (95% CI): 8.1–14.2 and Boruta Importance (Imp) of 93.6. Other main risk factors included history of attempted suicide and self-harm (OR 7.5, 95% CI: 5.6–9.9, Imp: 80.3), major depression (OR 2.0, 95% CI: 1.7–2.3, Imp: 39.7) and lower socioeconomic status (OR: 10.4, 95% CI: 4.6–23.4, Imp: 34.0). Conclusion Patients with a documented history of alcohol excess, severe psychological problems and those most socioeconomically deprived were found to have a higher risk of opioid-related hospitalisations. Medical providers should be made aware of psychosocial factors associated with opioid hospital admissions when prescribing opioids to patients with RMDs. By determining patient subgroups most vulnerable to opioid-related harms and further analysing patient risk factors, we hope to contribute to the development of targeted interventions for safer future clinical care. Acknowledgements Funded by a FOREUM Career Research Grant and NIHR. MJ is supported by an NIHR Advanced Fellowship [NIHR301413]. The views expressed in this publication are those of the authors and not necessarily those of the NIHR, NHS or the UK Department of Health and Social Care. Disclosure of Interests Carlos Ramirez Medina: None declared, David Jenkins: None declared, Niels Peek: None declared, Belay Birlie Yimer: None declared, Joyce (Yun-Ting) Huang: None declared, Mark Lunt: None declared, William Dixon Consultant of: WGD has received consultancy fees from Google unrelated to this work, Meghna Jani: None declared.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.