Investigating the psychosocial factors, pain characteristics, and biological markers for opioid use in chronic non-cancer pain patients: a UK biobank population study

Background Chronic pain has been identified as one of the ten most common reasons for primary care visits globally, being represented as a disease on its own. So far, opioid analgesics have been widely prescribed to patients suffering from chronic non-cancer pain (CNCP) due to their potential pain relief properties, particularly in the United States and Canada. However, the use of these medications in the context of CNCP has remained controversial, as there are concerns regarding reported public health challenges following long-term opioid therapy, such as opioid misuse and addiction. One major unanswered question is what makes a chronic pain patient more likely to be prescribed opioids among individuals recruited from the general population. Better understanding the characteristics of chronic non-cancer pain patients, determining prescribed opioid use, and opioid-related disorders will improve inform prescribing decisions on opioid analgesics. Objective To estimate the extent to which biological, psychological, and social factors predict opioid use in a large cohort of CNCP patients.Methods This population-based study used the prospective cohort of the UK Biobank. A machine learning approach was used to derive pain and pain-agnostic models predictive of opioid use. Models were developed using a sample of 178,763 CNCP patients from the baseline data (2006-2011) (i.e., train set) and validated using a left-out sample of 17,045 CNCP patients who have data available in a follow-up visit (6 to years later) (i.e., test set). Classification accuracy and correlation measures were used to evaluate the performance of the models. Regular prescription opioid use identified and confirmed at data collection visit was used as the outcome. Measures of C-reactive protein (CRP) collected from blood samples were assessed for their association with the predictive models. Diagnosis on opioid-related disorders, as per ICD-10, were tested for the associations with the expression of the pain-agnostic model. Results Of 195,808 CNCP patients included in the study, 110,712 (56.54%) were female and the mean (SD) age was 57.03 (8.02) years. 20,895 (11.7%) individuals from the train set, and 912 (5.4%) individuals from the test set used prescribed opioids. The pain and pain-agnostic models predicted opioid use with a good classification accuracy (AUC pain = 0.70, AUC pain-agnostic = 0.75). Models showed acceptable classification accuracy for predicting within-individual changes in opioid use between the baseline and follow-up visit. The pain-agnostic model was highly expressed in CNCP patients diagnosed with an opioid-related disorder. Levels of CRP were significantly associated with the expression of the pain-agnostic model (r = 0.26, p<0.001). Conclusion Our results show a dissociation between opioid users and non-opioid users at two time points. This study suggests that a pattern of psychosocial risk factors associated with a biological marker of inflammation could be a common predictor for opioid use among chronic non-cancer pain patients. Identifying the associated characteristics in these individuals could help improve the assessment of risks and benefits of chronic opioid use in certain subpopulations and will be a step towards improving the safety and effectiveness of chronic pain treatment

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