Clinical prediction of opioid use disorder in chronic pain patients: a cohort-retrospective study with a pharmacogenetic approach.

Abstract

Opioids are widely used in chronic non-cancer pain (CNCP) management. However, they remain controversial due to serious risk of causing opioid use disorder (OUD). Our main aim was to develop a predictive model for future clinical translation that include pharmacogenetic markers. An observational study was conducted in 806 pre-screened Spanish CNCP patients, under long-term use of opioids, to compare cases (with OUD, N.=137) with controls (without OUD, N.=669). Mu-opioid receptor 1 (OPRM1, A118G, rs1799971) and catechol-O-methyltransferase (COMT, G472A, rs4680) genetic variants plus cytochrome P450 2D6 (CYP2D6) liver enzyme phenotypes were analyzed. Socio-demographic, clinical and pharmacological outcomes were also registered. A logistic regression model was performed. The model performance and diagnostic accuracy were calculated. OPRM1-AA genotype and CYP2D6 poor and ultrarapid metabolizers together with three other potential predictors: 1) age; 2) work disability; 3) oral morphine equivalent daily dose (MEDD), were selected with a satisfactory diagnostic accuracy (sensitivity: 0.82 and specificity: 0.85), goodness of fit (P=0.87) and discrimination (0.89). Cases were ten-year younger with lower incomes, more sleep disturbances, benzodiazepines use, and history of substance use disorder in front of controls. Functional polymorphisms related to OPRM1 variant and CYP2D6 phenotypes may predict a higher OUD risk. Established risk factors such as young age, elevated MEDD and lower incomes were identified. A predictive model is expected to be implemented in clinical setting among CNCP patients under long-term opioids use.