Predictive Model for Opioid Use Disorder in Chronic Pain: A Development and Validation Study.

Background/objective: There are several questionnaires for the challenge of anticipating opioid use disorder (OUD). However, many are not specific for chronic non-cancer pain (CNCP) or have been developed in the American population, whose sociodemographic factors are very different from the Spanish population, leading to scarce translation into clinical practice. Thus, the aim of this study is to prospectively validate a predictive model for OUD in Spanish patients under long-term opioids.

Methods: An innovative two-stage predictive model was developed from retrospective ( = 129) and non-overlapping prospective ( = 100) cohorts of real-world CNCP outpatients. All subjects used prescribed opioids for 6 or more months. Sociodemographic, clinical and pharmacological covariates were registered. Mu-opioid receptor 1 (, A118G, rs1799971) and catechol-O-methyltransferase (, G472A, rs4680) genetic variants plus cytochrome P450 2D6 (CYP2D6) liver enzyme phenotypes were also analyzed. The model performance and diagnostic accuracy were calculated.

Results: The two-stage model comprised risk factors related to OUD (younger age, work disability and high daily opioid dose) and provided new useful information about other risk factors (low quality of life, -G allele and CYP2D6 extreme phenotypes). The validation showed a satisfactory accuracy (70% specificity and 75% sensitivity) for our predictive model with acceptable discrimination and goodness of fit.

Conclusions: Our study presents the results of an innovative model for predicting OUD in our setting. After external validation, it could represent a change in the paradigm of opioid treatment, helping clinicians to better identify and manage the risks and reduce the side effects and complications.