Mining reported adverse events induced by potential opioid-drug interactions.

Objective: Opioid-based analgesia is routinely used in clinical practice for the management of pain and alleviation of suffering at the end of life. It is well-known that opioid-based medications can be highly addictive, promoting not only abuse but also life-threatening overdoses. The scope of opioid-related adverse events (AEs) beyond these well-known effects remains poorly described. This exploratory analysis investigates potential AEs from drug-drug interactions between opioid and nonopioid medications (ODIs).

Materials And Methods: In this study, we conduct an initial exploration of the association between ODIs and severe AEs using millions of AE reports available in FDA Adverse Event Reporting System (FAERS). The odds ratio (OR)-based analysis and visualization are proposed for single drugs and pairwise ODIs to identify associations between AEs and ODIs of interest. Moreover, the multilabel (multi-AE) learning models are employed to evaluate the feasibility of AE prediction of polypharmacy.

Results: The top 12 most prescribed opioids in the FAERS are identified. The OR-based analysis identifies a diverse set of AEs associated with individual opioids. Moreover, the results indicate many ODIs can increase the risk of severe AEs dramatically. The area under the curve values of multilabel learning models of ODIs for oxycodone varied between 0.81 and 0.88 for 5 severe AEs.

Conclusions: The proposed data analysis and visualization are useful for mining FAERS data to identify novel polypharmacy associated AEs, as shown for ODIs. This approach was successful in recapitulating known drug interactions and also identified new opioid-specific AEs that could impact prescribing practices.