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Area of Science:

  • Pain Management
  • Pharmacogenomics
  • Clinical Prediction Models

Background:

  • Existing opioid use disorder (OUD) prediction tools lack specificity for chronic non-cancer pain (CNCP) patients.
  • Sociodemographic differences between American and Spanish populations limit the applicability of existing models in Spain.
  • There is a need for a validated predictive model for OUD tailored to the Spanish CNCP population.

Purpose of the Study:

  • To prospectively validate a predictive model for OUD in Spanish patients receiving long-term opioid therapy.
  • To identify key sociodemographic, clinical, pharmacological, and genetic risk factors for OUD in this population.

Main Methods:

  • A two-stage predictive model was developed using retrospective and prospective cohorts of CNCP outpatients on long-term opioids.
  • Data collected included sociodemographic, clinical, and pharmacological variables.
  • Genetic variants (OPRM1, COMT) and CYP2D6 phenotypes were analyzed.

Main Results:

  • The model identified risk factors including younger age, work disability, high opioid dose, low quality of life, OPRM1-G allele, and CYP2D6 extreme phenotypes.
  • The validated model demonstrated satisfactory accuracy with 70% specificity and 75% sensitivity.
  • The model showed acceptable discrimination and goodness of fit.

Conclusions:

  • An innovative, validated model for predicting OUD in Spanish CNCP patients has been developed.
  • This model could shift the paradigm of opioid treatment by improving risk identification and management.
  • Implementation could reduce opioid-related side effects and complications.