Predictors of relapse among patients with opioid use disorder treated with relapse prevention based cognitive behavior therapy: a prospective study

Predictors of relapse among treated opioid dependent patients

  • Ahmed Awad Gowaid Department of psychiatry, faculty of medicine, Alexandria University, Egypt
  • Tarek Kamal Molokhia
  • Ahmed Refat Rady
  • Ahmed Mohamed Abdel Karim
Keywords: relapse prevention, predictors, opioid use disorder, cognitive behavior disorder


Background: Opioid use disorder is defined as persistent opioid use that produces clinically substantial distress or impairment and necessitates effective treatment. According to epidemiological studies, 26–36 million persons globally abuse opiates, with extremely high relapse rates. Effective recovery is possible with sufficient therapy, but with a persistent propensity to relapse. The purpose of this study was to investigate the pretreatment patient’s characteristics that might predict relapse among patients with opioid use disorder after treatment with relapse prevention based cognitive behavior therapy for 12 weeks.

Methods: this was a prospective cohort study of 50 Egyptian opioid dependent patients who underwent detoxification with clonidine drug followed by assigning to relapse prevention based CBT sessions at outpatient psychiatry and addiction clinic of Alexandria main university hospital for 12 weeks then relapse into opioids was assessed at end of psychotherapy by urine screen test for opioids.

Results: of the 50 recruited patients, 15 patients didn’t complete the study with dropout rate 30%. 16 patients remained abstinent with negative urine test for opioids at end of CBT program while 19 one relapsed to opioid use with positive urine test. Statistical analysis of the results showed that having family history of substance abuse, using high doses of heroin per day and injection route of use were significantly associated with relapse at follow up while using high doses of heroin per day pretreatment was the most independent variable associated with relapse.

Conclusion: identification of predictors of relapse and hence high risk patients might be helpful in designing more effective and focused treatment plan to reduce relapse.