Researchers from Aurora-based University of Colorado Anschutz Medical Campus developed a prediction model to identify patients most likely to become chronic opioid users post-discharge.
They detailed their findings in the Journal of General Internal Medicine.
The researchers collected EMR data from Denver Health Medical Center. They pinpointed patient-specific variables strongly associated with the progression to chronic opioid therapy. They defined COT as either receiving a 90-day or more supply of oral opioids with less than a 30-day gap in supply over a 180-day period or filling 10 or more opioid prescriptions over one year.
The variables linked to progression to COT include:
• A history of substance use disorder
• Receipt in the past year of a benzodiazepine
• An opioid medication or a non-opioid analgesic
• Receipt of an opioid at hospital discharge
• High opioid requirements during hospitalization
The model correctly predicted COT in 79 percent of the patients and no COT correctly in 78 percent of the patients.
"This prediction model could be incorporated into the electronic health record and would activate when a physician orders opioid medication. It would inform the physician of their patient's risk for developing COT and may impact their prescribing practices," said Susan Calcaterra, a fellow in addiction medicine at the CU School of Medicine.