Abstract
OBJECTIVE: Compare and validate 5 algorithms to detect aberrant behavior with opioids: Opioid Misuse Score, Controlled Substance-Patterns of Utilization Requiring Evaluation (CS-PURE), Overutilization Monitoring System, Katz, and Cepeda algorithms. STUDY DESIGN AND SETTING: We identified new prescription opioid users from 2 insurance databases: Medicaid (2000-2006) and Clinformatics Data Mart (CDM; 2004-2013). Patients were followed 1 year, and aberrant opioid behavior was defined according to each algorithm, using Cohen's kappa to assess agreement. Risk differences were calculated comparing risk of opioid-related adverse events for identified aberrant and nonaberrant users. RESULTS: About 3.8 million Medicaid and 4.3 million CDM patients initiated prescription opioid use. Algorithms flagged potential aberrant behavior in 0.02% to 12.8% of initiators in Medicaid and 0.01% to 7.9% of initiators in CDM. Cohen's kappa values were poor to moderate (0.00 to 0.50 in Medicaid; 0.00 to 0.30 in CDM). Algorithms varied substantially in their ability to predict opioid-related adverse events; the Overutilization Monitoring System had the highest risk differences between aberrant and nonaberrant users (14.0% in Medicaid; 13.4% in CDM), and the Katz algorithm had the lowest (0.96% in Medicaid; 0.47% in CDM). CONCLUSIONS: In 2 large databases, algorithms applied to prescription data had varying accuracy in identifying increased risk of adverse opioid-related events.