Validation Study of a Predictive Algorithm to Evaluate Opioid Use Disorder in a Primary Care Setting

• Main Authors: Maneesh Sharma, Chee Lee, Svetlana Kantorovich, Maria Tedtaotao, Gregory A. Smith, Ashley Brenton
• Format: Article
• Language: English
• Published: SAGE Publishing 2017-08-01
• Series: Health Services Research & Managerial Epidemiology
• Online Access: https://doi.org/10.1177/2333392817717411

Background: Opioid abuse in chronic pain patients is a major public health issue. Primary care providers are frequently the first to prescribe opioids to patients suffering from pain, yet do not always have the time or resources to adequately evaluate the risk of opioid use disorder (OUD). Purpose: This study seeks to determine the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm (“profile”) incorporating phenotypic and, more uniquely, genotypic risk factors. Methods and Results: In a validation study with 452 participants diagnosed with OUD and 1237 controls, the algorithm successfully categorized patients at high and moderate risk of OUD with 91.8% sensitivity. Regardless of changes in the prevalence of OUD, sensitivity of the algorithm remained >90%. Conclusion: The algorithm correctly stratifies primary care patients into low-, moderate-, and high-risk categories to appropriately identify patients in need for additional guidance, monitoring, or treatment changes.