Journal de recherche et de thérapie en toxicomanie

Abstrait

Evaluation of a Predictive Algorithm that Detects Aberrant Use of Opioids in an Addiction Treatment Centre

J Ramsay Farah, Chee Lee, Svetlana Kantorovich, Gregory A Smith, Brian Meshkin and Ashley Brenton*

Introduction: Physicians prescribing opioids are at the front lines of the opioid abuse epidemic, battling to tip the scale between rising abuse rates and adequate pain control. This study evaluates the performance of an algorithm that incorporates genetic and non-genetic risk factors in accurately predicting patients at risk of Opioid Use Disorder (OUD). Materials and methods: In this study, we evaluated the ability of the Proove Opioid Risk (POR) algorithm to correctly identify OUD in patients at an addiction treatment facility versus healthy, non-addicted controls. The algorithm was applied to 186 participants: 94 patients at an addiction treatment facility who had documented opioid abuse and 92 healthy patients with no history of opioid use. OUD cases were diagnosed by an expert addictionologist using a predetermined set of criteria, including demonstrated tolerance to an opioid, dependence on an opioid for at least one year, and history of self-administration of an opioid on a daily basis. The performance of the POR using sensitivity, specificity, positive and negative predictive values, and area under the curve (AUC) measures was assessed in OUD cases versus the healthy controls. Results: The average POR score of patients with diagnosed OUD was significantly greater than those of the controls. The receiver operator characteristic (ROC) curve of the POR had an area under the curve (AUC) of 0.967, indicating the algorithm correctly categorizes those with OUD nearly 97% of the time. The sensitivity of the algorithm was 98% and the specificity was 100%, demonstrating that the POR is very unlikely to misclassify true positives and true negatives in this study. Conclusion: The POR reliably identified OUD in patients who were addicted to opioids, while classifying healthy controls as low risk. This can be used clinically to predict patients at high risk of OUD before prescribing opioid pain medications.