Observational study to calculate addictive risk to opioids: a validation study of a predictive algorithm to evaluate opioid use disorder
Authors Brenton A, Richeimer S, Sharma M, Lee C, Kantorovich S, Blanchard J, Meshkin B
Received 29 September 2016
Accepted for publication 28 February 2017
Published 18 May 2017 Volume 2017:10 Pages 187—195
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 4
Editor who approved publication: Dr Martin Bluth
Ashley Brenton,1 Steven Richeimer,2,3 Maneesh Sharma,4 Chee Lee,1 Svetlana Kantorovich,1 John Blanchard,1 Brian Meshkin1
1Proove Biosciences, Irvine, CA, 2Keck school of Medicine, University of Southern California, Los Angeles, CA, 3Departments of Anesthesiology and Psychiatry, University of Southern California, Los Angeles, CA, 4Interventional Pain Institute, Baltimore, MD, USA
Background: Opioid abuse in chronic pain patients is a major public health issue, with rapidly increasing addiction rates and deaths from unintentional overdose more than quadrupling since 1999.
Purpose: This study seeks to determine the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm incorporating phenotypic risk factors and neuroscience-associated single-nucleotide polymorphisms (SNPs).
Patients and methods: The Proove Opioid Risk (POR) algorithm determines the predictability of aberrant behavior to opioids using a comprehensive scoring algorithm incorporating phenotypic risk factors and neuroscience-associated SNPs. In a validation study with 258 subjects with diagnosed opioid use disorder (OUD) and 650 controls who reported using opioids, the POR successfully categorized patients at high and moderate risks of opioid misuse or abuse with 95.7% sensitivity. Regardless of changes in the prevalence of opioid misuse or abuse, the sensitivity of POR remained >95%.
Conclusion: The POR correctly stratifies patients into low-, moderate-, and high-risk categories to appropriately identify patients at need for additional guidance, monitoring, or treatment changes.
Keywords: opioid use disorder, addiction, personalized medicine, pharmacogenetics, genetic testing, predictive algorithm
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