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A prospective, longitudinal study to evaluate the clinical utility of a predictive algorithm that detects risk of opioid use disorder

Authors Brenton A, Lee C, Lewis K, Sharma M, Kantorovich S, Smith GA, Meshkin B

Received 8 April 2017

Accepted for publication 24 July 2017

Published 5 January 2018 Volume 2018:11 Pages 119—131

DOI https://doi.org/10.2147/JPR.S139189

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 4

Editor who approved publication: Dr Michael Schatman


Ashley Brenton,1 Chee Lee,1 Katrina Lewis,2 Maneesh Sharma,3 Svetlana Kantorovich,1 Gregory A Smith,4 Brian Meshkin1

1Proove Biosciences, Irvine, CA, 2Benefis Health Systems, Great Falls, MT, 3Interventional Pain Institute, Baltimore, MD, 4RedPill Medical Inc., Redondo Beach, CA, USA

Purpose: The purpose of this study was to determine the clinical utility of an algorithm-based decision tool designed to assess risk associated with opioid use. Specifically, we sought to assess how physicians were using the profile in patient care and how its use affected patient outcomes.
Patients and methods: A prospective, longitudinal study was conducted to assess the utility of precision medicine testing in 5,397 patients across 100 clinics in the USA. Using a patent-protected, validated algorithm combining specific genetic risk factors with phenotypic traits, patients were categorized into low-, moderate-, and high-risk patients for opioid abuse. Physicians who ordered precision medicine testing were asked to complete patient evaluations and document their actions, decisions, and perceptions regarding the utility of the precision medicine tests. The patient outcomes associated with each treatment action were carefully documented.
Results: Physicians used the profile to guide treatment decisions for over half of the patients. Of those, guided treatment decisions for 24.5% of the patients were opioid related, including changing the opioid prescribed, starting an opioid, or titrating a patient off the opioid. Treatment guidance was strongly influenced by profile-predicted opioid use disorder (OUD) risk. Most importantly, patients whose physicians used the profile to guide opioid-related treatment decisions had improved clinical outcomes, including better pain management by medication adjustments, with an average pain decrease of 3.4 points on a scale of 1–10.
Conclusion: Patients whose physicians used the profile to guide opioid-related treatment decisions had improved clinical outcomes, as measured by decreased pain levels resulting from better pain management with prescribed medications. The clinical utility of the profile is twofold. It provides clinically actionable recommendations that can be used to 1) prevent OUD through limiting initial opioid prescriptions and 2) reduce pain in patients at low risk of developing OUD.

Keywords: precision medicine, personalized medicine, opioid, pain management, opioid use disorder, clinical utility, patient outcomes

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