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

Farah, Jibril-Osman; Lee, Chee Khoon; Kantorovich, Svetlana; Smith, G. A.; Meshkin, Brian; Brenton, Ashley

doi:10.4172/2155-6105.1000312

Cited by 7 publications

(14 citation statements)

References 29 publications

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“…A previous discovery study demonstrated that the POR predicts risk of OUD with greater specificity than either the ORT or the SOAPP ® 25. This study confirms the utility of the POR, with an entirely new cohort of patients.…”

Section: Discussionsupporting

confidence: 78%

“…The POR algorithm is a patent-protected, validated measure of opioid risk 25. In short, it combines phenotypic and genotype information to calculate a risk score that correlates with high-, moderate-, or low-risk stratification of opioid dependence,25 such that a score of 1–11 is associated with low-risk, 12–23 with moderate-risk, and ≥24 with high-risk of OUD.…”

Section: Methodsmentioning

confidence: 99%

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Observational study to calculate addictive risk to opioids: a validation study of a predictive algorithm to evaluate opioid use disorder

Brenton

Richeimer

Sharma

et al. 2017

PGPM

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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.

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Section: Discussionsupporting

confidence: 78%

Section: Methodsmentioning

confidence: 99%

Observational study to calculate addictive risk to opioids: a validation study of a predictive algorithm to evaluate opioid use disorder

Brenton

Richeimer

Sharma

et al. 2017

PGPM

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“…Some of these gene panels and interpretative algorithms are available in the market from commercial vendors (48). While genetic approaches may be represented in the commercial marketplace as a panacea, we believe physicians should also be informed about the inherent predictive limitations in models from vendors of multi-gene panels.…”

Section: Further Research and Multi-gene Panelsmentioning

confidence: 99%

Fundamental Considerations for GeneticallyGuided Pain Management with Opioids Based on CYP2D6 and OPRM1 Polymorphisms

Ruaño

2018

Pain Phys

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Background: A major challenge for effective pharmacotherapy in pain management is to provide the drug best suited to the patient’s innate characteristics. Objective: The article illustrates pharmacogenetic principles to optimize treatments for patients and increase the likelihood of pain relief without dependence. Genetic variances are particularly relevant to opioid drugs used in pain control, and can now be harvested for predictive clinical decision support. Study Design: Clinically actionable polymorphisms in CYP2D6 (cytochrome p450 2D6) and OPRM1 (μ1 opioid receptor), the most important gene coding, respectively, for a metabolizing enzyme and receptor for opioids are reviewed, and functional effects described. Methods: Risk of dysfunction is calculated from the frequency of the alleles with null function for CYP2D6, and from the low function polymorphism for OPRM1. Integration of genetic variability was performed for 9 combinatorial scenarios for CYP2D6 and OPRM1. Each combination was quantified in frequency and classified for clinical impact. A rational and pharmacological basis for personalized pain management based on pharmacokinetic and pharmacodynamic modeling is extracted from the frequency of the combinations. Results: Patients can be classified in 3 broad risk categories for opioid side effects and dependence. Patients at high-risk with dysfunctional CYP2D6 or OPRM1 account for ~14% of the population and are best managed with non-opioids. Patients at medium risk with subnormal CYP2D6 or OPRM1 account for ~48% of the population and can be managed with dose monitoring. Patients at low risk with functional CYP2D6 and OPRM1 account for ~38% of the population and should be availed to opioid therapy. Limitations: Heuristic clinical decision support considerations are not validated yet by deployment in large clinical practices. Environmental modifiers such as other drugs and dietary supplements interact with innate characteristics to modify the genetic predictions. Conclusion: Through clinical decision support interpreting the genotyping data, drug choices and doses can then be tailored to provide safe and effective therapy for individual patients. This precision affords personalized medicine to be practiced in pain treatment. Genetic factors could help determine why some patients seem more vulnerable than others to opioid side effects and dependence. Key words: Pain management, opioids, CYP2D6, OPRM1, clinical decision support, pharmacokinetics, pharmacodynamics, pharmacogenetics, combinatorial genotypes

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“… 11 , 12 These screening assessments, however, are based completely on subjective information. Furthermore, three previous studies demonstrated that the algorithm described herein, referred to as “profile”, performed with greater sensitivity and specificity than either the ORT or the SOAPP-R. 13 – 15 …”

Section: Introductionmentioning

confidence: 82%

“…The improved sensitivity and specificity of the profile over other current screening measures stems from its unique algorithm that combines clinical risk factors with objective genotypic information. 13 – 15 The clinical validity of the profile has been examined in multiple clinical settings, including primary care, 14 addiction treatment centers, 15 pain clinics, 13 and orthopedic clinics, 13 among others. In these studies, the high accuracy of the profile, ranging from 76% to 97%, demonstrated its diagnostic potential as a predictor of risk to opioid abuse.…”

Section: Introductionmentioning

confidence: 99%

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

Brenton

Lee

Lewis

et al. 2018

JPR

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PurposeThe 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 methodsA 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.ResultsPhysicians 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.ConclusionPatients 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.

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

Cited by 7 publications

References 29 publications

Observational study to calculate addictive risk to opioids: a validation study of a predictive algorithm to evaluate opioid use disorder

Observational study to calculate addictive risk to opioids: a validation study of a predictive algorithm to evaluate opioid use disorder

Fundamental Considerations for GeneticallyGuided Pain Management with Opioids Based on CYP2D6 and OPRM1 Polymorphisms

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

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