ArticlePharmacogenomics and personalized medicine2017
Observational study to calculate addictive risk to opioids: a validation study of a predictive algorithm to evaluate opioid use disorder.
Article in Pharmacogenomics and personalized medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictive Models for Identifying Adult Patients at High Risk of Developing Opioid-Related Harms: a Systematic Review.Drug safety · 2025Pooled it
- Substance use risk and controlled substances discussions in cancer care: Observations from audio-recorded patient-clinician encounters.Palliative & supportive care · 2026Article
- A Study of the MTHFR Gene Prevalence in a Rural Tennessee Opioid Use Disorder Treatment Center Population.International journal of environmental research and public health · 2022Article
- Pharmacogenetics: A Precision Medicine Approach to Combatting the Opioid Epidemic.Journal of the American College of Clinical Pharmacy : JACCP · 2022Article
- Prospective Prediction of Lapses in Opioid Use Disorder: Protocol for a Personal Sensing Study.JMIR research protocols · 2021Article
- Identification of a sex-stratified genetic algorithm for opioid addiction risk.The pharmacogenomics journal · 2021Article
- Opioids, Polypharmacy, and Drug Interactions: A Technological Paradigm Shift Is Needed to Ameliorate the Ongoing Opioid Epidemic.Pharmacy (Basel, Switzerland) · 2020Article
- Individual differences in human opioid abuse potential as observed in a human laboratory study.Drug and alcohol dependence · 2019Article
- On the utilization of polygenic risk scores for therapeutic targeting.PLoS genetics · 2019Review
- A prospective, longitudinal study to evaluate the clinical utility of a predictive algorithm that detects risk of opioid use disorder.Journal of pain research · 2018Article
- Validation Study of a Predictive Algorithm to Evaluate Opioid Use Disorder in a Primary Care Setting.Health services research and managerial epidemiologyArticle
- A Predictive Algorithm to Detect Opioid Use Disorder: What Is the Utility in a Primary Care Setting?Health services research and managerial epidemiologyArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOpioid 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
methodsThe 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%.
conclusionThe 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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Registered trials
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