Proove Opioid Risk

  • Opioids are one of the most heavily prescribed and addictive drugs in the American healthcare system. It is estimated that 20-30% of patients on opioids misuse them and ~10% of patients become addicted to them1. In recent years, the consumption of opioid medication has increased 300%, and deaths associated with opioid poisoning more than tripled2.
  • It is clear from these staggering numbers that there is an unmet need to reliably identify, prevent, and treat opioid addiction in people with pain. The guidelines from the American Pain Society and American Academy of Pain Medicine (APS, AAPM) emphasize that every patient being considered for opiate therapy should have an evaluation that stratifies their risk for adverse response to opiates3. The recommendation that patients be evaluated for risk of addiction prior to initiation of therapy is also present in a number of other guidelines including the National Opiate Use Guideline Group (NOUGG)4 and the Veterans Administration/Department of Defense5.
  • However, currently available screening tools validated for the assessment of potential opioid addiction, such as Opioid Risk Tool (ORT) and the Screener and Opioid Assessment for Pain Patients (SOAPP®-R)6-8 are self-reported and influenced by trust issues and memory bias. This gives physicians a 50% chance of accurately predicting the development of opioid use disorder (OUD) and abuse9.
  • Therefore, the CMS Opioid Misuse Strategy released in January 2017 indicated that “data that can help identify drug seeking behaviors, inappropriate prescribing, and drug diversion must be utilized to minimize risk” and wrote in Objective 1-2 to “develop additional tools for states, beneficiaries, providers, and other stakeholders to use opioids appropriately”. The value of more accurately determining the risk of opioid abuse can lead to better selection and utilization of urine toxicology screens, opioid therapy agreements, abuse-deterrent opioids, and other interventions for high-risk patients10, or even selection of an alternative therapy than opiates3 thus dramatically reducing the possibility of opioid abuse or addiction.

  1. Vowles KE et al, Rates of opioid misuse, abuse, and addiction in chronic pain: a systematic review and data synthesis, Pain, 156 (2015) 569-576.
  2. CDC. Opioids drive continued increase in drug overdose deaths: Centers for Disease Control and Prevention; 2013 [2014]. Available from:
  3. Chou R et al, Clinical guidelines for the use of chronic opioid therapy in chronic noncancer pain, J Pain (2009), 10(2): 113-130.
  4. National Opiates Use Guideline Group, Canadian Guideline for Safe and Effective Use of Opioids for Chronic Non-Cancer Pain,, last accessed 9/5/15.
  5. VA/DoD Evidence Based Practice: Management of Opioid therapy for chronic pain, May 2010.
  6. Webster LR, Webster RM. Predicting aberrant behaviors in opioid-treated patients: preliminary validation of the Opioid Risk Tool. Pain Med 2005;6(6):432-42
  7. Ma JD, Horton JM, Hwang M, Atayee RS, Roeland EJ. A single-center, retrospective analysis evaluating the utilization of the opioid risk tool in opioid-treated cancer patients. J Pain Palliat Care Pharmacother 2014;28(1):4-9
  8. Butler SF, Budman SH, Fernandez KC, Fanciullo GJ, Jamison RN. Cross-Validation of a Screener to Predict Opioid Misuse in Chronic Pain Patients (SOAPP-R). J Addict Med 2009;3(2):66-73
  9. Bronstein K, Passik S, Munitz L, Leider H. Can clinicians accurately predict which patients are misusing their medications? J Pain 2011;12(4): P3
  10. Jamison RN et al, Assessment and treatment of abuse risk in opioid prescribing for chronic pain, Pain Res Treatment (2011), doi: 10.1155/2011/941808

  • The Proove Opioid Risk Profile offers physicians a more objective alternative to existing assessment tools used to evaluate patients for opioid therapy. It is based in part on genetic information which cannot be manipulated, and powered by an evidence-based algorithm that takes into account all major risk factors found to contribute to risk of aberrant behavior with opioid use.
  • Medical and scientific communities are in agreement that genetics impact the risk of opioid abuse, misuse, and addiction. The American Society of Addiction Medicine (ASAM) and the National Institute of Drug Abuse (NIDA) conclude that 50% of addiction is attributable to genetic factors. This fact alone suggests that genetic testing should be incorporated into an accurate risk profile for the use of medical professionals. Official Disability Guidelines, Medical Treatment Utilization Schedule (MTUS), and American College of Environmental Medicine (ACOEM), support the use of genetic testing by physicians as well. The MTUS guidelines, adapted from the Official Disability Guidelines (ODG) and adopted by 31 states, emphasizes genetics as a significant contributing factor in opioid risk and clearly indicates that a patient’s genetics should be evaluated. Recommendations involve “the physician tailor[ing] the medications and dosages to the individual, taking into consideration patient-specific variables.
  • A pharmacogenetics approach utilizing genetic testing improves the ability to identify opioid use disorder, as genes in the mesocorticolimbic system have been implicated in drug dependence and addiction in numerous scientific peer-reviewed studies. This information needs to be weighed with other clinical measures.


The Proove Opioid Risk algorithm was shown to successfully predict Opioid Use Disorder (OUD) in a discovery and 3 replication cohort studies (OSCAR) using clinical assessments and genetic markers, as shown in the table below. The algorithm relies on both genetic and phenotypic factors, with odds ratios of belonging to the high risk category ranging from 7.8 to 1600. These studies confirm the validity and replication validity of the algorithm in predicting OUD.

OSCAR 1: Discovery OSCAR 2: Validation 1, in Chronic Pain OSCAR 3: Validation 2, in Chronic Pain OSCAR 4: Validation 3, in Addiction
Participants 302 (80 OUD; 222 control) 471 (128 OUD; 343 control) 900 (258 OUD; 650 control) 187 (94 OUD, 93 control)
Sites 28 31 24 1
Algorithm performance PPV 100%
NPV 95.5%
OR (low) 0.16
OR (mod) 6.3
OR (high) 1600
PPV 79.8%
NPV 92.4%
OR (low) 0.16
OR (mod) 0.24
OR (high) 28.28
PLR (mod) 1.02-3.79
NLR (mod) 0.70-0.50
PLR (high) 5.00-17.64
NLR (high) 0.50-0.93
OR (mod) 1.5-7.6
OR (high) 7.8-25.7
RR (mod) 1.7
RR (high) 11.7
PPV 100%
NPV 95.4%
Receiver Operating Characteristics AUC 0.939 AUC 0.865 AUC 0.757 AUC 0.967

  • OSCAR 1: Onojighofia T et al. Observational Study to Calculate Addictive Risk (O.S.C.A.R.) to Opioids: A Pilot Study to Evaluate a Predictive Algorithm of Genetic and Clinical Data for Prescription Opioid Abuse. J Opioid Management. Under Review.
  • OSCAR 2: Clinical Validity of a Predictive Algorithm of Opioid Use Disorder: A Validation Study of the Proove Opioid Risk Algorithm. Postgraduate Medicine. Under Review.
  • OSCAR 3: Brenton et al. Observational Study to Calculate Addictive Risk (OSCAR 3) to Opioids: A Validation Study of a Predictive Algorithm to Evaluate Opioid Use Disorder. Pharmacogenetics and Personalized Medicine. In press.
  • OSCAR 4:Farah et al. Pilot study to evaluate a predictive algorithm that detects aberrant use of opioids in an addiction treatment center. Journal of Addiction Research and Therapy. Under Review.

  • The utility of the Proove Opioid Risk profile was evaluated in 2 studies, one including physicians in multiple specialties (n=5315, 76 clinics) and a second focused specifically in primary care (n=1822, 18 clinics). The POR was rated as beneficial for decision-making and patient improvement : 90% of all clinicians surveyed reported some benefit to their patient care, with 27% indicating the test provided significant benefit.

    • Lewis et al. An Observational Study to Evaluate the Clinical Utility of a Predictive Algorithm to Detect Opioid Use Disorder in the Clinical Management of Pain. Future Medicine: Personalized Medicine. Submitted
    • Lee et al. An Observational Study to Evaluate the Clinical Utility of a Predictive Algorithm to Detect Opioid Use Disorder in a Primary Care Setting. Annals of Family Medicine. Submitted
    • Lee et al. An Observational Study to Evaluate the Clinical Utility of a Predictive Algorithm to Detect Opioid Use Disorder in a Primary Care Setting. International Journal of General Medicine. Submitted

  • The utility of the profile varied based on clinician specialty, category of risk reported, and which actions were taken based on the results of the test. The benefit to patient care was greater if physicians discontinued or initiated an opioid prescription, made a change to an opioid prescription or dosage, advised another provider to make changes in the patient’s prescriptions, and/or used the results to verify and document their medical regimen with more confidence. The benefit of the profile to patient care was also reported to be greater in high risk than low or moderate risk patients.
    Decision n % of Guided P-value OR
    Decreased total opioid does or frequency 23 2.7 2.58×10-5* 15.2
    Changed opioid prescribed 108 12.5 1.64×10-5* 5.2
    Increased total opioid dose or frequency 25 2.9 2.66×10-4* 4.9
    Advised another provider to make changes in this patient’s prescriptions 45 5.2 5.15×10-4* 2.9
    Started an opioid rotation 13 1.5 0.55 1.4
    Switched from an opioid to an non-opioid pain medication 19 2.2 0.62 1.3
    Spent more time with the patient 846 97.9 4.99×10-3* 0.5

    aAdjusted model: age + gender + race + specialty

    (n=5,315 patients)
    Count “Yes” Percent “Yes” Avg. Rating Yes/No ORa P-valuea
    No changes were implemented (i.e. not guided) 3,242 61.0 3.1/3.9 0.31 7.34 x10-107 ***

    aAdjusted model: age + gender + race + specialty

    (n=2,595 patients (guided only))
    Count “Yes” Percent “Yes” Avg. Rating Yes/No ORa P-valuea
    Confidence in medical regimen 1,852 71.4 4.0/3.7 2 1.81 x10-15 ***
    Discontinued opioids 78 3 4.4/3.9 3.6 1.17 x10-6 ***
    Changed opioid or dosage 455 17.5 4.2/3.9 1.66 1.39 x 10-6 ***
    Advised another provider 66 2.5 4.0/3.9 2.15 3.16 x10-3 ***
    Initiated opioid 40 1.5 4.3/3.9 2.13 0.019 *
    Changed urine toxicology test frequency 28 1.1 4.3/3.9 2.14 0.059 trending
    Spent more time with patient 1,769 68.1 4.0/3.9 1.01 0.92
Protein Name Gene SNP Marker Associated Neuro-Psychiatric Disorders
Catechol-O-Methyltransferase COMT rs4680 Alcohol & Drug Abuse 1, 2
Anxiety 3
Dopamine Beta-Hydroxylase DBH rs1611115 Cocaine Addiction5, 6
Dopamine D1 Receptor DRD1 rs4532 Depression9
Heroin Addiction10
Ankyrin Repeat and Kinase Domain Containing 1/ Dopamine Receptor D2 ANKK1/DRD2 rs1800497 Alcohol & Cocaine Dependence11
Dopamine D4 Receptor DRD4 rs3758653 Anxiety12, 13
Dopamine Transporter SLC6A3 DAT rs27072 Methamphetamine Addiction14
Gamma Aminoburyric Acid Receptor A, gamma2 subunit GABRG2 rs211014 Alcohol Abuse15
Opioid Receptor, Kappa 1 OPRK1 rs1051660 Mood Disorders16
Alcohol Dependence17
Methylenetetrahydrofolate Reductase MTHFR rs1801133 Bipolar Disorder, Depression18
Opioid Receptor, Mu 1 OPRM1 rs1799971 Heroin Addiction19
Serotonin Receptor 2A HTR2A rs7997012 Drug Abuse1
Phenotypic Traits Risk Factors
Age 16-45 years old 21, 22
Personal history Mental health disorders1, 23-25
Alcoholism21, 29
Illicit drug use30, 31
Prescription drug abuse32

  1. Tassin, J.P., Uncoupling between noradrenergic and serotonergic neurons as a molecular basis of stable changes in behavior induced by repeated drugs of abuse. Biochem Pharmaco, 2008. 75(1): p. 85-97.
  2. Kranzler, H.R., V. Modesto-Lowe , and J. Van Kirk, Naltrexone vs. nefazodone for treatment of alcohol dependence. A placebo-controlled trial. Neuropsychopharmacology 2000. 22(5): p. 493-503.
  3. Koks, S. and E. Vasar, Deramciclane (Egis). Curr Opin Investig Drugs 2002. 3(2): p. 289-94.
  4. Wade, A.G., et al., Citalopram plus low-dose pipamperone versus citalopram plus placebo in patients with major depressive disorder: an 8-week, double-blind, randomized study on magnitude and timing of clinical response. Psychol Med, 2011. 41(10): p. 2089-97.
  5. Brousse, G., et al., Could the inter-individual variability in cocaine-induced psychotic effects influence the development of cocaine addiction? Towards a new pharmacogenetic approach to addictions. Med Hypotheses, 2010. 75(6): p. 600-4.
  6. Fernandez-Castillo, N., et al., Association study between the DAT1, DBH and DRD2 genes and cocaine dependence in a Spanish sample. Psychiatr Genet, 2010. 20(6): p. 317-20.
  7. Ji, N., et al., Dopamine beta-hydroxylase gene associates with stroop color-word task performance in Han Chinese children with attention deficit/hyperactivity disorder. Am J Med Genet B Neuropsychiatr Genet, 2011. 156B(6): p. 730-6.
  8. Cubells, J.F., et al., Linkage analysis of plasma dopamine beta-hydroxylase activity in families of patients with schizophrenia. Hum Genet, 2011. 130(5): p. 635-43.
  9. Nyman, E.S., et al., Interaction of early environment, gender and genes of monoamine neurotransmission in the aetiology of depression in a large population-based Finnish birth cohort. BMJ Open, 2011. 1(1): p. e000087.
  10. Le Foll, B., et al., Genetics of dopamine receptors and drug addiction: a comprehensive review. Behav Pharmacol, 2009. 20(1): p. 1-17.
  11. Noble, E.P., D2 dopamine receptor gene in psychiatric and neurologic disorders and its phenotypes. Am J Med Genet B Neuropsychiatr Genet, 2003. 116b(1): p. 103-25.
  12. Cao, B.J. and R.J. Rodgers, Dopamine D4 receptor and anxiety: behavioural profiles of clozapine, L-745,870 and L-741,742 in the mouse plus-maze. Eur J Pharmacol 1997. 335(2-3): p. 117-25.
  13. Navarro, J.F., et al., Effects of L-741,741, a selective dopamine receptor antagonist, on anxiety tested in the elevated plus-maze in mice. Methods Find Exp Clin Pharmacol 2003. 25(1): p. 45-7.
  14. Gross, N.B., P.C. Duncker, and J.F. Marshall, Striatal dopamine D1 and D2 receptors: widespread influences on methamphetamine-induced dopamine and serotonin neurotoxicity. Synapse, 2011. 65(11): p. 1144-55.
  15. Han, D.H., et al., Craving for alcohol and food during treatment for alcohol dependence: modulation by T allele of 1519T>C GABAAalpha6. Alcohol Clin Exp Res, 2008. 32(9): p. 1593-9.
  16. Carlezon, W.A., Jr., et al., Kappa-opioid ligands in the study and treatment of mood disorders. Pharmacol Ther, 2009. 123(3): p. 334-43.
  17. Gelernter, J., et al., Opioid receptor gene (OPRM1, OPRK1, and OPRD1) variants and response to naltrexone treatment for alcohol dependence: results from the VA Cooperative Study. Alcohol Clin Exp Res, 2007. 31(4): p. 555-63.
  18. Peerbooms, O.L., et al., Meta-analysis of MTHFR gene variants in schizophrenia, bipolar disorder and unipolar depressive disorder: evidence for a common genetic vulnerability? Brain Behav Immun, 2011. 25(8): p. 1530-43.
  19. Nelson, E.C., et al., Association of OPRD1 polymorphisms with heroin dependence in a large case-control series. Addict Biol, 2014. 19(1): p. 111-21.
  20. Celada, P., et al., The therapeutic role of 5-HT1A and 5-HT2A receptors in depression. J Psychiatry Neurosci, 2004. 29(4): p. 252-65.
  21. Lauschke, V.M. and M. Ingelman-Sundberg, Requirements for comprehensive pharmacogenetic genotyping platforms. Pharmacogenomics, 2016. 17(8): p. 917-24.
  22. Khazaal, Y., et al., Affective temperaments in alcohol and opiate addictions. Psychiatr Q, 2013. 84(4): p. 429-38.
  23. Compton, W.M. and N.D. Volkow, Abuse of prescription drugs and the risk of addiction. Drug Alcohol Depend, 2006. 83 Suppl 1: p. S4-7.
  24. Daigre, C., et al., Attention deficit hyperactivity disorder in cocaine-dependent adults: a psychiatric comorbidity analysis. Am J Addict, 2013. 22(5): p. 466-73.
  25. Fontenelle, L.F., et al., Obsessive-compulsive disorder, impulse control disorders and drug addiction: common features and potential treatments. Drugs, 2011. 71(7): p. 827-40.
  26. Manchikanti, L., et al., Psychological factors as predictors of opioid abuse and illicit drug use in chronic pain patients. J Opioid Manag, 2007. 3(2): p. 89-100.
  27. Brooner, R.K., et al., Psychiatric and substance use comorbidity among treatment-seeking opioid abusers. Arch Gen Psychiatry, 1997. 54(1): p. 71-80.
  28. Burke, J.D., Jr., K.C. Burke, and D.S. Rae, Increased rates of drug abuse and dependence after onset of mood or anxiety disorders in adolescence. Hosp Community Psychiatry, 1994. 45(5): p. 451-5.
  29. Cleland, C.M., ., et al., Age differences in heroin and prescription opioid abuse among enrolees into opioid treatment programs. Subst Abuse Treat Prev Policy, 2011. 6: p. 11.
  30. Sproule, B., et al., Changing patterns in opioid addiction: characterizing users of oxycodone and other opioids. Can Fam Physician, 2009. 55(1): p. 68-69.
  31. Edlund, M.J., et al., Risk factors for clinically recognized opioid abuse and dependence among veterans using opioids for chronic non-cancer pain. Pain, 2007. 129(3): p. 355-62.
  32. Institute of Medicine Committee on Advancing Pain Research, C.a.E., The National Academies Collection: Reports funded by National Institutes of Health, in Relieving Pain in America: A Blueprint for Transforming Prevention, Care, Education, and Research. National Academies Press (US), 2011.