Proven by Science
Backed by Data
Peer-reviewed studies validate the accuracy, clinical utility, and real-world impact of the Proove Opioid Risk Test.
97%
Accuracy
42%
Average Pain Reduction
91%
Reported Less Pain
Opioid Risk Index (ORI) Distribution
How patients are grouped by risk level using the test.
Patient Distribution by Risk Category
Source: O.S.C.A.R. Validation Study, Pharmacogenomics and Personalized Medicine, 2017
What This Means
- ~60% of patients are at Low Risk — standard precautions apply.
- ~29% of patients are at Moderate Risk — they are 4x more likely to develop addiction than low-risk patients.
- ~11% of patients are at High Risk — they are 16x more likely to develop addiction than low-risk patients.
How Doctors Used the Test
Real-world results from over 5,000 patients.
What Doctors Did With the Results
Source: Clinical Utility Study, Journal of Pain Research, 2018
had a change
- Changed opioid prescription (24.5%)
- Started new patient on opioid (15%)
- Tapered patient off opioids (10%)
- Other decisions (50.5%)
Key Takeaway
Doctors used the test to guide treatment for more than half of patients.
1 in 4 actions were directly related to opioids — changing, starting, or stopping them.
91% of patients reported less pain, with an average drop from 6.7 to 3.9 on a 0-10 scale (42% reduction).
Peer-Reviewed Studies & Publications
Clinical Utility & Validation Studies
A Prospective, Longitudinal Study to Evaluate the Clinical Utility of a Predictive Algorithm That Detects Risk of Opioid Use Disorder
Brenton A, et al. J Pain Res. 2018;11:119-131.
In 5,397 patients across 100 clinics, physicians used the algorithm to guide treatment for over half of patients, and patients whose treatment was guided by results saw an average pain reduction of 3.4 points on a 1-10 scale.
Read Full StudyObservational Study to Calculate Addictive Risk to Opioids: A Validation Study of a Predictive Algorithm to Evaluate Opioid Use Disorder
Brenton A, et al. Pharmacogenomics Pers Med. 2017;10:187-195.
Validated the algorithm in 258 patients with diagnosed opioid use disorder and 650 controls, achieving 95.7% sensitivity in identifying high- and moderate-risk patients — sensitivity stayed above 95% even as OUD prevalence varied.
Read Full StudyAdding Genetic Testing to Evidence-Based Guidelines to Determine the Safest and Most Effective Chronic Pain Treatment for Injured Workers
Meshkin B, et al. Int J Biomed Sci. 2015;11(4):157-165.
Makes the case for incorporating genetic testing into evidence-based chronic pain treatment guidelines for injured workers, given the documented genetic contribution to both pain experience and addiction susceptibility.
Read Full StudyA Precision Medicine Approach to a Patient With Unresolved Pain Following Orthopedic Surgery: A Case Report
Gazzaniga D, Brenton A, Meshkin B. J Med Case Rep. 2017;11:50.
A patient previously labeled a “drug seeker” had a Proove Opioid Risk profile showing low addiction risk, correcting the mislabeling and leading clinicians to resolve her treatment complications by narrowing to a single opioid.
Read Full StudyEvaluation of a Predictive Algorithm that Detects Aberrant Use of Opioids in an Addiction Treatment Centre
Farah J, et al. J Addict Res Ther. 2017;8:2.
The average POR score of patients with diagnosed OUD was significantly greater than controls. The algorithm's ROC curve had an AUC of 0.967 — correctly categorizing patients with OUD nearly 97% of the time, with 98% sensitivity and 100% specificity.
Read Full StudyAn Observational Study of the Impact of Genetic Testing for Pain Perception in the Clinical Management of Chronic Non-Cancer Pain
Sharma M, Kantorovich S, Lee C, et al. J Psychiatr Res. 2017;89:65-72.
Based on genetic testing results, physicians adjusted treatment plans for 40% of patients. When medication changes were made, 72% of patients showed clinical improvement, and physicians believed the genetic test results were consistent with patient pain levels in 85% of cases.
Read Full StudyA Predictive Algorithm to Detect Opioid Use Disorder: What Is the Utility in a Primary Care Setting?
Lee C, Sharma M, Kantorovich S, Brenton A. Health Serv Res Manag Epidemiol. 2018;5.
The profile gave primary care physicians a useful tool to stratify risk of opioid use disorder, rated as beneficial for decision-making and patient improvement by most physicians surveyed — including greater clinical improvement reported for minority patients.
Read Full StudyValidation Study of a Predictive Algorithm to Evaluate Opioid Use Disorder in a Primary Care Setting
Sharma M, Lee C, Kantorovich S, et al. Health Serv Res Manag Epidemiol. 2017;4.
In a validation study of 452 patients diagnosed with OUD and 1,237 controls, the algorithm categorized high- and moderate-risk patients with 91.8% sensitivity — staying above 90% regardless of changes in OUD prevalence.
Read Full StudyReady to See the Evidence?
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