| Title |
Observational study to calculate addictive risk to opioids: a validation study of a predictive algorithm to evaluate opioid use disorder
|
|---|---|
| Published in |
Pharmacogenomics and Personalized Medicine, May 2017
|
| DOI | 10.2147/pgpm.s123376 |
| Pubmed ID | |
| Authors |
Ashley Brenton, Steven Richeimer, Maneesh Sharma, Chee Lee, Svetlana Kantorovich, John Blanchard, Brian Meshkin |
| Abstract |
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. 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). 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%. 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. |
X Demographics
The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 43% |
| Japan | 1 | 14% |
| Unknown | 3 | 43% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 43% |
| Scientists | 2 | 29% |
| Science communicators (journalists, bloggers, editors) | 1 | 14% |
| Practitioners (doctors, other healthcare professionals) | 1 | 14% |
Mendeley readers
The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 36 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 17% |
| Researcher | 5 | 14% |
| Student > Doctoral Student | 4 | 11% |
| Student > Bachelor | 3 | 8% |
| Professor > Associate Professor | 3 | 8% |
| Other | 7 | 19% |
| Unknown | 8 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 5 | 14% |
| Nursing and Health Professions | 4 | 11% |
| Pharmacology, Toxicology and Pharmaceutical Science | 3 | 8% |
| Psychology | 3 | 8% |
| Biochemistry, Genetics and Molecular Biology | 2 | 6% |
| Other | 8 | 22% |
| Unknown | 11 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 August 2018.
All research outputs
#6,396,345
of 25,748,735 outputs
Outputs from Pharmacogenomics and Personalized Medicine
#1
of 1 outputs
Outputs of similar age
#92,272
of 325,543 outputs
Outputs of similar age from Pharmacogenomics and Personalized Medicine
#1
of 1 outputs
Altmetric has tracked 25,748,735 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.8. This one scored the same or higher as 0 of them.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 325,543 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them