Genetic Test and Predictive Algorithm Could Inform Prescriber Decisions
A new genetic test has the potential to aid clinicians in identifying patients at risk of addiction to opioids, according to the results of research led by Sherman Chang, PhD, vice president of research and development at AutoGenomics, Inc., Carlsbad, California. The team identified 16 genetic mutations associated with brain reward pathways, and designed a predictive algorithm that uses these variants to identify at-risk patients. PAINWeek faculty member Forest Tennant, MD, DrPH, who collaborated with AutoGenomics on this research, commented, “Test results show that many of the genetic mutations identified in this test panel-namely receptors and transporters--are present in most chronic pain patients and are helpful in identifying those subjects at risk for addiction. This test panel should become an indispensable tool in pain management and addiction risk assessment.”
Results of 2 independent clinical studies of the test panel and algorithm were presented earlier this week at the 69th AACC Annual Scientific Meeting & Clinical Lab Expo in San Diego. Both concluded that the genetic panel and predictive algorithm successfully differentiated between addicted and nonaddicted individuals. Study leader Keri Donaldson, MD, CEO at Prescient Medicine, stated, “The utility lies in identifying the at-risk population, if you can, before they get exposed to opioids. Using this type of testing in the normal workflow prior to elective surgeries--prior to patients getting that first exposure to opioids--that’s where this makes sense.”
Read more about the new screening approach, with links to the poster session abstracts.
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