New research tested the functionality of an algorithm that measures opioid use disorder (OUD) risk. The study was published in JAMA Network Open.
The team, led by Christal Davis, PhD, and Henry Kranzler, MD, studied “15 genetic variants from an algorithm meant to predict OUD risk that was recently granted pre-marketing approval by the Food and Drug Administration,” according to a Perelman School of Medicine at the University of Pennsylvania release.
Researchers found that the algorithm could generate false results, which could lead to possible opioid addiction in patients. It could also lead to certain patients being denied opioids.
The release compared the results to a “coin toss.” 47 out of 100 case results were false. “The case-control study drew upon health record data from more than 450,000 participants with opioid exposure in the Million Veteran Program (MVP), including more than 33,000 individuals with OUD. It showed that the 15 single nucleotide variants (SNVs) purported to predict OUD risk were not useful in identifying OUD, as they had high rates of both false negative and false positive results.”
Perelman School of Medicine at the University of Pennsylvania release on Newswise
