An FDA-approved genetic test to help physicians predict addiction risk in adults before prescribing opioids is no more accurate than a coin toss, according to a new study. Researchers at the University of Pennsylvania’s Perelman School of Medicine analyzed a diverse sample of nearly half a million people exposed to opioids, out of which nearly 34,000 had an opioid use disorder. The results showed that the genetic variants the researchers say underpin the test were accurate in about 53 percent of the cases. “It’s no better than chance,” Dr. Henry Kranzler, a psychiatry professor and the director of the Center for Studies of Addiction at the school, who led the study, told Carmen. How it works: The AvertD by AutoGenomics test uses 15 genetic variants and machine learning to determine whether a patient who has never been prescribed opioids is at risk of developing addiction if they get opioid pain medication for up to 30 days to treat acute pain. The information from the test shouldn’t be used alone but as part of a complete clinical evaluation and risk assessment, the FDA said when it approved the test a year ago. But Kranzler said tens of thousands of genetic variants in a person’s genome could contribute to addiction, and the 15 the test focuses on aren’t sufficient to predict someone’s opioid addiction risk. Why it matters: The test could harm patients by hampering access to pain medication for those at low risk for addiction or giving a false sense of security to those at high risk, Kranzler said. Clinicians could better predict opioid use disorder risk using an individual’s age and sex than the 15 genetic variants, Kranzler and his colleagues wrote in the study. He also pointed to a questionnaire that can be used successfully to predict the risk in many patients. “The whole issue of genetics and addiction is really understudied, it’s underfunded by the [National Institutes of Health], it’s not considered as high a priority as it needs to be,” he said. The FDA and AutoGenomics didn’t respond to a request for comment. |