Health care providers need better algorithms to predict opioid dependence, especially among Medicaid patients, according to new research from Stanford University and Gainwell.
Stanford researchers teamed up with health technology company Gainwell to use the company’s datasets of 180,000 de-identified Medicaid claims from six states (three in the Southeast, two in the West, and one in the Midwest). Their study revealed that among patients who had never taken opioids, 30% developed opioid dependence after their first prescription. Learning was published last week in PLOS Digital Health.
The study shows that a patient’s first experience receiving an opioid prescription is a crucial factor that can lead to opiate addiction, Tina Hernandez-Bussard, a professor at Stanford University School of Medicine and lead author of the study, said in an interview. Prescription quantity and duration are the two most significant predictors of opiate dependence, she added.
There are also some opioids that are more associated with addiction, according to the study. Prescriptions for tramadol, often sold under the brand name ConZip or Ultram, and long-acting oxycodone were found to have the highest risk of addiction.
“The risk factors aren’t always intuitive because I think intuitively a lot of people would think that shorter-acting opioids would be more prone to abuse,” said Gary Cole, Gainwell’s chief medical officer. “That’s kind of how we got to the opioid crisis. When the long-acting ones were first marketed, they were supposed to be less prone to abuse.
This finding is also interesting because tramadol has historically been touted as a “safe” opioid that may be less addictive than others, Hernandez-Bussard pointed out. Neither Sanofi nor Janssen — both known makers of the drug — responded to requests for comment.
Both Hernandez-Boussard and Call agreed that the goal of this research is to advance precision medicine by considering what risk prediction algorithms can be applied to opioid prescribing in the future, particularly among vulnerable Medicaid populations.
“Most of the opioid studies we see published are from privately insured patients at academic medical centers who may have different needs and different levels of risk,” Hernandez-Bussard said. “Medicaid is a really vulnerable population where we really don’t know if we should be treating them differently for pain management.”
Gainwell will use the study’s findings to develop new care management products and predictive modeling to predict opiate addiction risk, Kahl said. The company designed these algorithms to account for the social determinants of health, aiming to identify opioid-naïve patients who are at higher risk of opioid addiction.
Once the tools are developed, Gainwell hopes they will help providers become more aware of the possible consequences of long-acting or high-volume opioid prescriptions, potentially leading them to rethink the types of opioids they prescribe to non-adherent patients. opioids in Medicaid . These risk predictors can also be used to flag patients who may need educational information about the dangers of opiate addiction and how to safely stick to their treatment plan, Kahl said.
Gainwell’s development of these algorithms comes as the opioid crisis worsens — overdoses are now the leading cause of injury-related death in the US. In the 12-month period ending in April 2021, more than 100,000 Americans died of drug overdoses, according to data from the Centers for Disease Control and Prevention. Of those deaths, about three-quarters are opioid-related.
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