A patient's tolerance for risky decisions can predict chances of opioid relapse

A patient's tolerance for risky decisions can predict chances of opioid relapse

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Reviewed by Kate Anderton, B.Sc. (Editor)Dec 8 2019

People in treatment for opioid addiction are more likely to relapse when they become more tolerant of risks, according to a study by Rutgers and other institutions. The findings can help clinicians better predict which patients are most vulnerable.

In the study, published in JAMA Psychiatry, researchers followed 70 people during their first seven months of treatment for opioid addiction - the period associated with the highest relapse and overdose risk.

Forty-six percent returned to opioid use during that time. Most relapses occurred when patients exhibited a strong tolerance for risk-taking in situations where the risk associated with these decisions was not fully knowable, according to their performance in a computer game created for the study.

According to the National Institute on Drug Abuse, the relapse rate for substance use disorders is estimated to be between 40 percent and 60 percent.

Although it is well known that people addicted to opioids cycle through periods of abstinence and use, we lack the tools needed to prospectively identify when these transitions are more likely to occur. Here, given that opioid use during treatment is quite risky, we wanted to examine whether a patient's tolerance for risky decisions is informative about their vulnerability to relapse."

Anna Konova, assistant professor at Rutgers University Behavioral Health Care and Rutgers Robert Wood Johnson Medical School, and a faculty member in the Brain Health Institute

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Each patient completed up to 15 study visits over seven months, during which they had an opportunity to play the computer game for financial rewards. The computer game required patients to make decisions that involved two types of risk: Known risk, in which they had complete information on the likelihood of a decision's outcome to lead to reward; and ambiguous risk, in which they did not have full information on the possible outcomes.

The researchers measured the computer test results against clinical assessments of the patient's anxiety, craving, withdrawal and nonadherence to treatment. Opioid use was determined by random urine tests and self-reporting.

"Used in conjunction with clinical assessments, the computer model can be an important risk calculator, allowing clinicians in large, but short-staffed, treatment centers to allocate appropriate attention to those at greater risk for relapse and treatment failure," said Konova. "The goal is to eventually create a mobile app based on the game that people can play remotely, which could convey information about relapse risk in real time to the patient, clinician or caretaker."

This knowledge will allow clinicians to monitor vulnerable patients for changes that might affect their short-term and long-term vulnerability to relapse.

Source:

Rutgers University