Detecting and Predicting Risk for Prescription Opioid Misuse with an Innovative, Performance-Based Cognitive Measure of Attentional Bias in a Large Sample of Opioid-Treated Pain Patients

Background and Purpose: Prescription opioids, such as oxycodone, fentanyl and morphine, are common frontline treatments for chronic pain. Yet, a substantial subset of chronic pain patients misuse prescription opioids; meta-analyses indicate that approximately 25% of opioid-treated chronic pain patients exhibit opioid misuse behaviors that place them at risk for serious adverse consequences like overdose and addiction. This figure is likely an underestimate of opioid misuse, as patients may be reluctant to report due to fears of stigmatization, legal repercussions, and prescription limitations. Indeed, the sensitive nature of opioid misuse among chronic-pain patients calls for a valid means by which to assess the addictive drive toward opioids. The motivational drive to engage in opioid misuse involves unconscious neurocognitive processes. When chronic pain patients engage in recurrent opioid misuse, visual cues like a pill bottle or prescription slip may begin to implicitly signal relief and reward, and thereby automatically capture attention through the process of conditioning. This phenomenon, known as addiction attentional bias, has been demonstrated among other drug using populations and is associated with craving and poor treatment outcomes. Small pilot studies have provided preliminary evidence for a prescription opioid attentional bias among opioid misusers. Here we present findings from the first, large-scale study to test the hypothesis that relative to medication adherent patients, opioid-misusing chronic pain patients would exhibit a significantly greater opioid attentional bias (AB) that would in turn predict psychosocial treatment outcomes.

Methods: Chronic pain patients (N = 315) prescribed opioid analgesics for > 90 days were classified as opioid misusers (n = 186) or non-misusers (n = 128) according to a validated clinical cut-point on the Current Opioid Misuse Measure (COMM). Next, participants completed a dot probe task designed to measure opioid AB. In this cognitive task, participants are presented with pairs of opioid-related and neutral images side-by-side on a computer screen for 200 milliseconds, one of which is then replaced by a target probe. Participants press a key to indicate the location of the target probe, and reaction times are recorded. Attentional bias is evidenced by shorter reaction times to probes replacing opioid-related images relative to probes replacing neutral images. For a subsample of study participants (n = 53), baseline opioid AB was used to predict opioid misuse severity six months following psychosocial treatment.

Results: In support of our primary hypothesis, opioid misusers exhibited a significantly greater opioid AB than non-misusers, t(312) = 2.18, p = .03. Opioid AB predicted opioid misuse six months following treatment, β = .50, p = .0001, and remained a significant predictor even after controlling for baseline opioid misuse severity.

Conclusions and Implications: Findings demonstrate the presence of an opioid AB among chronic pain patients at risk for opioid misuse, and opioid AB predicted clinical outcomes six months following treatment. To our knowledge, this is the largest study to employ a cognitive task to detect risk for addictive behaviors. Social work researchers should consider using similar cognitive tasks to assess risk and protective factors in individuals with substance use disorders.