Computational Markers of Risky Decision-making for Identification of Temporal Windows of Vulnerability to Opioid Use in a Real-world Clinical Setting

Abstract

Importance: Opioid addiction is a major public health problem. Despite availability of evidence-based treatments, relapse and dropout are common outcomes. Efforts aimed at identifying reuse risk and gaining more precise understanding of the mechanisms conferring reuse vulnerability are needed.

Objective: To use tools from computational psychiatry and decision neuroscience to identify changes in decision-making processes preceding opioid reuse.

Design, setting, and participants: A cohort of individuals with opioid use disorder were studied longitudinally at a community-based treatment setting for up to 7 months (1-15 sessions per person). At each session, patients completed a risky decision-making task amenable to computational modeling and standard clinical assessments. Time-lagged mixed-effects logistic regression analyses were used to assess the likelihood of opioid use between sessions (t to t + 1; within the subsequent 1-4 weeks) from data acquired at the current session (t). A cohort of control participants completed similar procedures (1-5 sessions per person), serving both as a baseline comparison group and an independent sample in which to assess measurement test-retest reliability. Data were analyzed between January 1, 2018, and September 5, 2019.

Main outcomes and measures: Two individual model-based behavioral markers were derived from the task completed at each session, capturing a participant's current tolerance of known risks and ambiguity (partially unknown risks). Current anxiety, craving, withdrawal, and nonadherence were assessed via interview and clinic records. Opioid use was ascertained from random urine toxicology tests and self-reports.

Results: Seventy patients (mean [SE] age, 44.7 [1.3] years; 12 women and 58 men [82.9% male]) and 55 control participants (mean [SE] age, 42.4 [1.5] years; 13 women and 42 men [76.4% male]) were included. Of the 552 sessions completed with patients (mean [SE], 7.89 [0.59] sessions per person), 252 (45.7%) directly preceded opioid use events (mean [SE], 3.60 [0.44] sessions per person). From the task parameters, only ambiguity tolerance was significantly associated with increased odds of prospective opioid use (adjusted odds ratio, 1.37 [95% CI, 1.07-1.76]), indicating patients were more tolerant specifically of ambiguous risks prior to these use events. The association of ambiguity tolerance with prospective use was independent of established clinical factors (adjusted odds ratio, 1.29 [95% CI, 1.01-1.65]; P = .04), such that a model combining these factors explained more variance in reuse risk. No significant differences in ambiguity tolerance were observed between patients and control participants, who completed 197 sessions (mean [SE], 3.58 [0.21] sessions per person); however, patients were more tolerant of known risks (B = 0.56 [95% CI, 0.05-1.07]).

Conclusions and relevance: Computational approaches can provide mechanistic insights about the cognitive factors underlying opioid reuse vulnerability and may hold promise for clinical use.

Figure 1.. Longitudinal Study Design and Procedures

Participants with opioid use disorder completed up to 15 study sessions over 7 months. Control participants completed up to 5 sessions. Each session was made up of the decision-making task and clinical assessments of anxiety, craving, withdrawal, and adherence to treatment. Illicit opioid use was ascertained by random urine toxicology tests and self-reports.

Figure 2.. Decision-making Task and Model-Derived Parameter Test-Retest Reliability

A, Example task trial sequence. In known-risk trials, participants chose between a guaranteed $5 and a lottery that was defined by an explicit probability (p) (either 25%, 50%, or 75%) of receiving $5 to $66 (v). In ambiguity trials, the lotteries were defined by a partially unknown probability (ambiguity level, A), where either 24%, 50%, or 74% of the probability information was occluded and thus unknown. In the example shown, the probability of receiving $66 is anywhere between 25% and 75%. In reality, the true underlying probability in ambiguity trials was fixed at 50%. At the end of the task, a single trial was selected at random and played out to determine a participant’s bonus for the session completed, either $5 (if the guaranteed option was chosen on the selected trial) or by playing the lottery, which involved drawing a chip from the corresponding lottery bag to earn either v or $0 instead. No outcomes were shown during the task. B, Both model-derived known-risk and ambiguity tolerance parameters exhibited good test-retest reliability in control participants tested up to 5 times approximately 1 week apart. C, The 2 parameters were largely uncorrelated with each other across sessions. ITI indicates intertrial interval.

Figure 3.. Time-Lagged Association With Prospective Opioid Use

A, Raw data showing the ambiguity tolerance and opioid-use trajectories of 2 participants with opioid use disorder, illustrating how sessions were parsed in the group-level time-lagged analysis assessing prospective opioid use. B, Standardized odds ratios from the decision-making parameters only model as factors associated with prospective opioid use and expected probability of opioid use as a function of ambiguity tolerance level (based on the fitted parameter estimates from Table 2), showing opioid use was more likely than not to occur when a patient’s ambiguity tolerance surpassed the level observed in control participants and prior to ambiguity neutrality. The blue line in A and B represents the mean ambiguity tolerance observed in control participants; the line where ambiguity tolerance equals 1 on the y-axis represents ambiguity neutrality. The dark gray zone in B, demarcates an anticipated high opioid use–risk state. C, Standardized odds ratios from the full model, including the decision-making parameters and time-varying clinical variables as factors associated with prospective opioid use (Table 2; combined model).