Withdrawal catastrophizing scale: initial psychometric properties and implications for the study of opioid use disorder and hyperkatifeia.

Background: Discovery of modifiable factors influencing subjective withdrawal experience might advance opioid use disorder (OUD) research and precision treatment. This study explores one factor - withdrawal catastrophizing - a negative cognitive and emotional orientation toward withdrawal characterized by excessive fear, worry or inability to divert attention from withdrawal symptoms.Objectives: We define a novel concept - withdrawal catastrophizing - and present an initial evaluation of the Withdrawal Catastrophizing Scale (WCS).Methods: Prospective observational study (n = 122, 48.7% women). Factor structure (exploratory factor analysis) and internal consistency (Cronbach's α) were assessed. Predictive validity was tested via correlation between WCS and next-day subjective opiate withdrawal scale (SOWS) severity. The clinical salience of WCS was evaluated by correlation between WCS and withdrawal-motivated behaviors including risk taking, OUD maintenance, OUD treatment delay, history of leaving the hospital against medical advice and buprenorphine-precipitated withdrawal.Results: WCS was found to have a two-factor structure (distortion and despair), strong internal consistency (α = .901), and predictive validity - Greater withdrawal catastrophizing was associated with next-day SOWS (rs (99) = 0.237, p = .017). Withdrawal catastrophizing was also correlated with risk-taking behavior to relieve withdrawal (rs (119) = 0.357, p < .001); withdrawal-motivated OUD treatment avoidance (rs (119) = 0.421, p < .001), history of leaving the hospital against medical advice (rs (119) = 0.373, p < .001) and buprenorphine-precipitated withdrawal (rs (119) = 0.369, p < .001).Conclusion: This study provides first evidence of withdrawal catastrophizing as a clinically important phenomenon with implications for the future study and treatment of OUD.

Rapid, precise, and reliable measurement of delay discounting using a Bayesian learning algorithm.

Machine learning has the potential to facilitate the development of computational methods that improve the measurement of cognitive and mental functioning. In three populations (college students, patients with a substance use disorder, and Amazon Mechanical Turk workers), we evaluated one such method, Bayesian adaptive design optimization (ADO), in the area of delay discounting by comparing its test-retest reliability, precision, and efficiency with that of a conventional staircase method. In all three populations tested, the results showed that ADO led to 0.95 or higher test-retest reliability of the discounting rate within 10-20 trials (under 1-2 min of testing), captured approximately 10% more variance in test-retest reliability, was 3-5 times more precise, and was 3-8 times more efficient than the staircase method. The ADO methodology provides efficient and precise protocols for measuring individual differences in delay discounting.