UNLABELLED: The qualities of chronic neuropathic pain (NeP) may be informative about the different mechanisms of pain. We previously developed a 2-factor model of NeP that described an underlying structure among sensory descriptors on the Short-Form McGill Pain Questionnaire. The goal of this study was to confirm the correlated 2-factor model of NeP. Individual descriptive scores from the Short-Form McGill Pain Questionnaire were analyzed. Confirmatory factor analysis was used to test a correlated 2-factor model. Factor 1 (stabbing pain) was characterized by high loadings on stabbing, sharp, and shooting sensory items; factor 2 (heavy pain) was characterized by high loadings on heavy, gnawing, and aching items. Results of the confirmatory factor analysis strongly supported the correlated 2-factor model. PERSPECTIVE: This article validates a model that describes the qualities of neuropathic pain associated with diabetic peripheral neuropathy and postherpetic neuralgia. These data suggest that specific pain qualities may be associated with pain mechanisms or may be useful for predicting treatment response.

BACKGROUND: A patient s response to treatment may be influenced by the expectations that the patient has before initiating treatment. In the context of clinical trials, the influence of participant expectancy may blur the distinction between real and sham treatments, reducing statistical power to detect specific treatment effects. There is therefore a need for a tool that prospectively predicts expectancy effects on treatment outcomes across a wide range of treatment modalities. PURPOSE: To help assess expectancy effects, we created the Stanford Expectations of Treatment Scale (SETS): an instrument for measuring positive and negative treatment expectancies. Internal reliability of the instrument was tested in Study 1. Criterion validity of the instrument (convergent, discriminant, and predictive) was assessed in Studies 2 and 3. METHODS: The instrument was developed using 200 participants in Study 1. Reliability and validity assessments were made with an additional 423 participants in Studies 2 and 3. RESULTS: The final six-item SETS contains two subscales: positive expectancy (α = 0.81-0.88) and negative expectancy (α = 0.81-0.86). The subscales predict a significant amount of outcome variance (between 12% and 18%) in patients receiving surgical and pain interventions. The SETS is simple to administer, score, and interpret. CONCLUSION: The SETS may be used in clinical trials to improve statistical sensitivity for detecting treatment differences or in clinical settings to identify patients with poor treatment expectancies.

BACKGROUND: Determinants of the duration of opioid use after surgery have not been reported. We hypothesized that both preoperative psychological distress and substance abuse would predict more prolonged opioid use after surgery. METHODS: Between January 2007 and April 2009, a prospective, longitudinal inception cohort study enrolled 109 of 134 consecutively approached patients undergoing mastectomy, lumpectomy, thoracotomy, total knee replacement, or total hip replacement. We measured preoperative psychological distress and substance use, and then measured the daily use of opioids until patients reported the cessation of both opioid consumption and pain. The primary end point was time to opioid cessation. All analyses were controlled for the type of surgery done. RESULTS: Overall, 6% of patients continued on new opioids 150 days after surgery. Preoperative prescribed opioid use, depressive symptoms, and increased self-perceived risk of addiction were each independently associated with more prolonged opioid use. Preoperative prescribed opioid use was associated with a 73% (95% confidence interval [CI] 0.51%-87%) reduction in the rate of opioid cessation after surgery (P = 0.0009). Additionally, each 1-point increase (on a 4-point scale) of self-perceived risk of addiction was associated with a 53% (95% CI 23%-71%) reduction in the rate of opioid cessation (P = 0.003). Independent of preoperative opioid use and self-perceived risk of addiction, each 10-point increase on a preoperative Beck Depression Inventory II was associated with a 42% (95% CI 18%-58%) reduction in the rate of opioid cessation (P = 0.002). The variance in the duration of postoperative opioid use was better predicted by preoperative prescribed opioid use, self-perceived risk of addiction, and depressive symptoms than postoperative pain duration or severity. CONCLUSIONS: Preoperative factors, including legitimate prescribed opioid use, self-perceived risk of addiction, and depressive symptoms each independently predicted more prolonged opioid use after surgery. Each of these factors was a better predictor of prolonged opioid use than postoperative pain duration or severity.

Current views recognize the brain as playing a pivotal role in the arising and maintenance of pain experience. Real-time fMRI (rtfMRI) feedback is a potential tool for pain modulation that directly targets the brain with the goal of restoring regulatory function. Though still relatively new, rtfMRI is a rapidly developing technology that has evolved in the last 15 years from simple proof of concept experiments to demonstrations of learned control of single and multiple brain areas. Numerous studies indicate rtfMRI feedback assisted control over specific brain areas may have applications including mood regulation, language processing, neurorehabilitation in stroke, enhancement of perception and learning, and pain management. We discuss in detail earlier work from our lab in which rtfMRI feedback was used to train both healthy controls and chronic pain patients to modulate anterior cingulate cortex (ACC) activation for the purposes of altering pain experience. Both groups improved in their ability to control ACC activation and modulate their pain with rtfMRI feedback training. Furthermore, the degree to which participants were able to modulate their pain correlated with the degree of control over ACC activation. We additionally review current advances in rtfMRI feedback, such as real-time pattern classification, that bring the technology closer to more comprehensive control over neural function. Finally, remaining methodological questions concerning the further development of rtfMRI feedback and its implications for the future of pain research are also discussed.