The journal of pain : official journal of the American Pain Society
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Acute postoperative pain remains a significant health care issue. Development of anatomically relevant animal models of postoperative pain, with improved predictive validity, would advance understanding of postoperative pain mechanisms and improve treatment outcomes. This study aimed to develop, characterize, and validate a rat model of acute postoperative pain associated with inguinal hernia repair based on the Lichtenstein inguinal hernia repair procedure (without hernia induction). We hypothesized that the surgery would result in reduced spontaneous locomotor activity, which would represent a pain-related phenotype. Postsurgical characterization involved extensive monitoring of home cage and open field locomotor activity, as well as mechanical hypersensitivity and assessment of c-Fos expression in the dorsal horn of the spinal cord. In pharmacologic validation studies, rats received morphine, carprofen, or paracetamol 1 hour before, and/or immediately after, surgery. Rats that underwent hernia repair surgery exhibited significantly lower horizontal and vertical activities in the home cage and open field in the early postsurgical period, compared with sham rats or rats that underwent skin incision only. Morphine, carprofen, and paracetamol attenuated the surgery-induced reductions in locomotor activity, to varying degrees. Surgery was associated with significantly increased c-Fos expression in the ipsilateral dorsal horn of the spinal cord, an effect attenuated by carprofen treatment. These results support the development and characterization of a novel, anatomically relevant animal model of acute postoperative pain that may facilitate development of improved treatment regimens. ⋯ Acute pain following inguinal hernia repair can be difficult to treat. Here we report, for the first time, the development of a novel, anatomically relevant rat model to facilitate improved understanding and treatment of acute postoperative pain following inguinal hernia repair.
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Measurement of inappropriate medication use events (eg, abuse or misuse) in clinical trials is important in characterizing a medication's abuse potential. However, no gold standard assessment of inappropriate use events in clinical trials has been identified. In this systematic review, we examine the measurement properties (ie, content validity, cross-sectional reliability and construct validity, longitudinal construct validity, ability to detect change, and responder definitions) of instruments assessing inappropriate use of opioid and nonopioid prescription medications to identify any that meet U.S. and European regulatory agencies' rigorous standards for outcome measures in clinical trials. Sixteen published instruments were identified, most of which were not designed for the selected concept of interest and context of use. For this reason, many instruments were found to lack adequate content validity (or documentation of content validity) to evaluate current inappropriate medication use events; for example, evaluating inappropriate use across the life span rather than current use, including items that did not directly assess inappropriate use (eg, questions about anger), or failing to capture information pertinent to inappropriate use events (eg, intention and route of administration). In addition, the psychometric data across all instruments were generally limited in scope. A further limitation is the heterogeneous, nonstandardized use of inappropriate medication use terminology. These observations suggest that available instruments are not well suited for assessing current inappropriate medication use within the specific context of clinical trials. Further effort is needed to develop reliable and valid instruments to measure current inappropriate medication use events in clinical trials. ⋯ This systematic review evaluates the measurement properties of inappropriate medication use (eg, abuse or misuse) instruments to determine whether any meet regulatory standards for clinical trial outcome measures to assess abuse potential.
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Studies indicate that improving sleep decreases reported pain in patients with knee osteoarthritis, but it is unclear if this association extends to experimentally induced pain responses. A community-based sample of 88 African American and 52 non-Hispanic white adults (45-76 years) with knee osteoarthritis completed the Insomnia Severity Index and the arousal subscale of the Sleep Hygiene and Practices Scale. Participants underwent quantitative sensory testing, including measures of pain sensitivity and facilitation at the knee, and pain inhibition. Outcomes were analyzed with multiple Tobit hierarchical regression models, with adjustment for relevant covariates. Ethnicity and sex by sleep interactions were also entered into the models. After covariate adjustment, main associations were not observed. However, sex interacted with insomnia severity to predict greater temporal summation of heat and punctate pressure pain among women and lower heat temporal summation among men. Men and women who engaged in frequent arousal-associated sleep behaviors demonstrated higher and lower heat temporal summation, respectively. Non-Hispanic whites with greater insomnia severity displayed lower pressure pain thresholds and pain inhibition. Our findings are the first to demonstrate that disrupted sleep is associated with altered pain processing differentially by sex and ethnicity/race among people with knee osteoarthritis. ⋯ This article presents the association between insomnia severity, maladaptive sleep behaviors, and experimentally induced pain responses among people with knee osteoarthritis. Disrupted sleep was associated with altered pain processing by sex and ethnicity/race. Offering sleep interventions may help ameliorate pain, but treatment may need to be tailored by sex and ethnicity/race.
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Comparative Study
Comparison of machine classification algorithms for fibromyalgia: neuroimages versus self-report.
Recent studies have posited that machine learning (ML) techniques accurately classify individuals with and without pain solely based on neuroimaging data. These studies claim that self-report is unreliable, making "objective" neuroimaging classification methods imperative. However, the relative performance of ML on neuroimaging and self-report data have not been compared. This study used commonly reported ML algorithms to measure differences between "objective" neuroimaging data and "subjective" self-report (ie, mood and pain intensity) in their ability to discriminate between individuals with and without chronic pain. Structural magnetic resonance imaging data from 26 individuals (14 individuals with fibromyalgia and 12 healthy controls) were processed to derive volumes from 56 brain regions per person. Self-report data included visual analog scale ratings for pain intensity and mood (ie, anger, anxiety, depression, frustration, and fear). Separate models representing brain volumes, mood ratings, and pain intensity ratings were estimated across several ML algorithms. Classification accuracy of brain volumes ranged from 53 to 76%, whereas mood and pain intensity ratings ranged from 79 to 96% and 83 to 96%, respectively. Overall, models derived from self-report data outperformed neuroimaging models by an average of 22%. Although neuroimaging clearly provides useful insights for understanding neural mechanisms underlying pain processing, self-report is reliable and accurate and continues to be clinically vital. ⋯ The present study compares neuroimaging, self-reported mood, and self-reported pain intensity data in their ability to classify individuals with and without fibromyalgia using ML algorithms. Overall, models derived from self-reported mood and pain intensity data outperformed structural neuroimaging models.
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Use of multiple pharmacies concurrently (pharmacy shopping) and overlapping prescriptions may be indicators of potential misuse or abuse of prescription opioid medications. To evaluate strategies for identifying patients at high risk, we first compared different definitions of pharmacy shopping and then added the indicator of overlapping opioid prescriptions. We identified a cohort of 90,010 Medicaid enrollees who used ≥ 3 opioid prescriptions for ≥ 90 days during 2008 to 2010 from a multistate Medicaid claims database. We compared the diagnostic odds ratios for opioid overdose events of 9 pharmacy shopping definitions. Within a 90-day interval, a threshold of 4 pharmacies had the highest diagnostic odds ratio and was used to define pharmacy shopping. The overdose rate was higher in the subgroup with overlapping prescriptions (18.5 per 1,000 person-years [PYs]) than in the subgroup with pharmacy shopping as the sole indicator (10.7 per 1,000 PYs). Among the subgroup with both conditions, the overdose rate was 26.3 per 1,000 PYs, compared with 4.3 per 1,000 PYs for those with neither condition. Overlapping opioid prescriptions and pharmacy shopping measures had adjusted hazard ratios of 3.0 and 1.8, respectively, for opioid overdose. Using these measures will improve accurate identification of patients at highest risk of opioid overdose, the first step in implementing targeted prevention policies. ⋯ Long-term prescription opioid use may lead to adverse events, including overdose. Both pharmacy shopping and overlapping opioid prescriptions are associated with adverse outcomes. This study demonstrates that using both indicators will better identify those at high risk of overdose.