Pain
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Because chronic pain has been poorly represented in the International Statistical Classification of Diseases and Related Health Problems (ICD) despite its significant contribution to the burden of disease worldwide, the International Association for the Study of Pain (IASP) developed a classification of chronic pain that was included in the ICD-11 version as "MG30" and approved by the World Health Assembly in 2019. The objective of this field test was to determine how well the classification of chronic pain works in the context of the ICD-11. A web-based survey using the WHO-FiT platform recruited 177 healthcare professionals from all WHO regions. ⋯ The case coding was on average 83.9% accurate, only in 1.6% of cases any difficulty was perceived. The morbidity rules were applied correctly in 74.1% of cases. From a coding perspective, the ICD-11 is superior to the ICD-10 in every respect, offering better accuracy, difficulty, and ambiguity in coding chronic pain conditions.
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A cerebral upregulation of the translocator protein (TSPO), a biomarker of glial activation, has been reported in fibromyalgia subjects (FMS). The TSPO binding affinity is genetically regulated by the Ala147Thr polymorphism in the TSPO gene (rs6971) and allows for a subject classification into high affinity binders (HABs) and mixed/low affinity binders (MLABs). The aim of the present multimodal neuroimaging study was to examine the associations of the TSPO polymorphism with: (1) conditioned pain modulation, (2) expectancy-modulated pain processing assessed during functional magnetic resonance imaging, and (3) the concentration and balance of glutamate and γ-aminobutyric acid in the rostral anterior cingulate cortex and thalamus using proton magnetic resonance spectroscopy in FMS (n = 83) and healthy controls (n = 43). ⋯ Translocator protein HABs in both groups (FM and healthy controls) were found to have higher thalamic glutamate concentrations and exhibit a pattern of positive correlations between glutamate and γ-aminobutyric acid in the rostral anterior cingulate cortex, not seen in MLABs. Altogether, our findings point to TSPO-related mechanisms being HAB-dependent, brain region-specific, and non-FM-specific, although in FMS the disadvantage of an aberrant pain regulation combined with an HAB genetic set-up might hamper pain modulation more strongly. Our results provide evidence for an important role of TSPO in pain regulation and brain metabolism, thereby supporting the ongoing drug development targeting TSPO-associated mechanisms for pain relief.
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Tapering opioids for chronic pain can be challenging for both patients and prescribers, both of whom may be unsure of what to expect in terms of pain, distress, activity interference, and withdrawal symptoms over the first few weeks and months of the taper. To better prepare clinicians to provide patient-centred tapering support, the current research used prospective longitudinal qualitative methods to capture individual-level variation in patients' experience over the first few months of a voluntary physician-guided taper. The research aimed to identify patterns in individuals' experience of tapering and explore whether patient characteristics, readiness to taper, opioid tapering self-efficacy, or psychosocial context were related to tapering trajectory. ⋯ High and low readiness to taper was a defining characteristic of thriving and distressed trajectories, respectively. Life adversity was a prominent theme of resilient and distressed trajectories, with supportive relationships buffering the effects of adversity for those who followed a resilient trajectory. Discussion focuses on the implications of these findings for the preparation and support of patients with chronic pain who are commencing opioid tapering.
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Appropriate monitoring of opioid use in patients with pain conditions is paramount, yet it remains a very challenging task. The current work examined the use of a wearable sensor to detect self-administration of opioids after dental surgery using machine learning. Participants were recruited from an oral and maxillofacial surgery clinic. ⋯ The trained model had a validation sensitivity of 82%, a specificity of 85%, a positive predictive value of 85%, and a negative predictive value of 83%. A subsequent test of the trained model on withheld data had a sensitivity of 81%, a specificity of 88%, a positive predictive value of 87%, and a negative predictive value of 82%. Results from training and testing model of machine learning indicated that opioid self-administration could be identified with reasonable accuracy, leading to considerable possibilities of the use of wearable technology to advance prevention and treatment.