The journal of pain : official journal of the American Pain Society
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Recent attempts to utilize machine learning (ML) to predict pain-related outcomes from Electroencephalogram (EEG) data demonstrate promising results. The primary aim of this review was to evaluate the effectiveness of ML algorithms for predicting pain intensity, phenotypes or treatment response from EEG. Electronic databases MEDLINE, EMBASE, Web of Science, PsycINFO and The Cochrane Library were searched. ⋯ PERSPECTIVE: This systematic review explores the state-of-the-art machine learning methods for predicting pain intensity, phenotype or treatment response from EEG data. Results suggest that machine learning may demonstrate clinical utility, pending further research and development. Areas for improvement, including standardized processing, reporting and the need for better methodological assessment tools, are discussed.
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The Helping to End Addiction Long-term Initiative (NIH HEAL Initiative) is an aggressive trans-NIH effort to speed solutions to stem the national opioid public health crisis, including through improved pain management. Toward this end, the NIH HEAL Initiative launched a common data element (CDE) program to ensure that NIH-funded clinical pain research studies would collect data in a standardized way. NIH HEAL Initiative staff launched a process to determine which pain-related core domains should be assessed by every clinical pain study and what questionnaires are required to ensure that the data is collected uniformly. ⋯ The selection of core domains will ensure that valuable clinical pain data generated by the initiative is standardized, useable for secondary data analysis, and useful for guiding future research, clinical practice decisions, and policymaking. PERSPECTIVE: The NIH HEAL Initiative launched a common data element program to ensure that NIH-funded clinical pain research studies would collect data in a standardized way. Nine core pain domains and questionnaires to measure them were chosen for studies examining acute pain and chronic pain in adults and pediatric populations.
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Neuropathic pain research and clinical care is limited in low- and middle-income countries with high prevalence of chronic pain such as Nepal. We translated and cross-culturally adapted the Self-report version of the Leeds Assessment of Neuropathic Symptoms and Signs (S-LANSS)-a commonly used, reliable and valid instrument to screen for pain of predominantly neuropathic origin (POPNO)-into Nepali (S-LANSS-NP) and validated it using recommended guidelines. We recruited 30 patients with chronic pain in an outpatient setting for cognitive debriefing and recruited 287 individuals with chronic pain via door-to-door interviews for validation. ⋯ The S-LANSS-NP is a comprehensible, unidimensional, internally consistent, and valid instrument to screen POPNO in individuals with chronic pain with predominantly low-levels of literacy for clinical and research use. PERSPECTIVE: This paper shows that the Nepali version of the S-LANSS is comprehensible, reliable and valid in adults with chronic pain and predominantly low-levels of literacy in rural Nepal. The study could potentially develop research and clinical care of neuropathic pain in this resource-limited setting where chronic pain is a significant problem.
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Spinal cord stimulation (SCS) is a popular neurostimulation therapy for severe chronic pain. To improve stimulation efficacy, multiple modes are now used clinically, including conventional, burst, and 10-kHz SCS. Clinical observations have produced speculation that these modes target different neural elements and/or work via distinct mechanisms of action. ⋯ These results motivate future work to contextualize clinical observations across SCS paradigms. PERSPECTIVE: This article presents the first computational modeling study to investigate neural recruitment during conventional, burst, and 10-kilohertz spinal cord stimulation for chronic pain within a single modeling framework. The results provide insight into these treatments' unknown mechanisms of action and offer context to interpreting clinical observations.
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In a previous study, we demonstrated that the serum peptidase system might be less efficient in complex regional pain syndrome (CRPS). Since the neuropeptide substanc P (SP) contributes to inflammation in CRPS, we now investigated the metabolism of SP in CRPS specifically. An SP metabolism assay was performed in 24 CRPS patients, which constitute a subgroup of our previous investigation on BK degradation. ⋯ Collectively, our current and previous experimental results suggest that limb trauma reduces serum peptidase metabolism of SP ex vivo, specifically serum ACE activity. However, this finding is not CRPS-specific and seems to be rather a long-term consequence of the trauma itself. PERSPECTIVE: The experimental data from this study further support the hypothesis that impaired metabolism of inflammatory peptides potentially contribute to the development of posttraumatic pain in CRPS or limb trauma patients.