Articles: pain-clinics.
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In this study, we describe the development and validation of a revised Pediatric Chronic Pain Grading (P-CPG) for children aged 8 to 17 years that adds emotional impairment to previously used measures of pain intensity and functional impairment. Such a measure enables the assessment of chronic pain severity in different epidemiological and clinical populations, the stratification of treatment according to pain severity, and the monitoring of treatment outcome. The P-CPG was developed using a representative sample of school children with chronic pain (n = 454; M age = 12.95, SD = 2.22). ⋯ Convergent validity was demonstrated by significant positive correlations between the P-CPG and global ratings of pain severity as well as objective claims data; the latter reflects greater health care costs with increasing P-CPG scores. Sensitivity to change was supported by a significant reduction in baseline P-CPG grades 3 and 6 months after intensive interdisciplinary pain treatment in tertiary care sample. In conclusion, the P-CPG is an appropriate measure of pain severity in children and adolescents with chronic pain in clinical as well as epidemiological settings.
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Chronic pelvic pain is heterogeneous with potentially clinically informative subgroups. We aimed to identify subgroups of pelvic pain based on symptom patterns and investigate their associations with inflammatory and chronic pain-related comorbidities. Latent class analysis (LCA) identified subgroups of participants (n = 1255) from the Adolescence to Adulthood (A2A) cohort. ⋯ Migraines were associated with significant odds of membership in all 4 pelvic pain subgroups relative to those with no pelvic pain (adjusted odds ratios = 2.92-7.78), whereas back, joint, or leg pain each had significantly greater odds of membership in the latter 3 subgroups. Asthma or allergies had three times the odds of membership in the most severe pain group. Subgroups with elevated levels of cyclic or acyclic pain are associated with greater frequency of chronic overlapping pain conditions, suggesting an important role for central inflammatory and immunological mechanisms.
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Acute orthopedic traumatic musculoskeletal injuries are prevalent, costly, and often lead to persistent pain and functional limitations. Psychological risk factors (eg, pain catastrophizing and anxiety) exacerbate these outcomes but are often overlooked in acute orthopedic care. Addressing gaps in current treatment approaches, this mixed-methods pilot study explored the use of a therapeutic virtual reality (VR; RelieVRx ), integrating principles of mindfulness and cognitive-behavioral therapy, for pain self-management at home following orthopedic injury. ⋯ The results support a larger randomized clinical trial of RelieVRx versus a sham placebo control to replicate the findings and explore mechanisms. There is potential for self-guided VR to promote evidence-based pain management strategies and address the critical mental health care gap for patients following acute orthopedic injuries.
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Ecological momentary assessment (EMA) allows for the collection of participant-reported outcomes (PROs), including pain, in the normal environment at high resolution and with reduced recall bias. Ecological momentary assessment is an important component in studies of pain, providing detailed information about the frequency, intensity, and degree of interference of individuals' pain. However, there is no universally agreed on standard for summarizing pain measures from repeated PRO assessment using EMA into a single, clinically meaningful measure of pain. ⋯ However, linear mixed-effect modeling estimators that account for the nonlinear relationship between average and variability of pain scores perform better for quantifying the true average pain and reduce estimation error by up to 50%, with larger improvements for individuals with more variable pain scores. We also show that binarizing pain scores (eg, <3 and ≥3) can lead to a substantial loss of statistical power (40%-50%). Thus, when examining pain outcomes using EMA, the use of linear mixed models using the entire scale (0-10) is superior to splitting the outcomes into 2 groups (<3 and ≥3) providing greater statistical power and sensitivity.