Articles: chronic.
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Pain profiles (e.g. pro- and anti-nociceptive) can be developed using quantitative sensory testing (QST) but substantial variability exists. This study describes the variability in temporal summation of pain (TSP) and conditioned pain modulation (CPM) in chronic musculoskeletal pain patients, proposes cut-off values, and explores the association with clinical pain intensity. ⋯ This analysis shows that there is variability when assessing TSP and CPM in both pain-free subjects and patients with chronic pain. A cut-off for determining when a person is pain-sensitive is proposed, and data based on this cut-off approach suggest that significantly more patients with osteoarthritis and fibromyalgia are pain-sensitive (i.e. higher TSP and lower CPM) compared to pain-free subjects. This analysis does not find an association between pain sensitivity and clinical pain.
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Randomized Controlled Trial
METHA-NeP: effectiveness and safety of methadone for neuropathic pain: a controlled randomized trial.
In this randomized, double-blind, parallel placebo-controlled clinical trial, we evaluated the efficacy of methadone as an add-on therapy for people with chronic neuropathic pain (NP). Eighty-six patients were randomly assigned to receive methadone or placebo for 8 weeks. The primary outcome was the proportion of participants achieving at least 30% pain relief from baseline using a 100-mm pain Visual Analogue Scale. ⋯ No serious adverse events or deaths occurred. Discontinuation due to adverse events was reported in 2 participants in the methadone and none in the placebo arm. Methadone use as an add-on to an optimized treatment for NP with first- and/or second-line drugs provided superior analgesia, improved sleep, and enhanced global impression of change, without being associated with significant serious adverse effects that would raise safety concerns.
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While the development of artificial intelligence (AI) technologies in medicine has been significant, their application to acute and chronic pain management has not been well characterized. This systematic review aims to provide an overview of the current state of AI in acute and chronic pain management. ⋯ This review characterizes current applications of AI for pain management and discusses barriers to their clinical integration. Our findings support continuing efforts directed towards establishing comprehensive systems that integrate AI throughout the patient care continuum.
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Randomized Controlled Trial
A randomized clinical trial of emotional freedom techniques for chronic pain: Live versus self-paced delivery with 6-month follow-up.
Chronic pain represents a major global healthcare crisis, and current treatments are limited in effectiveness and safety. Emotional freedom techniques (EFTs) show promise as a potential psychological treatment. ⋯ An emerging body-based intervention for chronic pain may be a possible solution for remote clients who cannot attend in-person sessions. In this clinical trial Emotional Freedom Techniques (EFT) significantly reduced chronic pain severity and interference, and there were no differences between and online self-paced program toan online in-person EFT intervention. Both were equally effective, also enhancing quality of life without compromising outcomes. The results were significant at 6-month follow-up/. These findings highlight a body-based approach as a promising, accessible pain management strategy, and highlights that online programs may be part of the solution for chronic pain patients.
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Multicenter Study
Development and internal validation of a clinical risk tool to predict chronic postsurgical pain in adults: a prospective multicentre cohort study.
Chronic postsurgical pain (CPSP) is a highly prevalent condition. To improve CPSP management, we aimed to develop and internally validate generalizable point-of-care risk tools for preoperative and postoperative prediction of CPSP 3 months after surgery. A multicentre, prospective, cohort study in adult patients undergoing elective surgery was conducted between May 2021 and May 2023. ⋯ These models demonstrated good calibration and clinical utility. The primary CPSP model demonstrated fair predictive performance including 2 significant predictors. Derivation of a generalizable risk tool with point-of-care predictors was possible for the threshold-based CPSP models but requires independent validation.