Frontiers in pain research (Lausanne, Switzerland)最新文献

Chronic pain is an individual experience with physical and psychological dimensions. Ketamine is used in sub-anesthetic doses to treat chronic pain. We describe a proposed multidisciplinary approach with combined treatment of low-dose ketamine and pain-focused psychological and somatic therapies to benefit quality of life of disabled chronic pain patients. Beyond pain reduction, within the biopsychosocial approach, the treatment aims to achieve reduced suffering and improved pain management, functionality, and quality of life. Adopting a multidisciplinary approach can minimize exposure to ketamine and maintain a conservative ketamine dosing regimen. In this way, ketamine is not only used for the analgesic effects, but also to facilitate internal psychological processes of body-mind integration related to the pain identity and trauma. We illustrate the presented treatment approach with three cases of patients treated in a private clinic in Florida, United States. We describe the patients' original injury, ketamine and adjunct psychological and somatic therapies regimen, and short and longer-term outcomes from the patient's perspective. These results are preliminary, require replication with validated measures, and represent an opportunity for additional research and hypothesis formation. More clinical research on ketamine and adjunct therapies for chronic pain conditions is warranted to advance treatment options.

Neuropathic pain is defined as pain caused by a lesion or disease of the somatosensory nervous system. Current algorithms for neuropathic pain diagnosis include patient history, clinical examination, and complementary tests to confirm a lesion or disease of the somatosensory nervous system, able to change the diagnosis of neuropathic pain from probable to definite. These tests include clinical neurophysiology, such as pain-related evoked potentials, quantitative sensory testing, skin biopsy to measure intraepidermal nerve fiber density, or magnetic resonance imaging. However, these tests are especially relevant to demonstrate a structural lesion of the somatosensory system leading to sensory deficit, but they cannot establish a causal link between nervous lesion and the presence of pain. Similar lesions of the somatosensory nervous system may be accompanied by pain or not, while neuropathic pain can be a matter of sensitization or hyperexcitability of somatosensory structures without overt structural lesion. Even the existence of hyperexcitability of nociceptive pathways, revealed by neurophysiological or genetic tests, may contribute to the emergence of pain, but may not be sufficient to affirm that this results in ongoing neuropathic pain. Thus, various complementary tests can be useful to identify a lesion of the somatosensory nervous system, but not to confirm the presence of associated neuropathic pain. Clinical assessment, considering disease history, symptom descriptors and a plausible neuroanatomical distribution, remains the cornerstone of the diagnosis of neuropathic pain, while paraclinical findings must be interpreted with caution in this regard.

Introduction: We present connectivity-based features associated with fibromyalgia, derived from raw EEG data at the sensor level.

Methods: These connectivity features were identified through a data-driven method, employing machine learning. We carried out some automatic, moderate pre-processing and extracted spectral connectivity features. Machine learning experiments then followed, employing feature importance analyses and feature selection techniques for building high-performing classification models; finally, based on robust cross-validation and test evaluation, we obtained the features associated with fibromyalgia. The raw EEG signals from 463 participants are used in the primary analysis. An external dataset that consists of 48 participants is used to validate the identified connectivity features.

Results: Five features in the gamma band (Fz-Cz, Pz-P4, Fz-C3, Cz-P4, and Cz-Pz) were able to objectively detect the presence or absence of fibromyalgia with an accuracy of 99.57%. The identified connectivity features associated with fibromyalgia also show promising results on EEGs that are collected using a different type of device.

Discussion: EEG-based functional connectivity features associated with fibromyalgia, identified using machine learning in the gamma band at the sensor level, can distinguish between fibromyalgia participants and healthy controls with 99.57% accuracy. These findings advance our understanding of the brain-based mechanisms of fibromyalgia and provide novel targets for future non-invasive neuromodulation and neurofeedback trials. However, future studies need to replicate these findings in independent EEG datasets in people with fibromyalgia as well as compare with other clinical populations.

Background: Research has shown substantial evidence for the effectiveness of mindfulness-based interventions in the management of chronic pain. Less evidence is available whether a one-time mindfulness intervention may also be helpful in alleviating acute procedural pain. While anticipating a potentially painful procedure, people may worry about the upcoming pain. We investigated whether the time spent in a waiting room prior to an appointment could be used for a brief mindfulness intervention.

Methods: The sample consisted of 93 female students. Experimental pain was induced with a pressure pain algometer. Subjective pain ratings were recorded with a numerical rating scale in a 3 × 2 mixed design with the factors condition (mindfulness, worry, control) and measurement time (baseline, post). A situation corresponding to a waiting room in primary care was created. Participants received an audio recording of a mindfulness meditation, a worry instruction, or an instruction to wait. In addition, participants answered the state version of the Mindful Attention Awareness Scale (State) at both measurement times.

Results: 3 × 2 mixed-design ANOVA showed no main effect for the within-subjects factor "measurement time" F(1, 89) = 0.11, p = .74, no main effect for the between-subjects factor "condition" F(2, 89) = .24; p = .98, and no interaction effect of "measurement time×condition" F(2, 89) = 2.53, p = .09. Analyses showed that worrying led to an increase in perceived pain intensity [t(31) = 1.74, p = .046 (one-tailed), d = 0.31]. No further effects were observed. State mindfulness in the mindfulness condition increased between the measurement times [t(29) = 2.00, p = .03 (one-tailed), d = 0.37].

Discussion: Mindfulness increased through the induction; it did not affect perceived pain intensity. In the worry condition, perceived pain intensity increased, which is in accordance with research on detrimental effects of worry. Regarding the aim of the study, the experiment showed that a one-shot mindfulness intervention was able to promote state mindfulness, but not decrease perceived pain intensity. Future research should investigate whether mindfulness has more impact on the affective component of the pain, rather than on its sensory component.

Background: Neck pain (NP) is a common musculoskeletal disorder that significantly affects the physical function and quality of life of patients. Thumbtack needle therapy is widely used to manage NP. However, previous studies have reported inconsistent clinical outcomes. This study aims to systematically evaluate the efficacy and safety of thumbtack needle therapy for NP.

Methods: A systematic search was conducted in the Cochrane Library, Web of Science, Embase, PubMed, China National Knowledge Infrastructure (CNKI), China Science and Technology Journal (VIP), and Wanfang databases from their inception to 24 September 2023 for randomized controlled trials (RCTs) on thumbtack needle therapy for NP. Outcome measures included the visual analog scale (VAS) scores, neck disability index (NDI) scores, total effective rate, and adverse events. A meta-analysis was performed using Review Manager 5.3. The quality of evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system.

Results: Seven RCTs involving 425 patients were included. Compared with the control group, thumbtack needle therapy significantly reduced VAS scores (MD = -1.33, 95% confidence interval (CI): -1.63, -1.03; Z = 8.65; P < 0.05), reduced NDI scores (MD = -5.54, 95% CI: -9.73, -1.35; Z = 2.59; P < 0.05), and improved the total effective rate (OR = 0.27, 95% CI: 0.10, 0.70; Z = 2.67; P < 0.05). Adverse events were not reported in several studies, limiting conclusions on safety. A subgroup analysis revealed that heterogeneity may be related to the variation in combination therapies and treatment course. A sensitivity analysis confirmed the robustness of the results. The overall quality of evidence ranged from very low to moderate.

Conclusions: This study found that thumbtack needle therapy can effectively relieve pain and improve cervical mobility in patients with NP. The reduction in VAS scores reached the level of the minimum clinically important difference, indicating that thumbtack needle treatment for neck pain has a clinically significant impact. In the future, high-quality RCTs are needed to further validate the clinical efficacy of thumbtack needle therapy for NP.

Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD42025632076, PROSPERO CRD42025632076.

Objective: The combination of the pericapsular nerve group (PENG) block and lateral femoral cutaneous nerve (LFCN) block has garnered increasing attention as a postoperative analgesic strategy following hip surgery. Nevertheless, the clinical efficacy of this approach remains a subject of ongoing debate. Through a meta-analysis, the effects of the combined PENG and LFCN block in patients who underwent hip surgery were investigated.

Methods: We conducted a systematic search of relevant clinical randomized controlled trials (RCTs) available in English via online databases and grey literature resources. Quantitative analyses were performed to assess pain scores, time to first rescue analgesia, consumption of rescue analgesics, incidence of quadriceps weakness, time to first ambulation, and postoperative nausea and vomiting (PONV) to comprehensively evaluate the effects of the combined PENG and LFCN block in patients after hip surgery.

Results: A total of 12 RCTs involving 823 patients were included in this study. The findings from the quantitative synthesis indicate that the combined PENG and LFCN block significantly decreases postoperative pain scores at rest (p < 0.001) and during movement (p = 0.021), increases the time to first rescue analgesia (p < 0.001), and reduces the consumption of rescue analgesics (p < 0.001). Additionally, this combination decreases the incidence of quadriceps weakness (p < 0.001), shortens the time to first ambulation (p < 0.001), and decreases the incidence of PONV (p = 0.020).

Conclusion: The combined PENG and LFCN block has favourable clinical efficacy for postoperative analgesia in hip surgery patients and is recommended for use. However, more high-quality, large-scale RCTs are needed to further validate our findings.

Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420251142338, PROSPERO CRD420251142338.

Introduction: Substantial evidence suggests that experiences of discrimination negatively influence sleep, depressive symptoms, stress, and pain. The purpose of this study was to evaluate the strength of the associations between discrimination and pain, and to determine which psychosocial risk factors help explain these associations.

Methods: Participants (N = 208) underwent two study sessions, where they completed the Everyday Discrimination Scale, Perceived Stress Scale, Centers for Epidemiological Studies Depression Scale, Insomnia Severity Index, and the Brief Pain Inventory-Short Form. Demographic data was also obtained from participants.

Results: Majority of the participants self-identified as women (55.3%), and Black (62%). There were positive associations between discrimination and insomnia (p < .001), depressive symptoms (p < .001), perceived stress (p < .001), pain severity (p < .001) and pain interference (p < .001). Hierarchical regressions showed that identifying as Black (p < .001), having greater depressive symptoms (p = .03), and greater insomnia symptoms (p < .001) were associated with greater pain severity in the past 24 h. Similarly, older age (p = .01), identifying as Black (p = .002), having lower education (p = .04), taking medications (p = .04), greater depressive (p < .001) and insomnia symptoms (p < .001) were associated with greater pain interference. The indirect effect of discrimination on pain severity was significant (β = .015, Bootstrap 95% CI.003-.030). Additionally, there was a significant indirect effect of discrimination on pain interference (β = .015, Bootstrap 95% CI.004-.031). Exploratory models showed an indirect effect of pain severity (β = .014, Bootstrap 95% CI.001-.029) and interference (β = .012, Bootstrap 95% CI.000 to .029) on discrimination via psychosocial risk factors.

Discussion: Our findings highlight the harmful associations between discrimination, mental health outcomes, pain severity, and reduced quality of life. Additionally, these findings emphasize the need for more stress engaged research to continue exploring these potential relationships, identify cause-effect and inform the development of future interventions focused on reducing the negative impact of stress on pain outcomes - especially for minority groups who are disproportionately affected by pain disparities.

Objective: Clinical guidelines recommend opioid use only in severe and unresponsive cases of low back pain. Limited evidence exists on pain levels and opioid use in spine-related diagnoses (SRD). This study assessed the association between self-reported pain severity and opioid use among U.S. adults with SRD.

Methods: We conducted a retrospective cross-sectional analysis using 2018-2021 Medical Expenditure Panel Survey (MEPS) data. Adults aged ≥18 with SRD diagnoses and pain prescriptions were included. Pain severity was assessed using the Veterans Rand-12 item on pain interference with routine work. Pain medication use was determined from prescription records. Multivariable logistic regression was used to evaluate the association after controlling for other factors.

Results: According to the MEPS, there were 1.91 million (95% CI: 1.71-2.11) adults with SRD receiving pain medications annually, among whom 63.92% received opioid prescriptions. Most of the SRD patients were <66 years (69.71%), females (63.14%), and non-Hispanic whites (76.25%). Over half reported high pain interference with their routine activities (52.06%) Multivariable logistic regression analysis revealed that SRD patients who reported high pain interference with their routine work had higher odds of opioid use compared to those who reported low pain interference with their routine work (OR = 2.92, 95%CI: 1.49-5.70).

Conclusion: Over half of SRD patients receiving pain medications reported high pain severity levels, and these high pain levels were associated with higher odds of opioid use. While this evidence aligns with the clinical recommendations, more research is needed to understand the opioid use in SRD.

This rapid review examines the emerging role of voice-based artificial intelligence (AI) technologies in the objective assessment of chronic pain. It highlights promising applications of vocal biomarkers in pain quantification, particularly for populations with communication challenges or poorly understood conditions. AI is transforming healthcare, particularly for early detection of cardiovascular diseases and some neurological disorders, and it holds promise for managing chronic pain. This rapid literature review explores the potential of voice-based AI technologies to identify and analyze biomarkers that can objectively assess pain for populations with chronic pain conditions. These conditions are often complex and would benefit from more precise, reproducible measures of pain. While traditional pain scales heavily rely on self-reports, voice biomarkers are a non-invasive, scalable alternative. Studies show that changes in vocal characteristics-such as pitch, loudness, and jitter-correlate with pain intensity and quality; therefore, they offer insights that traditional, subjective measures may overlook. Machine learning models applied to voice data have demonstrated promise in detecting pain, particularly in vulnerable populations, such as those with intellectual and developmental disabilities. The review highlights how AI-driven voice analysis can complement cognitive behavioral therapy in pain management, enhancing accessibility and clinical outcomes. Despite the promise of AI-based approaches, challenges remain in standardizing these technologies for routine clinical use. Future research is needed to validate voice biomarkers across diverse pain conditions and to integrate them into clinical workflows to improve early diagnosis and personalized care, thus offering an innovative approach to chronic pain management. Key words: Voice biomarkers, Artificial intelligence, Pain Management, Machine Learning, Chronic Pain, Digital Health, Objective Pain Assessment.