Functional Magnetic Resonance Imaging Evaluation of Auricular Percutaneous Electrical Neural Field Stimulation for Fibromyalgia: Protocol for a Feasibility Study
Notice bibliographique
Résumé
BACKGROUND: Fibromyalgia is a chronic pain state that includes widespread musculoskeletal pain, fatigue, psychiatric symptoms, cognitive and sleep disturbances, and multiple somatic symptoms. Current therapies are often insufficient or come with significant risks, and while there is an increasing demand for non-pharmacologic and especially non-opioid pain management such as that offered through complementary and alternative medicine therapies, there is currently insufficient evidence to recommend these therapies. Percutaneous electrical neural stimulation (PENS) is an evidence-based treatment option for pain conditions that involves electrical current stimulation through needles inserted into the skin. Percutaneous electrical neural field stimulation (PENFS) of the auricle is similar to PENS, but instead of targeting a single neurovascular bundle, PENFS stimulates the entire ear, covering all auricular branches of the cranial nerves, including the vagus nerve. The neural mechanisms of PENFS for fibromyalgia symptom relief are unknown. OBJECTIVE: We hypothesize that PENFS treatment will decrease functional brain connectivity between the default mode network (DMN) and right posterior insula in fibromyalgia patients. We expect that the decrease in functional connectivity between the DMN and insula will correlate with patient-reported analgesic improvements as indicated by the Defense and Veterans Pain Rating Scale (DVPRS) and will be anti-correlated with patient-reported analgesic medication consumption. Exploratory analyses will be performed for further hypothesis generation. METHODS: A total of 20 adults from the Atlanta Veterans Affairs Medical Center diagnosed with fibromyalgia will be randomized into 2 groups: 10 subjects to a control (standard therapy) group and 10 subjects to a PENFS treatment group. The pragmatic, standard therapy group will include pharmacologic treatments such as anticonvulsants, non-steroidal anti-inflammatory drugs, topical agents and physical therapy individualized to patient comorbidities and preferences, prescribed by a pain management practitioner. The PENFS group will include the above therapies in addition to the PENFS treatments. The PENFS subject group will have the Neuro-Stim System placed on the ear for 5 days then removed and replaced once per week for 4 weeks. The primary outcome will be resting functional magnetic resonance imaging connectivity between DMN and insula, which will also be correlated with pain relief and functional improvements. This connectivity will be analyzed utilizing functional connectivity magnetic resonance imaging (fcMRI) and will be compared with patient-reported analgesic improvements as indicated by the DVPRS and patient-reported analgesic medication consumption. Pain and function will be further evaluated using Patient-Reported Outcomes Measurement Information System measures and measures describing a person's functional status from Activity and Participation section of the International Classification of Functioning Disability and Health. RESULTS: This trial has been funded by the Veterans Health Administration Program Office. This study attained approval by the Emory University/Veterans Affairs (VA) institutional review board and VA Research & Development committee. Institutional review board expedited approval was granted on 2/7/17 (IRB00092224). The study start date is 6/1/17 and estimated completion date is 5/31/20. The recruitment started in June 2017. CONCLUSIONS: This is a feasibility study that is meant to demonstrate the practicality of using fcMRI to study the neural correlates of PENFS outcomes and provide information regarding power calculations in order to design and execute a larger randomized controlled clinical trial to determine the efficacy of PENFS for improving pain and function. TRIAL REGISTRATION: ClinicalTrials.gov NCT03008837; https://clinicaltrials.gov/ct2/show/NCT03008837 (Archived by WebCite at http://www.webcitation.org/6wrY3NmaQ).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,004 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,004 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,041 | 0,012 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».