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NCT04852536
Electroencephalographia as Predictor of Effectiveness HD-tDCS in Neuropathic Pain: Machine Learning Approach
trial testing Neurostimulation (High Definition - transcranial Direct Current Stimulation) HD-tDCS in Traumatic Brachial Plexus Lesion in 30 participants. Status unknown.
15 December 2021
Quick facts
| Lead sponsor | Federal University of Paraíba |
|---|---|
| Status | Status unknown |
| Study type | OBSERVATIONAL |
| Enrollment | 30 |
| Start date | 15 June 2021 |
| Primary completion | 15 December 2021 |
| Estimated completion | 15 June 2022 |
| Sites | 1 location across Brazil |
Drugs / interventions tested
- Neurostimulation (High Definition - transcranial Direct Current Stimulation) HD-tDCS
Conditions studied
- Traumatic Brachial Plexus Lesion — all drugs for Traumatic Brachial Plexus Lesion →
- Chronic Pain — all drugs for Chronic Pain →
Sponsor
Federal University of Paraíba
Who can join
18 and older, any sex, with Traumatic Brachial Plexus Lesion or Chronic Pain. Patients with the condition only — healthy volunteers not accepted.
Sponsor's own description
Contextualization: Neuropathic pain is a complication present in the clinical picture of patients with traumatic Brachial Plexus injury (BPI). It is characterized by high intensity, severity and refractoriness to clinical treatments, resulting in high disability and loss of quality of life. Due to loss of afferent entry, it causes cortical and subcortical alterations and changes in somatotopic representation, from inadequate plastic adaptations in the Central and Peripheral Nervous System, one of the therapies with potential benefit in this population is the Transcranial High Definition Continuous Current Stimulation (HD-tDCS). Thus, by using connectivity-based response prediction and machine learning, it will allow greater assurance of efficiency and optimization of the application of this therapy, being directed to patients with greater potential to benefit from the application of this approach. Objective: Using connectivity-based prediction and machine learning, this study aims to assess whether baseline EEG related characteristics predict the response of patients with neuropathic pain after BPI to the effectiveness of HD-tDCS treatment. Materials and methods: A quantitative, applied, exploratory, open-label response prediction study will be conducted from data acquired from a pilot, triple-blind, cross-over, placebo-controlled, randomized clinical trial investigating the efficacy of applying HD-tDCS to patients with neuropathic brachial plexus trauma pain. Participants will be evaluated for eligibility and then randomly allocated into two groups to receive the active HD-tDCS or simulated HD-tDCS. The primary outcome will be pain intensity as measured by the numerical pain scale. Participants will be invited to participate in an EEG study before starting treatment. Clinical improvement labels used for machine learning classification will be determined based on data obtained from the clinical trial (baseline and post-treatment evaluations). The hypothesis adopted in this study is that the response prediction model constructed from EEG frequency band pattern data collected at baseline will be able to identify responders and non-responders to HD-tDCS treatment.
Publications & conference data
No peer-reviewed publications indexed yet for this trial.
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Verify against primary sources
- ClinicalTrials.gov — authoritative US registry record
- WHO ICTRP — international registry index
- EU Clinical Trials Register
- Sponsor press releases (Google)
- Trial protocol + status: ClinicalTrials.gov NCT04852536 (US National Library of Medicine, public domain)
- Drug + disease cross-links: matched in real time against Drug Landscape's normalised drug + company + condition tables
- Sponsor: as reported to ClinicalTrials.gov by Federal University of Paraíba
- Last refreshed: 21 April 2021
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