Electrical stimulation of the dorsal root ganglia in the context of pain: a systematic review of the in vivo and in vitro tests in animal model studies.
OBJECTIVE: The dorsal root ganglia (DRG) has emerged recently as an attractive objective for neuromodulation therapy since the primary sensory neurones of the DRG and their somata are important structures in the physiopathological changes that lead to neuropathic pain. Our objective has been to create a synthesis of the evidence about the effects of electrical stimulation of the DRG in the context of pain from the in vitro and in vivo animal studies and analyse the methodology and the quality of these studies.
METHODS: To conduct a systematic review we are looking at three databases: MEDLINE, Embase and Web of Science. The quality of the studies included was assessed with the Systematic Review Centre for Laboratory Animal Experimentation(SYRCLE) risk of bias tool for animal studies. The study was registered in the Collaborative Approach to Meta-Analysis and Review of Animal Data from Experimental Studies(CAMARADES) database.
RESULTS: Six in vitro and eight in vivo animal studies are included. All the in vitro studies combined neurostimulation with substances or pharmacological products and reported improvements in the pain parameters related to neurostimulation. Among the in vivo studies, six used pulsed radiofrequency and two electrical field stimulation. All the in vivo studies reported improvements in behaviour related to pain after the stimulation. The meta-analysis was not possible due the heterogenicity and lack of data. The quality of the studies was not optimal since all had an unclear risk of bias in multiple areas.
CONCLUSIONS: The limited data of the in vitro and in vivo animal studies that electrical stimulation of the DRG has a positive therapeutic effect in the context of the results related to pain. Additional studies with a methodological focus and standardised results would provide useful information on the electrical stimulation of the DRG in animal models.
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