Acupuncture methods for diabetic peripheral neuropathy: A protocol for a Bayesian network meta-analysis

Abstract

Background:Many clinical trials and systematic reviews have suggested that acupuncture (include moxibustion) could be effective in the treatment of diabetic peripheral neuropathy (DPN). However, clinical practices vary greatly leads to different choices which are mainly based on personal experience. The aim of this Bayesian network meta-analysis is to compare the efficacy of different acupuncture methods for DPN.Methods:Randomized controlled trials on acupuncture treatment of DPN published before January of 2021 will be searched in 9 databases including Medline, Web of Science, PubMed, Cochrane Library, Excerpta Medica Database, Sinomed, China National Knowledge Infrastructure, WanFang, and China Science and Technology Journal Database. The methodological assessment performed using the risk of bias assessment tool of Cochrane, and the level of evidence quality for the main results will be evaluated by a recommended grading, evaluation, formulation, and evaluation system approach. Bayesian network meta-analysis will be conducted using STATA V.14.0 and WinBUGS V.1.4.3.Results:The primary outcome involves: clinical efficacy. The secondary outcomes include: motor nerve conduction velocity, sensory nerve conduction velocity, Toronto clinical scoring system, Michigan neuropathy screening instrument, the modified Toronto Clinical Neuropathy Scale, the Utah early neuropathy scale, or the neuropathy disability score, and adverse reactions.Conclusion:To find the most effective acupuncture therapy for the treatment of DPN supported by evidence-based medicine.