Research on electroacupuncture parameters for knee osteoarthritis based on data mining

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Abstract

Background: Knee osteoarthritis, a common degenerative joint disease, has been widely treated by electroacupuncture in recent years. However, there are too many parameters of the treatment currently, resulting in various applications in clinical practice. This study aims to summarize the optimal stimulation parameters of electroacupuncture for knee osteoarthritis in clinical studies by applying data mining techniques. Methods: Four databases including Pubmed, Cochrane Library, Embase, and Web of Science were searched for clinical studies on electroacupuncture treating knee osteoarthritis from 2012 to 2021. A database was established by Microsoft Excel 2020 and analyzed by R Version 4.1.1. Results: Forty-six articles were included according to the established criteria. The most used electroacupuncture stimulation parameters were 0.30 mm × 40 mm needle, continuous wave, low frequency of current (mainly 2 Hz), stimulation duration for 30 min per treatment, and frequency of treatment for once a day. Eighteen acupoints were mentioned and the most used ones include Dubi (ST35), Liangqiu (ST34), Neixiyan (EX-LE4), Xuehai (SP10), Yanglingquan (GB34), and Yinlingquan (SP9), and those most generally used acupoints are closely arranged on the Stomach Channel of Foot Yangming. Cluster analysis showed two groups, one for obligatory acupoints and one for adjunctive ones. The association analysis showed the most supported acupoint pair was Liangqiu (ST34) and Xuehai (SP10). Conclusions: Continuous wave, low frequency of current (2 Hz), 30-min stimulation, and local acupoint selection are frequently used for electroacupuncture treating knee osteoarthritis. Due to the limitations of this study, further research and more standardized, multi-centered, and large-sample clinical trials should be conducted to provide more convincing evidence.

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Cai, F. hong, Li, F. lian, Zhang, Y. chen, Li, P. qi, & Xiao, B. (2022). Research on electroacupuncture parameters for knee osteoarthritis based on data mining. European Journal of Medical Research, 27(1). https://doi.org/10.1186/s40001-022-00795-9

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