Investigating Acupoint Selection and Combinations of Acupuncture for Tic Disorders: An Association Rule Mining and Network Analysis Study

Jieting Chen; Yufeng Xie; Qingchan Lin; Ziliang Qian; Jun Feng; Jianmei Zhang; Yun Chen; Wenhan Chen; Yueting Wu; Ziyi Guo

doi:10.3389/fneur.2022.894951

Investigating Acupoint Selection and Combinations of Acupuncture for Tic Disorders: An Association Rule Mining and Network Analysis Study

Front Neurol. 2022 Jun 10:13:894951. doi: 10.3389/fneur.2022.894951. eCollection 2022.

Authors

Jieting Chen^{1

2}, Yufeng Xie¹, Qingchan Lin^{1

2}, Ziliang Qian¹, Jun Feng^{1

2}, Jianmei Zhang^{1

2}, Yun Chen¹, Wenhan Chen¹, Yueting Wu^{1

2}, Ziyi Guo³

Affiliations

¹ Shenzhen Hospital of Guangzhou University of Chinese Medicine, Shenzhen, China.
² Sixth Clinical School of Medicine, Guangzhou University of Chinese Medicine, Shenzhen, China.
³ Macau University of Science and Technology, Taipa, Macao SAR, China.

Abstract

Objective: Tic disorders (TDs) are common mental disorders in children and adolescents, and the clinical application of acupuncture for treating TDs is becoming increasingly widespread. However, the criteria for selecting acupoint prescriptions and combinations have not been summarized. Therefore, data mining was used herein to determine the treatment principles and the most effective acupoint selection and compatibility criteria for the treatment of TDs.

Methods: Clinical studies and observations of the efficacy of acupuncture treatment for TDs were obtained from the PubMed, Cochrane Library, EMBASE, China National Knowledge Infrastructure (CNKI), Wanfang, VIP, and Chinese Biomedical (CBM) databases. The data on the acupoint prescriptions applied in these studies were collected, and network and association analyses were used to reveal the relationships between acupoints and to identify acupoint combinations. Additionally, the principles of acupuncture for TDs were determined through cluster analysis. Subgroup analysis of acupuncture prescriptions based on specific categorical diagnoses was performed to further assess the selection of acupoints.

Results: Eighty-six trials were identified, and 257 groups of effective prescriptions involving 121 acupoints were extracted. Bai-hui (DU20), Feng-chi (GB20), Tai-chong (LR3), He-gu (LI4), and San-yin-jiao (SP6) were the most regularly used acupoints for treating TDs. The Governor Vessel, gallbladder, and large intestine meridians were more commonly used than other meridians. Moreover, most acupoint sites focused on the head and neck. Network analysis revealed potentially effective acupoint prescriptions for their commonly used acupoints, namely, Bai-hui (DU20), Si-shen-cong (EX-HN1), Feng-chi (GB20), Nei-guan (PC6), Shen-men (HT7), He-gu (LI4), Zu-san-li (ST36), San-yin-jiao (SP6) and Tai-chong (LR3). Association rule mining indicated that potential point combinations that should be prioritized in TD treatment are Bai-hui (DU20), Neiguan (PC6) and Sanyinjiao (SP6). Cluster analysis revealed the treatment principle of "coordinating yin and yang, tonifying qi and blood, dispelling pathogenic wind and eliminating phlegm". The core acupoint prescription of TS treatment comprised He-gu (LI4), Feng-chi (GB20), Tai-chong (LR3), Bai-hui (DU20), Yin-tang (EX-HN3), Si-shen-cong (EX-HN1), San-yin-jiao (SP6), and Nei-guan (PC6). The core group included He-gu (LI4) and Feng-chi (GB20). Proximal points were usually used in TS as an additional method of point selection.

Conclusion: Using data mining analysis of published studies, this study provides valuable information regarding the selection of the most effective acupoints and point combinations for clinical acupuncture practice for treating TDs.

Keywords: acupuncture; association rule; cluster analysis; data mining; tic disorders acupoint.

Publication types

Systematic Review