The selection rules of acupoints and meridians of traditional acupuncture for postoperative nausea and vomiting: a data mining-based literature study
Journal Title: Traditional Medicine Research - Year 2020, Vol 5, Issue 4
Abstract
Background: Postoperative nausea and vomiting (PONV) refers to a problem commonly occurring after surgery. Acupuncture is considered a critical complementary alternative therapy for PONV. The acupoints selection critically determines the efficacy of acupuncture, whereas the selection rules remain unclear. The objective of the present study was to delve into the principles of acupoints selection for PONV using data mining technology. Methods: The clinical trials assessing the acupuncture effect for PONV were searched with the use of computer in PubMed, China National Knowledge Infrastructure, and Chinese Biomedical Database; the time span was confined as 2009-2019. The database of acupuncture prescriptions for PONV was built using Excel 2016; the description and association were analyzed by IBM SPSS modeler 18. Result: Eighty-three relevant literatures were screened out. The number of specific acupoints took up 72.5% of all acupoints; specific acupoints exhibited the frequency taking up 91.30% of the total frequency. As revealed from the result, Neiguan (PC 6), Zusanli (ST 36), Hegu (LI 4), and Zhongwan (CV 12) were most frequently applied, suggesting the tightest associations. Most acupoints were taken from the stomach meridian and pericardium meridian. The common acupoints were concentrated in the lower limbs, chest, as well as abdomen. Conclusion: Data mining acts as a feasible method to identify acupoints selection and compatibility characteristics. As suggested from our study, the acupoints selection for PONV prioritizes specific acupoints and related meridians. The selection and combination of acupoints comply with the theory of traditional Chinese medicine.
Authors and Affiliations
Li-Sha Liu,Jian Huo,Xiu-Li Yuan,Yi Lan,Jing-Yuan Zhang,Hong-Mei Zhong,Yu Wang,Yun-Sheng He
Keywords
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- EP ID EP683792
- DOI 10.12032/TMR20200114154
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