Auricular Acupoint Selection Patterns for Knee Osteoarthritis Pain Relief: A Systematic Review and Data Mining Analysis

耳穴选择模式在膝骨关节炎疼痛缓解中的应用:系统评价和数据挖掘分析

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Abstract

OBJECTIVE: To systematically evaluate randomized controlled trials of auricular therapy for knee osteoarthritis pain and to explore acupoint selection patterns using data mining techniques. METHODS: Eleven Chinese and English databases were searched from inception to September 1, 2025. Eligible randomized controlled trials were screened and assessed for methodological quality. Descriptive statistics, frequency analysis, association rule mining, and hierarchical cluster analysis were applied to identify commonly used auricular points and their combinations. RESULTS: Thirty-six studies involving 3,105 participants were included. Study quality was moderate, with frequent shortcomings in blinding and allocation reporting. The most frequently used acupoints were Shenmen (TF(4)), Xi (AH(4)), Jiaogan (AH(6) (a)), Pizhixia (AT(4)), Gan (CO(1) (2)), and Shen (CO(1) (0)). Association rule analysis showed Shenmen (TF(4))-Pizhixia (AT(4)) as the most frequent co-occurring pair (support = 97.22%), while Neifenmi (CO(1) (8))-Shenshangxian (TG(2p)) and Shenmen (TF(4))-Shenshangxian (TG(2p)) exhibited the strongest association (lift = 3.15). Cluster analysis identified three synergistic groups: Cluster 1: Shenmen (TF(4)), Xi (AH(4)), Jiaogan (AH(6) (a)), and Pizhixia (AT(4)), linked to nerve regulation; Cluster 2: Gan (CO(1) (2)) and Shen (CO(1) (0)), associated with zang-fu organ function; and Cluster 3: Yidan (CO(1) (1)), Xin (CO(1) (5)), Fei (CO(1) (4)), Pi (CO(1) (3)), and Sanjiao (CO(1) (7)), functioning as adjunctive regulators. CONCLUSION: Auricular therapy for knee osteoarthritis pain relief shows promising patterns in acupoint selection that align with both Traditional Chinese Medicine and neurophysiological mechanisms. Despite encouraging results, interpretation should be cautious due to methodological limitations, protocol heterogeneity, and the exploratory nature of data mining. Future research should include rigorously designed, preregistered RCTs with standardized intervention protocols and transparent reporting to validate these findings and evaluate clinical effectiveness.

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