Exploring traditional acupuncture point selection patterns for pain control: data mining of randomised controlled clinical trials

Abstract

Background The underlying principles of traditional acupuncture point selection for pain control are complex. Analysis of acupuncture treatments from clinical studies may provide us with a potential rule when selecting traditional acupuncture points (hereafter abbreviated as “points”) in treatment protocols for pain control. The aim of this study was to investigate which points were most commonly used to treat pain in randomised controlled clinical trials (RCTs). Methods We searched acupuncture treatment regimens in RCTs included in the Cochrane Database of Systematic Reviews for pain management. We analysed information on point selection (more than 10 RCTs included) from seven eligible systematic reviews on pain control. The frequency of the points used was calculated and visualised using a human body template. Results The points most commonly used across diseases were SP6, ST36, LI4 and LR3. However, the most frequently used points varied across individual conditions. For example, the most frequently used points to treat migraine were GB20, LR3, GV20, Taiyang, LI4 and TE5, while the most frequently used points to manage dysmenorrhoea were SP6, CV4, SP8, LR3 and BL32. Both regional and distal points were used for pain management with acupuncture. Conclusions Our findings suggest that local and segmental/extra-segmental neuromodulation appear to be the most common phenomena for pain control in acupuncture research. Analysis of information on point selection using a data-driven approach may unveil the hidden patterns of traditional acupuncture point utilisation in clinical practice.

Identification of Acupoint Indication from Reverse Inference: Data Mining of Randomized Controlled Clinical Trials

“…Among distal acupoints, low back pain was the indication for BL40 in the lower limb, while insomnia was the indication for acupoint HT7 in the wrist ( Figure 5 ). Classification of acupoint indications into regional and distal types is consistent with the approach used in previous studies, where both regional and distal acupoints were mainly used for pain management during acupuncture, based on data mining from clinical trials [ 1 , 8 , 11 ].…”

“…We found that acupoints ST36, LI4, and LR3 were the most commonly prescribed across all diseases [ 21 ]. Mining of CDSR data also revealed that the main acupoints commonly used for a variety of pain management approaches were SP6, ST36, LI4, and LR3 [ 1 ]. Consistent with the findings of previous studies, we found that the core acupoints were widely used for all 30 diseases, regardless of disease type.…”

“…The action of acupuncture can be explained by various levels of neuromodulations by means of local, segmental, and general effects (descending analgesia and central regulation) [ 1 , 2 ]. Many neuroimaging studies have shown common brain activations and deactivation patterns following acupuncture stimulations [ 3 ].…”

“…We also applied data mining techniques and Bayes factor (BF) correction to CDSR data. Data mining can reveal associations between diseases and acupoints [ 1 , 15 ]. Bayes factor correction has been suggested as a way to avoid logical errors and fallacies in forward and reverse inference analyses [ 17 , 18 , 19 , 20 ].…”

Acupoints for Tension-Type Headache: A Literature Study Based on Data Mining Technology

“…Analysis of treatment details applied in a multicentre randomised trial of acupuncture for tension-type headache (ARTTTH) from Germany has shown that GB20, LR3, and LI4 were the highfrequency acupoints, treated in 96%, 97%, and 67% of sessions, respectively [63]. Not only that, the combination of the above five acupoints also shows therapeutic advantages in the treatment of other primary headaches, such as migraine [64], and the application proportions of GB20, GV20, EX-HN5, LI4, and LR3 calculated from 11 studies of migraine are 82%, 55%, 55%, 46%, and 64%, respectively [65].…”