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

The specificity of acupoint indication (i.e., reverse inference-diseases for which an acupoint could be used) might differ from the specificity of acupoint selection (i.e., forward inference-acupoints used for a disease). In this study, we explore acupoint specificity through reverse inferences from the dataset of prescribed acupoints for a certain disease in clinical trials. We searched acupuncture treatment regimens in randomized controlled trials included in the Cochrane Database of Systematic Reviews. For forward inference, the acupoints prescribed for each disease were quantified. For reverse inference, diseases for each acupoint were quantified. Data were normalized using Z-scores. Bayes factor correction was performed to adjust for the prior probability of diseases. The specificities of acupoint selections in 30 diseases were determined using forward inference. The specificities of acupoint indications regarding 49 acupoints were identified using reverse inference and then subjected to Bayes factor correction. Two types of acupoint indications were identified for 24 acupoints: regional and distal. Our approach suggests that the specificity of acupoint indication can be inferred from clinical data using reverse inference. Acupoint indication will improve our understanding of acupoint specificity and will lead to the establishment of a new model of analysis and educational resources for acupoint characteristics.

Commonality and Specificity of Acupuncture Point Selections.

OBJECTIVE: Because individual acupoints have a wide variety of indications, it is difficult to accurately identify the associations between acupoints and specific diseases. Thus, the present study aimed at revealing the commonality and specificity of acupoint selections using virtual medical diagnoses based on several cases. METHODS: Eighty currently practicing Korean Medicine doctors were asked to prescribe acupoints for virtual acupuncture treatment after being presented with medical information extracted from 10 case reports. The acupoints prescribed for each case were quantified; the data were normalised and compared among the 10 cases using z-scores. A hierarchical cluster analysis was conducted to categorise diseases treated based on the acupoint prescription patterns. Additionally, network analyses were performed on the acupoint prescriptions, at the individual case and cluster level. RESULTS: Acupoints ST36, LI4, and LR3 were most commonly prescribed across all diseases. Regarding the specific acupoints prescribed in each cluster, acupoints around the disease site (knee and lower back) were frequently used in cluster A (musculoskeletal symptoms), acupoints LI4, LR3, PC6, and KI3 were frequently used in cluster B (psychiatric symptoms), and acupoints ST36, LI4, LR3, PC6, CV12, and SP6 were frequently used in cluster C (several symptoms of diseases of internal medicine). CONCLUSIONS: The present study identified the commonality and specificity of acupoint selections based on virtual acupuncture treatments prescribed by practicing clinicians. Acupoint selection patterns, which were defined using a top-down approach in previous studies and classical medical texts, may be further elucidated using a bottom-up approach based on patient medical records.

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

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.

Statistical inference of acupoint specificity: forward and reverse inference.

BACKGROUND: The acupoint specificity has been considered important issue in acupuncture research. In clinical aspects, it is essential to identify which acupoints are associated specifically with a particular disease. The present study aimed to identify the specificity of acupoint selection (forward inference) and the specificity of acupoint indication (reverse inference) from the online virtual diagnosis experiment. METHODS: Eighty Korean Medicine doctors conducted the virtual medical diagnoses provided for 10 different case reports. For forward inference, the acupoints prescribed for each disease were quantified and the data were normalised among 30 frequently used acupoints using Z-scores. For reverse inference, diseases for each acupoint were quantified and the data were normalized among 10 disease using Z-scores. RESULTS: Using forward inference we demonstrated the specificity of acupoint selection in each disease. Using reverse inference we identified the specificity of acupoint indication in each acupoint. In general, a certain acupoint can be selected specifically for a particular disease, and it has a specific indication for the disease. However, the specificity of acupoint indication and the specificity of acupoint selection are not always identical. CONCLUSIONS: The selection of an acupoint for a particular disease does not imply that the acupoint has specific indications for that disease. Inferring the specificity of acupoint indication from clinical observations should be considered.