Analysis of Acupoint Selection and Combinations in Acupuncture Treatment of Endometriosis: A Protocol for Data Mining

Endometriosis, characterized by dysmenorrhea and pelvic pain, is a common gynecological disease. Acupuncture, as a complementary and alternative therapy for endometriosis, has been supported by a large body of clinical evidence. However, due to the heterogeneity of acupoint prescriptions, the commonly used acupoints remain undetermined.

This study aims to identify the commonly used acupoint selection and combination patterns for the treatment of endometriosis through a comprehensive data mining analysis based on existing literature.

We will systematically search 8 databases (Web of Science (WOS), PubMed, Embase, Cochrane Library, Wanfang, Chongqing VIP database, China National Knowledge Infrastructure (CNKI), and the Chinese Biomedical Literature Database (CBM)) from their inception to September 30, 2026. Only acupuncture clinical trials will be included. All other types will be excluded. Literature deduplication will be performed using NoteExpress 4.3. Data management and descriptive statistical analysis will be completed using WPS Spreadsheets (version 12.1.0.26375). Association rule analysis will be conducted in SPSS Modeler 14.1. Cluster analysis, exploratory factor analysis, and decision tree analysis will be implemented using SPSS Statistics 26.0.

Our study will conduct an in-depth exploration of the frequently uesd acupoint selection and combination patterns for treating patients with endometriosis.

The findings of this study will identify commonly used acupoints and potentially relevant acupoint combinations for acupuncture treatment in patients with endometriosis, and provide a basis for subsequent clinical decision-making, and for the selection of acupoints in prospective randomized controlled trials, thereby helping both clinicians and patients make more informed choices.