Identifying Chinese herbal medicine network for treating acne: Implications from a nationwide database

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• 作者：Hsing-Yu Chen^a ; ^b ; ^c ; Yi-Hsuan Lin^a ; ^b ; ^c ; Yu-Chun Chen^d ; ^e ; ^f ; ^{yuchn.chen@googlemail.com" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Terms ; Abbreviation ; ARM ; Association rule mining ; CHM ; Chinese herbal medicine ; HF ; Herbal formula ; ICD-9-CM ; International Classification of Diseases ; Ninth Revision ; Clinical Modification ; NHI ; National Health Insurance ; NHIRD ; National Health Insurance Research Database ; SHrb ; Single he ; SNAis ; Social network analys ; TCM ; Traditional Chinese medicine
• 刊名：Journal of Ethnopharmacology
• 出版年：2016
• 出版时间：17 February 2016
• 年：2016
• 卷：179
• 期：Complete
• 页码：1-8
• 全文大小：1152 K

文摘

Acne is a highly prevalent inflammatory skin disease which causes patients great psychological stress, especially teenagers. Chinese herbal medicine (CHM) is commonly used to treat acne with personalized but complicated prescriptions. The aim of this study is to determine a CHM network and core CHM treatments for acne by analyzing a nationwide database.

Materials and methods

From January 1st to December 31st, 2011, all CHM prescriptions made for acne (ICD-9-CM code: 706.0 or 706.1) were included in this study. Visits with acupuncture, manual therapy or other treatment modalities were excluded, and CHM visits with other diagnoses were also excluded in final analysis. Association rule mining (ARM) and social network analysis (SNA) were used to explore and demonstrate a CHM network.

Results

A total of 91,129 patients used traditional Chinese medicine, and 99% of them chose CHM for acne treatment. Most CHM users were teenagers, and there were twice as many female patients as male patients. A total of 279,823 CHM prescriptions were made for acne in 2011. Qing-Shang-Fang-Feng-Tang was the most commonly used CHM (31.2% of all prescriptions), and Zhen-Ren-Huo-Ming-Yin combined with Forsythia suspensa (Thunb.) Vahl. (Lian Qiao) was the most commonly used CHM-CHM combination. Thirty-one important CHM-CHM combinations were identified, and the CHM network could be built. Extensive coverage of the known pathogenesis of acne could be found in the CHM network when incorporating CHM pharmacological mechanisms into the network. Anti-inflammatory and anti-bacterial effects were commonly found in the CHM network, and CHMs with anti-androgen, anti-depressive and skin whitening effects were frequently used in combination.

Conclusions

The CHM combination patterns and core treatments for acne were disclosed in this study by applying network analysis to a CHM prescription database. These results may be beneficial for further bench or clinical studies when choosing target CHM.