Abstract

Background Traditional Chinese medicine (TCM) has long been used to treat chronic kidney disease (CKD) in Asia. Its effectiveness and safety for CKD treatment have been confirmed in documented studies. However, the prescription rule of formulae for Chinese medicinal herbs is complicated and remains uncharacterized. Thus, we used data mining technology to evaluate the treatment principle and coprescription pattern of these formulae in CKD TCM treatment. Methods Data on patients with CKD were obtained from the outpatient system of a TCM hospital. We established a Chinese herb knowledge base based on the Chinese Pharmacopoeia and the Chinese Materia Medica. Then, following extraction of prescription information, we deweighted and standardized each prescribed herb according to the knowledge base to establish a database of CKD treatment formulae. We analyzed the frequency with which individual herbs were prescribed, as well as their properties, tastes, meridian tropisms, and categories. Then, we evaluated coprescription patterns and assessed medication rules by performing association rule learning, cluster analysis, and complex network analysis. Results We retrospectively analyzed 299 prescriptions of 166 patients with CKD receiving TCM treatment. The most frequently prescribed core herbs for CKD treatment were Rhizoma Dioscoreae (Shanyao), Spreading Hedyotis Herb (Baihuasheshecao), Root of Snow of June (Baimagu), Radix Astragali (Huangqi), Poria (Fulin), Rhizoma Atractylodis Macrocephalae (Baizhu), Radix Pseudostellariae (Taizishen), and Fructus Corni (Shanzhuyu). The TCM properties of the herbs were mainly being warm, mild, and cold. The tastes of the herbs were mainly sweet, followed by bitter. The main meridian tropisms were Spleen Meridian of Foot-Taiyin, Liver Meridian of Foot-Jueyi, Lung Meridian of Hand-Taiyin, Stomach Meridian of Foot-Yangming, and Kidney Meridian of Foot-Shaoyin. The top three categories were deficiency-tonifying, heat-clearing, and dampness-draining diuretic. Conclusion Using an integrated analysis method, we confirmed that the primary TCM pathogeneses of kidney disease were deficiency and dampness-heat. The primary treatment principles were tonifying deficiency and eliminating dampness-heat.

Highlights

  • Chronic kidney disease (CKD) is a worldwide public health problem and its prevalence is more than 10%

  • Another study was based on hierarchical clustering of herbal effects and determined standard prescription rules combined with the theory of qi and blood to explore the consistency of Traditional Chinese medicine (TCM) theory and herbs [13]

  • We aimed to identify the main treatment principle and herb prescriptions of TCM specialists for chronic kidney disease (CKD) treatment using modern data computer technology

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Summary

Background

Traditional Chinese medicine (TCM) has long been used to treat chronic kidney disease (CKD) in Asia. The prescription rule of formulae for Chinese medicinal herbs is complicated and remains uncharacterized. Us, we used data mining technology to evaluate the treatment principle and coprescription pattern of these formulae in CKD TCM treatment. En, following extraction of prescription information, we deweighted and standardized each prescribed herb according to the knowledge base to establish a database of CKD treatment formulae. We analyzed the frequency with which individual herbs were prescribed, as well as their properties, tastes, meridian tropisms, and categories. We retrospectively analyzed 299 prescriptions of 166 patients with CKD receiving TCM treatment. Using an integrated analysis method, we confirmed that the primary TCM pathogeneses of kidney disease were deficiency and dampness-heat. E primary treatment principles were tonifying deficiency and eliminating dampness-heat Using an integrated analysis method, we confirmed that the primary TCM pathogeneses of kidney disease were deficiency and dampness-heat. e primary treatment principles were tonifying deficiency and eliminating dampness-heat

Introduction
Materials and Methods
Data Processing
Results
Conclusions
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