Evidence map›Paper›PMID 32047530›Full record

ArticleEvidence-based complementary and alternative medicine : eCAM2020

Data Mining-Based Analysis of Chinese Medicinal Herb Formulae in Chronic Kidney Disease Treatment.

Ping Xia, Kun Gao, Jiadong Xie, Wei Sun, Ming Shi, Wei Li, Jing Zhao, Jin Yan, Qiong Liu, Min Zheng and 6 more

Open access · hybridAbstract read
In one paragraph

Article in Evidence-based complementary and alternative medicine : eCAM, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
10.9field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

26 citing papers in PubMed, 58 citations in OpenAlex.

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  20. An Association Rule Analysis of the Acupressure Effect on Sleep Quality.Evidence-based complementary and alternative medicine : eCAM · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors at 2 institutions in 1 country.

Ping XiaDivision of Nephrology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing 210029, China.ORCID https://orcid.org/0000-0001-7852-6764
Kun GaoDivision of Nephrology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing 210029, China.ORCID https://orcid.org/0000-0003-3236-4944
Jiadong XieSchool of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210029, China.
Wei SunDivision of Nephrology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing 210029, China.ORCID https://orcid.org/0000-0002-7366-1462
Ming ShiDivision of Gerontology, The Third Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210000, China.
Wei LiDivision of Nephrology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing 210029, China.
Jing ZhaoDivision of Nephrology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing 210029, China.
Jin YanDivision of Nephrology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing 210029, China.
Qiong LiuDivision of Nephrology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing 210029, China.ORCID https://orcid.org/0000-0002-0882-8991
Min ZhengDivision of Nephrology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing 210029, China.
Xin WangDivision of Nephrology, Suzhou Hospital of Integrated Medicine, Suzhou 215200, China.ORCID https://orcid.org/0000-0001-9448-7048
Qijing WuDivision of Nephrology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing 210029, China.
Enchao ZhouDivision of Nephrology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing 210029, China.
Jihong ChenDivision of Nephrology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing 210029, China.
Lingdong XvDivision of Nephrology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing 210029, China.
Weiming HeDivision of Nephrology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing 210029, China.ORCID https://orcid.org/0000-0002-9606-1427
Nanjing University of Chinese Medicine · CNSuzhou Traditional Chinese Medicine Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTraditional 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.

methodsData 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.

resultsWe 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.

conclusionUsing 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.

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PMID32047530
PMCPMC7003280
OpenAlexW3002838360

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