Evidence map›Paper›PMID 31723184›Full record

ArticleScientific reports2019

Identification of high-risk patients for ADR induced by traditional Chinese medicine injection: a nested case-control study.

Cheng Jiang, Jie Shen, Dan Shou, Nani Wang, Jing Jing, Guodi Zhang, Jing Gu, Yunlong Tian, Caihua Sun, Jiaqi He and 3 more

Abstract read
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
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

6 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Anaphylactic Rare Saponins Separated fromEvidence-based complementary and alternative medicine : eCAM · 2022
    Article
  5. Article
  6. Review
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

13 authors.

Cheng JiangDepartment of pharmacy, Tongde Hospital of Zhejiang Province, Hangzhou, China.
Jie ShenDepartment of pharmacy, Zhejiang Province Hospital of Traditional Chinese Medicine, Hangzhou, China.
Dan ShouDepartment of Medicine, Zhejiang Academy of Traditional Chinese Medicine, Hangzhou, China.ORCID http://orcid.org/0000-0003-3465-4072
Nani WangDepartment of Medicine, Zhejiang Academy of Traditional Chinese Medicine, Hangzhou, China.
Jing JingDepartment of oncology, Tongde Hospital of Zhejiang Province, Hangzhou, China.
Guodi ZhangDepartment of oncology, Tongde Hospital of Zhejiang Province, Hangzhou, China.
Jing GuDepartment of oncology, Tongde Hospital of Zhejiang Province, Hangzhou, China.
Yunlong TianDepartment of endocrinology, Tongde Hospital of Zhejiang Province, Hangzhou, China.
Caihua SunDepartment of pharmacy, Zhejiang Province Hospital of Traditional Chinese Medicine, Hangzhou, China.
Jiaqi HeDepartment of pharmacy, Zhejiang Province Hospital of Traditional Chinese Medicine, Hangzhou, China.
Jiaqi MaCollege of chemical engineering, Zhejiang University of Technology, Hangzhou, China.
Xiaojun WangDepartment of pharmacy, Tongde Hospital of Zhejiang Province, Hangzhou, China. 158838286@qq.com.
Gonghua LiDepartment of pharmacy, Tongde Hospital of Zhejiang Province, Hangzhou, China. ligonghua88@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The adverse drug reaction (ADR) of traditional Chinese medicine injection (TCMI) has become one of the major concerns of public health in China. There are significant advantages for developing methods to improve the use of TCMI in routine clinical practice. The method of predicting TCMI-induced ADR was illustrated using a nested case-control study in 123 cases and 123 controls. The partial least squares regression (PLSR) models, which mapped the influence of basic characteristics and routine examinations to ADR, were established to predict the risk of ADR. The software was devised to provide an easy-to-use tool for clinic application. The effectiveness of the method was evaluated through its application to new patients with 95.7% accuracy of cases and 91.3% accuracy of controls. By using the method, the patients at high-risk could be conveniently, efficiently and economically recognized without any extra financial burden for additional examination. This study provides a novel insight into individualized management of the patients who will use TCMI.

Indexed as

SoftwareCase-Control StudiesDrug-Related Side Effects and Adverse ReactionsHumansInjectionsMedicine, Chinese Traditional

Identifiers

PMID31723184
PMCPMC6853959

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