Evidence map›Paper›PMID 39691499›Full record

ReviewFrontiers in cardiovascular medicine2024

Traditional Chinese medicine for cardiovascular disease: efficacy and safety.

Youwei Lin, Yuanshan Han, Yuhong Wang

Abstract readReview
In one paragraph

Review in Frontiers in cardiovascular medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
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  5. Article
  6. Review
  7. Review
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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

3 authors.

Youwei LinInstitute of Innovation and Applied Research in Chinese Medicine, Hunan University of Chinese Medicine, Changsha, China.
Yuanshan HanScientific Research Department, The First Hospital of Hunan University of Chinese Medicine, Changsha, China.
Yuhong WangInstitute of Innovation and Applied Research in Chinese Medicine, Hunan University of Chinese Medicine, Changsha, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In China and other Asian nations, traditional medicine has long been utilized in the treatment of cardiovascular diseases (CVD). While Chinese authorities have incorporated traditional Chinese medicine (TCM) treatment experiences as a supplementary guide for CVD, its international recognition remains limited due to a scarcity of high-quality and reliable randomized controlled trials (RCTs) evidence. The purpose of this study was to examine the clinical outcomes with TCM for CVD after the recent publication of large trials adding >20,000 individuals to the published data. Here, we systematically reviewed 55 published RCTs (modified Jadad scores > 4) in the past 20 years, involving a total of 36,261 patients. In most studies, TCM has been associated with significant improvements in alternative endpoints such as hypertension, coronary heart disease, stroke and heart failure. A total of 19 trials reported on primary outcomes such as cardiovascular events and death events. During the follow-up period, some Chinese patent medicines can effectively reduce the "hard" endpoints of coronary heart disease, stroke, and heart failure, the overall trend of cardiovascular outcomes is lower. The risk of adverse effects was not significantly increased compared to the control group, suggesting its potential as an alternative approach for primary and secondary prevention of CVD based on the available evidence.

Indexed as

cardiovascular diseasescardiovascular eventsChinese patent medicinedeath eventshypertension

Identifiers

PMID39691499
PMCPMC11649660

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.