Evidence map›Paper›PMID 39170141›Full record

ArticleHeliyon2024

Effectiveness of seven oral traditional Chinese medicines against mild or moderate COVID-19: An updated systematic review and network meta-analysis.

Tian Zhang, Ting Li, Fei Zhao, Tongzhou Li, Miaomiao Zhang, Pengfei Jin

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

6 authors.

Tian ZhangDepartment of Pharmacy, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Key Laboratory of Assessment of Clinical Drugs Risk and Individual Application (Beijing Hospital), Beijing, 100730, PR China.
Ting LiDepartment of Pharmacy, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Key Laboratory of Assessment of Clinical Drugs Risk and Individual Application (Beijing Hospital), Beijing, 100730, PR China.
Fei ZhaoDepartment of Pharmacy, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Key Laboratory of Assessment of Clinical Drugs Risk and Individual Application (Beijing Hospital), Beijing, 100730, PR China.
Tongzhou LiDepartment of Pharmacy, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Key Laboratory of Assessment of Clinical Drugs Risk and Individual Application (Beijing Hospital), Beijing, 100730, PR China.
Miaomiao ZhangDepartment of Pharmacy, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Key Laboratory of Assessment of Clinical Drugs Risk and Individual Application (Beijing Hospital), Beijing, 100730, PR China.
Pengfei JinDepartment of Pharmacy, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Key Laboratory of Assessment of Clinical Drugs Risk and Individual Application (Beijing Hospital), Beijing, 100730, PR China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is responsible for the outbreak of COVID-19 in Wuhan, China. As a highly infectious epidemic, SARS-CoV-2 rapidly evolves. Presently, COVID-19 coexists with humans, mainly with mild or moderate disease. The latest Guidelines for the Diagnosis and Treatment of COVID-19 (trial version of the 10th Edition) recommend several oral traditional Chinese medicines (TCMs) for treatment. This study aims to evaluate the evidence-based benefits of these TCMs as adjunctive therapies to conventional western medicine (CWM) for patients with mild or moderate COVID-19. Methods: We conducted a systematic review and meta-analysis adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, utilizing the PRISMA checklist. We searched PubMed, Cochrane Library, Embase, CNKI, and Wan-Fang databases to retrieve randomized controlled trials and retrospective cohort studies of TCM in combination with CWM on the treatment of mild or moderate COVID-19 that were published as of December 25, 2023. A network meta-analysis using the frequency model was employed to evaluate the benefits of different interventions. Results: A total of 30 eligible studies, enrolling 4144 participants, utilized 7 marketed oral TCMs in China. Compared with CWM alone, the integration of TCMs with CWM can significantly reduce severe conversion rate. This combined approach also enhances the clinical effective rate, shortens the negative conversion time of nucleic acid, and improves both symptoms and blood biochemical markers in patients. The network meta-analysis provided preliminary evidence of the superiority of specific TCMs for various outcomes: Qingfei Paidu for raising the CT improvement rate and clinical effective rate, and shortening the negative conversion time of nucleic acid; Huashi Baidu for reducing severe conversion and improving cough; Xuanfei Baidu for improving fatigue; Jinhua Qinggan for improving fever; Lianhua Qingwen for shortening the recovery time of fatigue and cough; and Shufeng Jiedu for shortening the recovery time of fever. Conclusions: TCM in combination with CWM may be beneficial for patients with mild or moderate COVID-19. Each TCM may have distinct benefits in COVID-19.

Indexed as

COVID-19EffectivenessNetwork meta-analysisTraditional Chinese medicine

Identifiers

PMID39170141
PMCPMC11336365

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Registered trials

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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.