Evidence map›Paper›PMID 32792254›Full record

SynthesisJournal of integrative medicine2020

Chinese herbal medicine for COVID-19: Current evidence with systematic review and meta-analysis.

Arthur Yin Fan, Sherman Gu, Sarah Faggert Alemi, Research Group for Evidence-based Chinese Medicine

Open access · greenAbstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in Journal of integrative medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 11 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
52citing papers in PubMed, 11 pooled it
2.6field-weighted citation impact, top 6% 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

52 citing papers in PubMed, 11 syntheses or guidelines pooled it, 120 citations in OpenAlex.

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  4. Herbal medicine for COVID-19: An overview of systematic reviews and meta-analysis.Phytomedicine : international journal of phytotherapy and phytopharmacology · 2022
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  15. Antiviral Activity and Underlying Mechanism ofMolecules (Basel, Switzerland) · 2025
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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

4 authors at 1 institution in 1 country.

Arthur Yin FanAmerican TCM Association (ATCMA), Vienna, VA 22182, USA; McLean Center for Complementary and Alternative Medicine, PLC, Vienna, VA 22182, USA. Electronic address: ArthurFan@ChineseMedicineDoctor.US.
Sherman GuFederation of Chinese Medicine & Acupuncture Societies of Australia (FCMA), Victoria 3011, Australia; Knox Chinese Healing & Myotherapy, Wantirna South, Victoria 3152, Australia. Electronic address: contactus@fcma.org.au.
Sarah Faggert AlemiAmerican TCM Association (ATCMA), Vienna, VA 22182, USA; East Roots Wellness, PLC, McLean, VA 22101, USA.
Research Group for Evidence-based Chinese Medicine
National Center for Complementary and Integrative Health · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThere is currently no drug or therapy that cures COVID-19, a highly contagious and life-threatening disease.

objectiveThis systematic review and meta-analysis summarized contemporary studies that report the use of Chinese herbal medicine (CHM) to treat COVID-19. SEARCH STRATEGY: Six electronic databases (PubMed/MEDLINE, Cochrane Library, ScienceDirect, Google Scholar, Wanfang Data and China National Knowledge Infrastructure) were searched from their beginning to May 15, 2020 with the following search terms: traditional Chinese medicine, Chinese medicine, Chinese herbal medicine, COVID-19, new coronavirus pneumonia, SARS-CoV-2, and randomized controlled trial. INCLUSION CRITERIA: Randomized controlled trials (RCTs) from peer-reviewed journals and non-reviewed publications were included. Further, included RCTs had a control group that was given standard care (SC; such as conventional Western medicine treatments or routine medical care), and a treatment group that was given SC plus CHM. DATA EXTRACTION AND ANALYSIS: Two evaluators screened and collected literature independently; information on participants, study design, interventions, follow-up and adverse events were extracted, and risk of bias was assessed. The primary outcomes included scores that represented changes in symptoms and signs over the course of treatment. Secondary outcomes included the level of inflammatory markers, improvement of pneumonia confirmed by computed tomography (CT), and adverse events. Dichotomous data were expressed as risk ratio or hazard ratio with 95% confidence interval (CI); where time-to-event analysis was used, outcomes were expressed as odds ratio with 95% CI. Continuous data were expressed as difference in means (MD) with 95% CI, and standardized mean difference (SMD) was used when different outcome scales were pooled.

resultsSeven original studies, comprising a total of 732 adults, were included in this meta-analysis. Compared to SC alone, CHM plus SC had a superior effect on the change of symptom and sign score (-1.30 by SMD, 95% CI [-2.43, -0.16]; 3 studies; n = 261, P = 0.03), on inflammatory marker C-reactive protein (CRP, mg/L; -11.82 by MD, 95% CI [-17.95, -5.69]; 5 studies; n = 325, P = 0.0002), on number of patients with improved lung CT scans (1.34 by risk ratio, 95% CI [1.19, 1.51]; 4 studies; n = 489, P < 0.00001). No significant adverse events were recorded in the included RCTs.

conclusionCurrent evidence shows that CHM, as an adjunct treatment with standard care, helps to improve treatment outcomes in COVID-19 cases.

Indexed as

BetacoronavirusCoronavirus InfectionsCOVID-19 Drug TreatmentDrugs, Chinese HerbalHumansSARS-CoV-2Drugs, Chinese HerbalChinese herbal medicineCOVID-19Meta-analysisSARS-CoV-2Systematic reviewTraditional Chinese medicine

Identifiers

PMID32792254
PMCPMC7834293
OpenAlexW3046628749

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.