Evidence map›Paper›PMID 32896750›Full record

SynthesisInternational immunopharmacology2020

Immunomodulatory-based therapy as a potential promising treatment strategy against severe COVID-19 patients: A systematic review.

Mahdieh Razmi, Farideh Hashemi, Elmira Gheytanchi, Masoumeh Dehghan Manshadi, Roya Ghods, Zahra Madjd

Open access · greenAbstract readSystematic Review
In one paragraph

Synthesis in International immunopharmacology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 2 pooled it
0.3field-weighted citation impact, top 30% 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

5 citing papers in PubMed, 2 syntheses or guidelines pooled it, 12 citations in OpenAlex.

  1. Pooled it
  2. Current evidence for COVID-19 therapies: a systematic literature review.European respiratory review : an official journal of the European Respiratory Society · 2021
    Pooled it
  3. Existing Drugs Considered as Promising in COVID-19 Therapy.International journal of molecular sciences · 2021
    Review
  4. Review
  5. 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

6 authors at 1 institution in 1 country.

Mahdieh RazmiOncopathology Research Center, Iran University of Medical Sciences, Tehran, Iran.
Farideh HashemiOncopathology Research Center, Iran University of Medical Sciences, Tehran, Iran; Department of Molecular Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran.
Elmira GheytanchiOncopathology Research Center, Iran University of Medical Sciences, Tehran, Iran.
Masoumeh Dehghan ManshadiOncopathology Research Center, Iran University of Medical Sciences, Tehran, Iran; Department of Molecular Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran.
Roya GhodsOncopathology Research Center, Iran University of Medical Sciences, Tehran, Iran; Department of Molecular Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran. Electronic address: ghods.ro@iums.ac.ir.
Zahra MadjdOncopathology Research Center, Iran University of Medical Sciences, Tehran, Iran; Department of Molecular Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran. Electronic address: majdjabari.z@iums.ac.ir.
Iran University of Medical Sciences · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The global panic of the novel coronavirus disease 2019 (COVID-19) triggered by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to an urgent requirement for effective therapy. COVID-19 infection, especially in severely ill patients, is likely to be associated with immune dysregulation, prompting the development of novel treatment approaches. Therefore, this systematic review was designed to assess the available data regarding the efficacy of the immunomodulatory drugs used to manage COVID-19. A systematic literature search was carried out up to May 27, 2020, in four databases (PubMed, Scopus, Web of Science, and Embase) and also Clinicaltrials.gov. Sixty-six publications and 111 clinical trials were recognized as eligible, reporting the efficacy of the immunomodulatory agents, including corticosteroids, hydroxychloroquine, passive and cytokine-targeted therapies, mesenchymal stem cells, and blood-purification therapy, in COVID-19 patients. The data were found to be heterogeneous, and the clinical trials were yet to post any findings. Medicines were found to regulate the immune system by boosting the innate responses or suppressing the inflammatory reactions. Passive and cytokine-targeted therapies and mesenchymal stem cells were mostly safe and could regulate the disease much better. These studies underscored the significance of severity profiling in COVID-19 patients, along with appropriate timing, duration, and dosage of the therapies. Therefore, this review indicates that immunomodulatory therapies are potentially effective for COVID-19 and provides comprehensive information for clinicians to fight this outbreak. However, there is no consensus on the optimal therapy for COVID-19, reflecting that the immunomodulatory therapies still warrant further investigations.

Indexed as

Adaptive ImmunityBetacoronavirusClinical Trials as TopicCoronavirus InfectionsCOVID-19CytokinesHumansImmunity, InnateImmunotherapyPandemicsPneumonia, ViralSARS-CoV-2Severity of Illness IndexTreatment OutcomeCytokinesCOVID-19Cytokine-targeted therapyImmunomodulatory-based therapyMesenchymal stem cellsPassive immunotherapySARS-CoV-2

Identifiers

PMID32896750
PMCPMC7456184
OpenAlexW3080378540

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.