Evidence map›Paper›PMID 38097885›Full record

SynthesisInflammopharmacology2024

Intravenous immunoglobulin for treatment of hospitalized COVID-19 patients: an evidence mapping and meta-analysis.

Mei-Xuan Li, Yan-Fei Li, Xin Xing, Jun-Qiang Niu, Liang Yao, Meng-Ying Lu, Ke Guo, Mi-Na Ma, Xiao-Tian Wu, Ning Ma and 11 more

Abstract readMeta-AnalysisReview
PubMed Publisher
In one paragraph

Synthesis in Inflammopharmacology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 38% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Trial
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

21 authors at 7 institutions in 3 countries.

Mei-Xuan Li *Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China.
Yan-Fei Li *Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China.
Xin Xing *Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China.
Jun-Qiang NiuEvidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China.
Liang YaoDepartment of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Canada.
Meng-Ying LuEvidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China.
Ke GuoEvidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China.
Mi-Na MaEvidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China.
Xiao-Tian WuEvidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China.
Ning MaEvidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China.
Dan LiEvidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China.
Zi-Jun LiEvidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China.
Ling GuanEvidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China.
Xiao-Man WangEvidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China.
Bei PanEvidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China.
Wen-Ru ShangEvidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China.
Jing JiDepartment of Rehabilitation, Gansu Provincial Hospital of Traditional Chinese Medicine, Lanzhou, China.
Zhong-Yang SongAffiliated Hospital of Gansu University of Chinese Medicine, Lanzhou, China.
Zhi-Ming ZhangDepartment of Rehabilitation, Gansu Provincial Hospital of Traditional Chinese Medicine, Lanzhou, China. zhangzhimingys@163.com.
Yong-Feng WangGansu Medical College, Pingliang, China. wangyongfeng2020@126.com.
Ke-Hu YangEvidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China. yangkh-ebm@lzu.edu.cn.ORCID http://orcid.org/0000-0001-7864-3012
Lanzhou University · CNGansu University of Traditional Chinese Medicine · CNFirst Hospital of Lanzhou University · CNGansu Provincial Hospital · CNImpact · CALanzhou University of Technology · CNPingliang People's Hospital · CN

Funding

Science and technology Project of "Gansu Prescription" Prevention and treatment of COVID-19 22ZD1FA001
6 · The paper itself

Abstract

backgroundThe clinical efficacy and safety of intravenous immunoglobulin (IVIg) treatment for COVID-19 remain controversial. This study aimed to map the current status and gaps of available evidence, and conduct a meta-analysis to further investigate the benefit of IVIg in COVID-19 patients.

methodsElectronic databases were searched for systematic reviews/meta-analyses (SR/MAs), primary studies with control groups, reporting on the use of IVIg in patients with COVID-19. A random-effects meta-analysis with subgroup analyses regarding study design and patient disease severity was performed. Our outcomes of interest determined by the evidence mapping, were mortality, length of hospitalization (days), length of intensive care unit (ICU) stay (days), number of patients requiring mechanical ventilation, and adverse events.

resultsWe included 34 studies (12 SR/MAs, 8 prospective and 14 retrospective studies). A total of 5571 hospitalized patients were involved in 22 primary studies. Random-effects meta-analyses of very low to moderate evidence showed that there was little or no difference between IVIg and standard care or placebo in reducing mortality (relative risk [RR] 0.91; 95% CI 0.78-1.06; risk difference [RD] 3.3% fewer), length of hospital (mean difference [MD] 0.37; 95% CI - 2.56, 3.31) and ICU (MD 0.36; 95% CI - 0.81, 1.53) stays, mechanical ventilation use (RR 0.92; 95% CI 0.68-1.24; RD 2.8% fewer), and adverse events (RR 0.98; 95% CI 0.84-1.14; RD 0.5% fewer) of patients with COVID-19. Sensitivity analysis using a fixed-effects model indicated that IVIg may reduce mortality (RR 0.76; 95% CI 0.60-0.97), and increase length of hospital stay (MD 0.68; 95% CI 0.09-1.28).

conclusionVery low to moderate certainty of evidence indicated IVIg may not improve the clinical outcomes of hospitalized patients with COVID-19. Given the discrepancy between the random- and fixed-effects model results, further large-scale and well-designed RCTs are warranted.

Indexed as

COVID-19Immunoglobulins, IntravenousHumansProspective StudiesRetrospective StudiesSystematic Reviews as TopicImmunoglobulins, IntravenousCOVID-19EfficacyEvidence mappingIntravenous immunoglobulinMeta-analysisSafety

Identifiers

PMID38097885
OpenAlexW4389727139

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.