Evidence map›Paper›PMID 39117847›Full record

ReviewCurrent topics in microbiology and immunology2025

Generating the Evidence Base for Convalescent Plasma Use for a New Infectious Disease.

Hyunah Yoon, Liise-Anne Pirofski

Abstract readReview
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In one paragraph

Review in Current topics in microbiology and immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Hyunah YoonDivision of Infectious Diseases, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, USA.
Liise-Anne PirofskiDivision of Infectious Diseases, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, USA. l.pirofski@einsteinmed.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) swept across the world in the waning months of 2019 and emerged as the cause of the coronavirus disease 19 (COVID-19) pandemic in early 2020. The use of convalescent plasma (CP) for prior respiratory pandemics provided a strong biological rationale for the rapid deployment of COVID-19 convalescent plasma (CCP) in early 2020 when no validated treatments or prior immunity existed. CCP is an antiviral agent, with its activity against SARS-CoV-2 stemming from specific antibodies elicited by the virus. Early efforts to investigate the efficacy of CCP in randomized clinical trials (RCTs) that targeted hospitalized patients with COVID-19 did not demonstrate the overall efficacy of CCP despite signals of benefit in certain subgroups, such as those treated earlier in disease. In contrast, studies adhering to the principles of antibody therapy in their study design, choice of patient population, and product qualification, i.e., those that administered high levels of specific antibody during the viral phase of disease in immunocompromised or very early in immunocompetent individuals, demonstrated benefits. In this chapter, we leverage the knowledge gained from clinical studies of CCP for COVID-19 to propose a framework for future studies of CP for a new infectious disease. This framework includes obtaining high-quality CP and designing clinical studies that adhere to the principles of antibody therapy to generate a robust evidence base for using CP.

Indexed as

COVID-19Immunization, PassiveSARS-CoV-2Antibodies, ViralCOVID-19 SerotherapyHumansRandomized Controlled Trials as TopicAntibodies, ViralClinical researchCOVID-19 Convalescent PlasmaStudy design

Identifiers

PMID39117847

What OpenQuestion holds

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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.