Evidence map›Paper›PMID 39162921›Full record

ArticleAnnals of intensive care2024

Immunomodulators in patients receiving extracorporeal membrane oxygenation for COVID-19: a propensity-score adjusted analysis of the ELSO registry.

Ryan Ruiyang Ling, Kollengode Ramanathan, Liang Shen, Ryan P Barbaro, Kiran Shekar, Daniel Brodie, Graeme MacLaren

Abstract read
In one paragraph

Article in Annals of intensive care, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

7 authors.

Ryan Ruiyang Ling *Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Level 9, 1E Kent Ridge Road, Singapore, 119228, Singapore.ORCID http://orcid.org/0000-0002-8335-7640
Kollengode Ramanathan *Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Level 9, 1E Kent Ridge Road, Singapore, 119228, Singapore. surrkr@nus.edu.sg.ORCID http://orcid.org/0000-0003-1822-9455
Liang ShenBiostatistics Unit, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore, Singapore.ORCID http://orcid.org/0000-0001-7976-668X
Ryan P BarbaroDivision of Paediatrics Critical Care Medicine, University of Michigan, Ann Arbor, USA.ORCID http://orcid.org/0000-0002-3645-0359
Kiran ShekarAdult Intensive Care Services, Prince Charles Hospital, Brisbane, QLD, Australia.ORCID http://orcid.org/0000-0002-1239-7514
Daniel BrodieDivision of Pulmonary and Critical Care Medicine, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.ORCID http://orcid.org/0000-0002-0813-3145
Graeme MacLarenYong Loo Lin School of Medicine, National University of Singapore, National University Health System, Level 9, 1E Kent Ridge Road, Singapore, 119228, Singapore.ORCID http://orcid.org/0000-0002-1307-4274

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMortality for patients receiving extracorporeal membrane oxygenation (ECMO) for COVID-19 increased over the course of the pandemic. We investigated the association between immunomodulators and mortality for patients receiving ECMO for COVID-19.

methodsWe retrospectively analysed the Extracorporeal Life Support Organisation registry from 1 January, 2020, through 31 December, 2021, to compare the outcomes of patients who received no immunomodulators, only corticosteroids, only other immunomodulators (selective interleukin blockers, janus-kinase inhibitors, convalescent plasma, and intravenous immunoglobulin), and a combination of corticosteroids and other immunomodulators administered either before or during ECMO. We used Cox regression models to estimate survival time until 90 days. We estimated the propensity score of receiving different immunomodulators using multinomial regression, and incorporated these scores into the regression models.

resultsWe included 7181 patients in the final analysis; 6169 patients received immunomodulators either before or during ECMO. The 90-day survival was 58.1% (95%-CI 55.1-61.2%) for patients receiving no immunomodulators, 50.7% (95%-CI 49.0-52.5%) for those receiving only corticosteroids, 62.2% (95%-CI 57.4-67.0%) for those receiving other immunomodulators, and 48.5% (95%-CI 46.7-50.4%) for those receiving corticosteroids and other immunomodulators. Compared to patients without immunomodulators, patients receiving either corticosteroids alone (HR: 1.13, 95%-CI 1.01-1.28) or with other immunomodulators (HR: 1.21, 95%-CI: 1.07-1.54) had significantly shorter survival time, while patients receiving only other immunomodulators had significantly longer survival time (HR: 0.79, 95%-CI: 0.66-0.96). The receipt of immunomodulators (across all three groups) was associated with an increase in secondary infections.

conclusionsIn this cohort study, we found that immunomodulators, in particular corticosteroids, were associated with significantly higher mortality amongst patients receiving ECMO for COVID-19, after adjusting for potential confounding variables and propensity score. In addition, patients receiving corticosteroids with or without other immunomodulators had longer ECMO runs, which has potential implications for resource allocation. While residual confounding likely remains, further studies are required to evaluate the timing of immunomodulators and better understand the possible mechanisms behind this association, including secondary infections.

Indexed as

Coronavirus disease 2019CorticosteroidsExtracorporeal membrane oxygenationImmunomodulatory treatment

Identifiers

PMID39162921
PMCPMC11336150

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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.