Evidence map›Paper›PMID 39449081›Full record

Observational studyCritical care (London, England)2024

Identification of novel sub-phenotypes of severe ARDS requiring ECMO using latent class analysis.

Mitsuaki Nishikimi, Shinichiro Ohshimo, Giacomo Bellani, Wataru Fukumoto, Tatsuhiko Anzai, Keibun Liu, Junki Ishii, Michihito Kyo, Kazuo Awai, Kunihiko Takahashi and 2 more

Abstract readObservational StudyMulticenter Study
In one paragraph

Observational study in Critical care (London, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
  2. Review
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  5. Article
  6. Review
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  11. Article
  12. Targeting PANoptosis: a promising therapeutic strategy for ALI/ARDS.Apoptosis : an international journal on programmed cell death · 2025
    Review
  13. Article
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  15. Review
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

12 authors.

Mitsuaki NishikimiDepartment of Emergency and Critical Care Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan. m0528332626@yahoo.co.jp.
Shinichiro OhshimoDepartment of Emergency and Critical Care Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
Giacomo BellaniCentre for Medical Sciences-CISMed, University of Trento, Trento, Italy.
Wataru FukumotoDepartment of Diagnostic Radiology, Graduate School of Biomedical and Health Science, Hiroshima University, Hiroshima, Japan.
Tatsuhiko AnzaiM&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan.
Keibun LiuCritical Care Research Group, The Prince Charles Hospital, Brisbane, Australia.
Junki IshiiDepartment of Emergency and Critical Care Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
Michihito KyoDepartment of Emergency and Critical Care Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
Kazuo AwaiDepartment of Diagnostic Radiology, Graduate School of Biomedical and Health Science, Hiroshima University, Hiroshima, Japan.
Kunihiko TakahashiM&D Data Science Center, Tokyo Medical and Dental University, Tokyo, Japan.
Nobuaki ShimeDepartment of Emergency and Critical Care Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan.
J-CARVE registry group

Funding

Japanese Association for Acute Medicine, and Japan Agency for Medical Research and Development JP23fk0108654JSPS KAKENHI JP 22K09120
6 · The paper itself

Abstract

backgroundSub-phenotyping of acute respiratory distress syndrome (ARDS) could be useful for evaluating the severity of ARDS or predicting its responsiveness to given therapeutic strategies, but no studies have yet investigated the heterogeneity of patients with severe ARDS requiring veno-venous extracorporeal membrane oxygenation (V-V ECMO).

methodsWe conducted this retrospective multicenter observational study in adult patients with severe ARDS treated by V-V ECMO. We performed latent class analysis (LCA) for identifying sub-phenotypes of severe ARDS based on the radiological and clinical findings at the start of ECMO support. Multivariate Cox regression analysis was conducted to investigate the differences in mortality and association between the PEEP setting of ≥ 10 cmH

resultsWe identified three sub-phenotypes from analysis of the data of a total of 544 patients with severe ARDS treated by V-V ECMO, as follows: Dry type (n = 185; 34%); Wet type (n = 169; 31%); and Fibrotic type (n = 190; 35%). The 90-days in-hospital mortality risk was higher in the patients with the Fibrotic type than in those with the Dry type (adjusted hazard ratio [95% confidence interval] 1.75 [1.10-2.79], p = 0.019) or the Wet type (1.50 [1.02-2.23], p = 0.042). The PEEP setting of ≥ 10 cmH

conclusionsThe three sub-phenotypes showed different mortality rates and different relationships between higher PEEP settings in the early phase of V-V ECMO and patient outcomes. Our data suggest that we may need to change our management approach to patients with severe ARDS during V-V ECMO according to their clinical sub-phenotype.

Indexed as

Extracorporeal Membrane OxygenationLatent Class AnalysisRespiratory Distress SyndromeAdultAgedFemaleHospital MortalityHumansMaleMiddle AgedPhenotypeProportional Hazards ModelsRetrospective StudiesComputed tomographyIn-hospital mortalityLatent class analysisSevere acute respiratory distress syndromeVeno-venous extracorporeal membrane oxygenation

Identifiers

PMID39449081
PMCPMC11515347

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.