Evidence map›Paper›PMID 37366530›Full record

ArticleHeliyon2023

Machine learning-based COVID-19 acute respiratory distress syndrome phenotyping and clinical outcomes: A systematic review.

Eric Daniel Tenda, Joshua Henrina, Jistrani Samosir, Ridha Amalia, Mira Yulianti, Ceva Wicaksono Pitoyo, Siti Setiati

Abstract read
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

7 authors.

Eric Daniel TendaDivision of Respirology and Critical Care, Department of Internal Medicine, Faculty of Medicine Universitas Indonesia - Cipto Mangunkusumo Hospital, Jakarta, Indonesia.
Joshua HenrinaDivision of Respirology and Critical Care, Department of Internal Medicine, Faculty of Medicine Universitas Indonesia - Cipto Mangunkusumo Hospital, Jakarta, Indonesia.
Jistrani SamosirDepartment of Internal Medicine, Dr.T.C. Hillers General Public Hospital, East Nusa Tenggara, Kabupaten Sikka, Indonesia.
Ridha AmaliaUniversity of Indonesia Hospital, Depok, Indonesia.
Mira YuliantiDivision of Respirology and Critical Care, Department of Internal Medicine, Faculty of Medicine Universitas Indonesia - Cipto Mangunkusumo Hospital, Jakarta, Indonesia.
Ceva Wicaksono PitoyoDivision of Respirology and Critical Care, Department of Internal Medicine, Faculty of Medicine Universitas Indonesia - Cipto Mangunkusumo Hospital, Jakarta, Indonesia.
Siti SetiatiDivision of Geriatric Medicine, Department of Internal Medicine, Faculty of Medicine Universitas Indonesia - Cipto Mangunkusumo Hospital, Jakarta, Indonesia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

COVID-19-related acute respiratory distress syndrome (CARDS) has been suggested to differ from the typical ARDS. While distinct phenotypes of ARDS have been identified through latent class analysis (LCA), it is unclear whether such phenotypes exist for CARDS and how they affect clinical outcomes. To address this question, we conducted a systematic review of the current evidence.We searched several, including PubMed, EBSCO Host, and Web of Science, from inception to July 1, 2022. Our exposure and outcome of interest were different CARDS phenotypes identified and their associated outcomes, such as 28-day, 90-day, 180-day mortality, ventilator-free days, and other relevant outcomes.We identified four studies comprising a total of 1776 CARDS patients.Of the four studies, three used LCA to identify subphenotypes (SPs) of CARDS. One study based on longitudinal data identified two SPs, with SP2 associated with worse ventilation and mechanical parameters than SP1. The other two studies based on baseline data also identified two SPs, with SP2 and SP1 were associated with hyperinflammatory and hypoinflammatory CARDS, respectively. The fourth study identified three SPs primarily stratified by comorbidities using multifactorial analysis.All studies identified a subphenotype associated with poorer outcomes, including mortality, ventilator-free days, multiple-organ injury, and pulmonary embolism. Two studies reported differential responses to corticosteroids among the SPs, with improved mortality in the hyperinflammatory and worse in the hypoinflammatory SPs.Overall, our review highlights the importance of phenotyping in understanding CARDS and its impact on disease management and prognostication. However, a consensus approach to phenotyping is necessary to ensure consistency and comparability across studies. We recommend that randomized clinical trials stratified by phenotype should only be initiated after such consensus is reached. Short title: COVID-19 ARDS subphenotypes and outcomes.

Indexed as

ARDSConference presentation: noneCOVID-19COVID-19 ARDSOutcomesPhenotyping

Identifiers

PMID37366530
PMCPMC10275654

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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.