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

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...

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Autores principales: Tenda, Eric Daniel, Henrina, Joshua, Samosir, Jistrani, Amalia, Ridha, Yulianti, Mira, Pitoyo, Ceva Wicaksono, Setiati, Siti
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10275654/
https://www.ncbi.nlm.nih.gov/pubmed/37366530
http://dx.doi.org/10.1016/j.heliyon.2023.e17276
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author Tenda, Eric Daniel
Henrina, Joshua
Samosir, Jistrani
Amalia, Ridha
Yulianti, Mira
Pitoyo, Ceva Wicaksono
Setiati, Siti
author_facet Tenda, Eric Daniel
Henrina, Joshua
Samosir, Jistrani
Amalia, Ridha
Yulianti, Mira
Pitoyo, Ceva Wicaksono
Setiati, Siti
author_sort Tenda, Eric Daniel
collection PubMed
description 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.
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spelling pubmed-102756542023-06-21 Machine learning-based COVID-19 acute respiratory distress syndrome phenotyping and clinical outcomes: A systematic review Tenda, Eric Daniel Henrina, Joshua Samosir, Jistrani Amalia, Ridha Yulianti, Mira Pitoyo, Ceva Wicaksono Setiati, Siti Heliyon Research Article 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. Elsevier 2023-06-17 /pmc/articles/PMC10275654/ /pubmed/37366530 http://dx.doi.org/10.1016/j.heliyon.2023.e17276 Text en © 2023 Published by Elsevier Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Tenda, Eric Daniel
Henrina, Joshua
Samosir, Jistrani
Amalia, Ridha
Yulianti, Mira
Pitoyo, Ceva Wicaksono
Setiati, Siti
Machine learning-based COVID-19 acute respiratory distress syndrome phenotyping and clinical outcomes: A systematic review
title Machine learning-based COVID-19 acute respiratory distress syndrome phenotyping and clinical outcomes: A systematic review
title_full Machine learning-based COVID-19 acute respiratory distress syndrome phenotyping and clinical outcomes: A systematic review
title_fullStr Machine learning-based COVID-19 acute respiratory distress syndrome phenotyping and clinical outcomes: A systematic review
title_full_unstemmed Machine learning-based COVID-19 acute respiratory distress syndrome phenotyping and clinical outcomes: A systematic review
title_short Machine learning-based COVID-19 acute respiratory distress syndrome phenotyping and clinical outcomes: A systematic review
title_sort machine learning-based covid-19 acute respiratory distress syndrome phenotyping and clinical outcomes: a systematic review
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10275654/
https://www.ncbi.nlm.nih.gov/pubmed/37366530
http://dx.doi.org/10.1016/j.heliyon.2023.e17276
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