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Difference in mortality rates in hospitalized COVID-19 patients identified by cytokine profile clustering using a machine learning approach: An outcome prediction alternative
COVID-19 is a disease caused by the novel Coronavirus SARS-CoV-2 causing an acute respiratory disease that can eventually lead to severe acute respiratory syndrome (SARS). An exacerbated inflammatory response is characteristic of SARS-CoV-2 infection, which leads to a cytokine release syndrome also...
Autores principales: | , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9530472/ https://www.ncbi.nlm.nih.gov/pubmed/36203752 http://dx.doi.org/10.3389/fmed.2022.987182 |
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author | Castro-Castro, Ana Cristina Figueroa-Protti, Lucia Molina-Mora, Jose Arturo Rojas-Salas, María Paula Villafuerte-Mena, Danae Suarez-Sánchez, María José Sanabría-Castro, Alfredo Boza-Calvo, Carolina Calvo-Flores, Leonardo Solano-Vargas, Mariela Madrigal-Sánchez, Juan José Sibaja-Campos, Mario Silesky-Jiménez, Juan Ignacio Chaverri-Fernández, José Miguel Soto-Rodríguez, Andrés Echeverri-McCandless, Ann Rojas-Chaves, Sebastián Landaverde-Recinos, Denis Weigert, Andreas Mora, Javier |
author_facet | Castro-Castro, Ana Cristina Figueroa-Protti, Lucia Molina-Mora, Jose Arturo Rojas-Salas, María Paula Villafuerte-Mena, Danae Suarez-Sánchez, María José Sanabría-Castro, Alfredo Boza-Calvo, Carolina Calvo-Flores, Leonardo Solano-Vargas, Mariela Madrigal-Sánchez, Juan José Sibaja-Campos, Mario Silesky-Jiménez, Juan Ignacio Chaverri-Fernández, José Miguel Soto-Rodríguez, Andrés Echeverri-McCandless, Ann Rojas-Chaves, Sebastián Landaverde-Recinos, Denis Weigert, Andreas Mora, Javier |
author_sort | Castro-Castro, Ana Cristina |
collection | PubMed |
description | COVID-19 is a disease caused by the novel Coronavirus SARS-CoV-2 causing an acute respiratory disease that can eventually lead to severe acute respiratory syndrome (SARS). An exacerbated inflammatory response is characteristic of SARS-CoV-2 infection, which leads to a cytokine release syndrome also known as cytokine storm associated with the severity of the disease. Considering the importance of this event in the immunopathology of COVID-19, this study analyses cytokine levels of hospitalized patients to identify cytokine profiles associated with severity and mortality. Using a machine learning approach, 3 clusters of COVID-19 hospitalized patients were created based on their cytokine profile. Significant differences in the mortality rate were found among the clusters, associated to different CXCL10/IL-38 ratio. The balance of a CXCL10 induced inflammation with an appropriate immune regulation mediated by the anti-inflammatory cytokine IL-38 appears to generate the adequate immune context to overrule SARS-CoV-2 infection without creating a harmful inflammatory reaction. This study supports the concept that analyzing a single cytokine is insufficient to determine the outcome of a complex disease such as COVID-19, and different strategies incorporating bioinformatic analyses considering a broader immune profile represent a more robust alternative to predict the outcome of hospitalized patients with SARS-CoV-2 infection. |
format | Online Article Text |
id | pubmed-9530472 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95304722022-10-05 Difference in mortality rates in hospitalized COVID-19 patients identified by cytokine profile clustering using a machine learning approach: An outcome prediction alternative Castro-Castro, Ana Cristina Figueroa-Protti, Lucia Molina-Mora, Jose Arturo Rojas-Salas, María Paula Villafuerte-Mena, Danae Suarez-Sánchez, María José Sanabría-Castro, Alfredo Boza-Calvo, Carolina Calvo-Flores, Leonardo Solano-Vargas, Mariela Madrigal-Sánchez, Juan José Sibaja-Campos, Mario Silesky-Jiménez, Juan Ignacio Chaverri-Fernández, José Miguel Soto-Rodríguez, Andrés Echeverri-McCandless, Ann Rojas-Chaves, Sebastián Landaverde-Recinos, Denis Weigert, Andreas Mora, Javier Front Med (Lausanne) Medicine COVID-19 is a disease caused by the novel Coronavirus SARS-CoV-2 causing an acute respiratory disease that can eventually lead to severe acute respiratory syndrome (SARS). An exacerbated inflammatory response is characteristic of SARS-CoV-2 infection, which leads to a cytokine release syndrome also known as cytokine storm associated with the severity of the disease. Considering the importance of this event in the immunopathology of COVID-19, this study analyses cytokine levels of hospitalized patients to identify cytokine profiles associated with severity and mortality. Using a machine learning approach, 3 clusters of COVID-19 hospitalized patients were created based on their cytokine profile. Significant differences in the mortality rate were found among the clusters, associated to different CXCL10/IL-38 ratio. The balance of a CXCL10 induced inflammation with an appropriate immune regulation mediated by the anti-inflammatory cytokine IL-38 appears to generate the adequate immune context to overrule SARS-CoV-2 infection without creating a harmful inflammatory reaction. This study supports the concept that analyzing a single cytokine is insufficient to determine the outcome of a complex disease such as COVID-19, and different strategies incorporating bioinformatic analyses considering a broader immune profile represent a more robust alternative to predict the outcome of hospitalized patients with SARS-CoV-2 infection. Frontiers Media S.A. 2022-09-20 /pmc/articles/PMC9530472/ /pubmed/36203752 http://dx.doi.org/10.3389/fmed.2022.987182 Text en Copyright © 2022 Castro-Castro, Figueroa-Protti, Molina-Mora, Rojas-Salas, Villafuerte-Mena, Suarez-Sánchez, Sanabría-Castro, Boza-Calvo, Calvo-Flores, Solano-Vargas, Madrigal-Sánchez, Sibaja-Campos, Silesky-Jiménez, Chaverri-Fernández, Soto-Rodríguez, Echeverri-McCandless, Rojas-Chaves, Landaverde-Recinos, Weigert and Mora. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Medicine Castro-Castro, Ana Cristina Figueroa-Protti, Lucia Molina-Mora, Jose Arturo Rojas-Salas, María Paula Villafuerte-Mena, Danae Suarez-Sánchez, María José Sanabría-Castro, Alfredo Boza-Calvo, Carolina Calvo-Flores, Leonardo Solano-Vargas, Mariela Madrigal-Sánchez, Juan José Sibaja-Campos, Mario Silesky-Jiménez, Juan Ignacio Chaverri-Fernández, José Miguel Soto-Rodríguez, Andrés Echeverri-McCandless, Ann Rojas-Chaves, Sebastián Landaverde-Recinos, Denis Weigert, Andreas Mora, Javier Difference in mortality rates in hospitalized COVID-19 patients identified by cytokine profile clustering using a machine learning approach: An outcome prediction alternative |
title | Difference in mortality rates in hospitalized COVID-19 patients identified by cytokine profile clustering using a machine learning approach: An outcome prediction alternative |
title_full | Difference in mortality rates in hospitalized COVID-19 patients identified by cytokine profile clustering using a machine learning approach: An outcome prediction alternative |
title_fullStr | Difference in mortality rates in hospitalized COVID-19 patients identified by cytokine profile clustering using a machine learning approach: An outcome prediction alternative |
title_full_unstemmed | Difference in mortality rates in hospitalized COVID-19 patients identified by cytokine profile clustering using a machine learning approach: An outcome prediction alternative |
title_short | Difference in mortality rates in hospitalized COVID-19 patients identified by cytokine profile clustering using a machine learning approach: An outcome prediction alternative |
title_sort | difference in mortality rates in hospitalized covid-19 patients identified by cytokine profile clustering using a machine learning approach: an outcome prediction alternative |
topic | Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9530472/ https://www.ncbi.nlm.nih.gov/pubmed/36203752 http://dx.doi.org/10.3389/fmed.2022.987182 |
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