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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
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.
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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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