Difference in mortality rates in hospitalized COVID-19 patients identified by cytokine profile clustering using a machine learning approach. An outcome prediction alternative

 

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Detalles Bibliográficos
Autores: Castro Castro, Ana Cristina, Figueroa Protti, Lucía, Molina Mora, José Arturo, Rojas Salas, María Paula, Villafuerte Mena, Danae, Suárez Sánchez, María José, Sanabria 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, Mario Andrés, Echeverri McCandless, Ann, Rojas Chaves, Sebastián, Landaverde Recinos, Denis, Weigert, Andreas, Mora Rodríguez, Javier Francisco
Formato: artículo original
Fecha de Publicación:2022
Descripción: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-CoV2 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-CoV2 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-CoV2 infection.
País:Kérwá
Institución:Universidad de Costa Rica
Repositorio:Kérwá
Lenguaje:Inglés
OAI Identifier:oai:kerwa.ucr.ac.cr:10669/87283
Acceso en línea:https://www.frontiersin.org/journals/medicine
https://hdl.handle.net/10669/87283
Palabra clave:COVID-19
SARS-CoV2
CXCL10
IL-38
Cytokine profile

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