Details

Autor(en) / Beteiligte

• 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

Titel

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

Ist Teil von

• Frontiers in medicine, 2022-09, Vol.9, p.987182-987182

Ort / Verlag

Frontiers Media S.A

Erscheinungsjahr

2022

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• 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.