Difference in mortality rates in hospitalized COVID-19 patients identified by cytokine profile clustering using a machine learning approach. An outcome prediction alternative
artículo original
Fecha
2022Autor
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
Metadatos
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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.