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

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

Abstract

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.

Publisher

Frontiers Media SA

Subject

General Medicine

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