Multisystem Inflammatory Syndrome in Children in Western Countries? Decreasing Incidence as the Pandemic Progresses?: An Observational Multicenter International Cross-sectional Study

COVID-19, Multisystemic inflammatory syndrome; Children; EpidemiologyCOVID-19, Síndrome inflamatorio multisistémico; Niños; EpidemiologíaCOVID-19, Síndrome inflamatòria multisistèmica; Nens; EpidemiologiaBackground: SARS-CoV-2 variations as well as immune protection after previous infections and/or vaccination may have altered the incidence of multisystemic inflammatory syndrome in children (MIS-C). We aimed to report an international time-series analysis of the incidence of MIS-C to determine if there was a shift in the regions or countries included into the study. Methods: This is a multicenter, international, cross-sectional study. We collected the MIS-C incidence from the participant regions and countries for the period July 2020 to November 2021. We assessed the ratio between MIS-C cases and COVID-19 pediatric cases in children <18 years diagnosed 4 weeks earlier (average time for the temporal association observed in this disease) for the study period. We performed a binomial regression analysis for 8 participating sites [Bogotá (Colombia), Chile, Costa Rica, Lazio (Italy), Mexico DF, Panama, The Netherlands and Catalonia (Spain)]. Results: We included 904 cases of MIS-C, among a reference population of 17,906,432 children. We estimated a global significant decrease trend ratio in MIS-C cases/COVID-19 diagnosed cases in the previous month ( P < 0.001). When analyzing separately each of the sites, Chile and The Netherlands maintained a significant decrease trend ( P < 0.001), but this ratio was not statistically significant for the rest of sites. Conclusions: To our knowledge, this is the first international study describing a global reduction in the trend of the MIS-C incidence during the pandemic. COVID-19 vaccination and other factors possibly linked to the virus itself and/or community transmission may have played a role in preventing new MIS-C cases

Multisystem inflammatory syndrome in children in western countries: decreasing incidence as the pandemic progresses? An observational multicenter international cross-sectional study

Multisystemic inflammatory syndrome temporally associated with SARS-CoV-2 infection in children (MIS-C) has been reported worldwide.1–7 The case definition of MIS-C has been estab- lished by different institutions and organizations such as the US Centers for Disease Control and Prevention (CDC) (May 14, 2020),8 the Royal College of Paediatrics and Child Health in the United Kingdom (RCPCH) (May 1, 2020)9,10 and the World Health Organi- zation (WHO) (May 15, 2020).1Postprint (published version

Investigación (Arte + Diseño): desarrollo de discusiones críticas sobre múltiples experiencias, perspectivas y prácticas investigativas en el CIDEA.

Modalidad de graduación: Seminario para optar por el grado de licenciatrua en Arte y Comunicación VisualEntendiendo que el Seminario constituye una modalidad comprendida en el ámbito de los Trabajos Finales de Graduación como una actividad teórico - práctica dirigida al desarrollo de proyectos de investigación circunscritos a un tema propuesto por la Unidad Académica. El tema de este seminario de graduación, avalado por la mencionada entidad, coincide con el proyecto (PPAA): Nodos activos (Investigación + práctica artística). En síntesis, este tema es la exploración crítica sobre los procesos, formas prácticas y modelos destinados a diferentes formas de vivenciar, asumir y aplicar el concepto de Investigación artística y diseñística1. Por su amplitud y complejidad, este tema se ofrece como matriz para el desarrollo de proyectos de investigación alternos o asociados partiendo de una perspectiva interdisciplinaria y transdisciplinaria capaz de comprender diferentes áreas de especialización más allá incluso de las fronteras de las artes visuales y el diseño.Escuela de Arte y Comunicación Visua

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-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.Universidad de Costa Rica/[807-C1-357]/UCR/Costa RicaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias de la Salud::Centro de Investigación en Hematología y Trastornos Afines (CIHATA)UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias de la Salud::Centro de Investigación en Enfermedades Tropicales (CIET)UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias de la Salud::Centro de Investigación en Cirugía y Cáncer (CICICA

Multisystem Inflammatory Syndrome in Children in Western Countries? Decreasing Incidence as the Pandemic Progresses?: An Observational Multicenter International Cross-sectional Study

BACKGROUND: SARS-CoV-2 variations as well as immune protection after previous infections and/or vaccination may have altered the incidence of multisystemic inflammatory syndrome in children (MIS-C). We aimed to report an international time-series analysis of the incidence of MIS-C to determine if there was a shift in the regions or countries included into the study. METHODS: This is a multicenter, international, cross-sectional study. We collected the MIS-C incidence from the participant regions and countries for the period July 2020 to November 2021. We assessed the ratio between MIS-C cases and COVID-19 pediatric cases in children <18 years diagnosed 4 weeks earlier (average time for the temporal association observed in this disease) for the study period. We performed a binomial regression analysis for 8 participating sites [Bogotá (Colombia), Chile, Costa Rica, Lazio (Italy), Mexico DF, Panama, The Netherlands and Catalonia (Spain)]. RESULTS: We included 904 cases of MIS-C, among a reference population of 17,906,432 children. We estimated a global significant decrease trend ratio in MIS-C cases/COVID-19 diagnosed cases in the previous month ( P < 0.001). When analyzing separately each of the sites, Chile and The Netherlands maintained a significant decrease trend ( P < 0.001), but this ratio was not statistically significant for the rest of sites. CONCLUSIONS: To our knowledge, this is the first international study describing a global reduction in the trend of the MIS-C incidence during the pandemic. COVID-19 vaccination and other factors possibly linked to the virus itself and/or community transmission may have played a role in preventing new MIS-C cases

Table_2_Difference in mortality rates in hospitalized COVID-19 patients identified by cytokine profile clustering using a machine learning approach: An outcome prediction alternative.XLSX

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.</p

Image_1_Difference in mortality rates in hospitalized COVID-19 patients identified by cytokine profile clustering using a machine learning approach: An outcome prediction alternative.TIF

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.</p

Image_3_Difference in mortality rates in hospitalized COVID-19 patients identified by cytokine profile clustering using a machine learning approach: An outcome prediction alternative.TIF

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.</p

Image_2_Difference in mortality rates in hospitalized COVID-19 patients identified by cytokine profile clustering using a machine learning approach: An outcome prediction alternative.TIF

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.</p