Sara Domínguez-Rodríguez1,2,3, Serena Villaverde1,2,3, Francisco J Sanz-Santaeufemia4, Carlos Grasa2,5, Antoni Soriano-Arandes6, Jesús Saavedra-Lozano7, Victoria Fumadó8, Cristina Epalza1,2,3, Miquel Serna-Pascual1,2,3, José A Alonso-Cadenas4, Paula Rodríguez-Molino2,5, Joan Pujol-Morro6, David Aguilera-Alonso7, Silvia Simó8, Sara Villanueva-Medina1,2,3, M Isabel Iglesias-Bouzas9, M José Mellado4, Blanca Herrero4, Susana Melendo6, Mercedes De la Torre4, Teresa Del Rosal2,5, Pere Soler-Palacin6, Cristina Calvo4, María Urretavizcaya-Martínez10, Marta Pareja11, Fátima Ara-Montojo12, Yolanda Ruiz Del Prado13, Nerea Gallego14, Marta Illán Ramos15, Elena Cobos1,2,3, Alfredo Tagarro1,2,3,16, Cinta Moraleda1,2,3. 1. From the Fundación de Investigación Biomédica Hospital 12 de Octubre, Instituto de Investigación 12 de Octubre (imas12), Madrid, Spain. 2. RITIP (Translational Research Network in Paediatric Infectious Diseases), Madrid, Spain. 3. Paediatric Infectious Diseases Unit, Department of Paediatrics, Hospital Universitario 12 Octubre, Madrid, Spain. 4. Hospital Universitario Niño Jesús, Madrid, Spain. 5. Paediatrics, Infectious and Tropical Diseases Department, Hospital Universitario La Paz, Instituto Investigación Hospital La Paz (IDIPaz), Madrid, Spain. 6. Pediatric Infectious Diseases and Immunodeficiencies Unit, Hospital Universitari Vall d'Hebron, Vall d'Hebron Research Institute, Barcelona, Catalonia, Spain. 7. Paediatric Infectious Diseases Unit, Department of Paediatrics, Hospital Universitario Gregorio Marañón, Madrid, Spain. 8. Paediatric Infectious Diseases Unit, Department of Paediatrics, Hospital Universitario Sant Joan de Deu Barcelona, Barcelona, Spain. 9. Paediatric Intensive Care Unit, Hospital Universitario Niño Jesús, Madrid, Spain. 10. Paediatrics Department, Complejo Hospitalario de Navarra, Pamplona, Spain. 11. Paediatrics Department, Complejo Hospitalario Universitario de Albacete, Albacete, Spain. 12. Paediatrics Department, Hospital Universitario Quironsalud Madrid, Madrid, Spain. 13. Paediatrics Department, Hospital San Pedro, Logroño, Spain. 14. Paediatrics Department, Hospital Universitari Son Espases, Palma de Mallorca, Spain. 15. Paediatrics Department, Hospital Universitario Clínico San Carlos, Madrid, Spain. 16. Paediatrics Department, Hospital Universitario Infanta Sofía, Paediatrics Research Group, Universidad Europea de Madrid, Madrid, Spain.
Abstract
BACKGROUND: We aimed to identify risk factors causing critical disease in hospitalized children with COVID-19 and to build a predictive model to anticipate the probability of need for critical care. METHODS: We conducted a multicenter, prospective study of children with SARS-CoV-2 infection in 52 Spanish hospitals. The primary outcome was the need for critical care. We used a multivariable Bayesian model to estimate the probability of needing critical care. RESULTS: The study enrolled 350 children from March 12, 2020, to July 1, 2020: 292 (83.4%) and 214 (73.7%) were considered to have relevant COVID-19, of whom 24.2% required critical care. Four major clinical syndromes of decreasing severity were identified: multi-inflammatory syndrome (MIS-C) (17.3%), bronchopulmonary (51.4%), gastrointestinal (11.6%), and mild syndrome (19.6%). Main risk factors were high C-reactive protein and creatinine concentration, lymphopenia, low platelets, anemia, tachycardia, age, neutrophilia, leukocytosis, and low oxygen saturation. These risk factors increased the risk of critical disease depending on the syndrome: the more severe the syndrome, the more risk the factors conferred. Based on our findings, we developed an online risk prediction tool (https://rserver.h12o.es/pediatria/EPICOAPP/, username: user, password: 0000). CONCLUSIONS: Risk factors for severe COVID-19 include inflammation, cytopenia, age, comorbidities, and organ dysfunction. The more severe the syndrome, the more the risk factor increases the risk of critical illness. Risk of severe disease can be predicted with a Bayesian model.
BACKGROUND: We aimed to identify risk factors causing critical disease in hospitalized children with COVID-19 and to build a predictive model to anticipate the probability of need for critical care. METHODS: We conducted a multicenter, prospective study of children with SARS-CoV-2 infection in 52 Spanish hospitals. The primary outcome was the need for critical care. We used a multivariable Bayesian model to estimate the probability of needing critical care. RESULTS: The study enrolled 350 children from March 12, 2020, to July 1, 2020: 292 (83.4%) and 214 (73.7%) were considered to have relevant COVID-19, of whom 24.2% required critical care. Four major clinical syndromes of decreasing severity were identified: multi-inflammatory syndrome (MIS-C) (17.3%), bronchopulmonary (51.4%), gastrointestinal (11.6%), and mild syndrome (19.6%). Main risk factors were high C-reactive protein and creatinine concentration, lymphopenia, low platelets, anemia, tachycardia, age, neutrophilia, leukocytosis, and low oxygen saturation. These risk factors increased the risk of critical disease depending on the syndrome: the more severe the syndrome, the more risk the factors conferred. Based on our findings, we developed an online risk prediction tool (https://rserver.h12o.es/pediatria/EPICOAPP/, username: user, password: 0000). CONCLUSIONS: Risk factors for severe COVID-19 include inflammation, cytopenia, age, comorbidities, and organ dysfunction. The more severe the syndrome, the more the risk factor increases the risk of critical illness. Risk of severe disease can be predicted with a Bayesian model.
Authors: Junyi Gao; Chaoqi Yang; Joerg Heintz; Scott Barrows; Elise Albers; Mary Stapel; Sara Warfield; Adam Cross; Jimeng Sun Journal: iScience Date: 2022-08-17
Authors: Lauren A Henderson; Scott W Canna; Kevin G Friedman; Mark Gorelik; Sivia K Lapidus; Hamid Bassiri; Edward M Behrens; Kate F Kernan; Grant S Schulert; Philip Seo; Mary Beth F Son; Adriana H Tremoulet; Christina VanderPluym; Rae S M Yeung; Amy S Mudano; Amy S Turner; David R Karp; Jay J Mehta Journal: Arthritis Rheumatol Date: 2022-02-03 Impact factor: 15.483