%0 Journal Article 
%A de Gonzalo Calvo, David
%A Molinero, Marta
%A Benítez, Iván D.
%A Perez Pons, Manel
%A García Mateo, Nadia
%A Ortega, Alicia
%A Postigo, Tamara
%A García Hidalgo, María C.
%A Belmonte, Thalía
%A Rodríguez Muñoz, Carlos
%A González, Jessica
%A Torres, Gerard
%A Gort Paniello, Clara
%A Moncusí Moix, Anna
%A Estella García, Ángel
%A Tamayo Lomas, Luis
%A Martínez de la Gándara, Amalia
%A Socias, Lorenzo
%A Peñasco, Yhivian
%A de la Torre, María del Carmen
%A Bustamante Munguira, Elena
%A Gallego Curto, Elena
%A Martínez Varela, Ignacio
%A Martín Delgado, María Cruz
%A Vidal Cortés, Pablo
%A López Messa, Juan
%A Pérez García, Felipe
%A Caballero, Jesús
%A Añón, José Manuel
%A Loza Vázquez, Ana
%A Carbonell, Nieves
%A Marin Corral, Judith
%A Jorge García, Ruth Noemí
%A Barberà, Carmen
%A Ceccato, Adrián
%A Fernández Barat, Laia
%A Ferrer, Ricard
%A García Gasulla, Dario
%A Lorente Balanza, Jose Ángel
%A Menéndez, Rosario
%A Motos, Ana
%A Peñuelas, Oscar
%A Riera, Jordi
%A Bermejo Martin, Jesús F.
%A Torres, Antoni
%A Barbé, Ferrán
%T A blood microRNA classifier for the prediction of ICU mortality in COVID-19 patients: a multicenter validation study
%D 2023
%@ 1465-9921
%U http://hdl.handle.net/10498/29728
%X Background: The identification of critically ill COVID-19 patients at risk of fatal outcomes remains a challenge. Here, we first validated candidate microRNAs (miRNAs) as biomarkers for clinical decision-making in critically ill patients. Second, we constructed a blood miRNA classifier for the early prediction of adverse outcomes in the ICU. Methods: This was a multicenter, observational and retrospective/prospective study including 503 critically ill patients admitted to the ICU from 19 hospitals. qPCR assays were performed in plasma samples collected within the first 48 h upon admission. A 16-miRNA panel was designed based on recently published data from our group. Results: Nine miRNAs were validated as biomarkers of all-cause in-ICU mortality in the independent cohort of critically ill patients (FDR < 0.05). Cox regression analysis revealed that low expression levels of eight miRNAs were associated with a higher risk of death (HR from 1.56 to 2.61). LASSO regression for variable selection was used to construct a miRNA classifier. A 4-blood miRNA signature composed of miR-16-5p, miR-192-5p, miR-323a-3p and miR-451a predicts the risk of all-cause in-ICU mortality (HR 2.5). Kaplan‒Meier analysis confirmed these findings. The miRNA signature provides a significant increase in the prognostic capacity of conventional scores, APACHE-II (C-index 0.71, DeLong test p-value 0.055) and SOFA (C-index 0.67, DeLong test p-value 0.001), and a risk model based on clinical predictors (C-index 0.74, DeLong test-p-value 0.035). For 28-day and 90-day mortality, the classifier also improved the prognostic value of APACHE-II, SOFA and the clinical model. The association between the classifier and mortality persisted even after multivariable adjustment. The functional analysis reported biological pathways involved in SARS-CoV infection and inflammatory, fibrotic and transcriptional pathways. Conclusions: A blood miRNA classifier improves the early prediction of fatal outcomes in critically ill COVID-19 patients.
%K COVID-19
%K Biomarker
%K ICU
%K microRNA
%K Prognosis
%K SARS-CoV-2
%~ Universidad de Cádiz