Este articulo es un Preprint
Los preprints son informes de investigación preliminares que no han sido certificados por revisión por pares. No deben considerarse para guiar la práctica clínica o los comportamientos relacionados con la salud y no deben publicarse en los medios como información establecida.
Los preprints publicados en línea permiten a los autores recibir comentarios rápidamente, y toda la comunidad científica puede evaluar de forma independiente el trabajo y responder adecuadamente. Estos comentarios se publican junto con los preprints para que cualquiera pueda leer y servir como una revisión pospublicación.
Gene Expression Risk Scores for COVID-19 Illness Severity (preprint)
biorxiv; 2021.
Preprint
en Inglés
| bioRxiv | ID: ppzbmed-10.1101.2021.08.24.457521
ABSTRACT
BackgroundThe correlates of COVID-19 illness severity following infection with SARS-Coronavirus 2 (SARS-CoV-2) are incompletely understood. MethodsWe assessed peripheral blood gene expression in 53 adults with confirmed SARS-CoV-2-infection clinically adjudicated as having mild, moderate or severe disease. Supervised principal components analysis was used to build a weighted gene expression risk score (WGERS) to discriminate between severe and non-severe COVID. ResultsGene expression patterns in participants with mild and moderate illness were similar, but significantly different from severe illness. When comparing severe versus non-severe illness, we identified >4000 genes differentially expressed (FDR<0.05). Biological pathways increased in severe COVID-19 were associated with platelet activation and coagulation, and those significantly decreased with T cell signaling and differentiation. A WGERS based on 18 genes distinguished severe illness in our training cohort (cross-validated ROC-AUC=0.98), and need for intensive care in an independent cohort (ROC-AUC=0.85). Dichotomizing the WGERS yielded 100% sensitivity and 85% specificity for classifying severe illness in our training cohort, and 84% sensitivity and 74% specificity for defining the need for intensive care in the validation cohort. ConclusionThese data suggest that gene expression classifiers may provide clinical utility as predictors of COVID-19 illness severity.
Texto completo:
Disponible
Colección:
Preprints
Base de datos:
bioRxiv
Asunto principal:
Enfermedad Crítica
/
Infecciones por Coronavirus
/
Trastornos de la Coagulación Sanguínea Heredados
/
COVID-19
Idioma:
Inglés
Año:
2021
Tipo del documento:
Preprint
Similares
MEDLINE
...
LILACS
LIS