CONICET | Buscador de Institutos y Recursos Humanos

The COVID-19 disease presents a dynamic nature characterized by distinct and well-defined athophysiological phases. As the disease progresses, an exacerbated inflammatory response accompanied by marked lymphopenia and dysfunctional immune response that could lead to the sepsis progression and dead. Nevertheless, clinically, accurately determining the patient´s stage oridentifying those individuals at advanced risk of progressing to more severe conditions remains challenging. Technologies such as proteomics and predictive models based on artificial intelligence have raised significant expectations in the study of various pathologies. Our aim was to investigate the usefulness of plasma peptidome fingerprints as a tool for severity stratification of patients withCOVID-19 using MALDI-TOF MS technology. For this, we conducted an observational and analytical prospective cohort study in which the analysis unit consisted of the peptidome biomolecular fingerprints in plasma, along with clinical information collected from patients admitted to the intensive Care Unit (ICU). We followed up 37 patients admitted to the ICU of the Hospital de Clinicas "José de San Martín" -UBA during the August-September 2020 period. Plasma samples (n = 87) and clinical information were collected during their ICU stay. Patients were categorized into survivors (S) (n=12) and non-survivors (NS) (n=19). A control group (C) (n=6) consisted of ICU patients without COVID-19. Multivariate analyses and machine learning algorithms were applied todiscriminate between the classification stages in patients. Clinical and biochemical parameters were also evaluated. Neutrophil-Lymphocyte Ratio (NLR), which serves as a predictor of mortality in COVID-19 ICU patients (NLR (median (RIQ): S=7.5 (4.5-9.7); NS=16.6 (10.4-31.6)*#; C=4.8 (2.6-7.1),p

enviar mensaje