RT Journal Article
T1 Clinical, laboratory data and inflammatory biomarkers at baseline as early discharge predictors in hospitalized SARS-CoV-2 infected patients.
A1 Trujillo-Rodriguez, Maria
A1 Muñoz-Muela, Esperanza
A1 Serna-Gallego, Ana
A1 Praena-Fernandez, Juan Manuel
A1 Perez-Gomez, Alberto
A1 Gasca-Capote, Carmen
A1 Vitalle, Joana
A1 Peraire, Joaquim
A1 Palacios-Baena, Zaira R
A1 Cabrera, Jorge Julio
A1 Ruiz-Mateos, Ezequiel
A1 Poveda, Eva
A1 Lopez-Cortes, Luis Eduardo
A1 Rull, Anna
A1 Gutierrez-Valencia, Alicia
A1 Lopez-Cortes, Luis Fernando
K1 COVID-19
K1 Humans
K1 Patient Discharge
AB The SARS-CoV-2 pandemic has overwhelmed hospital services due to the rapid transmission of the virus and its severity in a high percentage of cases. Having tools to predict which patients can be safely early discharged would help to improve this situation. Patients confirmed as SARS-CoV-2 infection from four Spanish hospitals. Clinical, demographic, laboratory data and plasma samples were collected at admission. The patients were classified into mild and severe/critical groups according to 4-point ordinal categories based on oxygen therapy requirements. Logistic regression models were performed in mild patients with only clinical and routine laboratory parameters and adding plasma pro-inflammatory cytokine levels to predict both early discharge and worsening. 333 patients were included. At admission, 307 patients were classified as mild patients. Age, oxygen saturation, Lactate Dehydrogenase, D-dimers, neutrophil-lymphocyte ratio (NLR), and oral corticosteroids treatment were predictors of early discharge (area under curve (AUC), 0.786; sensitivity (SE) 68.5%; specificity (S), 74.5%; positive predictive value (PPV), 74.4%; and negative predictive value (NPV), 68.9%). When cytokines were included, lower interferon-γ-inducible protein 10 and higher Interleukin 1 beta levels were associated with early discharge (AUC, 0.819; SE, 91.7%; S, 56.6%; PPV, 69.3%; and NPV, 86.5%). The model to predict worsening included male sex, oxygen saturation, no corticosteroids treatment, C-reactive protein and Nod-like receptor as independent factors (AUC, 0.903; SE, 97.1%; S, 68.8%; PPV, 30.4%; and NPV, 99.4%). The model was slightly improved by including the determinations of interleukine-8, Macrophage inflammatory protein-1 beta and soluble IL-2Rα (CD25) (AUC, 0.952; SE, 97.1%; S, 98.1%; PPV, 82.7%; and NPV, 99.6%). Clinical and routine laboratory data at admission strongly predict non-worsening during the first two weeks; therefore, these variables could help identify those patients who do not need a long hospitalization and improve hospital overcrowding. Determination of pro-inflammatory cytokines moderately improves these predictive capacities.
PB Public Library of Science
YR 2022
FD 2022-07-14
LK http://hdl.handle.net/10668/20431
UL http://hdl.handle.net/10668/20431
LA en
NO Trujillo-Rodriguez M, Muñoz-Muela E, Serna-Gallego A, Praena-Fernández JM, Pérez-Gómez A, Gasca-Capote C, et al. Clinical, laboratory data and inflammatory biomarkers at baseline as early discharge predictors in hospitalized SARS-CoV-2 infected patients. PLoS One. 2022 Jul 14;17(7):1-15.
DS RISalud
RD Apr 9, 2025