@misc{10481/81702,
year = {2022},
month = {7},
url = {https://hdl.handle.net/10481/81702},
abstract = {Background
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.
Methods
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.
Results
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%).
Conclusions
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.},
organization = {Consejeria de Salud y Familia	COVID-00052020
RH-0037-2020},
organization = {Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades	PY20/01276},
organization = {Instituto de Salud Carlos III	CP19/00159
CP19/00146
FI19/00304
FI19/00083
COV20/00698},
organization = {Red Tematica de Investigacion Cooperativa en SIDA	RD16/0025/0020
RD16/0025/0006
RD16/0025/0026},
organization = {European Commission},
organization = {Centro de Investigacion Biomedica en Red de Enfermedades Infecciosas-ISCIII Madrid, Spain	CB21/13/00020},
organization = {Spanish Research Council (CSIC)},
organization = {IISPV	2019/IISPV/05},
organization = {GeSIDA},
publisher = {Plos},
title = {Clinical, laboratory data and inflammatory biomarkers at baseline as early discharge predictors in hospitalized SARS-CoV-2 infected patients},
doi = {10.1371/journal.pone.0269875},
author = {Trujillo Rodríguez, María and Praena Fernández, Juan Manuel},
}