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):e0269875. doi: 10.1371/journal.pone.0269875. eCollection 2022.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biomarkers
  • COVID-19*
  • Cytokines
  • Humans
  • Male
  • Patient Discharge
  • SARS-CoV-2*

Substances

  • Biomarkers
  • Cytokines

Grants and funding

This work was supported by Consejeria de Salud y Familia (research Project COVID-0005-2020 and Research Contract RH-0037-2020 to JV); Consejería de Transformación Económica, Industria, Conocimiento y Universidades (PY20/01276 to APG); Instituto de Salud Carlos III (CP19/00159 to AGV, CP19/00146 to AR, FI19/00304 to EMM, FI19/00083 to MCGC, "a way to make Europe, and COV20/00698 to support COHVID-GS), Red Temática de Investigación Cooperativa en SIDA (RD16/0025/0020, RD16/0025/0006 and RD16/0025/0026), Fondos FEDER; Centro de Investigación Biomédica en Red de Enfermedades Infecciosas-ISCIII (CB21/13/00020) Madrid, Spain. ERM was supported by the Spanish Research Council (CSIC). AR is also supported by a grant from IISPV through the project “2019/IISPV/05” (Boosting Young Talent), by GeSIDA through the “III Premio para Jóvenes Investigadores”. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.