Clinical, laboratory data and inflammatory biomarkers at baseline as early discharge predictors in hospitalized SARS-CoV-2 infected patients

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.

Fig 1. Plasma cytokine concentrations at baseline according to four-point ordinal categories based on oxygen at admission.

Mild, oxygen therapy with mask or nasal prongs. Severe/Critical, high-flow oxygen requirement, non-invasive ventilation, mechanical ventilation or extracorporeal membrane oxygenation, and death. IL-6, interleukine-6; IL-8, interleukine-8; MIP-1β, macrophage inflammatory proteins 1β; sCD25, soluble receptor interleukine-2; TNF-α, tumor necrosis factor α; IL-1β, interleukine-1β; IP-10, interferon γ-induced protein 10; MIP-1α, macrophage inflammatory proteins 1α; IFN-γ, interferon-gamma.

Fig 2. Flow chart of the evolution during hospitalization.

Fig 3. Differences in plasma cytokine concentrations at baseline in mild patients according to whether or not they were discharged during the first week of hospitalization.

IL-6, interleukine-6; IL-8, interleukine-8; IL-1β, interleukine-1β; MIP-1β, macrophage inflammatory proteins 1β; sCD25, soluble receptor interleukine-2; IP-10, interferon γ-induced protein 10; TNF-α, tumor necrosis factor α; MIP-1α, macrophage inflammatory proteins 1α; IFN-γ, interferon-gamma.

Fig 4. Differences in baseline plasma cytokine concentrations in mild patients according to whether they worsened during the first week of hospitalization or not.

IL-6, interleukine-6; IL-8, interleukine-8; TNF-α, tumor necrosis factor α; IL-1β, interleukine-1β; MIP-1β, macrophage inflammatory proteins 1β; sCD25, soluble receptor interleukine-2; IP-10, interferon γ-induced protein 10; MIP-1α, macrophage inflammatory proteins 1α; IFN-γ, interferon-gamma.