Increased Blood Monocytic Myeloid Derived Suppressor Cells but Low Regulatory T Lymphocytes in Patients with Mild COVID-19.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may produce a systemic disease, the coronavirus disease-19 (COVID-19), with high morbidity and mortality. Even though we do not fully understand the interaction of innate and adaptive immunity in the control and complications of the viral infection, it is well recognized that SARS-CoV-2 induces an immunodepression that impairs the elimination of the virus and favors its rapid dissemination in the organism. Even less is known about the possible participation of inhibitory cells of the innate immune system, such as the myeloid-derived suppressor cells (MDSCs), or the adaptive immune system, such as the T regulatory cells (Tregs). That is why we aimed to study blood levels of MDSCs, as well as lymphocyte subpopulations, including Tregs, and activated (OX-40+) and inhibited (PD-1) T lymphocytes in patients with mild COVID-19 in comparison with data obtained from control donors. We have found that 20 hospitalized patients with COVID-19 and no health history of immunosuppression had a significant increase in the number of peripheral monocytic MDSCs (M-MDSC), but a decrease in Tregs, as well as an increase in the number of inhibited or exhausted T cells, whereas the number of activated T cells was significantly decreased compared with that from 20 healthy controls. Moreover, there was a significant negative correlation (r = 0.496) between the number of M-MDSC and the number of activated T cells. Therefore, M-MDSC rather than Tregs may contribute to the immunosuppression observed in patients with COVID-19.

Development and validation of a laboratory-based risk score to predict the occurrence of critical illness in hospitalized patients with COVID-19.

BACKGROUND: Early identification of patients with COVID-19 who may develop critical illness is of great importance. METHODS: In this study a retrospective cohort of 264 COVID-19 cases admitted at Macarena University was used for development and internal validation of a risk score to predict the occurrence of critical illness in hospitalized patients with COVID-19. Backward stepwise logistic regression was used to derive the model, including clinical and laboratory variables predictive of critical illness. Internal validation of the final model used bootstrapped samples and the model scoring derived from the coefficients. External validation was performed in a cohort of 154 cases admitted at Valme and Virgen del Rocio University Hospital. RESULTS: A total of 62 (23.5%) patients developed a critical illness during their hospitalization stay, 21 (8.0%) patients needed invasive ventilation, 34 (12.9%) were admitted at the ICU and the overall mortality was of 14.8% (39 cases). 5 variables were included in the final model: age >59.5 years (OR: 3.11;95%CI 1.39-6.97), abnormal CRP results (OR: 5.76;95%CI 2.32-14.30), abnormal lymphocytes count (OR: 3.252;95%CI 1.56-6.77), abnormal CK results (OR: 3.38;95%CI 1.59-7.20) and abnormal creatinine (OR: 3.30;95%CI 1.42-7.68). The AUC of this model was 0.850 with sensitivity of 65% and specificity of 87% and the IDI and NRI were 0.1744 and 0.2785, respectively. The validation indicated a good discrimination for the external population. CONCLUSIONS: Biomarkers add prognostic information in COVID-19 patients. Our risk-score provides an easy to use tool to identify patients who are likely to develop critical illness during their hospital stay.