Low quantity and quality of anti-spike humoral response is linked to CD4 T-cell apoptosis in COVID-19 patients.

In addition to an inflammatory reaction, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)-infected patients present lymphopenia, which we recently reported as being related to abnormal programmed cell death. As an efficient humoral response requires CD4 T-cell help, we hypothesized that the propensity of CD4 T cells to die may impact the quantity and quality of the humoral response in acutely infected individuals. In addition to specific immunoglobulins (Ig)A, IgM, and IgG against SARS-CoV-2 nucleocapsid (N), membrane (M), and spike (S1) proteins, we assessed the quality of IgG response by measuring the avidity index. Because the S protein represents the main target for neutralization and antibody-dependent cellular cytotoxicity responses, we also analyzed anti-S-specific IgG using S-transfected cells (S-Flow). Our results demonstrated that most COVID-19 patients have a predominant IgA anti-N humoral response during the early phase of infection. This specific humoral response preceded the anti-S1 in time and magnitude. The avidity index of anti-S1 IgG was low in acutely infected individuals compared to convalescent patients. We showed that the percentage of apoptotic CD4 T cells is inversely correlated with the levels of specific IgG antibodies. These lower levels were also correlated positively with plasma levels of CXCL10, a marker of disease severity, and soluble Fas ligand that contributes to T-cell death. Finally, we found lower S-Flow responses in patients with higher CD4 T-cell apoptosis. Altogether, these results demonstrate that individuals with high levels of CD4 T-cell apoptosis and CXCL10 have a poor ability to build an efficient anti-S response. Consequently, preventing CD4 T-cell death might be a strategy for improving humoral response during the acute phase, thereby reducing COVID-19 pathogenicity.

Prediction of noninvasive ventilation failure in COVID-19 patients: A retrospective observational study (preprint)

Background: In COVID-19, there are no available tools or guidelines for the difficult task of recognizing which patients do not benefit from keeping advanced respiratory support, such as noninvasive ventilation (NIV). Identifying failure is crucial to deliver the best possible care and optimize resources in overloaded healthcare systems. Therefore, this study aimed to build a model that predicts NIV failure and consequent death in COVID-19 patients.Methods: This is a retrospective observational study with critical COVID-19 patients who needed NIV but were not candidates or did not need invasive mechanical ventilation, admitted between 1st October 2020 and 31st March 2021, in a tertiary Portuguese hospital. The measure of interest was NIV failure, defined as COVID-19 related in-hospital death. A binary logistic regression was performed, where the outcome (dead vs alive) was set as the dependent variable. Using the independent variables of the logistic regression a decision tree classification model was implemented. Results: The 103 patients included had a mean age of 66.3 years old (SD=14.9), 38.8% were female and 82.5% autonomous. The developed prediction model was statistically significant (X2= 119.865; p< .001) with an area under the curve (AUC) of 0.994. Higher age, higher number of days with increases on FiO2, higher number of days of maximum expiratory positive airway pressure, lower number of days on NIV, and lower number of days from disease onset to hospital admission were, with statistical significance, associated with increased odds of death. A decision-tree classification model was obtained to achieve the best combination of variables to predict the outcome of interest.  Conclusions: This study presents a model to predict death in COVID-19 patients treated with NIV (AUC of 0.994), including easily applicable variables that mainly reflect patients’ evolution during hospitalization. Along with the decision-tree classification model, these original findings have the potential to help clinicians define the best therapeutical approach to each patient and optimize medical resources. However, further research is needed on this subject of treatment failure, not only to understand if these results are reproducible but also, in a broader sense, to help fill this gap in modern medicine guidelines.