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Whether immunosuppression impairs severe acute respiratory syndrome coronavirus 2-specific T cell-mediated immunity (SARS-CoV-2-CMI) after liver transplantation (LT) remains unknown. We included 31 LT recipients in whom SARS-CoV-2-CMI was assessed by intracellular cytokine staining (ICS) and interferon (IFN)-γ FluoroSpot assay after a median of 103 days from COVID-19 diagnosis. Serum SARS-CoV-2 IgG antibodies were measured by ELISA. A control group of nontransplant immunocompetent patients were matched (1:1 ratio) by age and time from diagnosis. Post-transplant SARS-CoV-2-CMI was detected by ICS in 90.3% (28/31) of recipients, with higher proportions for IFN-γ-producing CD4 + than CD8 + responses (93.5% versus 83.9%). Positive spike-specific and nucleoprotein-specific responses were found by FluoroSpot in 86.7% (26/30) of recipients each, whereas membrane protein-specific response was present in 83.3% (25/30). An inverse correlation was observed between the number of spike-specific IFN-γ-producing SFUs and time from diagnosis (Spearman's rho: -0.418; p value = .024). Two recipients (6.5%) failed to mount either T cell-mediated or IgG responses. There were no significant differences between LT recipients and nontransplant patients in the magnitude of responses by FluoroSpot to any of the antigens. Most LT recipients mount detectable-but declining over time-SARS-CoV-2-CMI after a median of 3 months from COVID-19, with no meaningful differences with immunocompetent patients., (© 2021 The American Society of Transplantation and the American Society of Transplant Surgeons.)

Keywords: Hematological malignancies; IL-1R blockade; SARS-CoV-2 EN Hematological malignancies IL-1R blockade SARS-CoV-2 2953 2956 4 11/25/20 20201201 NES 201201 Key points • We report 5 hematological patients diagnosed with SARS-CoV-2 infection (COVID-19) treated with anakinra without any clinical benefit. All patients were undergoing active treatment of first to third-line therapy, and the hematological malignancy status was complete remission ( I n i = 1), partial remission ( I n i = 2), progression ( I n i = 1), and pending evaluation ( I n i = 1). [Extracted from the article]

Coronavirus disease 2019 (COVID-19) has rapidly become a global pandemic. Because the severity of the disease is highly variable, predictive models to stratify patients according to their mortality risk are needed. Our aim was to develop a model able to predict the risk of fatal outcome in patients with COVID-19 that could be used easily at the time of patients' arrival at the hospital. We constructed a prospective cohort with 611 adult patients in whom COVID-19 was diagnosed between March 10 and April 12, 2020, in a tertiary hospital in Madrid, Spain. The analysis included 501 patients who had been discharged or had died by April 20, 2020. The capacity of several biomarkers, measured at the beginning of hospitalization, to predict mortality was assessed individually. Those biomarkers that independently contributed to improve mortality prediction were included in a multivariable risk model. High IL-6 level, C-reactive protein level, lactate dehydrogenase (LDH) level, ferritin level, d -dimer level, neutrophil count, and neutrophil-to-lymphocyte ratio were all predictive of mortality (area under the curve >0.70), as were low albumin level, lymphocyte count, monocyte count, and ratio of peripheral blood oxygen saturation to fraction of inspired oxygen (SpO 2 /FiO 2). A multivariable mortality risk model including the SpO 2 /FiO 2 ratio, neutrophil-to-lymphocyte ratio, LDH level, IL-6 level, and age was developed and showed high accuracy for the prediction of fatal outcome (area under the curve 0.94). The optimal cutoff reliably classified patients (including patients with no initial respiratory distress) as survivors and nonsurvivors with 0.88 sensitivity and 0.89 specificity. This mortality risk model allows early risk stratification of hospitalized patients with COVID-19 before the appearance of obvious signs of clinical deterioration, and it can be used as a tool to guide clinical decision making. [ABSTRACT FROM AUTHOR]