Impacts of delta and omicron variants on inactivated SARS-CoV-2 vaccine-induced T cell responses in patients with autoimmune diseases and healthy controls.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection causes coronavirus disease 2019 (COVID-19), which is still devastating economies and communities globally. The increasing infections of variants of concern (VOCs) in vaccinated population have raised concerns about the effectiveness of current vaccines. Patients with autoimmune diseases (PAD) under immunosuppressant treatments are facing higher risk of infection and potentially lower immune responses to SARS-CoV-2 vaccination. METHODS: Blood samples were collected from PAD or healthy controls (HC) who finished two or three doses of inactivated vaccines. Spike peptides derived from wild-type strain, delta, omicron BA.1 were utilised to evaluate T cell responses and their cross-recognition of delta and omicron in HC and PAD by flow cytometry and ex vivo IFNγ-ELISpot. RESULTS: We found that inactivated vaccine-induced spike-specific memory T cells were long-lasting in both PAD and HC. These spike-specific T cells were highly conserved and cross-recognized delta and omicron. Moreover, a third inactivated vaccine expanded spike-specific T cells that responded to delta and omicron spike peptides substantially in both PAD and HC. Importantly, the polyfunctionality of spike-specific memory T cells was preserved in terms of cytokine and cytotoxic responses. Although the extent of T cell responses was lower in PAD after two-dose, T cell responses were boosted to a greater magnitude in PAD by the third dose, bringing comparable spike-specific T cell immunity after the third dose. CONCLUSION: Inactivated vaccine-induced spike-specific T cells remain largely intact against delta and omicron variants. This study expands our understanding of inactivated vaccine-induced T cell responses in PAD and HC, which could have important indications for vaccination strategy.

Development and Validation of a Clinical Risk Score to Predict the Occurrence of Critical Illness in Hospitalized Patients With COVID-19.

Importance: Early identification of patients with novel coronavirus disease 2019 (COVID-19) who may develop critical illness is of great importance and may aid in delivering proper treatment and optimizing use of resources. Objective: To develop and validate a clinical score at hospital admission for predicting which patients with COVID-19 will develop critical illness based on a nationwide cohort in China. Design, Setting, and Participants: Collaborating with the National Health Commission of China, we established a retrospective cohort of patients with COVID-19 from 575 hospitals in 31 provincial administrative regions as of January 31, 2020. Epidemiological, clinical, laboratory, and imaging variables ascertained at hospital admission were screened using Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression to construct a predictive risk score (COVID-GRAM). The score provides an estimate of the risk that a hospitalized patient with COVID-19 will develop critical illness. Accuracy of the score was measured by the area under the receiver operating characteristic curve (AUC). Data from 4 additional cohorts in China hospitalized with COVID-19 were used to validate the score. Data were analyzed between February 20, 2020 and March 17, 2020. Main Outcomes and Measures: Among patients with COVID-19 admitted to the hospital, critical illness was defined as the composite measure of admission to the intensive care unit, invasive ventilation, or death. Results: The development cohort included 1590 patients. the mean (SD) age of patients in the cohort was 48.9 (15.7) years; 904 (57.3%) were men. The validation cohort included 710 patients with a mean (SD) age of 48.2 (15.2) years, and 382 (53.8%) were men and 172 (24.2%). From 72 potential predictors, 10 variables were independent predictive factors and were included in the risk score: chest radiographic abnormality (OR, 3.39; 95% CI, 2.14-5.38), age (OR, 1.03; 95% CI, 1.01-1.05), hemoptysis (OR, 4.53; 95% CI, 1.36-15.15), dyspnea (OR, 1.88; 95% CI, 1.18-3.01), unconsciousness (OR, 4.71; 95% CI, 1.39-15.98), number of comorbidities (OR, 1.60; 95% CI, 1.27-2.00), cancer history (OR, 4.07; 95% CI, 1.23-13.43), neutrophil-to-lymphocyte ratio (OR, 1.06; 95% CI, 1.02-1.10), lactate dehydrogenase (OR, 1.002; 95% CI, 1.001-1.004) and direct bilirubin (OR, 1.15; 95% CI, 1.06-1.24). The mean AUC in the development cohort was 0.88 (95% CI, 0.85-0.91) and the AUC in the validation cohort was 0.88 (95% CI, 0.84-0.93). The score has been translated into an online risk calculator that is freely available to the public (http://118.126.104.170/). Conclusions and Relevance: In this study, a risk score based on characteristics of COVID-19 patients at the time of admission to the hospital was developed that may help predict a patient's risk of developing critical illness.