Multi-center study of COVID-19 infection in elderly patients with lymphoma: on behalf of Jiangsu Cooperative Lymphoma Group (JCLG)

Elderly patients with lymphoproliferative diseases (LPD) are vulnerable to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Here, we retrospectively described the clinical features and outcomes of the first time infection of Omicron SARS-CoV-2 in 364 elderly patients with lymphoma enrolled in Jiangsu Cooperative Lymphoma Group (JCLG) between November 2022 and April 2023 in China. Median age was 69 years (range 60-92). 54.4% (198/364) of patients were confirmed as severe and critical COVID-19 infection. In univariable analysis, Age > 70 years (OR 1.88, p = 0.003), with multiple comorbidities (OR 1.41, p = 0.005), aggressive lymphoma (OR 2.33, p < 0.001), active disease (progressive or relapsed/refractory, OR 2.02, p < 0.001), and active anti-lymphoma therapy (OR 1.90, p < 0.001) were associated with severe COVID-19. Multiple (three or more) lines of previous anti-lymphoma therapy (OR 3.84, p = 0.021) remained an adverse factor for severe COVID-19 in multivariable analysis. Moreover, CD20 antibody (Rituximab or Obinutuzumab)-based treatments within the last 6 months was associated with severe COVID-19 in the entire cohort (OR 3.42, p < 0.001). Continuous BTK inhibitors might be protective effect on the outcome of COVID-19 infection (OR 0.44, p = 0.043) in the indolent lymphoma cohort. Overall, 7.7% (28/364) of the patients ceased, multiple lines of previous anti-lymphoma therapy (OR 3.46, p = 0.016) remained an adverse factor for mortality.

Development and validation of a predicting nomogram for in-hospital mortality of COVID-19 Omicron variant: A cohort study of 1324 cases in Beijing Anzhen Hospital

Coronavirus disease 2019 (COVID-19) is continuously posing high global public health concerns due to its high morbidity and mortality. This study aimed to construct a convenient risk model for predicting in-hospital mortality of COVID-19 Omicron variant. A total of 1324 hospitalized patients with Omicron variant were enrolled from Beijing Anzhen Hospital. During hospitalization, the Omicron variant mortality rate was found to be 24.4%. Using the datasets of clinical demographics and laboratory tests, three machine learning algorithms, including best subset selection, stepwise selection, and least absolute shrinkage and selection operator regression analyses were employed to identify the potential predictors of in-hospital mortality. The results found that a panel of twenty-four clinical variables (including age, hyperlipemia, stroke, tumor, and several cardiovascular markers) identified by stepwise selection model exhibited significant performances in predicting the in-hospital mortality of COVID-19. The resultant nomogram showed good discrimination, highlighted by the areas under the curve values of 0.88 for 10 days, 0.81 for 20 days, and 0.82 for 30 days, respectively. Furthermore, decision curve analysis showed a significant reliability and precision for the established stepwise selection model. Collectively, this study developed an accurate and convenience risk model for predicting the in-hospital mortality of COVID-19 Omicron.

Integrative proteomics and metabolomics study reveal enhanced immune responses by COVID-19 vaccine booster shot against Omicron SARS-CoV-2 infection

Since its outbreak in late 2021, the Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been widely reported to be able to evade neutralizing antibodies, becoming more transmissible while causing milder symptoms than previous SARS-CoV-2 strains. Understanding the underlying molecular changes of Omicron SARS-CoV-2 infection and corresponding host responses are important to the control of Omicron COVID-19 pandemic. In this study, we report an integrative proteomics and metabolomics investigation of serum samples from 80 COVID-19 patients infected with Omicron SARS-CoV-2, as well as 160 control serum samples from 80 healthy individuals and 80 patients who had flu-like symptoms but were negative for SARS-CoV-2 infection. The multiomics results indicated that Omicron SARS-CoV-2 infection caused significant changes to host serum proteome and metabolome comparing to the healthy controls and patients who had flu-like symptoms without COVID-19. Protein and metabolite changes also pointed to liver dysfunctions and potential damage to other host organs by Omicron SARS-CoV-2 infection. The Omicron COVID-19 patients could be roughly divided into two subgroups based on their proteome differences. Interestingly, the subgroup who mostly had received full vaccination with booster shot had fewer coughing symptom, changed sphingomyelin lipid metabolism, and stronger immune responses including higher numbers of lymphocytes, monocytes, neutrophils, and upregulated proteins related to CD4+ T cells, CD8+ effector memory T cells (Tem), and conventional dendritic cells, revealing beneficial effects of full COVID-19 vaccination against Omicron SARS-CoV-2 infection through molecular changes.