Characteristics, outcomes, and mortality amongst 133,589 patients with prevalent autoimmune diseases diagnosed with, and 48,418 hospitalised for COVID-19: a multinational distributed network cohort analysis

Mouse models in COVID-19 research: analyzing the adaptive immune response

The emergence of SARS-CoV-2, the severe acute respiratory syndrome coronavirus type 2 causing the COVID-19 pandemic, resulted in a major necessity for scientific countermeasures. Investigations revealing the exact mechanisms of the SARS-CoV-2 pathogenesis provide the basis for the development of therapeutic measures and protective vaccines against COVID-19. Animal models are inevitable for infection and pre-clinical vaccination studies as well as therapeutic testing. A well-suited animal model, mimicking the pathology seen in human COVID-19 patients, is an important basis for these investigations. Several animal models were already used during SARS-CoV-2 studies with different clinical outcomes after SARS-CoV-2 infection. Here, we give an overview of different animal models used in SARS-CoV-2 infection studies with a focus on the mouse model. Mice provide a well-established animal model for laboratory use and several different mouse models have been generated and are being used in SARS-CoV-2 studies. Furthermore, the analysis of SARS-CoV-2-specific T cells during infection and in vaccination studies in mice is highlighted.

A novel evidence-based predictor tool for hospitalization and length of stay: insights from COVID-19 patients in New York city

Predictive models for key outcomes of coronavirus disease 2019 (COVID-19) can optimize resource utilization and patient outcome. We aimed to design and internally validate a web-based calculator predictive of hospitalization and length of stay (LOS) in a large cohort of COVID-19-positive patients presenting to the Emergency Department (ED) in a New York City health system. The study cohort consisted of consecutive adult (> 18 years) patients presenting to the ED of Mount Sinai Health System hospitals between March 2020 and April 2020, diagnosed with COVID-19. Logistic regression was utilized to construct predictive models for hospitalization and prolonged (> 3 days) LOS. Discrimination was evaluated using area under the receiver operating curve (AUC). Internal validation with bootstrapping was performed, and a web-based calculator was implemented. From 5859 patients, 65% were hospitalized. Independent predictors of hospitalization and extended LOS included older age, chronic kidney disease, elevated maximum temperature, and low minimum oxygen saturation (p < 0.001). Additional predictors of hospitalization included male sex, chronic obstructive pulmonary disease, hypertension, and diabetes. AUCs of 0.881 and 0.770 were achieved for hospitalization and LOS, respectively. Elevated levels of CRP, creatinine, and ferritin were key determinants of hospitalization and LOS (p < 0.05). A calculator was made available under the following URL: https://covid19-outcome-prediction.shinyapps.io/COVID19_Hospitalization_Calculator/ . This study yielded internally validated models that predict hospitalization risk in COVID-19-positive patients, which can be used to optimize resource allocation. Predictors of hospitalization and extended LOS included older age, CKD, fever, oxygen desaturation, elevated C-reactive protein, creatinine, and ferritin.

Incidence and factors related to SARS-CoV-2 infection in a cohort of patients with rheumatoid arthritis from a health service provider in Colombia during the COVID-19 pandemic

Background:

Patients with rheumatoid arthritis (RA) have an increased risk of SARS-CoV-2 infection due to intrinsic characteristics of the pathology and the medications used to treat it. The aim was to evaluate the incidence of and factors related to SARS-CoV-2 infection in patients with RA in Colombia.

Methods:

This was an observational study of patients diagnosed with RA who were treated at a health care institution in Colombia. The study evaluated whether the patients presented SARS-CoV-2 infection and other clinical variables. Variables associated with the risk of SARS-CoV-2 infection were identified.

Results:

A total of 2566 patients with RA were identified. They had a median age of 61.9 years, and 81.1% were women. They were mainly treated with synthetic disease-modifying antirheumatic drugs (DMARDs) (85.3%), glucocorticoids (52.2%), and biological DMARDs (26.8%). The incidence of SARS-CoV-2 infection was 5.1%, and the factors that increased the risk included treatment with synthetic DMARDs with or without biological DMARDs but with concomitant systemic glucocorticoids [odds ratio (OR): 2.18, 95% confidence interval (CI): 1.21–3.93 and OR: 1.69, 95% CI: 1.05–2.74, respectively] and receiving antidiabetic drugs (OR: 2.24, 95% CI: 1.27–3.94). A total of 20.8% of patients with COVID-19 required hospitalization and 3.8% died.

Conclusion:

The incidence of COVID-19 is higher among patients with RA who receive DMARDs and glucocorticoids simultaneously or who have diabetes mellitus than among patients with RA not receiving these drug combinations, which should guide treatment strategies.