https://www.selleckchem.com/products/peficitinb-asp015k-jnj-54781532.html Clinical risk scores and machine learning models based on routine laboratory values could assist in automated early identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients at risk for severe clinical outcomes. They can guide patient triage, inform allocation of health care resources, and contribute to the improvement of clinical outcomes. In- and out-patients tested positive for SARS-CoV-2 at the Insel Hospital Group Bern, Switzerland, between February 1st and August 31st ('first wave', n = 198) and September 1st through November 16th 2020 ('second wave', n = 459) were used as training and prospective validation cohort, respectively. A clinical risk stratification score and machine learning (ML) models were developed using demographic data, medical history, and laboratory values taken up to 3 days before, or 1 day after, positive testing to predict severe outcomes of hospitalization (a composite endpoint of admission to intensive care, or death from any cause). Test accurac were predictive for severe outcomes at our tertiary hospital center, and performed well in prospective validation. Anxiety is reportedly prevalent in older adults with dementia living in care homes and, within this population, is most often assessed through caregiver reports. Heart rate variability (HRV) is a physiological indicator of autonomic function, whereby reduced vagally-mediated HRV is associated with a variety of anxiety symptoms and disorders. This study evaluates the feasibility of collecting HRV data within this population, presents HRV data for older adults with dementia living in a care home, and examines HRV in the context of self-reported anxiety. These data were collected during a larger study examining an exercise intervention. HRV data, in the form of log-transformed root mean square of the successive differences (lnRMSSD), were in line with transformed data from previous