Mortality of hospitalized COVID-19 patients that were residents of nursing homes

Introduction: During the COVID-19 pandemic many outbreaks in nursing homes were reported. Aim(s): To determine the clinical characteristics and outcomes of hospitalized COVID-19 patients previously living in nursing homes. Method(s): This prospective study collected and evaluated data related to demographics, comorbidities, laboratory findings and outcomes of hospitalized COVID-19 patients from 4 clinics in Greece. Result(s): 185 patients (74.1% female), median age of 85(IQR 77-90) years were recruited. 29.7% of patients died. Parameters that influenced the high mortality rate were older age, the presence of dementia and atrial fibrillation. Furthermore, delay admission, fever >=38oC, dyspnea, low lymphocytes, high neutrophils, elevated LDH and DDimers were reported. Cardiovascular events, acute kidney and liver injury were more frequent in the group of patients who did not survive (40%vs14.6%, p<0.001, 50.9%vs13.1%, p<0.001, and 18.2%vs1.5%,p<0.001 respectively). In cox regression analysis independent risk factors for fatal outcome were dementia [HR (95%CI):5.067(1.512-16.981),p=0.009] and cardiovascular events [HR(95%CI):2.709(1.191-6.165),p=0.018]. Conclusion(s): Mortality rate is high in COVID-19 patients, residents of nursing homes. Comorbidities with predominance dementia and cardiovascular diseases, specific laboratory findings and delayed hospital referral were the main aspects contributing to adverse outcomes.

A hybrid approach based on dynamic trajectories to predict mortality in COVID-19 patients upon steroids administration

Since the World Health Organization (WHO) has declared Artificial Intelligence (AI) as a powerful tool in the fight against COVID-19, multiple studies have been launched aiming to shed light into risk factors for ICU admission and mortality. None of the existing studies, however, have captured the dynamic trajectories of hospitalized COVID-19 patients who receive steroids nor have explored trajectory-based mortality indicators. In this work, we present a novel, hybrid approach to address this need. Latent Growth Mixture Modelling (LGMM) was used to analyze the trajectories of patients who received steroids. The patients were then grouped into clusters based on the similarity of their dynamic trajectories. State-of-the art machine learning classifiers are trained on the original dataset with and without dynamic trajectories to assess whether their inclusion can enhance the prediction of mortality. Our results highlight the importance of trajectories for predicting mortality in patients who receive steroids yielding 4% and 5% increase in the sensitivity (0.84) and specificity (0.85). The FiO2 and percentage of neutrophils at day 5, along with the percentage of lymphocytes at day 7, were identified as the main causes for mortality in patients who receive steroids, where the SatO2 levels showed significant alterations in the dynamic trajectories. © 2022 IEEE.

Baseline characteristics and outcome predictors in patients hospitalized with COVID-19 in the University General Hospital of Ioannina, Greece

INTRODUCTION The novel Severe Acute Respiratory Syndrome Coronavirus-2, which causes the coronavirus disease COVID-19, is a highly infectious viral pathogen that is responsible for the ongoing pandemic. The aim of the present study was to illustrate the pre-hospitalization baseline characteristics and comorbidities of patients admitted with COVID-19 and their association with patient outcomes. METHODS This was a retrospective observational study of consecutive patients who were admitted to the COVID-19 departments of the University General Hospital of Ioannina, Greece (March 2020 - August 2021). Patients' demographic data, chronic disease medication use, and comorbidities were recorded upon their admission. RESULTS A total of 627 patients were hospitalized with mean age 62.5 years, 65.2% with at least one comorbidity, and 43.1% female. The median hospitalization duration was 11 days;554 (88.4%) of the patients were discharged and the mortality rate was 11.6%. Older age, admission during the second pandemic wave, arterial hypertension, and diabetes mellitus were associated with longer hospitalization. In multivariate analyses, cardiovascular disease was an independent predictor of hospitalization length (OR=1.834;95% CI: 1.039-3.228), whereas age (HR=1.079;95% CI: 1.045-1.115), history of malignancy (HR=1.246;95% CI: 1.002-1.595), and a diagnosis of COPD (HR=1.989;95% CI: 1.025-7.999) remained the independent mortality predictors. CONCLUSIONS Our data highlight the effect of COPD and malignancy on mortality risk in COVID-19 patients and the association of cardiovascular disease with a longer hospitalization.

Monitoring human activity at a very local scale with ground-motion records: The early stage of covid-19 pandemic in california, u.s.a., new york city, u.s.a., and mexicali, mexico

In this article, we analyze the change in anthropogenic seismic noise level within a frequency range of 4-14 Hz, through a survey of seismic stations in California, United States, New York City, United States, and Mexicali, Baja California, Mexico from early December 2019 to late April 2020. Our analysis shows that some stations recorded a drop in anthropogenic seismic noise during the COVID-19 pandemic, and the timing of the anthropogenic noise decrease typically correlates with the timing of a strict curtailment of personal and economic activity issued by the local government. In other locations, the drop in the anthropogenic seismic noise appears not to follow the lockdown timing perfectly.Duringour analysis,weobservedthatmanystations didnot recordadropduring the early stageofCOVID-19pandemic. Ofthe 19 stationsof the Southern California Seismic Network that were surveyed, we found that only five show a similar extent of drop in anthropogenic seismic noise comparable to the Christmas holiday break in 2019. This suggests that the human activity that caused seismic noise did not significantly reduce during the COVID-19 pandemic near most surveyed stations in southern California. A further analysis implies that the primary seismic noise source in southern California might be traffic, and the continuation of industrial traffic, such as cargo transportation, during the COVID-19 pandemic may be the reason why many stations did not record a noise drop. Our results show that the anthropogenic seismic noise recorded by seismic stations is capable of indicating human activity, and that thismetric is, particularly, powerful inmeasuring how localized communities initially responded to the COVID-19 pandemic. © 2021 Seismological Society of America. All rights reserved.