[Bed capacity management in times of the COVID-19 pandemic : A simulation-based prognosis of normal and intensive care beds using the descriptive data of the University Hospital Augsburg]. / Bettenkapazitätssteuerung in Zeiten der COVID-19-Pandemie : Eine simulationsbasierte Prognose der Normal- und Intensivstationsbetten anhand der deskriptiven Daten des Universitätsklinikums Augsburg.

BACKGROUND: Following the regional outbreak in China, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread all over the world, presenting the healthcare systems with huge challenges worldwide. In Germany the coronavirus diseases 2019 (COVID-19) pandemic has resulted in a slowly growing demand for health care with a sudden occurrence of regional hotspots. This leads to an unpredictable situation for many hospitals, leaving the question of how many bed resources are needed to cope with the surge of COVID-19 patients. OBJECTIVE: In this study we created a simulation-based prognostic tool that provides the management of the University Hospital of Augsburg and the civil protection services with the necessary information to plan and guide the disaster response to the ongoing pandemic. Especially the number of beds needed on isolation wards and intensive care units (ICU) are the biggest concerns. The focus should lie not only on the confirmed cases as the patients with suspected COVID-19 are in need of the same resources. MATERIAL AND METHODS: For the input we used the latest information provided by governmental institutions about the spreading of the disease, with a special focus on the growth rate of the cumulative number of cases. Due to the dynamics of the current situation, these data can be highly variable. To minimize the influence of this variance, we designed distribution functions for the parameters growth rate, length of stay in hospital and the proportion of infected people who need to be hospitalized in our area of responsibility. Using this input, we started a Monte Carlo simulation with 10,000 runs to predict the range of the number of hospital beds needed within the coming days and compared it with the available resources. RESULTS: Since 2 February 2020 a total of 306 patients were treated with suspected or confirmed COVID-19 at this university hospital. Of these 84 needed treatment on the ICU. With the help of several simulation-based forecasts, the required ICU and normal bed capacity at Augsburg University Hospital and the Augsburg ambulance service in the period from 28 March 2020 to 8 June 2020 could be predicted with a high degree of reliability. Simulations that were run before the impact of the restrictions in daily life showed that we would have run out of ICU bed capacity within approximately 1 month. CONCLUSION: Our simulation-based prognosis of the health care capacities needed helps the management of the hospital and the civil protection service to make reasonable decisions and adapt the disaster response to the realistic needs. At the same time the forecasts create the possibility to plan the strategic response days and weeks in advance. The tool presented in this study is, as far as we know, the only one accounting not only for confirmed COVID-19 cases but also for suspected COVID-19 patients. Additionally, the few input parameters used are easy to access and can be easily adapted to other healthcare systems.

Subjective passage of time during the pandemic: Routine, boredom, and memory

Over the weeks of social isolation during the COVID-19 pandemic, people typically reported that time had passed comparably quickly. Although time might have passed slowly during moments of anxiety and boredom for some, many felt a speeding up of the passing days and weeks. Here I attempt to explain the experience of time during the pandemic with cognitive models of time perception as related to the present moment (prospective time) and in hindsight (retrospective time). Retrospective judgments of time intervals rely on memory traces. The more contextual changes experienced during a given time interval, the longer duration is judged when looking back over past time intervals. More routine activities, as experienced by many during the pandemic, even when under time pressure, lead to fewer memorable events stored in autobiographical memory. This creates the impression that time has passed considerably more quickly. © Koninklijke Brill NV, Leiden, 2020

Parameters predicting COVID-19-induced myocardial injury and mortality.

Clinical manifestations of COVID-19 affect many organs, including the heart. Cardiovascular disease is a dominant comorbidity and prognostic factors predicting risk for critical courses are highly needed. Moreover, immunomechanisms underlying COVID-induced myocardial damage are poorly understood. OBJECTIVE: To elucidate prognostic markers to identify patients at risk. RESULTS: Only patients with pericardial effusion (PE) developed a severe disease course, and those who died could be identified by a high CD8/Treg/monocyte ratio. Ten out of 19 COVID-19 patients presented with PE, 7 (78%) of these had elevated APACHE-II mortality risk-score, requiring mechanical ventilation. At admission, PE patients showed signs of systemic and cardiac inflammation in NMR and impaired cardiac function as detected by transthoracic echocardiography (TTE), whereas parameters of myocardial injury e.g. high sensitive troponin-t (hs-TnT) were not yet increased. During the course of disease, hs-TnT rose in 8 of the PE-patients above 16 ng/l, 7 had to undergo ventilatory therapy and 4 of them died. FACS at admission showed in PE patients elevated frequencies of CD3+CD8+ T cells among all CD3+ T-cells, and lower frequencies of Tregs and CD14+HLA-DR+-monocytes. A high CD8/Treg/monocyte ratio predicted a severe disease course in PE patients, and was associated with high serum levels of antiviral cytokines. By contrast, patients without PE and PE patients with a low CD8/Treg/monocyte ratio neither had to be intubated, nor died. CONCLUSIONS: PE predicts cardiac injury in COVID-19 patients. Therefore, TTE should be performed at admission. Immunological parameters for dysfunctional antiviral immunity, such as the CD8/Treg/monocyte ratio used here, supports risk assessment by predicting poor prognosis.

Can chlorine dioxide prevent the spreading of coronavirus or other viral infections? Medical hypotheses.

Motivation: Viruses have caused many epidemics throughout human history. The novel coronavirus [10] is just the latest example. A new viral outbreak can be unpredictable, and development of specific defense tools and countermeasures against the new virus remains time-consuming even in today's era of modern medical science and technology. In the lack of effective and specific medication or vaccination, it would be desirable to have a nonspecific protocol or substance to render the virus inactive, a substance/protocol, which could be applied whenever a new viral outbreak occurs. This is especially important in cases when the emerging new virus is as infectious as SARS-CoV-2 [4]. Aims and structure of the present communication: In this editorial, we propose to consider the possibility of developing and implementing antiviral protocols by applying high purity aqueous chlorine dioxide (ClO2) solutions. The aim of this proposal is to initiate research that could lead to the introduction of practical and effective antiviral protocols. To this end, we first discuss some important properties of the ClO2 molecule, which make it an advantageous antiviral agent, then some earlier results of ClO2 gas application against viruses will be reviewed. Finally, we hypothesize on methods to control the spread of viral infections using aqueous ClO2 solutions.