Vancomycin-Resistant Enterococci Outbreak, Germany, and Calculation of Outbreak Start

On the basis of a large outbreak of vancomycin-resistant Enterococcus faecium in a German university hospital, we estimated costs (≈1 million Euros) that could have been avoided by early detection of the imminent outbreak. For this purpose, we demonstrate an easy-to-use statistical method

Disease severity in hospitalized COVID-19 patients: comparing routine surveillance with cohort data from the LEOSS study in 2020 in Germany

Introduction Studies investigating risk factors for severe COVID-19 often lack information on the representativeness of the study population. Here, we investigate factors associated with severe COVID-19 and compare the representativeness of the dataset to the general population. Methods We used data from the Lean European Open Survey on SARS-CoV-2 infected patients (LEOSS) of hospitalized COVID-19 patients diagnosed in 2020 in Germany to identify associated factors for severe COVID-19, defined as progressing to a critical disease stage or death. To assess the representativeness, we compared the LEOSS cohort to cases of hospitalized patients in the German statutory notification data of the same time period. Descriptive methods and Poisson regression models were used. Results Overall, 6672 hospitalized patients from LEOSS and 132,943 hospitalized cases from the German statutory notification data were included. In LEOSS, patients above 76 years were less likely represented (34.3% vs. 44.1%). Moreover, mortality was lower (14.3% vs. 21.5%) especially among age groups above 66 years. Factors associated with a severe COVID-19 disease course in LEOSS included increasing age, male sex (adjusted risk ratio (aRR) 1.69, 95% confidence interval (CI) 1.53–1.86), prior stem cell transplantation (aRR 2.27, 95% CI 1.53–3.38), and an elevated C-reactive protein at day of diagnosis (aRR 2.30, 95% CI 2.03–2.62). Conclusion We identified a broad range of factors associated with severe COVID-19 progression. However, the results may be less applicable for persons above 66 years since they experienced lower mortality in the LEOSS dataset compared to the statutory notification data.Peer Reviewe

Smoking Cessation Counselling: What Makes Her or Him a Good Counsellor? Can Counselling Technique Be Deduced to Other Important Lifestyle Counselling Competencies?

Smoking is a major health concern in both developed and developing countries. Smoking cessation counselling is of major importance for health care providers such as physicians, psychologists, nurses and many further therapeutic workers. We recently have demonstrated feasibility of a 4-hour “student-to-student course” (1 hour of scientific background and 3 hours of role plays and intervision) that provided knowledge, skills and attitude to smoking cessation counselling. A key question remains whether such knowledge, skills and attitude can be further deduced to key public health or lifestyle counselling areas like body weight management in overweight persons, management of addictions like alcohol and substance or situation (e.g., Internet and shopping) abuse, management of physical activity/exercise or lifestyle modification like workaholic lifestyle. The authors try to develop such a base for enabling patients to adapt healthier behaviour and give objectives for such counselling situations including the elaboration of clear therapeutic aims for counsellors

Machine learning based prediction of COVID-19 mortality suggests repositioning of anticancer drug for treating severe cases

Despite available vaccinations COVID-19 case numbers around the world are still growing, and effective medications against severe cases are lacking. In this work, we developed a machine learning model which predicts mortality for COVID-19 patients using data from the multi-center ‘Lean European Open Survey on SARS-CoV-2-infected patients’ (LEOSS) observational study (>100 active sites in Europe, primarily in Germany), resulting into an AUC of almost 80%. We showed that molecular mechanisms related to dementia, one of the relevant predictors in our model, intersect with those associated to COVID-19. Most notably, among these molecules was tyrosine kinase 2 (TYK2), a protein that has been patented as drug target in Alzheimer's Disease but also genetically associated with severe COVID-19 outcomes. We experimentally verified that anti-cancer drugs Sorafenib and Regorafenib showed a clear anti-cytopathic effect in Caco2 and VERO-E6 cells and can thus be regarded as potential treatments against COVID-19. Altogether, our work demonstrates that interpretation of machine learning based risk models can point towards drug targets and new treatment options, which are strongly needed for COVID-19

Hospitalized patients dying with SARS-CoV-2 infection—an analysis of patient characteristics and management in ICU and general ward of the LEOSS registry

BACKGROUND: COVID-19 is a severe disease with a high need for intensive care treatment and a high mortality rate in hospitalized patients. The objective of this study was to describe and compare the clinical characteristics and the management of patients dying with SARS-CoV-2 infection in the acute medical and intensive care setting. METHODS: Descriptive analysis of dying patients enrolled in the Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS), a non-interventional cohort study, between March 18 and November 18, 2020. Symptoms, comorbidities and management of patients, including palliative care involvement, were compared between general ward and intensive care unit (ICU) by univariate analysis. RESULTS: 580/4310 (13%) SARS-CoV-2 infected patients died. Among 580 patients 67% were treated on ICU and 33% on a general ward. The spectrum of comorbidities and symptoms was broad with more comorbidities (≥ four comorbidities: 52% versus 25%) and a higher age distribution (>65 years: 98% versus 70%) in patients on the general ward. 69% of patients were in an at least complicated phase at diagnosis of the SARS-CoV-2 infection with a higher proportion of patients in a critical phase or dying the day of diagnosis treated on ICU (36% versus 11%). While most patients admitted to ICU came from home (71%), patients treated on the general ward came likewise from home and nursing home (44% respectively) and were more frequently on palliative care before admission (29% versus 7%). A palliative care team was involved in dying patients in 15%. Personal contacts were limited but more often documented in patients treated on ICU (68% versus 47%). CONCLUSION: Patients dying with SARS-CoV-2 infection suffer from high symptom burden and often deteriorate early with a demand for ICU treatment. Therefor a demand for palliative care expertise with early involvement seems to exist

Экспериментальное исследование процесса влагоудаления из различных типов древесной хвойной биомассы при подготовке к получению тепловой энергии.

В результате эксперимента получены результаты изменения массовой скорости испарения, коэффициента аккомодации и парциального давления для хвойных пород древесины. Получены зависимости массовой скорости испарения от температуры, массовой скорости испарения от времени испарения, а также проведен расчет коэффициента аккомодации.As a result of the experiment, the results of changes in the mass evaporation rate, accommodation coefficient and partial pressure for coniferous wood species were obtained. Dependences of the mass evaporation rate on temperature, mass evaporation rate on evaporation time, and calculation of the accommodation coefficient are obtained

COVID-19 severity and thrombo-inflammatory response linked to ethnicity

Although there is strong evidence that SARS-CoV-2 infection is associated with adverse outcomes in certain ethnic groups, the association of disease severity and risk factors such as comorbidities and biomarkers with racial disparities remains undefined. This retrospective study between March 2020 and February 2021 explores COVID-19 risk factors as predictors for patients’ disease progression through country comparison. Disease severity predictors in Germany and Japan were cardiovascular-associated comorbidities, dementia, and age. We adjusted age, sex, body mass index, and history of cardiovascular disease comorbidity in the country cohorts using a propensity score matching (PSM) technique to reduce the influence of differences in sample size and the surprisingly young, lean Japanese cohort. Analysis of the 170 PSM pairs confirmed that 65.29% of German and 85.29% of Japanese patients were in the uncomplicated phase. More German than Japanese patients were admitted in the complicated and critical phase. Ethnic differences were identified in patients without cardiovascular comorbidities. Japanese patients in the uncomplicated phase presented a suppressed inflammatory response and coagulopathy with hypocoagulation. In contrast, German patients exhibited a hyperactive inflammatory response and coagulopathy with hypercoagulation. These differences were less pronounced in patients in the complicated phase or with cardiovascular diseases. Coagulation/fibrinolysis-associated biomarkers rather than inflammatory-related biomarkers predicted disease severity in patients with cardiovascular comorbidities: platelet counts were associated with severe illness in German patients. In contrast, high D-dimer and fibrinogen levels predicted disease severity in Japanese patients. Our comparative study indicates that ethnicity influences COVID-19-associated biomarker expression linked to the inflammatory and coagulation (thrombo-inflammatory) response. Future studies will be necessary to determine whether these differences contributed to the less severe disease progression observed in Japanese COVID-19 patients compared with those in Germany

Molecular and functional profiling identifies therapeutically targetable vulnerabilities in plasmablastic lymphoma

Plasmablastic lymphoma (PBL) represents a rare and aggressive lymphoma subtype frequently associated with immunosuppression. Clinically, patients with PBL are characterized by poor outcome. The current understanding of the molecular pathogenesis is limited. A hallmark of PBL represents its plasmacytic differentiation with loss of B-cell markers and, in 60% of cases, its association with Epstein-Barr virus (EBV). Roughly 50% of PBLs harbor a MYC translocation. Here, we provide a comprehensive integrated genomic analysis using whole exome sequencing (WES) and genome-wide copy number determination in a large cohort of 96 primary PBL samples. We identify alterations activating the RAS-RAF, JAK-STAT, and NOTCH pathways as well as frequent high-level amplifications in MCL1 and IRF4. The functional impact of these alterations is assessed using an unbiased shRNA screen in a PBL model. These analyses identify the IRF4 and JAK-STAT pathways as promising molecular targets to improve outcome of PBL patients

Covid-19 triage in the emergency department 2.0: how analytics and AI transform a human-made algorithm for the prediction of clinical pathways

The Covid-19 pandemic has pushed many hospitals to their capacity limits. Therefore, a triage of patients has been discussed controversially primarily through an ethical perspective. The term triage contains many aspects such as urgency of treatment, severity of the disease and pre-existing conditions, access to critical care, or the classification of patients regarding subsequent clinical pathways starting from the emergency department. The determination of the pathways is important not only for patient care, but also for capacity planning in hospitals. We examine the performance of a human-made triage algorithm for clinical pathways which is considered a guideline for emergency departments in Germany based on a large multicenter dataset with over 4,000 European Covid-19 patients from the LEOSS registry. We find an accuracy of 28 percent and approximately 15 percent sensitivity for the ward class. The results serve as a benchmark for our extensions including an additional category of palliative care as a new label, analytics, AI, XAI, and interactive techniques. We find significant potential of analytics and AI in Covid-19 triage regarding accuracy, sensitivity, and other performance metrics whilst our interactive human-AI algorithm shows superior performance with approximately 73 percent accuracy and up to 76 percent sensitivity. The results are independent of the data preparation process regarding the imputation of missing values or grouping of comorbidities. In addition, we find that the consideration of an additional label palliative care does not improve the results