End-of-life perceptions among physicians in intensive care units managed by anesthesiologists in Germany: a survey about structure, current implementation and deficits.

BACKGROUND: Structural aspects and current practice about end-of-life (EOL) decisions in German intensive care units (ICUs) managed by anesthesiologists are unknown. A survey among intensive care anesthesiologists has been conducted to explore current practice, barriers and opinions on EOL decisions in ICU. METHODS: In November 2015, all members of the German Society of Anesthesiology and Intensive Care Medicine (DGAI) and the Association of German Anesthesiologists (BDA) were asked to participate in an online survey to rate the presence or absence and the importance of 50 items. Answers were grouped into three categories considering implementation and relevance: Category 1 reflects high implementation and high relevance, Category 2 low and low, and Category 3 low and high. RESULTS: Five-hundred and forty-one anesthesiologists responded. Only four items reached ≥90% agreement as being performed "yes, always" or "mostly", and 29 items were rated "very" or "more important". A profound discrepancy between current practice and attributed importance was revealed. Twenty-eight items attributed to Category 1, six to Category 2 and sixteen to Category 3. Items characterizing the most urgent need for improvement (Category 3) referred to patient outcome data, preparation of health care directives and interdisciplinary discussion, standard operating procedures, implementation of practical instructions and inclusion of nursing staff and families in the process. CONCLUSION: The present survey affirms an urgent need for improvement in EOL practice in German ICUs focusing on advanced care planning, distinct aspects of changing goals of care, implementation of standard operating procedures, continuing education and reporting of outcome data.

Clinical and lifestyle related factors influencing whole blood metabolite levels - A comparative analysis of three large cohorts.

OBJECTIVE: Human blood metabolites are influenced by a number of lifestyle and environmental factors. Identification of these factors and the proper quantification of their relevance provides insights into human biological and metabolic disease processes, is key for standardized translation of metabolite biomarkers into clinical applications, and is a prerequisite for comparability of data between studies. However, so far only limited data exist from large and well-phenotyped human cohorts and current methods for analysis do not fully account for the characteristics of these data. The primary aim of this study was to identify, quantify and compare the impact of a comprehensive set of clinical and lifestyle related factors on metabolite levels in three large human cohorts. To achieve this goal, we improve current methodology by developing a principled analysis approach, which could be translated to other cohorts and metabolite panels. METHODS: 63 Metabolites (amino acids, acylcarnitines) were quantified by liquid chromatography tandem mass spectrometry in three cohorts (total N = 16,222). Supported by a simulation study evaluating various analytical approaches, we developed an analysis pipeline including preprocessing, identification, and quantification of factors affecting metabolite levels. We comprehensively identified uni- and multivariable metabolite associations considering 29 environmental and clinical factors and performed metabolic pathway enrichment and network analyses. RESULTS: Inverse normal transformation of batch corrected and outlier removed metabolite levels accompanied by linear regression analysis proved to be the best suited method to deal with the metabolite data. Association analyses revealed numerous uni- and multivariable significant associations. 15 of the analyzed 29 factors explained >1% of variance for at least one of the metabolites. Strongest factors are application of steroid hormones, reticulocytes, waist-to-hip ratio, sex, haematocrit, and age. Effect sizes of factors are comparable across studies. CONCLUSIONS: We introduced a principled approach for the analysis of MS data allowing identification, and quantification of effects of clinical and lifestyle factors with metabolite levels. We detected a number of known and novel associations broadening our understanding of the regulation of the human metabolome. The large heterogeneity observed between cohorts could almost completely be explained by differences in the distribution of influencing factors emphasizing the necessity of a proper confounder analysis when interpreting metabolite associations.

Comparison of differential gene expression of hot and cold thyroid nodules with primary epithelial cell culture models by investigation of co-regulated gene sets.

Both autonomously functioning thyroid nodules (AFTNs) and cold thyroid nodules (CTNs) are characterized by an increased proliferation, however, they have opposite functional activities. Therefore, with the aim to further understand the distinct molecular pathology of each entity and to discover common mechanisms like those leading to increased proliferation in both, AFTNs and CTNs, we now compared gene expression of AFTNs and CTNs with in vitro model systems (TSH-stimulated and ras-transfected primary cultures (PC)) whose gene expression patterns can be attributed to specific molecular alterations. Since combinations of co-regulated genes are more likely to reveal molecular mechanisms, we used a procedure which groups co-regulated genes within "gene sets". We found a co-regulated gene set in the AFTNs that overlaps with differential expression in TSH-stimulated PCs but not in CTNs or ras-transfected PCs. In addition to thyroid peroxidase and sialyltransferase 1, this set of co-regulated genes comprises metallothioneins and the G-protein-coupled receptor 56. Although their role in the thyroid is unknown so far, their appearance in one group indicates a functional relevance in TSH-TSH receptor-stimulated mechanisms. Furthermore, we identified down-regulated gene sets with concordant expression patterns in AFTNs, CTNs and ras-transfected PCs. However, these expression patterns are not of relevance in the TSH-stimulated PCs. These findings suggest that TSH-stimulated PCs can be used as a model of increased thyroid function (AFTNs), whereas the ras-transfected PCs better reflect the increased proliferation of both AFTNs and CTNs.