Subclinical thyroid dysfunction and the risk of heart failure events: an individual participant data analysis from 6 prospective cohorts.

BACKGROUND: American College of Cardiology/American Heart Association guidelines for the diagnosis and management of heart failure recommend investigating exacerbating conditions such as thyroid dysfunction, but without specifying the impact of different thyroid-stimulation hormone (TSH) levels. Limited prospective data exist on the association between subclinical thyroid dysfunction and heart failure events. METHODS AND RESULTS: We performed a pooled analysis of individual participant data using all available prospective cohorts with thyroid function tests and subsequent follow-up of heart failure events. Individual data on 25 390 participants with 216 248 person-years of follow-up were supplied from 6 prospective cohorts in the United States and Europe. Euthyroidism was defined as TSH of 0.45 to 4.49 mIU/L, subclinical hypothyroidism as TSH of 4.5 to 19.9 mIU/L, and subclinical hyperthyroidism as TSH <0.45 mIU/L, the last two with normal free thyroxine levels. Among 25 390 participants, 2068 (8.1%) had subclinical hypothyroidism and 648 (2.6%) had subclinical hyperthyroidism. In age- and sex-adjusted analyses, risks of heart failure events were increased with both higher and lower TSH levels (P for quadratic pattern <0.01); the hazard ratio was 1.01 (95% confidence interval, 0.81-1.26) for TSH of 4.5 to 6.9 mIU/L, 1.65 (95% confidence interval, 0.84-3.23) for TSH of 7.0 to 9.9 mIU/L, 1.86 (95% confidence interval, 1.27-2.72) for TSH of 10.0 to 19.9 mIU/L (P for trend <0.01) and 1.31 (95% confidence interval, 0.88-1.95) for TSH of 0.10 to 0.44 mIU/L and 1.94 (95% confidence interval, 1.01-3.72) for TSH <0.10 mIU/L (P for trend=0.047). Risks remained similar after adjustment for cardiovascular risk factors. CONCLUSION: Risks of heart failure events were increased with both higher and lower TSH levels, particularly for TSH ≥10 and <0.10 mIU/L.

Synapses on motoneuron dendrites in the brachial section of the frog spinal cord: a computer-aided electron microscopic study of cobalt-filled cells.

Cobalt-labelled motoneuron dendrites of the frog spinal cord at the level of the second spinal nerve were photographed in the electron microscope from long series of ultrathin sections. Three-dimensional computer reconstructions of 120 dendrite segments were analysed. The samples were taken from two locations: proximal to cell body and distal, as defined in a transverse plane of the spinal cord. The dendrites showed highly irregular outlines with many 1-2 microns-long 'thorns' (on average 8.5 thorns per 100 microns 2 of dendritic area). Taken together, the reconstructed dendrite segments from the proximal sites had a total length of about 250 microns; those from the distal locations, 180 microns. On all segments together there were 699 synapses. Nine percent of the synapses were on thorns, and many more close to their base on the dendritic shaft. The synapses were classified in four groups. One third of the synapses were asymmetric with spherical vesicles; one half were symmetric with spherical vesicles; and one tenth were symmetric with flattened vesicles. A fourth, small class of asymmetric synapses had dense-core vesicles. The area of the active zones was large for the asymmetric synapses (median value 0.20 microns 2), and small for the symmetric ones (median value 0.10 microns 2), and the difference was significant. On average, the areas of the active zones of the synapses on thin dendrites were larger than those of synapses on large calibre dendrites. About every 4 microns 2 of dendritic area received one contact. There was a significant difference between the areas of the active zones of the synapses at the two locations. Moreover, the number per unit dendritic length was correlated with dendrite calibre. On average, the active zones covered more than 4% of the dendritic area; this value for thin dendrites was about twice as large as that of large calibre dendrites. We suggest that the larger active zones and the larger synaptic coverage of the thin dendrites compensate for the longer electrotonic distance of these synapses from the soma.

The Patientʼs Perception of Eosinophilic Esophagitis Related Symptoms: a Qualitative Content Analysis

Background and Aims: The international EEsAI study group is currently developing an activity index for Eosinophilic Esophagitis (EoE). A potential discrepancy between patient and physician reported EoE symptoms has not been assessed yet. Therefore, we aimed to evaluate patient reported items describing their EoE activity and to compare these with the physicianʼs perception. Methods: A questionnaire was sent to 100 EoE patients in Switzerland. EoE-related symptoms dependent and independent of food intake were reported by patients. Results were analyzed using a qualitative content analysis and compared with symptoms reported by international EoE experts in Delphi rounds. Results: The questionnaire response rate was 64/100. The following items were developed by combining categories based on patients answers: food-consistency related dysphagia, frequency and severity of dysphagia, food impaction, strategies to avoid food impaction, food allergy, drinking-related retrosternal pain. The following food categories associated with dysphagia were identified: meat, rice, dry bread, French fries, raw, fibrous foods, others. Sports and psychological stress were identified as triggers for non-food intake related EoE symptoms. A good correlation was found between patient and physicianʼs reported EoE related symptoms. Conclusions: There is a good correlation between patient reported symptoms and the physicianʼs perception of clinical items as reported by international EoE experts. These patient reported outcomes will now be incorporated into the EEsAI questionnaire that measures EoE activity.

On the Schoenberg transformations in data analysis: theory and illustrations

The class of Schoenberg transformations, embedding Euclidean distances into higher dimensional Euclidean spaces, is presented, and derived from theorems on positive definite and conditionally negative definite matrices. Original results on the arc lengths, angles and curvature of the transformations are proposed, and visualized on artificial data sets by classical multidimensional scaling. A distance-based discriminant algorithm and a robust multidimensional centroid estimate illustrate the theory, closely connected to the Gaussian kernels of Machine Learning.

Video analysis software increases the interrater reliability of video gait assessments in children with cerebral palsy.

The aim of this study was to determine the effect of using video analysis software on the interrater reliability of visual assessments of gait videos in children with cerebral palsy. Two clinicians viewed the same random selection of 20 sagittal and frontal video recordings of 12 children with cerebral palsy routinely acquired during outpatient rehabilitation clinics. Both observers rated these videos in a random sequence for each lower limb using the Observational Gait Scale, once with standard video software and another with video analysis software (Dartfish(®)) which can perform angle and timing measurements. The video analysis software improved interrater agreement, measured by weighted Cohen's kappas, for the total score (κ 0.778→0.809) and all of the items that required angle and/or timing measurements (knee position mid-stance κ 0.344→0.591; hindfoot position mid-stance κ 0.160→0.346; foot contact mid-stance κ 0.700→0.854; timing of heel rise κ 0.769→0.835). The use of video analysis software is an efficient approach to improve the reliability of visual video assessments.

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

Computer-Aided Business Model Design

There is a lack of dedicated tools for business model design at a strategic level. However, in today's economic world the need to be able to quickly reinvent a company's business model is essential to stay competitive. This research focused on identifying the functionalities that are necessary in a computer-aided design (CAD) tool for the design of business models in a strategic context. Using design science research methodology a series of techniques and prototypes have been designed and evaluated to offer solutions to the problem. The work is a collection of articles which can be grouped into three parts: First establishing the context of how the Business Model Canvas (BMC) is used to design business models and explore the way in which CAD can contribute to the design activity. The second part extends on this by proposing new technics and tools which support elicitation, evaluation (assessment) and evolution of business models design with CAD. This includes features such as multi-color tagging to easily connect elements, rules to validate coherence of business models and features that are adapted to the correct business model proficiency level of its users. A new way to describe and visualize multiple versions of a business model and thereby help in addressing the business model as a dynamic object was also researched. The third part explores extensions to the business model canvas such as an intermediary model which helps IT alignment by connecting business model and enterprise architecture. And a business model pattern for privacy in a mobile environment, using privacy as a key value proposition. The prototyped techniques and proposition for using CAD tools in business model modeling will allow commercial CAD developers to create tools that are better suited to the needs of practitioners.

Corporate responsibility in the postnational constellation : a multiple-case study

In my thesis I present the findings of a multiple-case study on the CSR approach of three multinational companies, applying Basu and Palazzo's (2008) CSR-character as a process model of sensemaking, Suchman's (1995) framework on legitimation strategies, and Habermas (1996) concept of deliberative democracy. The theoretical framework is based on the assumption of a postnational constellation (Habermas, 2001) which sends multinational companies onto a process of sensemaking (Weick, 1995) with regards to their responsibilities in a globalizing world. The major reason is that mainstream CSR-concepts are based on the assumption of a liberal market economy embedded in a nation state that do not fit the changing conditions for legitimation of corporate behavior in a globalizing world. For the purpose of this study, I primarily looked at two research questions: (i) How can the CSR approach of a multinational corporation be systematized empirically? (ii) What is the impact of the changing conditions in the postnational constellation on the CSR approach of the studied multinational corporations? For the analysis, I adopted a holistic approach (Patton, 1980), combining elements of a deductive and inductive theory building methodology (Eisenhardt, 1989b; Eisenhardt & Graebner, 2007; Glaser & Strauss, 1967; Van de Ven, 1992) and rigorous qualitative data analysis. Primary data was collected through 90 semi-structured interviews in two rounds with executives and managers in three multinational companies and their respective stakeholders. Raw data originating from interview tapes, field notes, and contact sheets was processed, stored, and managed using the software program QSR NVIVO 7. In the analysis, I applied qualitative methods to strengthen the interpretative part as well as quantitative methods to identify dominating dimensions and patterns. I found three different coping behaviors that provide insights into the corporate mindset. The results suggest that multinational corporations increasingly turn towards relational approaches of CSR to achieve moral legitimacy in formalized dialogical exchanges with their stakeholders since legitimacy can no longer be derived only from a national framework. I also looked at the degree to which they have reacted to the postnational constellation by the assumption of former state duties and the underlying reasoning. The findings indicate that CSR approaches become increasingly comprehensive through integrating political strategies that reflect the growing (self-) perception of multinational companies as political actors. Based on the results, I developed a model which relates the different dimensions of corporate responsibility to the discussion on deliberative democracy, global governance and social innovation to provide guidance for multinational companies in a postnational world. With my thesis, I contribute to management research by (i) delivering a comprehensive critique of the mainstream CSR-literature and (ii) filling the gap of thorough qualitative research on CSR in a globalizing world using the CSR-character as an empirical device, and (iii) to organizational studies by further advancing a deliberative view of the firm proposed by Scherer and Palazzo (2008).

How to interprete subclinical thyroid dysfunction in the prevention and management of heart failure events? Individual participant data analysis from six prospective cohorts

Background: Guidelines of the Diagnosis and Management of Heart Failure (HF) recommend investigating exacerbating conditions, such as thyroid dysfunction, but without specifying impact of different TSH levels. Limited prospective data exist regarding the association between subclinical thyroid dysfunction and HF events. Methods: We performed a pooled analysis of individual participant data using all available prospective cohorts with thyroid function tests and subsequent follow-up of HF events. Individual data on 25,390 participants with 216,247 person-years of follow-up were supplied from 6 prospective cohorts in the United States and Europe. Euthyroidism was defined as TSH 0.45-4.49 mIU/L, subclinical hypothyroidism as TSH 4.5-19.9 mIU/L and subclinical hyperthyroidism as TSH <0.45 mIU/L, both with normal free thyroxine levels. HF events were defined as acute HF events, hospitalization or death related to HF events. Results: Among 25,390 participants, 2068 had subclinical hypothyroidism (8.1%) and 648 subclinical hyperthyroidism (2.6%). In age- and gender-adjusted analyses, risks of HF events were increased with both higher and lower TSH levels (P for quadratic pattern<0.01): hazard ratio (HR) was 1.01 (95% confidence interval [CI] 0.81-1.26) for TSH 4.5-6.9 mIU/L, 1.65 (CI 0.84-3.23) for TSH 7.0-9.9 mIU/L, 1.86 (CI 1.27-2.72) for TSH 10.0-19.9 mIUL/L (P for trend <0.01), and was 1.31 (CI 0.88-1.95) for TSH 0.10-0.44 mIU/L and 1.94 (CI 1.01-3.72) for TSH <0.10 mIU/L (P for trend=0.047). Risks remained similar after adjustment for cardiovascular risk factors. Conclusion: Risks of HF events were increased with both higher and lower TSH levels, particularly for TSH ≥10 mIU/L and for TSH <0.10 mIU/L. Our findings might help to interpret TSH levels in the prevention and investigation of HF.

Thyroid antibody status, subclinical hypothyroidism, and the risk of coronary heart disease: an individual participant data analysis.

CONTEXT: Subclinical hypothyroidism has been associated with increased risk of coronary heart disease (CHD), particularly with thyrotropin levels of 10.0 mIU/L or greater. The measurement of thyroid antibodies helps predict the progression to overt hypothyroidism, but it is unclear whether thyroid autoimmunity independently affects CHD risk. OBJECTIVE: The objective of the study was to compare the CHD risk of subclinical hypothyroidism with and without thyroid peroxidase antibodies (TPOAbs). DATA SOURCES AND STUDY SELECTION: A MEDLINE and EMBASE search from 1950 to 2011 was conducted for prospective cohorts, reporting baseline thyroid function, antibodies, and CHD outcomes. DATA EXTRACTION: Individual data of 38 274 participants from six cohorts for CHD mortality followed up for 460 333 person-years and 33 394 participants from four cohorts for CHD events. DATA SYNTHESIS: Among 38 274 adults (median age 55 y, 63% women), 1691 (4.4%) had subclinical hypothyroidism, of whom 775 (45.8%) had positive TPOAbs. During follow-up, 1436 participants died of CHD and 3285 had CHD events. Compared with euthyroid individuals, age- and gender-adjusted risks of CHD mortality in subclinical hypothyroidism were similar among individuals with and without TPOAbs [hazard ratio (HR) 1.15, 95% confidence interval (CI) 0.87-1.53 vs HR 1.26, CI 1.01-1.58, P for interaction = .62], as were risks of CHD events (HR 1.16, CI 0.87-1.56 vs HR 1.26, CI 1.02-1.56, P for interaction = .65). Risks of CHD mortality and events increased with higher thyrotropin, but within each stratum, risks did not differ by TPOAb status. CONCLUSIONS: CHD risk associated with subclinical hypothyroidism did not differ by TPOAb status, suggesting that biomarkers of thyroid autoimmunity do not add independent prognostic information for CHD outcomes.