Preoperative Indicators of the Effectiveness of Surgical Release in Patients with de Quervain Disease:A Prospective Cohort Study

Background: A significant proportion of patients report persistent pain after surgical release for de Quervain disease (DQ). This study aimed to investigate the effectiveness of a surgical release for DQ and to identify the preoperative factors associated with pain after a surgical release for DQ. Methods: This prospective cohort study included 707 patients who underwent surgical release and completed a visual analogue scale questionnaire (VAS; range 0 to 100). We used a paired t test to analyze the effectiveness of the surgical release on pain at 3 months postoperatively compared with the preoperative measure. A hierarchical multivariable linear regression model was created to investigate the contribution of patient-related and disease-related characteristics to postoperative pain. Results: All VAS domains showed improvement after surgical release. On average, the mean VAS pain decreased by 44 points (95% CI, 42, 46). Smoking (B = 6.37; P &lt; 0.01), younger age (B = -0.35; P &lt; 0.01), longer duration of complaints (B = 0.13; P &lt; 0.01), concomitant surgery (B = 14.40; P &lt; 0.01), and higher VAS pain scores at intake (B = 0.15; P &lt; 0.01) were associated with worse VAS pain scores postoperatively. Together, the variables explained 11% of the variance in mean VAS pain score at 3 months follow-up. Conclusions: This study confirms that surgical treatment for DQ significantly reduces patient-reported pain. Smoking, younger age, concomitant surgery, duration of complaints, and higher VAS pain scores at intake are associated with worse patient-reported pain 3 months after surgical release. However, the small effects suggest that these factors should not be considered the only important factors. CLINICAL QUESTION/LEVEL OF EVIDENCE: Risk, III.</p

Bragatston study protocol: a multicentre cohort study on automated quantification of cardiovascular calcifications on radiotherapy planning CT scans for cardiovascular risk prediction in patients with breast cancer

Introduction Cardiovascular disease (CVD) is an important cause of death in breast cancer survivors. Some breast cancer treatments including anthracyclines, trastuzumab and radiotherapy can increase the risk of CVD, especially for patients with pre-existing CVD risk factors. Early identification of patients at increased CVD risk may allow switching to less cardiotoxic treatments, active surveillance or treatment of CVD risk factors. One of the strongest independent CVD risk factors is the presence and extent of coronary artery calcifications (CAC). In clinical practice, CAC are generally quantified on ECGtriggered cardiac CT scans. Patients with breast cancer treated with radiotherapy routinely undergo radiotherapy planning CT scans of the chest, and those scans could provide the opportunity to routinely assess CAC before a potentially cardiotoxic treatment. The Bragatston study aims to investigate the association between calcifications in the coronary arteries, aorta and heart valves (hereinafter called ‘cardiovascular calcifications’) measured automatically on planning CT scans of patients with breast cancer and CVD risk. Methods and analysis In a first step, we will optimise and validate a deep learning algorithm for automated quantification of cardiovascular calcifications on planning CT scans of patients with breast cancer. Then, in a multicentre cohort study (University Medical Center Utrecht, Utrecht, Erasmus MC Cancer Institute, Rotterdam and Radboudumc, Nijmegen, The Netherlands), the association between cardiovascular calcifications measured on planning CT scans of patients with breast cancer (n≈16 000) and incident (non-)fatal CVD events will be evaluated. To assess the added predictive value of these calcifications over traditional CVD risk factors and treatment characteristics, a case-cohort analysis will be performed among all cohort members diagnosed with a CVD event during follow-up (n≈200) and a random sample of the baseline cohort (n≈600). Ethics and dissemination The Institutional Review Boards of the participating hospitals decided that the Medical R

The changing landscape of financial markets in Europe, the United States and Japan. Bruegel Working Paper 2013/02, 18 March 2013

We compare the structure of the financial sectors of the EU27, Japan and the United States, looking at a set of 23 indicators. We find a large variation within the European Union in the structure of the financial sector. Using principal components analysis, we identify robust groups of EU countries. One group consists of the eastern European members that entered the EU more recently.These have substantially smaller financial sectors than the old member states. A second group can be classified as market-based (MBEU) and the third group is more bank-based (BBEU). We compare US, MBEU, BBEU, Eastern EU and Japan with the following main results. First, the groups within Europe are geographically related. Second, in many indicators, MBEU countries are closer to the (market-based) US, while BBEU countries more closely resemble Japan. Paradoxically, however, market-based EU countries also have large banking sectors. Banks in market-based countries have larger cross-border assets and liabilities, and derive a larger fraction of their income from fees, rather than interest income, than banks in bank-based countries. Finally, for most indicators, the ordering of groups of countries is quite stable over time, but while the crisis has had no impact on the relative ordering of the groups, it has slightly widened the gap between the US and all EU regions insome respects. We also find that during the crisis, substitution between market-based and bank-based sources of finance occurred in the US, and to a lesser extent in MBEU and BBEU countries

Cross-country insurance mechanisms in currency unions. Bruegel Working Paper 2014/04, 27 March 2014

Countries in a monetary union can adjust to shocks either through internal or external mechanisms. We quantitatively assess for the European Union a number of relevant mechanisms suggested by Mundell’s optimal currency area theory, and compare them to the United States. For this purpose, we update a number of empirical analyses in the economic literature that identify (1) the size of asymmetries across countries and (2) the magnitude of insurance mechanisms relative to similar mechanisms and compare results for the European Monetary Union (EMU) with those obtained for the US. To study the level of synchronization between EMU countries we follow Alesina et al. (2002) and Barro and Tenreyro (2007). To measure the effect of an employment shock on employment levels, unemployment rates and participation rates we perform an analysis based on Blanchard and Katz (1992) and Decressin and Fatas (1995). We measure consumption smoothing through capital markets, fiscal transfers and savings, using the approach by Asdrubali et al. (1996) and Afonso and Furceri (2007). To analyze risk sharing through a common safety net for banks we perform a rudimentary simulation analysis.

Glia Open Access Database (GOAD):A comprehensive gene expression encyclopedia of glia cells in health and disease

Recently, the number of genome-wide transcriptome profiles of pure populations of glia cells has drastically increased, resulting in an unprecedented amount of data that offer opportunities to study glia phenotypes and functions in health and disease. To make genome-wide transcriptome data easily accessible, we developed the Glia Open Access Database (GOAD), available via cation. GOAD contains a collection of previously published and unpublished transcriptome data, including datasets from isolated microglia, astrocytes and oligodendrocytes both at homeostatic and pathological conditions. It contains an intuitive web-based interface that consists of three features that enable searching, browsing, analyzing, and downloading of the data. The first feature is differential gene expression (DE) analysis that provides genes that are significantly up and down-regulated with the associated fold changes and p-values between two conditions of interest. In addition, an interactive Venn diagram is generated to illustrate the overlap and differences between several DE gene lists. The second feature is quantitative gene expression (QE) analysis, to investigate which genes are expressed in a particular glial cell type and to what degree. The third feature is a search utility, which can be used to find a gene of interest and depict its expression in all available expression data sets by generating a gene card. In addition, quality guidelines and relevant concepts for transcriptome analysis are discussed. Finally, GOAD is discussed in relation to several online transcriptome tools developed in neuroscience and immunology. In conclusion, GOAD is a unique platform to facilitate integration of bioinformatics in glia biology. GLIA 2015;63:1495-150