A clinical prediction model to identify patients at high risk of death in the emergency department.

PURPOSE Rapid assessment and intervention is important for the prognosis of acutely ill patients admitted to the emergency department (ED). The aim of this study was to prospectively develop and validate a model predicting the risk of in-hospital death based on all available information available at the time of ED admission and to compare its discriminative performance with a non-systematic risk estimate by the triaging first health-care provider. METHODS Prospective cohort analysis based on a multivariable logistic regression for the probability of death. RESULTS A total of 8,607 consecutive admissions of 7,680 patients admitted to the ED of a tertiary care hospital were analysed. Most frequent APACHE II diagnostic categories at the time of admission were neurological (2,052, 24 %), trauma (1,522, 18 %), infection categories [1,328, 15 %; including sepsis (357, 4.1 %), severe sepsis (249, 2.9 %), septic shock (27, 0.3 %)], cardiovascular (1,022, 12 %), gastrointestinal (848, 10 %) and respiratory (449, 5 %). The predictors of the final model were age, prolonged capillary refill time, blood pressure, mechanical ventilation, oxygen saturation index, Glasgow coma score and APACHE II diagnostic category. The model showed good discriminative ability, with an area under the receiver operating characteristic curve of 0.92 and good internal validity. The model performed significantly better than non-systematic triaging of the patient. CONCLUSIONS The use of the prediction model can facilitate the identification of ED patients with higher mortality risk. The model performs better than a non-systematic assessment and may facilitate more rapid identification and commencement of treatment of patients at risk of an unfavourable outcome.

The Use of New Technology and Occupational Mobility: An Event History Analysis of the Swiss Labor Market

The investigation of the consequences of new technologies has a long standing tradition within economics. Particularly, labor economists are wondering how the introduction of new technologies, e.g. Personal Computers, have shaped labor markets. Former research has concentrated on the question of whether on-the-job use of PCs creates a wage bonus for employees. In this paper, we investigate whether the use of PCs increases employees’ probability of an upward shift in their employment status and whether it reduces the risk of involuntary labor market exits. We do so by applying event history analysis to the Swiss Labor Market Survey, a random sample of 3028 respondents, and by analyzing a Panel sub-sample of 650 respondents conducted recently in Switzerland. Our results show that on-the-job use of PCs was beneficial for employees in the past by increasing their probability of an upward shift by approximately 50%. The analysis also suggests that PC use reduces the risk and duration of unemployment. However, these latter results fail to reach statistical significance.

Localization and Segmentation of 3D Intervertebral Discs in MR Images by Data Driven Estimation

This paper addresses the problem of fully-automatic localization and segmentation of 3D intervertebral discs (IVDs) from MR images. Our method contains two steps, where we first localize the center of each IVD, and then segment IVDs by classifying image pixels around each disc center as foreground (disc) or background. The disc localization is done by estimating the image displacements from a set of randomly sampled 3D image patches to the disc center. The image displacements are estimated by jointly optimizing the training and test displacement values in a data-driven way, where we take into consideration both the training data and the geometric constraint on the test image. After the disc centers are localized, we segment the discs by classifying image pixels around disc centers as background or foreground. The classification is done in a similar data-driven approach as we used for localization, but in this segmentation case we are aiming to estimate the foreground/background probability of each pixel instead of the image displacements. In addition, an extra neighborhood smooth constraint is introduced to enforce the local smoothness of the label field. Our method is validated on 3D T2-weighted turbo spin echo MR images of 35 patients from two different studies. Experiments show that compared to state of the art, our method achieves better or comparable results. Specifically, we achieve for localization a mean error of 1.6-2.0 mm, and for segmentation a mean Dice metric of 85%-88% and a mean surface distance of 1.3-1.4 mm.

Implications for the Discovery of Extraterrestrial Life: A Theological Approach

Recent remarks of Pope Francis spark anew an important discussion: are we alone in the Universe? The article follows traces of the idea of extraterrestrial life throughout philosophy, evaluates the current considerations about the probability of extraterrestrial life and discusses the potential implications for the discovery of such life from a theological point of view. This “thought experiment” covers basic insights on creation, revelation and redemption.