Implementation and evaluation of a nurse-administered dysphagia screening tool to identify patient’s at high risk for post-extubation dysphagia

Purpose: Post-extubation dysphagia (PED) occurs in 3% to 62% of intensive care unit patients. Patients with moderate or severe PED are more likely to experience pneumonia, reintubation, or death. Early identification of post-extubation dysphagia is crucial so diet modifications, temporary feeding measures, and/or advanced swallow evaluations and therapies can be implemented. The purpose of this quality improvement project was to implement a nurse-administered dysphagia screening tool (NADST) for post-extubated patients in a 21-bed mixed medical intensive care unit (MICU) at a large academic medical center. Methods: Utilizing quality improvement methods, a modified dysphagia screening tool was trialed in a MICU for two months. Eight Super Users (RNs) were recruited and attended one of three train the trainer sessions taught by a Speech Language Pathologist. The Super Users trained the remaining unit nurses (RNs). A 5-minute video for the unit nurses was created to supplement the trainings. Pre- and post-intervention surveys were administered to measure changes in knowledge, beliefs, and practices around PED screening. Patient electronic health records were reviewed to identify all patients eligible for PED screening and screening dispositions. Results: Of the 59 eligible patients, 34 patients were screened utilizing the NADST. Nurses had a high level of knowledge but varying practices and comfort with dysphagia screening prior to the intervention. The intervention increased the comfort level and screening frequencies for PED. The NADST was found to be useful for improving nursing practice. Conclusions: Through the utilization of a Super User training model, this quality improvement project demonstrated that implementing a standardized PED screening tool does improve PED screening frequencies

Responder Identification in Clinical Trials with Censored Data

We present a newly developed technique for identification of positive and negative responders to a new treatment which was compared to a classical treatment (or placebo) in a randomized clinical trial. This bump-hunting-based method was developed for trials in which the two treatment arms do not differ in survival overall. It checks in a systematic manner if certain subgroups, described by predictive factors do show difference in survival due to the new treatment. Several versions of the method were discussed and compared in a simulation study. The best version of the responder identification method employs martingale residuals to a prognostic model as response in a stabilized through bootstrapping bump hunting procedure. On average it recognizes 90% of the time the correct positive responder group and 99% of the time the correct negative responder group

NEFI: Network Extraction From Images

Networks and network-like structures are amongst the central building blocks of many technological and biological systems. Given a mathematical graph representation of a network, methods from graph theory enable a precise investigation of its properties. Software for the analysis of graphs is widely available and has been applied to graphs describing large scale networks such as social networks, protein-interaction networks, etc. In these applications, graph acquisition, i.e., the extraction of a mathematical graph from a network, is relatively simple. However, for many network-like structures, e.g. leaf venations, slime molds and mud cracks, data collection relies on images where graph extraction requires domain-specific solutions or even manual. Here we introduce Network Extraction From Images, NEFI, a software tool that automatically extracts accurate graphs from images of a wide range of networks originating in various domains. While there is previous work on graph extraction from images, theoretical results are fully accessible only to an expert audience and ready-to-use implementations for non-experts are rarely available or insufficiently documented. NEFI provides a novel platform allowing practitioners from many disciplines to easily extract graph representations from images by supplying flexible tools from image processing, computer vision and graph theory bundled in a convenient package. Thus, NEFI constitutes a scalable alternative to tedious and error-prone manual graph extraction and special purpose tools. We anticipate NEFI to enable the collection of larger datasets by reducing the time spent on graph extraction. The analysis of these new datasets may open up the possibility to gain new insights into the structure and function of various types of networks. NEFI is open source and available http://nefi.mpi-inf.mpg.de

Long-term care and intermediary structures for frail older people : Switzerland and Germany in comparison

This is a post-peer-review, pre-copy edited version of an article published in [International journal of care and caring]. The definitive publisher-authenticated version is available online at: https://doi.org/10.1332/239788218X15265697287824Long-term care not only includes residential care, home care and familial care, but services ‘in-between’, such as day and night care, temporary (short-term) stays in nursing homes, respite care, and local infrastructure giving informed advice and conveying informal support. In both Switzerland and Germany, the role of such intermediary structures has been debated and affected by social policy reforms. The authors analyse different functions of intermediary structures, discuss their access and use, and show that intermediary structures can have a different impact on care regimes

Mintaelemszám tervezés Likert-skálás lekérdezések esetén klasszikus és bayesi keretek között

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