Diagnosis isn\u27t enough: Understanding the connections between high health care utilization, chronic conditions and disabilities among U.S. working age adults

Background Under the ACA, new programs are being developed to enhance care coordination and reduce health care costs among people with chronic conditions, disabilities, and high utilization of health care. However, the relationships between these groups are not well understood. Objectives Our aims were to (1) identify high utilizers of health care in the U.S. working age (18–64) population, (2) examine the overlap between this group and people with chronic conditions and/or disabilities, (3) identify predictors of high service use or cost among these subpopulations, and (4) recommend approaches for stratification of individuals with high health care utilization. Methods Using pooled national data from the Medical Expenditure Panel Survey (2006–2008), we created indices to identify elevated or high utilization and cost groups. We performed descriptive analyses, bivariate comparisons and multivariate analyses to examine the relations between these populations and individuals with chronic conditions and/or disabilities. Results While the large majority of persons with high use/cost had chronic conditions, the minority of persons with chronic conditions had high health care utilization. However, among persons with chronic conditions, disability was a significant predictor of high utilization. Annual expenditures were significantly elevated among people with disabilities, particularly when activities of daily living were limited. Conclusions We conclude that medical diagnosis alone is insufficient for the development of eligibility criteria for, or the evaluation of, programs intended to better the delivery or coordination of services for high utilizers of health care services. New approaches are needed to assess functional limitations and identify ongoing needs for services and supports

TBI Contusion Segmentation from MRI using Convolutional Neural Networks

Traumatic brain injury (TBI) is caused by a sudden trauma to the head that may result in hematomas and contusions and can lead to stroke or chronic disability. An accurate quantification of the lesion volumes and their locations is essential to understand the pathophysiology of TBI and its progression. In this paper, we propose a fully convolutional neural network (CNN) model to segment contusions and lesions from brain magnetic resonance (MR) images of patients with TBI. The CNN architecture proposed here was based on a state of the art CNN architecture from Google, called Inception. Using a 3-layer Inception network, lesions are segmented from multi-contrast MR images. When compared with two recent TBI lesion segmentation methods, one based on CNN (called DeepMedic) and another based on random forests, the proposed algorithm showed improved segmentation accuracy on images of 18 patients with mild to severe TBI. Using a leave-one-out cross validation, the proposed model achieved a median Dice of 0.75, which was significantly better (p<0.01) than the two competing methods.Comment: https://ieeexplore.ieee.org/abstract/document/8363545/, IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018

ROC Analysis for Phase II Group Sequential Basket Clinical Trial

The basket trial is a recent development in the clinical trial practice. It conducts the test of the same treatment on several different related diseases in a single trial, and has the advantage of reduced cost and enhanced efficiency. A natural question is how to assess the performance of the group sequential basket trial against the classical group sequential trial? To our knowledge, a formal assessment hasn't been seen in the literature, and is the goal of this study. Specifically, we use the receiver operating characteristic curve to assess the performance of the mentioned two trials. We considered two cases, parametric and nonparametric settings. The former is efficient when the parametric model is correctly specified, but can bemis-leading if the model is incorrect; the latter is less efficient but is robust in that it cannot be wrong no matter what the true data generating model is. Simulation studies are conducted to evaluate the experiments, and it suggests that the group sequential basket trial generally outperforms the group sequential trial in either the parametric and nonparametric cases, and that the nonparametric method gives more accurate evaluation than the parametric one for moderate to large sample sizes

Vertex Sparsifiers: New Results from Old Techniques

Given a capacitated graph $G = (V,E)$ and a set of terminals $K \subseteq V$, how should we produce a graph $H$ only on the terminals $K$ so that every (multicommodity) flow between the terminals in $G$ could be supported in $H$ with low congestion, and vice versa? (Such a graph $H$ is called a flow-sparsifier for $G$.) What if we want $H$ to be a "simple" graph? What if we allow $H$ to be a convex combination of simple graphs? Improving on results of Moitra [FOCS 2009] and Leighton and Moitra [STOC 2010], we give efficient algorithms for constructing: (a) a flow-sparsifier $H$ that maintains congestion up to a factor of $O(\log k/\log \log k)$, where $k = |K|$, (b) a convex combination of trees over the terminals $K$ that maintains congestion up to a factor of $O(\log k)$, and (c) for a planar graph $G$, a convex combination of planar graphs that maintains congestion up to a constant factor. This requires us to give a new algorithm for the 0-extension problem, the first one in which the preimages of each terminal are connected in $G$. Moreover, this result extends to minor-closed families of graphs. Our improved bounds immediately imply improved approximation guarantees for several terminal-based cut and ordering problems.Comment: An extended abstract appears in the 13th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems (APPROX), 2010. Final version to appear in SIAM J. Computin

Robust Test for Multiple Endpoints in Group Sequential Design

Group sequential design is widely used in today’s phase II/III clinical trials where testing multiple endpoints is quite often performed. In such tests, a basic requirement is to control the family-wise error rate at a given nominal level. The design is determined by a set of testing statistic and stopping boundaries (rules). Existing methods compute the stopping boundaries use Normal approximations, which work well when the true underlying data distribution is approximately Normal, but with small sample sizes the Normal approximation may not be valid. In an attempt to overcome these difficulties, we propose a robust method to compute the stopping boundaries in which it is assumed only that the data distributions are symmetric about their means. The null and alternative distributions are then constructed via the empirical distribution as well as the stopping boundaries for the given nominal level. Powers for the test statistics are obtained by bootstrap simulation, which is always valid for any sample size, and correlations between test statistics are automatically taken care of. Simulation examples are given to illustrate the proposed method

Major trends in mobility technology research and development: Overview of the results of the NSF-WTEC European study

Mobility technologies, including wheelchairs, prostheses, joint replacements, assistive devices, and therapeutic exercise equipment help millions of people participate in desired life activities. Yet, these technologies are not yet fully transformative because many desired activities cannot be pursued or are difficult to pursue for the millions of individuals with mobility related impairments. This WTEC study, initiated and funded by the National Science Foundation, was designed to gather information on European innovations and trends in technology that might lead to greater mobility for a wider range of people. What might these transformative technologies be and how might they arise? Based on visits to leading mobility technology research labs in western Europe, the WTEC panel identified eight major trends in mobility technology research. This commentary summarizes these trends, which are then described in detail in companion papers appearing in this special issue

Airspace Technology Demonstration 2 (ATD-2) Project: Integrated Arrival/Departure/Surface Metroplex Traffic Management

Airspace Technology Demonstration 2, or ATD-2, is the integration of existing and emerging NASA, FAA, and industry technologies to significantly benefit arrival, departure, and surface operations. It provides solutions to several problems in the complicated, multi-airport metroplex environment

Rehabilitation medicine summit: building research capacity Executive Summary

The general objective of the "Rehabilitation Medicine Summit: Building Research Capacity" was to advance and promote research in medical rehabilitation by making recommendations to expand research capacity. The five elements of research capacity that guided the discussions were: 1) researchers; 2) research culture, environment, and infrastructure; 3) funding; 4) partnerships; and 5) metrics. The 100 participants included representatives of professional organizations, consumer groups, academic departments, researchers, governmental funding agencies, and the private sector. The small group discussions and plenary sessions generated an array of problems, possible solutions, and recommended actions. A post-Summit, multi-organizational initiative is called to pursue the agendas outlined in this report (see Additional File 1)

Core competencies for scientific editors of biomedical journals: consensus statement

Background&nbsp; Scientific editors are responsible for deciding which articles to publish in their journals. However, we have not found documentation of their required knowledge, skills, and characteristics, or the existence of any formal core competencies for this role.&nbsp; Methods&nbsp; We describe the development of a minimum set of core competencies for scientific editors of biomedical journals.&nbsp; Results&nbsp; The 14 key core competencies are divided into three major areas, and each competency has a list of associated elements or descriptions of more specific knowledge, skills, and characteristics that contribute to its fulfillment.&nbsp; Conclusions&nbsp; We believe that these core competencies are a baseline of the knowledge, skills, and characteristics needed to perform competently the duties of a scientific editor at a biomedical journal.Additional co-authors: Kurinchi Gurusamy, Farrokh Habibzadeh, Stefanie Jewell-Thomas, Diane Kelsall, José Florencio LapeñaJr, Harriet MacLehose, Ana Marusic, Joanne E. McKenzie, Jay Shah, Larissa Shamseer, Sharon Straus, Peter Tugwell, Elizabeth Wager, Margaret Winker and Getu Zhaor