第964回千葉医学会例会・第31回麻酔科例会

BACKGROUND:Disease-associated malnutrition has been identified as a prevalent condition, particularly for the elderly, which has often been overlooked in the U.S. healthcare system. The state-level burden of community-based disease-associated malnutrition is unknown and there have been limited efforts by state policy makers to identify, quantify, and address malnutrition. The objective of this study was to examine and quantify the state-level economic burden of disease-associated malnutrition. METHODS:Direct medical costs of disease-associated malnutrition were calculated for 8 diseases: Stroke, Chronic Obstructive Pulmonary Disease, Coronary Heart Failure, Breast Cancer, Dementia, Musculoskeletal Disorders, Depression, and Colorectal Cancer. National disease and malnutrition prevalence rates were estimated for subgroups defined by age, race, and sex using the National Health and Nutrition Examination Survey and the National Health Interview Survey. State prevalence of disease-associated malnutrition was estimated by combining national prevalence estimates with states' demographic data from the U.S. Census. Direct medical cost for each state was estimated as the increased expenditures incurred as a result of malnutrition. PRINCIPAL FINDINGS:Direct medical costs attributable to disease-associated malnutrition vary among states from an annual cost of $36 per capita in Utah to$65 per capita in Washington, D.C. Nationally the annual cost of disease-associated malnutrition is over $15.5 billion. The elderly bear a disproportionate share of this cost on both the state and national level. CONCLUSIONS:Additional action is needed to reduce the economic impact of disease-associated malnutrition, particularly at the state level. Nutrition may be a cost-effective way to help address high health care costs

Nutrition Risk Classification: A Reproducible and Valid Tool for Nurses

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141661/1/ncp0020.pd

Tumor–Stroma Ratio in Colorectal Cancer—Comparison between Human Estimation and Automated Assessment

Simple Summary A lower tumor–stroma ratio within a tumor correlates with a poorer outcome, i.e., with a higher risk of death. The assessment of this ratio by humans is prone to errors, and when presented the same case, the ratios reported by multiple pathologists will oftentimes deviate significantly. The aim of our work was to predict the tumor–stroma ratio automatically using deep neural segmentation networks. The assessment comprises two steps: recognizing the different tissue types and estimating their ratio. We compared both steps individually to human observers and showed that (i) the outlined automatic method yields good segmentation results and (ii) that human estimations are consistently higher than the automated estimation and deviate significantly for a hand-annotated ground truth. We showed that including an additional evaluation step for our segmentation results and relating the segmentation quality to deviations in tumor–stroma assessment provides helpful insights. Abstract The tumor–stroma ratio (TSR) has been repeatedly shown to be a prognostic factor for survival prediction of different cancer types. However, an objective and reliable determination of the tumor–stroma ratio remains challenging. We present an easily adaptable deep learning model for accurately segmenting tumor regions in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of colon cancer patients into five distinct classes (tumor, stroma, necrosis, mucus, and background). The tumor–stroma ratio can be determined in the presence of necrotic or mucinous areas. We employ a few-shot model, eventually aiming for the easy adaptability of our approach to related segmentation tasks or other primaries, and compare the results to a well-established state-of-the art approach (U-Net). Both models achieve similar results with an overall accuracy of 86.5% and 86.7%, respectively, indicating that the adaptability does not lead to a significant decrease in accuracy. Moreover, we comprehensively compare with TSR estimates of human observers and examine in detail discrepancies and inter-rater reliability. Adding a second survey for segmentation quality on top of a first survey for TSR estimation, we found that TSR estimations of human observers are not as reliable a ground truth as previously thought

Aqueous batteries as grid scale energy storage solutions

Energy storage technologies are required to make full use of renewable energy sources, and electrochemical cells offer a great deal flexibility in the design of energy systems. For large scale electrochemical storage to be viable, the materials employed and device production methods need to be low cost, devices should be long lasting and safety during operation is of utmost importance. Energy and power densities are of lesser concern. For these reasons, battery chemistries that make use of aqueous electrolytes are favorable candidates where large quantities of energy need to be stored. Herein we describe several different aqueous based battery chemistries and identify some of the research challenges currently hindering their wider adoption. Lead acid batteries represent a mature technology that currently dominates the battery market, however there remain challenges that may prevent their future use at the large scale. Nickel–iron batteries have received a resurgence of interest of late and are known for their long cycle lives and robust nature however improvements in efficiency are needed in order to make them competitive. Other technologies that use aqueous electrolytes and have the potential to be useful in future large-scale applications are briefly introduced. Recent investigations in to the design of nickel–iron cells are reported with it being shown that electrolyte decomposition can be virtually eliminated by employing relatively large concentrations of iron sulfide in the electrode mixture, however this is at the expense of capacity and cycle life

Medical Spending on DAM per Capita.

<p>Medical Spending on DAM per Capita.</p

Estimated Direct Medical Cost of Disease-Associated Malnutrition.

<p>Estimated Direct Medical Cost of Disease-Associated Malnutrition.</p

Burden of Direct Medical Expenditures related to Malnutrition by Disease (Million Dollars).

<p>Monte Carlo Simulation Confidence Intervals (90%) in brackets.<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0161833#t002fn001" target="_blank">¹</a></p

National Cost of DAM by condition.

<p>National Cost of DAM by condition.</p