49 research outputs found

    In Reply to Mr. Charles T. Gorham.

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    Home-monitoring reduces hospital stay for COVID-19 patients

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    Home monitoring reduces hospital stay of patients with COVID-19, which increases regular healthcare capacity https://bit.ly/3CdFp1

    Novel insights into an old controversy: Is coronary artery ectasia a variant of coronary atherosclerosis?

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    Coronary artery ectasia (CAE) is defined as a localized or diffuse non-obstructive lesion of the epicardial coronary arteries with a luminal dilation exceeding 1.5-fold the diameter of the normal adjacent arterial segment. The incidence of CAE has been reported to range between 2% and 4%, which might be an overestimation of the true frequency. The coincidence of CAE with other systemic vascular dilatations has suggested that the mechanism underlying CAE is not only localized to coronary arteries, but also to other vascular compartments such as aorta or peripheral veins. Although the pathophysiology of CAE remains largely unknown, it was supposed to represent a variant of coronary atherosclerosis. This review focuses on this controversy of whether CAE and coronary artery disease (CAD) are two manifestations of the same underlying process. There are clear differences between CAD and CAE with respect to cardiovascular risk factors such as diabetes mellitus, and pathogenic steps in disease progress such as inflammation or extracellular matrix remodeling. As this review will underscore, the current knowledge of the field is insufficient to finally clarify the causative interrelation between CAE and CAD. The clinical course and treatment of CAE mainly depends on its coexistence with CAD. When coexisting with CAD, the prognosis and treatment of CAE are the same as for CAD alone. In isolated CAE, prognosis is better and anti-platelet drugs are the mainstay of treatment. Surgical treatment can be considered in selected patients. For clarifying the mechanism underlying CAE, additional clinical, histopathological and pathophysiological investigations are required. In fact, every patient with CAE should be evaluated systematically for pathological changes in other vascular territories, both in the arterial system as well as in the venous system, which might occur in the disease process

    The Nachtlichter app: a citizen science tool for documenting outdoor light sources in public space

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    The relationship between satellite based measurements of city radiance at night and the numbers and types of physical lights installed on the ground is not well understood. Here we present the "Nachtlichter app", which was developed to enable citizen scientists to classify and count light sources along street segments over large spatial scales. The project and app were co-designed: citizen scientists played key roles in the app development, testing, and recruitment, as well as in analysis of the data. In addition to describing the app itself and the data format, we provide a general overview of the project, including training materials, data cleaning, and the result of some basic data consistency checks

    Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

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    Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified. Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study. Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations

    Variation in Structure and Process of Care in Traumatic Brain Injury: Provider Profiles of European Neurotrauma Centers Participating in the CENTER-TBI Study.

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    INTRODUCTION: The strength of evidence underpinning care and treatment recommendations in traumatic brain injury (TBI) is low. Comparative effectiveness research (CER) has been proposed as a framework to provide evidence for optimal care for TBI patients. The first step in CER is to map the existing variation. The aim of current study is to quantify variation in general structural and process characteristics among centers participating in the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. METHODS: We designed a set of 11 provider profiling questionnaires with 321 questions about various aspects of TBI care, chosen based on literature and expert opinion. After pilot testing, questionnaires were disseminated to 71 centers from 20 countries participating in the CENTER-TBI study. Reliability of questionnaires was estimated by calculating a concordance rate among 5% duplicate questions. RESULTS: All 71 centers completed the questionnaires. Median concordance rate among duplicate questions was 0.85. The majority of centers were academic hospitals (n = 65, 92%), designated as a level I trauma center (n = 48, 68%) and situated in an urban location (n = 70, 99%). The availability of facilities for neuro-trauma care varied across centers; e.g. 40 (57%) had a dedicated neuro-intensive care unit (ICU), 36 (51%) had an in-hospital rehabilitation unit and the organization of the ICU was closed in 64% (n = 45) of the centers. In addition, we found wide variation in processes of care, such as the ICU admission policy and intracranial pressure monitoring policy among centers. CONCLUSION: Even among high-volume, specialized neurotrauma centers there is substantial variation in structures and processes of TBI care. This variation provides an opportunity to study effectiveness of specific aspects of TBI care and to identify best practices with CER approaches
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