Objective: Due to the diversity of the elderly population and medical practices, the decision to transfer elderly patients to an intensive care unit is complex. This study aimed to identify the criteria used to take an advance decision to limit transfer to an intensive care unit of patients hospitalised in an acute geriatric unit.
Methods: This retrospective study included, over a ten-month period, patients >75 years and hospitalised in an acute geriatric unit. They were divided into two groups according to whether or not an advanced decision to limit transfer to an intensive care unit had been taken.
Results: In total, 906 elderly patients were included in the study. Of them, 446 had no advance decision to limit transfer to an ICU. Univariate analysis showed a correlation between an advance decision to limit transfer to an ICU and a Mini Mental State Examination (MMSE) score of less than 20/30. Malnutrition had no impact on the advance decision. In multivariate analysis, the factors associated with an advance decision to limit transfer to an ICU were an age > 85 years, a hospitalisation in the last six months (Odds Ratio (OR) = 1.72, Confidence Interval (CI) 95% [1.23-2.39]), residence in a nursing home (OR = 1.93, 95% CI [1.18-0.16]) and the presence of bedsores (OR = 2.44, 95% CI [1.20-0.98]). A zero Charlson score was associated with the absence of an advance decision to limit transfer to an ICU (OR = 0.42, 95% CI [0.26-0.67]).
Conclusion: Some criteria are common to geriatricians, intensive care doctors and emergency physicians, while others are discordant, illustrating differences in physicians' practices.
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http://dx.doi.org/10.1684/pnv.2021.0989 | DOI Listing |
Sci Rep
December 2024
School of Public Administration, Guangzhou University, Guangzhou, 510006, China.
With the accelerated urbanization and economic development in Northwest China, the efficiency of urban wastewater treatment and the importance of water quality management have become increasingly significant. This work aims to explore urban wastewater treatment and carbon reduction mechanisms in Northwest China to alleviate water resource pressure. By utilizing online monitoring data from pilot systems, it conducts an in-depth analysis of the impacts of different wastewater treatment processes on water quality parameters.
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December 2024
Advanced Research Institute for Digital-Twin Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.
Traditional hydraulic structures rely on manual visual inspection for apparent integrity, which is not only time-consuming and labour-intensive but also inefficient. The efficacy of deep learning models is frequently constrained by the size of available data, resulting in limited scalability and flexibility. Furthermore, the paucity of data diversity leads to a singular function of the model that cannot provide comprehensive decision support for improving maintenance measures.
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December 2024
Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran.
Surface electromyography (sEMG) data has been extensively utilized in deep learning algorithms for hand movement classification. This paper aims to introduce a novel method for hand gesture classification using sEMG data, addressing accuracy challenges seen in previous studies. We propose a U-Net architecture incorporating a MobileNetV2 encoder, enhanced by a novel Bidirectional Long Short-Term Memory (BiLSTM) and metaheuristic optimization for spatial feature extraction in hand gesture and motion recognition.
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December 2024
Department of Computer Sciences and Industries, Universidad Católica del Maule, Talca, Chile.
Antimicrobial resistance (AMR) poses a significant global health challenge, necessitating advanced predictive models to support clinical decision-making. In this study, we explore multi-label classification as a novel approach to predict antibiotic resistance across four clinically relevant bacteria: E. coli, S.
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December 2024
Department of Pharmacoepidemiology, Graduate School of Medicine and PublicHealth, Kyoto University, Kyoto, Japan.
Although conservative treatment is commonly used for osteoporotic vertebral fracture (OVF), some patients experience functional disability following OVF. This study aimed to develop prediction models for new-onset functional impairment following admission for OVF using machine learning approaches and compare their performance. Our study consisted of patients aged 65 years or older admitted for OVF using a large hospital-based database between April 2014 and December 2021.
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