Critical care : the official journal of the Critical Care Forum
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Randomized Controlled Trial
Fluid management for sepsis-induced hypotension in patients with advanced chronic kidney disease: a secondary analysis of the CLOVERS trial.
Early fluid management in patients with advanced chronic kidney disease (CKD) and sepsis-induced hypotension is challenging with limited evidence to support treatment recommendations. We aimed to compare an early restrictive versus liberal fluid management for sepsis-induced hypotension in patients with advanced CKD. ⋯ In patients with advanced CKD and sepsis-induced hypotension, an early restrictive fluid strategy, prioritizing vasopressor use, was associated with a lower risk of death from any cause before discharge home by day 90 as compared with an early liberal fluid strategy.
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Conflicts with patients and relatives occur frequently in intensive care units (ICUs), driven by factors that are intensified by critical illness and its treatments. A majority of ICU healthcare professionals have experienced verbal and/or physical violence. There is a need to understand how healthcare professionals in ICUs experience and manage this workplace violence. ⋯ This study contributes to understanding the normalisation of violence in ICU care and gives a possible explanation for its origins. The paradox involves a multifaceted approach that acknowledges and confronts the structural and cultural dimensions of violence in healthcare. Such an approach will lay the foundations for a more sustainable healthcare system.
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Observational Study
A proof of concept for microcirculation monitoring using machine learning based hyperspectral imaging in critically ill patients: a monocentric observational study.
Impaired microcirculation is a cornerstone of sepsis development and leads to reduced tissue oxygenation, influenced by fluid and catecholamine administration during treatment. Hyperspectral imaging (HSI) is a non-invasive bedside technology for visualizing physicochemical tissue characteristics. Machine learning (ML) for skin HSI might offer an automated approach for bedside microcirculation assessment, providing an individualized tissue fingerprint of critically ill patients in intensive care. The study aimed to determine if machine learning could be utilized to automatically identify regions of interest (ROIs) in the hand, thereby distinguishing between healthy individuals and critically ill patients with sepsis using HSI. ⋯ Based on this proof of concept, the integration of automated and standardized ROIs along with automated skin HSI analyzes, was able to differentiate between healthy individuals and patients with sepsis. This approach offers a reliable and objective assessment of skin microcirculation, facilitating the rapid identification of critically ill patients.