Journal of critical care
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Journal of critical care · Apr 2024
Effects of structured protocolized physical therapy on the duration of mechanical ventilation in patients with prolonged weaning.
20% of patients with mechanical ventilation (MV) have a prolonged, complex weaning process, often experiencing a condition of ICU-acquired weakness (ICUAW), with a severe decrease in muscle function and restricted long-term prognosis. We aimed to analyze a protocolized, systematic approach of physiotherapy in prolonged weaning patients and hypothesized that the duration of weaning from MV would be shortened. ⋯ Protocolized, systematic physiotherapy resulted in an improvement of the clinical outcome in patients with prolonged weaning. Results were objectifiable with the SOMS and the handgrip test.
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Assessment of the IVC by point-of-care ultrasound in the context of resuscitation has been a controversial topic in the last decades. Most of the focus had been on its use as a surrogate marker for fluid responsiveness, with results being equivocal. We review its important anatomical aspects as well as the physiological rationale behind ultrasound assessment and propose a new way to do so, as well as explain its central role in the concept of fluid tolerance.
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Journal of critical care · Apr 2024
ReviewAn environmental scan of online resources for informal family caregivers of ICU survivors.
To collate a comprehensive repository of online resources for family caregivers of intensive care survivors to inform a recovery website and digital peer support programme. ⋯ This environmental scan identifies multiple resources addressing informational needs of family caregivers and highlights areas for resource development.
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Journal of critical care · Apr 2024
Multicenter StudyDevelopment and validation of a machine learning model to predict the use of renal replacement therapy in 14,374 patients with COVID-19.
To develop a model to predict the use of renal replacement therapy (RRT) in COVID-19 patients. ⋯ An early ML model using easily available clinical and laboratory data accurately predicted the use of RRT in critically ill patients with COVID-19. Our study demonstrates that using ML techniques is feasible to provide early prediction of use of RRT in COVID-19 patients.