Critical care : the official journal of the Critical Care Forum
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Acute kidney injury (AKI) is a frequent and severe complication of both COVID-19-related acute respiratory distress syndrome (ARDS) and non-COVID-19-related ARDS. The COVID-19 Critical Care Consortium (CCCC) has generated a global data set on the demographics, management and outcomes of critically ill COVID-19 patients. The LUNG-SAFE study was an international prospective cohort study of patients with severe respiratory failure, including ARDS, which pre-dated the pandemic. ⋯ AKI is a common and serious complication of COVID-19, with a high mortality rate, which differs by geo-economic location. Important differences exist in the profile of AKI in COVID-19 versus non-COVID-19 ARDS in terms of their haemodynamic profile, time of onset and clinical outcomes.
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Review Meta Analysis
The rate and assessment of muscle wasting during critical illness: a systematic review and meta-analysis.
Patients with critical illness can lose more than 15% of muscle mass in one week, and this can have long-term detrimental effects. However, there is currently no synthesis of the data of intensive care unit (ICU) muscle wasting studies, so the true mean rate of muscle loss across all studies is unknown. The aim of this project was therefore to systematically synthetise data on the rate of muscle loss and to identify the methods used to measure muscle size and to synthetise data on the prevalence of ICU-acquired weakness in critically ill patients. ⋯ On average, critically ill patients lose nearly 2% of skeletal muscle per day during the first week of ICU admission.
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Observational Study
Clustering of critically ill patients using an individualized learning approach enables dose optimization of mobilization in the ICU.
While early mobilization is commonly implemented in intensive care unit treatment guidelines to improve functional outcome, the characterization of the optimal individual dosage (frequency, level or duration) remains unclear. The aim of this study was to demonstrate that artificial intelligence-based clustering of a large ICU cohort can provide individualized mobilization recommendations that have a positive impact on the likelihood of being discharged home. ⋯ An artificial intelligence-based learning approach was able to divide a heterogeneous critical care cohort into four clusters, which differed significantly in their clinical characteristics and in their mobilization parameters. Depending on the cluster, different mobilization strategies supported the likelihood of being discharged home enabling an individualized and resource-optimized mobilization approach.