Critical care : the official journal of the Critical Care Forum
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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.
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To investigate the effects of ICU quality control indicators on the VAP incidence rate and mortality in China throughout 2019. ⋯ This study highlights the association between the ICU quality control (QC) factors and VAP incidence rate and mortality. The process factors rather than the structural factors need to be further improved for the QC of VAP in the ICU.