Journal of critical care
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Journal of critical care · Jun 2023
Randomized Controlled TrialA randomized controlled trial comparing non-invasive ventilation delivered using neurally adjusted ventilator assist (NAVA) or adaptive support ventilation (ASV) in patients with acute exacerbation of chronic obstructive pulmonary disease.
No study has compared neurally adjusted ventilator assist (NAVA) with adaptive support ventilation (ASV) during non-invasive ventilation (NIV) in subjects with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). ⋯ The use of NAVA-NIV was not superior to ASV-NIV in reducing NIV failure rates in AECOPD. Both NAVA-NIV and ASV-NIV had similar asynchrony index and 90-day mortality.
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Journal of critical care · Jun 2023
Gender distribution of editorial board members in critical care journals: Assessment of gender parity.
To reveal factors related to gender parity on editorial boards of critical care journals indexing in SCI-E. ⋯ Further efforts are needed to expand diversity policies in critical care medicine.
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Journal of critical care · Jun 2023
Multicenter StudyDefinitions, rates and associated mortality of ICU-acquired pneumonia: A multicenter cohort study.
We aimed to analyze intensive care unit (ICU)-acquired pneumonia according to 7 definitions, estimating associated hospital mortality. ⋯ Rates of ICU-acquired pneumonia vary by definition and are associated with differential increased risk of death.
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Journal of critical care · Jun 2023
Observational StudyReverse triggering neural network and rules-based automated detection in acute respiratory distress syndrome.
Dyssynchrony may cause lung injury and is associated with worse outcomes in mechanically ventilated patients. Reverse triggering (RT) is a common type of dyssynchrony presenting with several phenotypes which may directly cause lung injury and be difficult to identify. Due to these challenges, automated software to assist in identification is needed. ⋯ Automated detection of RT demonstrated good performance, with the potential application of these programs for research and clinical care.
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We developed and validated two parsimonious algorithms to predict the time of diagnosis of any stage of acute kidney injury (any-AKI) or moderate-to-severe AKI in clinically actionable prediction windows. ⋯ The two AKI prediction models have good discriminative performance using common features, which can aid in accurately and informatively monitoring AKI risk in ICU patients.