Articles: mechanical-ventilation.
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The timing of tracheostomy for critically ill patients on mechanical ventilation (MV) is a topic of controversy. Our objective was to determine the most suitable timing for tracheostomy in patients undergoing MV. ⋯ In a mixed ICU population, delayed tracheostomy prolonged ICU and hospital stays, sedation durations, and time receiving MV. Multinomial logistic regression analysis identified delayed tracheostomies as independently correlated with worse outcomes.
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This study was aimed to explore the protective effect of electroacupuncture (EA) pretreatment at Zusanli point (ST36) on ventilation-induced lung injury (VILI) and its potential anti-inflammatory mechanism. ⋯ EA pretreatment at ST36 could alleviate the inflammatory response for VILI via inhibiting TLR4/NF- κB pathway.
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Observational Study
Driving pressure of respiratory system and lung stress in mechanically ventilated patients with active breathing.
During control mechanical ventilation (CMV), the driving pressure of the respiratory system (ΔPrs) serves as a surrogate of transpulmonary driving pressure (ΔPlung). Expiratory muscle activity that decreases end-expiratory lung volume may impair the validity of ΔPrs to reflect ΔPlung. This prospective observational study in patients with acute respiratory distress syndrome (ARDS) ventilated with proportional assist ventilation (PAV+), aimed to investigate: (1) the prevalence of elevated ΔPlung, (2) the ΔPrs-ΔPlung relationship, and (3) whether dynamic transpulmonary pressure (Plungsw) and effort indices (transdiaphragmatic and respiratory muscle pressure swings) remain within safe limits. ⋯ In patients with ARDS ventilated with PAV+, injurious tidal lung stress and effort were infrequent. In the presence of expiratory muscle activity, ΔPrs underestimated ΔPlung. This phenomenon limits the usefulness of ΔPrs as a surrogate of tidal lung stress, regardless of the mode of support.
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Intensive care medicine · Jan 2024
Prediction of post-traumatic stress disorder in family members of ICU patients: a machine learning approach.
Post-traumatic stress disorder (PTSD) can affect family members of patients admitted to the intensive care unit (ICU). Easily accessible patient's and relative's information may help develop accurate risk stratification tools to direct relatives at higher risk of PTSD toward appropriate management. ⋯ We propose a machine learning-based approach to predict PTSD in relatives of ICU patients at an individual level. In this model, PTSD is mostly influenced by non-modifiable factors.