Critical care : the official journal of the Critical Care Forum
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The oxygen reactivity index (ORx) reflects the correlation between focal brain tissue oxygen (pbtO2) and the cerebral perfusion pressure (CPP). Previous, small cohort studies were conflicting on whether ORx conveys cerebral autoregulatory information and if it is related to outcome in traumatic brain injury (TBI). Thus, we aimed to investigate these issues in a larger TBI cohort. ⋯ ORx seemed to be sensitive to the lower, but not the upper, limit of autoregulation, in contrast to PRx which was sensitive to both. The combination of high values for both ORx and PRx was particularly associated with worse outcome and, thus, ORx may provide a complementary value to the global index PRx. ORx could also be useful to determine the safe and dangerous perfusion target intervals.
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Double cycling with breath-stacking (DC/BS) during controlled mechanical ventilation is considered potentially injurious, reflecting a high respiratory drive. During partial ventilatory support, its occurrence might be attributable to physiological variability of breathing patterns, reflecting the response of the mode without carrying specific risks. ⋯ DC/BS events during partial ventilatory support were infrequent and linked to breathing variability. Their frequency and physiological effects on lung compliance and EELI resemble spontaneous sighs and may not be considered a priori as harmful.
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Pulse pressure variation (PPV) is limited in low tidal volume mechanical ventilation. We conducted this systematic review and meta-analysis to evaluate whether passive leg raising (PLR)-induced changes in PPV can reliably predict preload/fluid responsiveness in mechanically ventilated patients with low tidal volume in the intensive care unit. ⋯ PLR-induced change in absolute PPV has good diagnostic performance in predicting preload/fluid responsiveness in ICU patients on mechanical ventilation with low tidal volume. Trial registration PROSPERO (CRD42024496901). Registered on 15 January 2024.
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Patients supported by extracorporeal membrane oxygenation (ECMO) are at a high risk of brain injury, contributing to significant morbidity and mortality. This study aimed to employ machine learning (ML) techniques to predict brain injury in pediatric patients ECMO and identify key variables for future research. ⋯ The Random Forest model based on machine learning demonstrates high accuracy and robustness in predicting brain injury in pediatric patients supported by ECMO, with strong generalization capabilities and promising clinical applicability.