Neurocritical care
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We aimed to identify continuous electroencephalogram (cEEG) markers associated with survival and death in patients with extracorporeal membrane oxygenation (ECMO) support under standardized sedation cessation protocol. ⋯ Although future multicenter studies with larger patient cohorts are certainly warranted, we were able to validate the feasibility of protocolized sedation cessation and cEEG analyses in the absence of a confounding effect from sedating medications. Moreover, we demonstrate some evidence that cEEG features of intact reactivity, present state changes, and fair/good variability in comatose patients on ECMO may be associated with survival at hospital discharge.
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Blood pressure variability (BPV) is associated with outcome after endovascular thrombectomy in acute large vessel occlusion stroke. We aimed to provide the optimal sampling frequency and BPV index for outcome prediction by using high-resolution blood pressure (BP) data. ⋯ Using high-resolution BP data of 1 Hz, downsampling by averaging all BP values within 5-min intervals is essential to find relevant differences in systolic BPV, as noise can be avoided (confirmed by the significance of the power of midrange frequencies). These results demonstrate how high-resolution BP data can be processed for effective outcome prediction.
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Large and complex data sets are increasingly available for research in critical care. To analyze these data, researchers use techniques commonly referred to as statistical learning or machine learning (ML). The latter is known for large successes in the field of diagnostics, for example, by identification of radiological anomalies. ⋯ To conclude, there are important opportunities but also pitfalls to consider when performing clustering or predictive studies with ML techniques. We advocate careful valuation of new data-driven findings. More interaction is needed between the engineer mindset of experts in ML methods, the insight in bias of epidemiologists, and the probabilistic thinking of statisticians to extract as much information and knowledge from data as possible, while avoiding harm.
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Cerebral blood flow (CBF) plays an important role in neurological recovery after cardiac arrest (CA) resuscitation. However, the variations of CBF recovery in distinct brain regions and its correlation with neurologic recovery after return of spontaneous circulation (ROSC) have not been characterized. This study aimed to investigate the characteristics of regional cerebral reperfusion following resuscitation in predicting neurological recovery. ⋯ Early magnetic resonance imaging analyses showed early rCBF recovery in thalamus, hippocampus, and cortex post ROSC was positively correlated with neurological outcomes at 24 h. Our findings suggest new translational insights into the regional reperfusion and the time window that may be critical in neurological recovery and warrant further validation.