Anesthesiology
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Unfractionated heparin, administered during veno-arterial extracorporeal membrane oxygenation to prevent thromboembolic events, largely depend on plasma antithrombin for its antithrombotic effects. Decreased heparin responsiveness seems frequent on extracorporeal membrane oxygenation however its association with acquired antithrombin deficiency is poorly understood. Our objective was to describe longitudinal changes in plasma antithrombin levels during extracorporeal membrane oxygenation support and evaluate the association between antithrombin levels and heparin responsiveness. We hypothesized that extracorporeal membrane oxygenation support would be associated with acquired antithrombin deficiency and related decreased heparin responsiveness. ⋯ Veno-arterial extracorporeal membrane oxygenation support was associated with a moderate acquired antithrombin deficiency, mainly during the first 72 hours, that did not correlate with heparin responsiveness.
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The use of anesthetics may result in depression of the hypoxic ventilatory response. Since there are no receptor-specific antagonists for most anesthetics, there is the need for agnostic respiratory stimulants, that increase respiratory drive irrespective of its cause. We tested whether ENA-001, an agnostic respiratory stimulant that blocks carotid body BK-channels, could restore the hypoxic ventilatory response during propofol infusion. We hypothesize that ENA-001 is able to fully restore the hypoxic ventilatory response. ⋯ In this pilot study, we demonstrated that ENA-001 restored the hypoxic ventilatory response impaired by propofol. This finding is not only of clinical importance, but also provides mechanistic insights into the peripheral stimulation of breathing with ENA-001 overcoming central depression by propofol.
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The utilization of artificial intelligence and machine learning as diagnostic and predictive tools in perioperative medicine holds great promise. Indeed, many studies have been performed in recent years to explore the potential. The purpose of this systematic review is to assess the current state of machine learning in perioperative medicine, its utility in prediction of complications and prognostication, and limitations related to bias and validation. ⋯ The findings indicate that the development of this field is still in its early stages. This systematic review indicates that application of machine learning in perioperative medicine is still at an early stage. While many studies suggest potential utility, several key challenges must be first overcome before their introduction into clinical practice.