Anesthesiology
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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.
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Anesthesiologists are experiencing unprecedented levels of workplace stress and staffing shortages. This analysis aims to assess how U.S. attending anesthesiologist burnout changed since the onset of the COVID-19 pandemic and target well-being efforts. ⋯ Burnout is more prevalent in anesthesiology since early 2020, with workplace factors of perceived support and staffing being the predominant associated variables. Interventions focused on the drivers of burnout are needed to improve well-being among U.S. attending anesthesiologists.
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Propofol causes significant cardiovascular depression and a slowing of neurophysiological activity. However, literature on its effect on the heart rate remains mixed, and it is not known whether cortical slow waves are related to cardiac activity in propofol anesthesia. ⋯ The authors observed a robust increase in heart rate with increasing propofol concentrations in healthy volunteers and patients. This was likely due to decreased parasympathetic cardioinhibition. Similar to non-rapid eye movement sleep, cortical slow waves are coupled to the cardiac rhythm, perhaps due to a common brainstem generator.