Anesthesiology
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Randomized Controlled Trial
Standardized Unloading of Respiratory Muscles during Neurally Adjusted Ventilatory Assist: A Randomized Crossover Pilot Study.
WHAT THIS ARTICLE TELLS US THAT IS NEW: BACKGROUND:: Currently, there is no standardized method to set the support level in neurally adjusted ventilatory assist (NAVA). The primary aim was to explore the feasibility of titrating NAVA to specific diaphragm unloading targets, based on the neuroventilatory efficiency (NVE) index. The secondary outcome was to investigate the effect of reduced diaphragm unloading on distribution of lung ventilation. ⋯ In this pilot study, NAVA could be titrated to different diaphragm unloading levels based on the NVE index. Less unloading was associated with greater diaphragm activity and improved ventilation of the dependent lung regions.
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WHAT THIS ARTICLE TELLS US THAT IS NEW: BACKGROUND:: The incidence of substance use disorders in the United States among residents in anesthesiology is between 1% and 2%. A recent study reported that the incidence of substance use disorders in U.S. anesthesiology residents has been increasing. There are no reports of effective methods to prevent substance use disorder in residents. A comprehensive drug testing program including a random component may reduce the incidence of substance use disorders. ⋯ This single-center, comprehensive program including preplacement and random drug testing was associated with a reduction of the incidence of substance use disorders among our residents in anesthesiology. There were no instances of substance use disorders in our residents over the recent 13 yr. A large, multicenter trial of a more diverse sample of academic, government, and community institutions is needed to determine if such a program can predictably reduce the incidence of substance use disorders in a larger group of anesthesiology residents.
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WHAT THIS ARTICLE TELLS US THAT IS NEW: BACKGROUND:: With appropriate algorithms, computers can learn to detect patterns and associations in large data sets. The authors' goal was to apply machine learning to arterial pressure waveforms and create an algorithm to predict hypotension. The algorithm detects early alteration in waveforms that can herald the weakening of cardiovascular compensatory mechanisms affecting preload, afterload, and contractility. ⋯ The results demonstrate that a machine-learning algorithm can be trained, with large data sets of high-fidelity arterial waveforms, to predict hypotension in surgical patients' records.