Anesthesiology
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Research on electronic health record physiologic data is common, invariably including artifacts. Traditionally, these artifacts have been handled using simple filter techniques. The authors hypothesized that different artifact detection algorithms, including machine learning, may be necessary to provide optimal performance for various vital signs and clinical contexts. ⋯ No single artifact detection method consistently performed well across different vital signs and clinical settings. Neural networks may be a promising artifact detection method for specific vital signs.
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The objective of this study was to examine insurance-based disparities in mortality, nonhome discharges, and extracorporeal membrane oxygenation utilization in patients hospitalized with COVID-19. ⋯ Among patients with COVID-19, insurance-based disparities in mortality, nonhome discharges, and extracorporeal membrane oxygenation utilization were substantial, but these disparities did not increase as the hospital COVID-19 burden increased.
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Data on assessment and management of dyspnea in patients on venoarterial extracorporeal membrane oxygenation (ECMO) for cardiogenic shock are lacking. The hypothesis was that increasing sweep gas flow through the venoarterial extracorporeal membrane oxygenator may decrease dyspnea in nonintubated venoarterial ECMO patients exhibiting clinically significant dyspnea, with a parallel reduction in respiratory drive. ⋯ In critically ill patients with venoarterial ECMO, an increase in sweep gas flow through the oxygenation membrane decreases dyspnea, possibly mediated by a decrease in respiratory drive.