Anesthesiology
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Events occurring after randomization, such as use of rescue medication, treatment discontinuation, or death, are common in randomized trials. These events can change either the existence or interpretation of the outcome of interest. ⋯ This article describes how the estimand framework can be used in anesthesia trials to precisely define the treatment effect to be estimated, key attributes of an estimand, common intercurrent events in anesthesia trials with strategies for handling them, and use of the estimand framework in a hypothetical anesthesia trial on postoperative delirium. When planning anesthesia trials, clearly defining the estimand is vital to ensure that what is being estimated is clearly understood, is clinically relevant, and helps answer the clinical questions of interest.
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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.