Anesthesiology
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Automated medical technology is becoming an integral part of routine anesthetic practice. Automated technologies can improve patient safety, but may create new workflows with potentially surprising adverse consequences and cognitive errors that must be addressed before these technologies are adopted into clinical practice. Industries such as aviation and nuclear power have developed techniques to mitigate the unintended consequences of automation, including automation bias, skill loss, and system failures. ⋯ Medical device manufacturers now evaluate usability of equipment using the principles of human performance and should be encouraged to develop comprehensive training materials that describe possible system failures. Additional research in human-system interaction can improve the ways in which automated medical devices communicate with clinicians. These steps will ensure that medical practitioners can effectively use these new devices while being ready to assume manual control when necessary and prepare us for a future that includes automated health care.
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Review Case Reports
Determining Associations and Estimating Effects with Regression Models in Clinical Anesthesia.
There are an increasing number of "big data" studies in anesthesia that seek to answer clinical questions by observing the care and outcomes of many patients across a variety of care settings. This Readers' Toolbox will explain how to estimate the influence of patient factors on clinical outcome, addressing bias and confounding. One approach to limit the influence of confounding is to perform a clinical trial. ⋯ Logistic regression is used when the outcome is binary (e.g., intracranial hemorrhage: yes or no), by modeling the natural log for the odds of an outcome. Because outcomes are influenced by many factors, we commonly use multivariable logistic regression to estimate the unique influence of each factor. From this tutorial, one should acquire a clearer understanding of how to perform and assess multivariable logistic regression.
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Comparative Study
Benefit and Risk Evaluation of Biased μ-Receptor Agonist Oliceridine versus Morphine.
To improve understanding of the respiratory behavior of oliceridine, a μ-opioid receptor agonist that selectively engages the G-protein-coupled signaling pathway with reduced activation of the β-arrestin pathway, the authors compared its utility function with that of morphine. It was hypothesized that at equianalgesia, oliceridine will produce less respiratory depression than morphine and that this is reflected in a superior utility. ⋯ These data indicate a favorable oliceridine safety profile over morphine when considering analgesia and respiratory depression over the clinical concentration range.