J Med Syst
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Children undergoing general anesthesia require airway monitoring by an anesthesia provider. The airway may be supported with noninvasive devices such as face mask or invasive devices such as a laryngeal mask airway or an endotracheal tube. The physiologic data stored provides an opportunity to apply machine learning algorithms distinguish between these modes based on pattern recognition. ⋯ In contrast, the sensitivity, specificity, and accuracy of support vector machine are 89.1%, 92.3%, and 88.3% and with the boosted tree classifier they are 93.8%, 92.1%, and 91.4%. We describe a method to automatically distinguish between noninvasive and invasive airway device support in a pediatric surgical setting based on respiratory monitoring parameters. The results show that the neural network classifier algorithm can accurately classify noninvasive and invasive airway device support.
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While a number of studies have examined efficiency metrics in the operating rooms (ORs), there are few studies addressing non-operating room anesthesia (NORA) metrics. The standards established in the realm of OR studies may not apply to ongoing investigations of NORA efficiency. We hypothesize that there are significant differences in these commonly used metrics. ⋯ Case times for NORA settings tended to be overestimated (-4.07 min versus -2.12 min), but showed less variation (8.61 min vs. 17.92 min). In short, there are significant differences in common efficiency metrics between OR and NORA cases. Future studies should elucidate and validate appropriate efficiency benchmarks for the NORA setting.