Anesthesiology
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The utilization of artificial intelligence and machine learning as diagnostic and predictive tools in perioperative medicine holds great promise. Indeed, many studies have been performed in recent years to explore the potential. The purpose of this systematic review is to assess the current state of machine learning in perioperative medicine, its utility in prediction of complications and prognostication, and limitations related to bias and validation. ⋯ The findings indicate that the development of this field is still in its early stages. This systematic review indicates that application of machine learning in perioperative medicine is still at an early stage. While many studies suggest potential utility, several key challenges must be first overcome before their introduction into clinical practice.
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Multicenter Study
Hypoxemia in School Age Children Undergoing One-Lung Ventilation: A Retrospective Cohort Study from the Multicenter Perioperative Outcomes Group (MPOG).
Risk factors for hypoxemia in school-age children undergoing one-lung ventilation remain poorly understood. The hypothesis was that certain modifiable and nonmodifiable factors may be associated with increased risk of hypoxemia in school-age children undergoing one-lung ventilation and thoracic surgery. ⋯ An initial room air oxygen saturation of less than 98% was associated with an increased risk of hypoxemia in all children 4 to 17 yr of age. Extremes of weight, right-sided cases, and decreasing age were associated with an increased risk of hypoxemia in children 10 to 17 yr of age.