British journal of anaesthesia
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Multicenter Study
Frailty and decisional regret after elective noncardiac surgery: a multicentre prospective cohort study.
Frailty is associated with patient surgical-regret at 1 year post-op among older patients, particularly for non-orthopaedic surgery.
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Multicenter Study
Preoperative neurologic comorbidity and unanticipated early postoperative reintubation: a multicentre cohort study.
The risk of respiratory complications is highest in the first 72 h post-surgery. Postoperative respiratory events can exacerbate pre-existing respiratory compromise and lead to reintubation of the trachea, particularly in patients with neurologic disorders. This study examined the association between neurologic comorbidities and unanticipated early postoperative reintubation in children. ⋯ Children with neurologic comorbidities have an increased risk of unanticipated early postoperative reintubation and prolonged mechanical ventilation. Given the high mortality risk associated with these outcomes, children with neurologic comorbidities require heightened monitoring and risk assessment.
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Randomized Controlled Trial
Effect of machine learning models on clinician prediction of postoperative complications: the Perioperative ORACLE randomised clinical trial.
Anaesthesiologists might be able to mitigate risk if they know which patients are at greatest risk for postoperative complications. This trial examined the impact of machine learning models on clinician risk assessment. ⋯ NCT05042804.
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The issue of potentially harmful effects of neurotoxicity or anaesthesia management on children undergoing general anaesthesia is still not resolved. Studies have so far been limited by methodological problems. In a retrospective cohort study, a new noninvasive method was used to demonstrate visual processing changes in children with a single previous exposure to anaesthesia. We need new noninvasive methods that can be used before and after exposure to anaesthesia and surgery to detemine possible effects on long-term neurodevelopment.
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Preoperative risk prediction is an important component of perioperative medicine. Machine learning is a powerful tool that could lead to increasingly complex risk prediction models with improved predictive performance. Careful consideration is required to guide the machine learning approach to ensure appropriate decisions are made with regard to what we are trying to predict, when we are trying to predict it, and what we seek to do with the results.