Surgery
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Postoperative acute kidney injury is common after major vascular surgery and is associated with increased morbidity, mortality, and cost. High-performance risk stratification using a machine learning model can inform strategies that mitigate harm and optimize resource use. It is hypothesized that incorporating intraoperative data would improve machine learning model accuracy, discrimination, and precision in predicting acute kidney injury among patients undergoing major vascular surgery. ⋯ In predicting acute kidney injury after major vascular surgery, machine learning approaches that incorporate dynamic intraoperative data had greater accuracy, discrimination, and precision than models using either preoperative data alone or the American Society of Anesthesiologists physical status classification. Machine learning methods have the potential for real-time identification of high-risk patients who may benefit from personalized risk-reduction strategies.
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Preoperative frailty is associated with poor outcomes in major surgery. Postoperative delirium is common after neurosurgery. To date, the association of preoperative frailty with postoperative delirium after neurosurgery has not been established. We aimed to determine the association between preoperative frailty and postoperative delirium in patients undergoing elective brain tumor resection. ⋯ Preoperative frailty is associated with postoperative delirium, length of hospital stay, and total costs in patients undergoing elective brain tumor resection. Preoperative frailty assessment and appropriate management strategies should be involved in the perioperative management of postoperative delirium.
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New surgeons are faced with inadequate mentoring when first entering practice. Our study examined challenges faced by young surgeons during their transition in practice and their mentoring experience when entering practice. ⋯ Our survey highlights the importance of mentoring for young surgeons during their transition into practice. With many young surgeons being enthusiastic about mentoring by retired surgeons, specific programs are necessary to better use their expertise.
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This review describes the steps and conclusions from the development and validation of an artificial intelligence algorithm (the Hypotension Prediction Index), one of the first machine learning predictive algorithms used in the operating room environment. The algorithm has been demonstrated to reduce intraoperative hypotension in two randomized controlled trials via real-time prediction of upcoming hypotensive events prompting anesthesiologists to act earlier, more often, and differently in managing impending hypotension. ⋯ Clinical validation of such algorithms is relatively new and requires more standardization, as guidelines are lacking or only now start to be drafted. Before adaptation in clinical practice, impact of artificial intelligence algorithms on clinical behavior, outcomes and economic advantages should be studied too.
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The coronavirus disease 2019 (COVID-19) pandemic has seen transplant volume decrease nationwide, resulting in a 2.2-fold increase in waitlist mortality. In particular, solid organ transplant patients are subjected to increased morbidity and mortality from infection. In the face of these challenges, transplant centers need to develop innovative protocols to ensure high-quality care. ⋯ Orthotopic liver transplant can be accomplished safely and effectively in the COVID-19 era without compromising outcomes through increasing utilization of telehealth, rapid COVID-19 testing, and multidisciplinary protocols for managing immunosuppressed patients.