Surgery
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Review Meta Analysis
Ambulatory laparoscopic cholecystectomy: Systematic review and meta-analysis of predictors of failure.
Outpatient laparoscopic cholecystectomy has proven to be a safe and cost-effective technique; however, it is not yet a universally widespread procedure. The aim of the study was to determine the predictive factors of outpatient laparoscopic cholecystectomy failure. ⋯ Our meta-analysis allowed us to identify the predictors of outpatient laparoscopic cholecystectomy failure. The knowledge of these factors could help surgeons in their decision-making process for the selection of patients who are suitable for outpatient laparoscopic cholecystectomy.
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Multicenter Study Comparative Study
Reduced duration of stay after elective colorectal surgery during the peak phase of COVID-19 pandemic: A positive effect of infection risk awareness?
While elective surgery was shut down in most settings during the 2019 novel coronavirus pandemic, some referral centers were designated as surgery hubs. We sought to investigate how the pandemic scenario impacted the quality of a long-established enhanced recovery protocol colorectal surgery program in 2 referral centers, designated as colorectal surgery hubs, located in the epicentral Italian regions hardest hit by the pandemic. ⋯ Under special precautionary measures, major colorectal surgery can be undertaken on elective basis even during coronavirus disease 2019 pandemic with reasonable results. A reduction of duration of stay within a long-established enhanced recovery protocol colorectal surgery program was observed during the coronavirus disease 2019 pandemic occurred in 2020 in comparison with the correspondent timeframe of the previous year without compromising short-term outcomes. The pandemic uncovered the positive impact of patients' commitment to reducing duration of stay as the empowered risk awareness likely promoted their compliance to the enhanced recovery protocol.
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The aim of this analysis was to determine whether optimal outcomes have increased in recent years. Hepatic surgery is high risk, but regionalization and minimally invasive approaches have evolved. Best practices also have been defined with the goal of improving outcomes. ⋯ Over a 5-year period in North America, minimally invasive hepatectomies have increased, while operative time, postoperative sepsis, bile leaks, liver failure, and prolonged length of stay have decreased. Optimal hepatic surgery has increased for partial and all hepatectomies and is achieved more often in partial than in major resections.
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The transoral endoscopic thyroidectomy vestibular approach has been demonstrated to have similar surgical outcomes as open thyroidectomy for selected papillary thyroid carcinomas. This study aimed to evaluate and compare the surgical outcomes and safety of the transoral endoscopic thyroidectomy vestibular approach with those of open thyroidectomy in the treatment of papillary thyroid carcinoma with a diameter between >1 cm and ≤3.5cm. ⋯ The transoral endoscopic thyroidectomy vestibular approach is feasible in selected patients with papillary thyroid carcinoma, not only because it is cosmetically advantageous but also because it is surgical and oncologically safe and may be an optional surgical method for treating papillary thyroid carcinomas having a diameter between >1 cm and ≤3.5 cm.
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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.