Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Advancing mid‐rectal cancer surgery: Unveiling the potential of natural orifice specimen extraction surgery in comparison to conventional laparoscopic‐assisted resection

Abstract Background Mid‐rectal cancer treatment traditionally involves conventional laparoscopic‐assisted resection (CLAR). This study aimed to assess the clinical and therapeutic advantages of Natural Orifice Specimen Extraction Surgery (NOSES) over CLAR. Aims To compare the clinical outcomes, intraoperative metrics, postoperative recovery, complications, and long‐term prognosis between NOSES and CLAR groups. Materials & Methods A total of 136 patients were analyzed, with 92 undergoing CLAR and 44 undergoing NOSES. Clinical outcomes were evaluated, and propensity score matching (PSM) was employed to control potential biases. Results The NOSES group exhibited significant improvements in postoperative recovery, including lower pain scores on days 1, 3, and 5 (p < .001), reduced need for additional analgesics (p = .02), shorter hospital stays (10.8 ± 2.3 vs. 14.2 ± 5.3 days; p < .001), and decreased intraoperative blood loss (48.1 ± 52.7 mL vs. 71.0 ± 55.0 mL; p = .03). Patients undergoing NOSES also reported enhanced satisfaction with postoperative abdominal appearance and better quality of life. Additionally, the NOSES approach resulted in fewer postoperative complications. Conclusion While long‐term outcomes (overall survival, disease‐free survival, and local recurrence rates) were comparable between the two methods, NOSES demonstrated superior postoperative outcomes compared to CLAR in mid‐rectal cancer treatment, while maintaining similar long‐term oncological safety. These findings suggest that NOSES could serve as an effective alternative to CLAR without compromising long‐term results

Stratification of Length of Stay Prediction following Surgical Cytoreduction in Advanced High-Grade Serous Ovarian Cancer Patients Using Artificial Intelligence; the Leeds L-AI-OS Score

(1) Background: Length of stay (LOS) has been suggested as a marker of the effectiveness of short-term care. Artificial Intelligence (AI) technologies could help monitor hospital stays. We developed an AI-based novel predictive LOS score for advanced-stage high-grade serous ovarian cancer (HGSOC) patients following cytoreductive surgery and refined factors significantly affecting LOS. (2) Methods: Machine learning and deep learning methods using artificial neural networks (ANN) were used together with conventional logistic regression to predict continuous and binary LOS outcomes for HGSOC patients. The models were evaluated in a post-hoc internal validation set and a Graphical User Interface (GUI) was developed to demonstrate the clinical feasibility of sophisticated LOS predictions. (3) Results: For binary LOS predictions at differential time points, the accuracy ranged between 70–98%. Feature selection identified surgical complexity, pre-surgery albumin, blood loss, operative time, bowel resection with stoma formation, and severe postoperative complications (CD3–5) as independent LOS predictors. For the GUI numerical LOS score, the ANN model was a good estimator for the standard deviation of the LOS distribution by ± two days. (4) Conclusions: We demonstrated the development and application of both quantitative and qualitative AI models to predict LOS in advanced-stage EOC patients following their cytoreduction. Accurate identification of potentially modifiable factors delaying hospital discharge can further inform services performing root cause analysis of LOS