Background: Robotics represents the most technologically advanced approach in minimally invasive surgery (MIS). Its application in general surgery has increased progressively, with some early experience reported in emergency settings. The present position paper, supported by the World Society of Emergency Surgery (WSES), aims to provide a systematic review of the literature to develop consensus statements about the potential use of robotics in emergency general surgery. Methods: This position paper was conducted according to the WSES methodology. A steering committee was constituted to draft the position paper according to the literature review. An international expert panel then critically revised the manuscript. Each statement was voted through a web survey to reach a consensus. Results: Ten studies (3 case reports, 3 case series, and 4 retrospective comparative cohort studies) have been published regarding the applications of robotics for emergency general surgery procedures. Due to the paucity and overall low quality of evidence, 6 statements are proposed as expert opinions. In general, the experts claim for a strict patient selection while approaching emergent general surgery procedures with robotics, eventually considering it for hemodynamically stable patients only. An emergency setting should not be seen as an absolute contraindication for robotic surgery if an adequate training of the operating surgical team is available. In such conditions, robotic surgery can be considered safe, feasible, and associated with surgical outcomes related to an MIS approach. However, there are some concerns regarding the adoption of robotic surgery for emergency surgeries associated with the following: (i) the availability and accessibility of the robotic platform for emergency units and during night shifts, (ii) expected longer operative times, and (iii) increased costs. Further research is necessary to investigate the role of robotic surgery in emergency settings and to explore the possibility of performing telementoring and telesurgery, which are particularly valuable in emergency situations. Conclusions: Many hospitals are currently equipped with a robotic surgical platform which needs to be implemented efficiently. The role of robotic surgery for emergency procedures remains under investigation. However, its use is expanding with a careful assessment of costs and timeliness of operations. The proposed statements should be seen as a preliminary guide for the surgical community stressing the need for reevaluation and update processes as evidence expands in the relevant literature.

Background: Non-traumatic emergency general surgery involves a heterogeneous population that may present with several underlying diseases. Timeous emergency surgical treatment should be supplemented with high-quality perioperative care, ideally performed by multidisciplinary teams trained to identify and handle complex postoperative courses. Uncontrolled or poorly controlled acute postoperative pain may result in significant complications. While pain management after elective surgery has been standardized in perioperative pathways, the traditional perioperative treatment of patients undergoing emergency surgery is often a haphazard practice. The present recommended pain management guidelines are for pain management after non-traumatic emergency surgical intervention. It is meant to provide clinicians a list of indications to prescribe the optimal analgesics even in the absence of a multidisciplinary pain team. Material and methods: An international expert panel discussed the different issues in subsequent rounds. Four international recognized scientific societies: World Society of Emergency Surgery (WSES), Global Alliance for Infection in Surgery (GAIS), Italian Society of Anesthesia, Analgesia Intensive Care (SIAARTI), and American Association for the Surgery of Trauma (AAST), endorsed the project and approved the final manuscript. Conclusion: Dealing with acute postoperative pain in the emergency abdominal surgery setting is complex, requires special attention, and should be multidisciplinary. Several tools are available, and their combination is mandatory whenever is possible. Analgesic approach to the various situations and conditions should be patient based and tailored according to procedure, pathology, age, response, and available expertise. A better understanding of the patho-mechanisms of postoperative pain for short- and long-term outcomes is necessary to improve prophylactic and treatment strategies.

Background: CME is a radical resection for colon cancer, but the procedure is technically demanding with significant variation in its practice. A standardised approach to the optimal technique and training is, therefore, desirable to minimise technical hazards and facilitate safe dissemination. The aim is to develop an expert consensus on the optimal technique for Complete Mesocolic Excision (CME) for right-sided and transverse colon cancer to guide safe implementation and training pathways. Methods: Guidance was developed following a modified Delphi process to draw consensus from 55 international experts in CME and surgical education representing 18 countries. Domain topics were formulated and subdivided into questions pertinent to different aspects of CME practice. A three-round Delphi voting on 25 statements based on the specific questions and 70% agreement was considered as consensus. Results: Twenty-three recommendations for CME procedure were agreed on, describing the technique and optimal training pathway. CME is recommended as the standard of care resection for locally advanced colon cancer. The essential components are central vascular ligation, exposure of the superior mesenteric vein and excision of an intact mesocolon. Key anatomical landmarks to perform a safe CME dissection include identification of the ileocolic pedicle, superior mesenteric vein and root of the mesocolon. A proficiency-based multimodal training curriculum for CME was proposed including a formal proctorship programme. Conclusions: Consensus on standardisation of technique and training framework for complete mesocolic excision was agreed upon by a panel of experts to guide current practice and provide a quality control framework for future studies.

Laparoscopic complete mesocolic excision (CME) of the right colon with central vascular ligation (CVL) is a technically demanding procedure. This study retrospectively evaluated the feasibility, safety and oncological outcomes of the procedure when performed using the da Vinci^® robotic system. A prospective case series was collected over 3 years for patients with right colonic cancers treated by standardized robotic CME with CVL using the superior mesenteric vessels first approach. The CME group was compared to a 2 : 1 propensity score-matched non-CME group who had conventional laparoscopic right colectomy with D2 nodal dissection. Primary outcomes were total lymph node harvest and length of specimen. Secondary outcomes were operative time, postoperative complications, and disease-free and overall survival. The study included 120 patients (40 in the CME group and 80 in the non-CME group). Lymph node yield was higher (29 versus 18, P = 0.006), the specimen length longer (322 versus 260 mm, P = 0.001) and median operative time was significantly longer (180 versus 130 min, P < 0.001) with robotic CME versus laparoscopy, respectively. Duration of hospital stay was longer with robotic CME, although not significantly (median 6 versus 5 days, P = 0.088). There were no significant differences in R0 resection rate, complications, readmission rates and local recurrence. A trend in survival benefit with robotic CME for disease-free (P = 0.0581) and overall survival (P = 0.0454) at 3 years was documented. Robotic CME with CVL is feasible and, although currently associated with a longer operation time, it provides good specimen quality, higher lymph node yield and acceptable morbidity, with a disease-free survival advantage.

Data analytics and artificial intelligence (AI) have been used to predict patient outcomes after colorectal cancer surgery. A prospectively maintained colorectal cancer database was used, covering 4336 patients who underwent colorectal cancer surgery between 2003 and 2019. The 47 patient parameters included demographics, peri- and post-operative outcomes, surgical approaches, complications, and mortality. Data analytics were used to compare the importance of each variable and AI prediction models were built for length of stay (LOS), readmission, and mortality. Accuracies of at least 80% have been achieved. The significant predictors of LOS were age, ASA grade, operative time, presence or absence of a stoma, robotic or laparoscopic approach to surgery, and complications. The model with support vector regression (SVR) algorithms predicted the LOS with an accuracy of 83% and mean absolute error (MAE) of 9.69 days. The significant predictors of readmission were age, laparoscopic procedure, stoma performed, preoperative nodal (N) stage, operation time, operation mode, previous surgery type, LOS, and the specific procedure. A BI-LSTM model predicted readmission with 87.5% accuracy, 84% sensitivity, and 90% specificity. The significant predictors of mortality were age, ASA grade, BMI, the formation of a stoma, preoperative TNM staging, neoadjuvant chemotherapy, curative resection, and LOS. Classification predictive modelling predicted three different colorectal cancer mortality measures (overall mortality, and 31- and 91-days mortality) with 80–96% accuracy, 84–93% sensitivity, and 75–100% specificity. A model using all variables performed only slightly better than one that used just the most significant ones.