An Application of the Multi-Level Heuristic for the Heterogeneous Fleet Vehicle Routing Problem

The Multi-Level heuristic is used to investigate the heterogeneous fleet vehicle routing problem (HFVRP). The initial solution for the Multi-Level heuristic is obtained by Dijkstra\u27s algorithm based on a cost network constructed by the sweep algorithm and the 2-opt procedure. The proposed algorithm uses a number of local search operators such as swap, 1-0 insertion, 2-opt, and Dijkstra\u27s Algorithm. In addition, in order to improve the search process, a diversification procedure is applied. The proposed algorithm is thentested on the data sets from the literature

Revision of Laparoscopic Adjustable Gastric Banding: Success or Failure?

BACKGROUND: Laparoscopic adjustable gastric banding (LAGB) is a safe and frequently performed bariatric procedure. Unfortunately, re-operations are often necessary. Reports on the success of revisional procedures are scarce and show variable results, either supporting or declining the idea of revising LAGB. This study describes a large cohort of re-operations after failed LAGB to determine the success of revision. METHODS: By use of a prospective cohort, all LAGB revisions performed between 1996 and 2008 were identified. From 301 primary LAGB procedures in our centre, 43 patients (14.3%) required a band revision. In addition, 51 patients were referred from other centres. Our analysis included in total 94 patients with a mean follow-up period of 38 months after revision. RESULTS: Revision was mainly necessary due to anterior slippage (46%) and symmetrical pouch dilatation (36%), which could be resolved by replacing (70%) or refixating the band (27%). Weight loss significantly increased after revision (excess BMI loss (EBMIL), 37.2 +/- 36.3% versus 47.5 +/- 30.4%, P < 0.05). After revision, 23 patients (24%) needed a second re-operation. Patients converted to other procedures (16%) during the second re-operation showed larger weight loss than the revised group (EBMIL, 64.3 +/- 28.1% versus 44.3 +/- 28.7%, P < 0.05). CONCLUSIONS: We report on a large cohort of LAGB revisions with 38 months of follow-up. Revision of failed LAGB by either refixation or replacement of the band is successful and further increases weight loss

Robot-Assisted vs. Conventional Laparoscopic Rectopexy for Rectal Prolapse: A Comparative Study on Costs and Time

PURPOSE: Laparoscopic rectopexy has become one of the most advocated treatments for full-thickness rectal prolapse, offering good functional results compared with open surgery and resulting in less postoperative pain and faster convalescence. However, laparoscopic rectopexy can be technically demanding. Once having mastered dexterity, with robotic assistance, laparoscopic rectopexy can be performed faster. Moreover, it shortens the learning curve in simple laparoscopic tasks. This may lead to faster and safer laparoscopic surgery. Robot-assisted rectopexy has been proven safe and feasible; however, until now, no study has been performed comparing costs and time consumption in conventional laparoscopic rectopexy vs. robot-assisted rectopexy. METHODS: Our first 14 cases of robot-assisted laparoscopic rectopexy were reviewed and compared with 19 patients who underwent conventional laparoscopic rectopexy in the same period. RESULTS: Robot-assisted laparoscopic rectopexy did not show more complications. However, the average operating time was 39 minutes longer, and costs were 557.29 (or: $745.09) higher. CONCLUSION: Robot-assisted laparoscopic rectopexy is a safe and feasible procedure but results in increased time and higher costs than conventional laparoscopy. AD - Department of Surgery, Maastricht University Hospital, Maastricht, The Netherlands

EAES Recommendations for Recovery Plan in Minimally Invasive Surgery Amid COVID-19 Pandemic

Background: COVID-19 pandemic presented an unexpected challenge for the surgical community in general and Minimally Invasive Surgery (MIS) specialists in particular. This document aims to summarize recent evidence and experts’ opinion and formulate recommendations to guide the surgical community on how to best organize the recovery plan for surgical activity across diferent sub-specialities after the COVID-19 pandemic. Methods: Recommendations were developed through a Delphi process for establishment of expert consensus. Domain topics were formulated and subsequently subdivided into questions pertinent to diferent surgical specialities following the COVID-19 crisis. Sixty-fve experts from 24 countries, representing the entire EAES board, were invited. Fifty clinicians and six engineers accepted the invitation and drafted statements based on specifc key questions. Anonymous voting on the statements was performed until consensus was achieved, defned by at least 70% agreement. Results: A total of 92 consensus statements were formulated with regard to safe resumption of surgery across eight domains, addressing general surgery, upper GI, lower GI, bariatrics, endocrine, HPB, abdominal wall and technology/research. The statements addressed elective and emergency services across all subspecialties with specifc attention to the role of MIS during the recovery plan. Eighty-four of the statements were approved during the frst round of Delphi voting (91.3%) and another 8 during the following round after substantial modifcation, resulting in a 100% consensus. Conclusion: The recommendations formulated by the EAES board establish a framework for resumption of surgery following COVID-19 pandemic with particular focus on the role of MIS across surgical specialities. The statements have the potential for wide application in the clinical setting, education activities and research work across diferent healthcare systems

Comparison of three different application routes of butyrate to improve colonic anastomotic strength in rats

Despite extensive research, anastomotic leakage (AL) remains one of the most dreaded complications after colorectal surgery. Since butyrate enemas are known to enhance anastomotic healing, several administration routes have been explored in this study. Three intraluminal approaches involving butyrate were investigated: (1) butyrin-elucidating patch, (2) a single injection of hyaluronan-butyrate (HA-But) prior to construction of the proximal anastomosis and (3) rectal hyaluronan-butyrate (HA-But) enemas designed for distal anastomoses. The main outcome was AL and secondary outcomes were bursting pressure, histological analysis of the anastomosis, zymography to detect MMP activity and qPCR for gene expression of MMP2, MMP9, MUC2 and TFF3. RESULTS: Neither the patches nor the injections led to a reduction of AL in experiments 1 and 2. In experiment 3, a significant reduction of AL was accomplished with the (HA-But) enema compared to the control group together with a higher bursting pressure. Histological analysis detected only an increased inflammation in experiment 2 in the hyaluronan injection group compared to the control group. No other differences were found regarding wound healing. Zymography identified a decreased proenzyme of MMP9 when HA-But was administered as a rectal enema. qPCR did not show any significant differences between groups in any experiment. CONCLUSION: Butyrate enemas are effective in the enhancement of colonic anastomosis. Enhanced butyrate-based approaches designed to reduce AL in animal models for both proximal and distal anastomoses were not more effective than were butyrate enemas alone. Further research should focus on how exogenous butyrate can improve anastomotic healing after gastrointestinal surgery

Common Problems, Common Data Model Solutions: Evidence Generation for Health Technology Assessment

There is growing interest in using observational data to assess the safety, effectiveness, and cost effectiveness of medical technologies, but operational, technical, and methodological challenges limit its more widespread use. Common data models and federated data networks offer a potential solution to many of these problems. The open-source Observational and Medical Outcomes Partnerships (OMOP) common data model standardises the structure, format, and terminologies of otherwise disparate datasets, enabling the execution of common analytical code across a federated data network in which only code and aggregate results are shared. While common data models are increasingly used in regulatory decision making, relatively little attention has been given to their use in health technology assessment (HTA). We show that the common data model has the potential to facilitate access to relevant data, enable multidatabase studies to enhance statistical power and transfer results across populations and settings to meet the needs of local HTA decision makers, and validate findings. The use of open-source and standardised analytics improves transparency and reduces coding errors, thereby increasing confidence in the results. Further engagement from the HTA community is required to inform the appropriate standards for mapping data to the common data model and to design tools that can support evidence generation and decision making

Common Problems, Common Data Model Solutions: Evidence Generation for Health Technology Assessment

There is growing interest in using observational data to assess the safety, effectiveness, and cost effectiveness of medical technologies, but operational, technical, and methodological challenges limit its more widespread use. Common data models and federated data networks offer a potential solution to many of these problems. The open-source Observational and Medical Outcomes Partnerships (OMOP) common data model standardises the structure, format, and terminologies of otherwise disparate datasets, enabling the execution of common analytical code across a federated data network in which only code and aggregate results are shared. While common data models are increasingly used in regulatory decision making, relatively little attention has been given to their use in health technology assessment (HTA). We show that the common data model has the potential to facilitate access to relevant data, enable multidatabase studies to enhance statistical power and transfer results across populations and settings to meet the needs of local HTA decision makers, and validate findings. The use of open-source and standardised analytics improves transparency and reduces coding errors, thereby increasing confidence in the results. Further engagement from the HTA community is required to inform the appropriate standards for mapping data to the common data model and to design tools that can support evidence generation and decision making