A study of simulation training in laparoscopic bilioenteric anastomosis on a 3D-printed dry lab model.

BACKGROUND

There are few studies on simulation training in laparoscopic bilioenteric anastomosis. There is also a lack of mature and reliable training models for bilioenteric anastomosis. In this study, we aimed to assess a feasible training model for bilioenteric anastomosis. Surgeons can improve their surgical ability by performing laparoscopic bilioenteric anastomosis on this model through repeated training.

METHOD

The original articles related to simulation training in surgical anastomosis were identified from January 2000 to November 2021 in the Clarivate Analytics Web of Science Core Collection database. We conducted a bibliometric analysis based on the country of these publications and the type of anastomosis. A 3D-printed bilioenteric anastomosis model was applied in this study. Baseline data of 15 surgeons (5 surgeons of Attendings, 5 surgeons of Fellows, and 5 surgeons of Residents) were collected. The bilioenteric anastomosis data, including the operation time and operation score, were recorded and analyzed. A study of the learning curve was also performed for further assessment.

RESULT

Surgeons at different levels of experience exhibited different levels of performance in conducting laparoscopic bilioenteric anastomosis on this model. Experienced surgeons completed their first training session in a shorter time and obtained a higher surgical score. In turn, repeated training significantly shortened the time of laparoscopic bilioenteric anastomosis for each trainer and improved the surgical score. Surgeons with different levels of experience needed different numbers of cases to reach the stable period of the learning curve. Experienced surgeons were able to reach a proficient level through fewer training cases.

CONCLUSION

A suitable biliary-enteric anastomosis model can help surgeons conduct simulation training and provide experience and skill accumulation for future real operations. Our training model performed well in this study and can effectively accomplish this goal.