ERCP训练模型的现况与问题及未来发展设想
Current status and problems of ERCP training models,and future development prospects
李锦阳 1王玮琛 1谢婉莹 1陈晓晓 2谭淞文 2何超 1刘威 1熊力1
作者信息
- 1. 中南大学湘雅二医院 普通外科,湖南 长沙 410011
- 2. 中南大学湘雅药学院,湖南 长沙 410013
- 折叠
摘要
内镜逆行胰胆管造影术(ERCP)是临床上常用的胆胰系统疾病的诊断方法和治疗手段.为了缩短训练期限和减少因操作问题产生的手术并发症的发生,操作者需要通过训练模型大量训练才能达到能够独立完成1次手术的水平.在此,笔者总结分析了目前国内外投入使用的模型类型的现状与存在的问题:机械模拟操作模型和计算机模拟模型具有灵活性、可视性等特点,适合初学者了解操作流程,但真实性不如其他模型;活体动物模型具有可以进行完整的操作训练和并发症的处理的优点,但价格相对昂贵,且没有完全匹配人体结构适合训练的动物品种;离体动物模型价格低廉,具有较好真实性,适用于进阶学习与初步开展并发症的应对训练.最后,笔者根据不同模型的特点,创新性提出一种阶段性联合ERCP训练方案—五阶段法,并提出ERCP训练模型发展迭代的新方向.
Abstract
Endoscopic retrograde cholangiopancreatography(ERCP)is a common diagnostic method and therapeutic technique for biliary and pancreatic diseases in clinical practice.In order to shorten the training period and reduce the occurrence of surgical complications due to operational issues,operators need extensive training through training models to reach the level of independently performing a procedure.Here,the authors summarize and analyze the current status and existing problems of different models used at home and abroad:mechanical simulation operation models and computer simulation models have characteristics such as flexibility and visibility,suitable for beginners to understand the procedural flow,but lack realism compared to other models;live animal models have the advantage of complete operational training and management of complications,but they are relatively expensive and do not fully match the human anatomy for training purposes;ex vivo animal models have the advantages of low cost and better realism,suitable for advanced learning and initial training in managing complications.Finally,based on the characteristics of different models,the authors innovatively propose a phased joint ERCP training scheme—the five-stage method,and also suggest new directions for the iterative development of ERCP training models.
关键词
胰胆管造影术,内窥镜逆行/模拟培训/元宇宙Key words
Cholangiopancreatography,Endoscopic Retrograde/Simulation Training/Metaverse引用本文复制引用
基金项目
湖南省科技厅重点研发基金(2023DK2003)
中南大学前沿交叉基金(2023QYJC028)
湖南省科技厅颠覆性技术十强基金(2022)(2022GK3003)
湖南省自然科学基金-部门联合基金(2023JJ60506)
湖南省卫生健康委科研重点指导基金(C202204017138)
出版年
2024
国家科技期刊平台
NSTL
万方数据