腹腔镜与机器人腹股沟疝修补术:普外科住院医师学习曲线与技能转移比较

Kristen M. Quinn, Louis T. Runge, Claire Griffiths, Hannah Harris, Heidi Pieper, Michael Meara, Ben Poulose, Vimal Narula, David Renton, Courtney Collins, Alan Harzman, Syed Husain
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引用次数: 0

摘要

背景关于腹腔镜经验是否应作为机器人培训的先决条件,目前还没有达成共识。此外,关于腹腔镜和机器人技术之间技能转换的信息也很有限。本研究的重点是普外科住院医师在两种微创平台中的学习曲线和技能转移。方法 观察普外科住院医师在腹腔镜和机器人腹股沟疝修补术中的表现。记录的数据包括客观指标(手术时间、住院医师的参与度(以控制台或腹腔镜上的活跃时间占总手术时间的百分比表示)、住院医师与主治医师之间的交接次数)和主观评价(导师和受训者对手术表现的评估),同时控制了病例的复杂性、患者的并发症和住院医师之前的手术经验。结果观察了 20 例腹腔镜手术和 44 例机器人手术。机器人手术和腹腔镜手术的平均手术时间分别为 90 分钟和 95 分钟(P = 0.4590)。在机器人平台上,住院医师的主动参与时间为66%,腹腔镜手术为37%(P = 0.0001)。机器人手术病例中平均交接 9.7 次,腹腔镜手术病例中平均交接 6.3 次(P = 0.0131)。每位住院医师的机器人手术和腹腔镜手术的平均病例数分别为 5.86 例和 1.67 例(P = 0.0312)。就机器人手术病例而言,住院医师积极参与的百分比与他们之前的机器人手术经验有很强的相关性(r = 0.78),而与之前的腹腔镜手术经验的相关性较弱(r = 0.47)。另一方面,之前的机器人经验与住院医师腹腔镜病例参与率的相关性很小(r = 0.12),与之前的腹腔镜经验的相关性较弱(r = 0.37)。我们观察到从腹腔镜到机器人的技能转移程度更高,这体现在住院医师之前的腹腔镜经验与机器人病例控制台时间百分比之间的相关性更高。住院医师之前的机器人经验与他们参与腹腔镜病例的相关性很小。我们的研究结果表明,机器人的学习曲线可能更短,因为与腹腔镜相比,之前的机器人经验与未来的机器人表现有更强的相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Laparoscopic vs robotic inguinal hernia repair: a comparison of learning curves and skill transference in general surgery residents

Laparoscopic vs robotic inguinal hernia repair: a comparison of learning curves and skill transference in general surgery residents

Background

There is no consensus on whether laparoscopic experience should be a prerequisite for robotic training. Further, there is limited information on skill transference between laparoscopic and robotic techniques. This study focused on the general surgery residents’ learning curve and skill transference within the two minimally invasive platforms.

Methods

General surgery residents were observed during the performance of laparoscopic and robotic inguinal hernia repairs. The recorded data included objective measures (operative time, resident participation indicated by percent active time on console or laparoscopy relative to total case time, number of handoffs between the resident and attending), and subjective evaluations (preceptor and trainee assessments of operative performance) while controlling for case complexity, patient comorbidities, and residents’ prior operative experience. Wilcoxon two-sample tests and Pearson Correlation coefficients were used for analysis.

Results

Twenty laparoscopic and forty-four robotic cases were observed. Mean operative times were 90 min for robotic and 95 min for laparoscopic cases (P = 0.4590). Residents’ active participation time was 66% on the robotic platform and 37% for laparoscopic (P = < 0.0001). On average, hand-offs occurred 9.7 times during robotic cases and 6.3 times during laparoscopic cases (P = 0.0131). The mean number of cases per resident was 5.86 robotic and 1.67 laparoscopic (P = 0.0312). For robotic cases, there was a strong correlation between percent active resident participation and their prior robotic experience (r = 0.78) while there was a weaker correlation with prior laparoscopic experience (r = 0.47). On the other hand, prior robotic experience had minimal correlation with the percent active resident participation in laparoscopic cases (r = 0.12) and a weak correlation with prior laparoscopic experience (r = 0.37).

Conclusion

The robotic platform may be a more effective teaching tool with a higher degree of entrustability indicated by the higher mean resident participation. We observed a greater degree of skill transference from laparoscopy to the robot, indicated by a higher degree of correlation between the resident’s prior laparoscopic experience and the percent console time in robotic cases. There was minimal correlation between residents’ prior robotic experience and their participation in laparoscopic cases. Our findings suggest that the learning curve for the robot may be shorter as prior robotic experience had a much stronger association with future robotic performance compared to the association observed in laparoscopy.

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