Department of Surgery, Section of Minimally Invasive Surgery, Washington University School of Medicine, 660 South Euclid Avenue, Mailstop 8109-22-9905, Campus Box 8109, St. Louis, MO, 63110-1093, USA.
Data and Analytics, Intuitive Surgical, Inc., Peachtree Corners, GA, 30092, USA.
J Robot Surg. 2023 Oct;17(5):2117-2123. doi: 10.1007/s11701-023-01628-5. Epub 2023 May 26.
Trainee participation and progression in robotic general surgery remain poorly defined. Computer-assisted technology offers the potential to provide and track objective performance metrics. In this study, we aimed to validate the use of a novel metric-active control time (ACT)-for assessing trainee participation in robotic-assisted cases. Performance data from da Vinci Surgical Systems was retrospectively analyzed for all robotic cases involving trainees with a single minimally invasive surgeon over 10 months. The primary outcome metric was percent ACT-the amount of trainee console time spent in active system manipulations over total active time from both consoles. Kruskal-Wallis and Mann-Whitney U statistical tests were applied in analyses. A total of 123 robotic cases with 18 general surgery residents and 1 fellow were included. Of these, 56 were categorized as complex. Median %ACT was statistically different between trainee levels for all case types taken in aggregate (PGY1s 3.0% [IQR 2-14%], PGY3s 32% [IQR 27-66%], PGY4s 42% [IQR 26-52%], PGY5s 50% [IQR 28-70%], and fellow 61% [IQR 41-85%], p = < 0.0001). When stratified by complexity, median %ACT was higher in standard versus complex cases for PGY5 (60% vs. 36%, p = 0.0002) and fellow groups (74% vs. 47%, p = 0.0045). In this study, we demonstrated an increase in %ACT with trainee level and with standard versus complex robotic cases. These findings are consistent with hypotheses, providing validity evidence for ACT as an objective measurement of trainee participation in robotic-assisted cases. Future studies will aim to define task-specific ACT to guide further robotic training and performance assessments.
受训者在机器人辅助手术中的参与度和进展情况仍定义不明确。计算机辅助技术具有提供和跟踪客观绩效指标的潜力。在这项研究中,我们旨在验证一种新指标——主动控制时间(ACT)——用于评估受训者在机器人辅助手术中的参与度。对 10 个月内一位微创手术医生进行的所有涉及受训者的机器人手术的达芬奇手术系统的性能数据进行了回顾性分析。主要的结果衡量指标是 ACT 百分比——受训者控制台主动操作时间占两个控制台的总主动时间的比例。分析中应用了 Kruskal-Wallis 和 Mann-Whitney U 统计检验。共纳入 123 例机器人手术和 18 名普通外科住院医师和 1 名研究员。其中,56 例被归类为复杂病例。对于所有类型的病例,受训者水平之间的中位数 ACT 百分比存在统计学差异(PGY1 为 3.0%[2-14%],PGY3 为 32%[27-66%],PGY4 为 42%[26-52%],PGY5 为 50%[28-70%],研究员为 61%[41-85%],p<0.0001)。当按复杂性分层时,PGY5 和研究员组的标准病例与复杂病例相比,ACT 中位数更高(60%对 36%,p=0.0002;74%对 47%,p=0.0045)。在这项研究中,我们发现 ACT 百分比随着受训者水平的提高以及标准病例与复杂病例的增加而增加。这些发现与假设一致,为 ACT 作为机器人辅助手术中受训者参与度的客观衡量标准提供了有效性证据。未来的研究将旨在定义特定任务的 ACT,以指导进一步的机器人培训和绩效评估。