Center for Robotic Simulation and Education, Catherine and Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, California.
Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, California.
J Urol. 2022 Aug;208(2):414-424. doi: 10.1097/JU.0000000000002691. Epub 2022 Apr 8.
Previously, we identified 8 objective suturing performance metrics highly predictive of urinary continence recovery after robotic-assisted radical prostatectomy. Here, we aimed to test the feasibility of providing tailored feedback based upon these clinically relevant metrics and explore the impact on the acquisition of robotic suturing skills.
Training surgeons were recruited and randomized to a feedback group or a control group. Both groups completed a baseline, midterm and final dry laboratory vesicourethral anastomosis (VUA) and underwent 4 intervening training sessions each, consisting of 3 suturing exercises. Eight performance metrics were recorded during each exercise: 4 automated performance metrics (derived from kinematic and system events data of the da Vinci® Robotic System) representing efficiency and console manipulation competency, and 4 suturing technical skill scores. The feedback group received tailored feedback (a visual diagram+verbal instructions+video examples) based on these metrics after each session. Generalized linear mixed model was used to compare metric improvement (Δ) from baseline to the midterm and final VUA.
Twenty-three participants were randomized to the feedback group (11) or the control group (12). Demographic data and baseline VUA metrics were comparable between groups. The feedback group showed greater improvement than the control group in aggregate suturing scores at midterm (mean Δ feedback group 4.5 vs Δ control group 1.1) and final VUA (Δ feedback group 5.3 vs Δ control group 4.9). The feedback group also showed greater improvement in the majority of the included metrics at midterm and final VUA.
Tailored feedback based on specific, clinically relevant performance metrics is feasible and may expedite the acquisition of robotic suturing skills.
此前，我们确定了 8 项客观缝合表现指标，这些指标高度预测了机器人辅助根治性前列腺切除术后尿控的恢复。在这里，我们旨在测试基于这些临床相关指标提供定制反馈的可行性，并探讨其对机器人缝合技能获取的影响。
招募培训外科医生并将其随机分为反馈组或对照组。两组均完成基线、中期和最终的干实验室膀胱尿道吻合术（VUA），并各进行 4 次干预培训，每次培训包括 3 次缝合练习。在每次练习中记录了 8 项性能指标：4 项自动性能指标（源自达芬奇®机器人系统的运动学和系统事件数据），代表效率和控制台操作能力，以及 4 项缝合技术技能评分。反馈组在每次训练后会根据这些指标收到定制反馈（图表+口头说明+视频示例）。使用广义线性混合模型比较从基线到中期和最终 VUA 的指标改善（Δ）。
23 名参与者被随机分配到反馈组（11 名）或对照组（12 名）。两组的人口统计学数据和基线 VUA 指标相似。与对照组相比，反馈组在中期（反馈组平均Δ为 4.5，对照组为 1.1）和最终 VUA（反馈组平均Δ为 5.3，对照组为 4.9）的总体缝合评分上的改善更大。在中期和最终 VUA 时，反馈组在大多数包含的指标上也显示出更大的改善。
基于特定的、临床相关的绩效指标的定制反馈是可行的，并且可能加速机器人缝合技能的掌握。
