Department of Gynecology and Obstetrics, Johns Hopkins University, Baltimore, MD, USA.
Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA.
Int Urogynecol J. 2023 Nov;34(11):2751-2758. doi: 10.1007/s00192-023-05595-1. Epub 2023 Jul 14.
The objective was to study the effect of immediate pre-operative warm-up using virtual reality simulation on intraoperative robot-assisted laparoscopic hysterectomy (RALH) performance by gynecology trainees (residents and fellows).
We randomized the first, non-emergent RALH of the day that involved trainees warming up or not warming up. For cases assigned to warm-up, trainees performed a set of exercises on the da Vinci Skills Simulator immediately before the procedure. The supervising attending surgeon, who was not informed whether or not the trainee was assigned to warm-up, assessed the trainee's performance using the Objective Structured Assessment for Technical Skill (OSATS) and the Global Evaluative Assessment of Robotic Skills (GEARS) immediately after each surgery.
We randomized 66 cases and analyzed 58 cases (30 warm-up, 28 no warm-up), which involved 21 trainees. Attending surgeons rated trainees similarly irrespective of warm-up randomization with mean (SD) OSATS composite scores of 22.6 (4.3; warm-up) vs 21.8 (3.4; no warm-up) and mean GEARS composite scores of 19.2 (3.8; warm-up) vs 18.8 (3.1; no warm-up). The difference in composite scores between warm-up and no warm-up was 0.34 (95% CI: -1.44, 2.13), and 0.34 (95% CI: -1.22, 1.90) for OSATS and GEARS respectively. Also, we did not observe any significant differences in each of the component/subscale scores within OSATS and GEARS between cases assigned to warm-up and no warm-up.
Performing a brief virtual reality-based warm-up before RALH did not significantly improve the intraoperative performance of the trainees.
本研究旨在探讨妇科住院医师和研究员等受训者在机器人辅助腹腔镜子宫切除术（RALH）前进行虚拟现实模拟预热对术中操作表现的影响。
我们随机选择当天的首例非紧急 RALH，其中部分受训者进行预热，部分不进行预热。对于安排预热的病例，受训者在手术前立即在达芬奇技能模拟器上进行一组练习。主刀医师在不知道受训者是否被安排预热的情况下，在每次手术后立即使用客观结构化手术技能评估（OSATS）和机器人技能综合评估（GEARS）对受训者的表现进行评估。
我们随机分配了 66 例病例，分析了 58 例病例（30 例预热，28 例未预热），涉及 21 名受训者。主刀医师对受训者的评价不受预热随机分组的影响，OSATS 综合评分的平均值（标准差）分别为 22.6（4.3；预热）和 21.8（3.4；未预热），GEARS 综合评分的平均值（标准差）分别为 19.2（3.8；预热）和 18.8（3.1；未预热）。预热和未预热组之间的综合评分差异为 0.34（95%置信区间：-1.44，2.13），OSATS 和 GEARS 分别为 0.34（95%置信区间：-1.22，1.90）。此外，我们还没有观察到在 OSATS 和 GEARS 的各个组成部分/子量表评分中，分配到预热组和未预热组的病例之间存在任何显著差异。
在 RALH 前进行简短的基于虚拟现实的预热并不能显著提高受训者的术中表现。
