USC Institute of Urology, Los Angeles, California.
J Urol. 2021 May;205(5):1294-1302. doi: 10.1097/JU.0000000000001557. Epub 2020 Dec 24.
Automated performance metrics provide a novel approach to the assessment of surgical performance. Herein, we present a construct validation of automated performance metrics during robotic assisted partial nephrectomy.
Automated performance metrics (instrument motion tracking/system events) and synchronized surgical videos from da Vinci® Si systems during robotic assisted partial nephrectomy were recorded using a system data recorder. Each case was segmented into 7 steps: colon mobilization, ureteral identification/dissection, hilar dissection, exposure of tumor within Gerota's fascia, intraoperative ultrasound/tumor scoring, tumor excision, and renorrhaphy. Automated performance metrics from each step were compared between expert (≥150 cases) and trainee (<150 cases) surgeons by Mann-Whitney U test (continuous variables) and Pearson's chi-squared test (categorical variables). Clinical outcomes were collected prospectively and correlated to automated performance metrics and R.E.N.A.L. (radius, exophytic/endophytic, nearness of tumor to collecting system, anterior/posterior, location relative to polar line) nephrometry score by Spearman's correlation coefficients (r).
A total of 50 robotic assisted partial nephrectomy cases were included for analysis, performed by 7 expert and 10 trainee surgeons. Automated performance metric profiles significantly differed between experts and novices in the initial 5 steps (p <0.05). Specifically, experts exhibited faster dominant instrument movement and greater dominant instrument usage (bimanual dexterity) than trainees in select steps (p ≤0.045). Automated performance metrics during tumor excision and renorrhaphy were significantly correlated with R.E.N.A.L. score (r ≥0.364; p ≤0.041). These included metrics related to instrument efficiency, task duration, and dominant instrument use.
Experts are more efficient and directed in their movement during robotic assisted partial nephrectomy. Automated performance metrics during key steps correlate with objective measures of tumor complexity and may serve as predictors of clinical outcomes. These data help establish a standardized metric for surgeon assessment and training during robotic assisted partial nephrectomy.
自动化绩效指标为评估手术绩效提供了一种新方法。本文旨在对达芬奇机器人辅助部分肾切除术的自动化绩效指标进行构建验证。
使用系统数据记录器记录达芬奇 Si 系统的机器人辅助部分肾切除术中的自动化绩效指标（器械运动跟踪/系统事件）和同步手术视频。将每个病例分为 7 个步骤：结肠游离、输尿管识别/解剖、肾门解剖、在肾筋膜内显露肿瘤、术中超声/肿瘤评分、肿瘤切除和肾缝合。采用曼-惠特尼 U 检验（连续变量）和皮尔逊卡方检验（分类变量）比较专家（≥150 例）和新手（<150 例）外科医生在各步骤的自动化绩效指标差异。前瞻性收集临床结果，并通过 Spearman 相关系数（r）与自动化绩效指标和 R.E.N.A.L.（半径、外生性/内生性、肿瘤与集合系统的接近程度、前后、相对于极线的位置）肾切除术评分相关联。
共纳入 50 例机器人辅助部分肾切除术进行分析，由 7 名专家和 10 名新手外科医生完成。在最初的 5 个步骤中，专家和新手的自动化绩效指标图谱存在显著差异（p<0.05）。具体而言，在某些步骤中，专家表现出更快的主导器械运动和更大的主导器械使用（双手灵巧性）（p≤0.045）。肿瘤切除和肾缝合期间的自动化绩效指标与 R.E.N.A.L.评分显著相关（r≥0.364；p≤0.041）。这些指标包括与器械效率、任务持续时间和主导器械使用相关的指标。
专家在机器人辅助部分肾切除术中的动作更有效率和有针对性。关键步骤中的自动化绩效指标与肿瘤复杂性的客观指标相关，可能成为临床结果的预测指标。这些数据有助于为达芬奇机器人辅助部分肾切除术建立标准化的外科医生评估和培训指标。
