Surgeon Automated Performance Metrics as Predictors of Early Urinary Continence Recovery After Robotic Radical Prostatectomy-A Prospective Bi-institutional Study.
Surgeon Automated Performance Metrics as Predictors of Early Urinary Continence Recovery After Robotic Radical Prostatectomy-A Prospective Bi-institutional Study.
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DOI：
10.1016/j.euros.2021.03.005
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发表时间：
2021-05
影响因子：
2.5
通讯作者：
Wagner C
中科院分区：
文献类型：
--
作者：
Hung AJ;Ma R;Cen S;Nguyen JH;Lei X;Wagner C
在机器人手术中，运动学指标客观地量化外科医生的表现。目的：确定临床因素是否影响外科医生预测机器人辅助前列腺癌根治术(RARP)术后尿控恢复的能力。前瞻性收集了2016年7月至2018年11月期间进行的RARP的临床数据(患者特征、大小便恢复情况和治疗因素)和外科医生数据。外科医生的数据包括来自机器人系统(器械运动学和事件)的40个自动化性能指标(APM)，并在每个标准化的RARP步骤中进行汇总。这些数据是从美国和德国的两个大容量机器人中心收集的。这两家机构的外科医生都进行了RARP。纳入标准是具有临床和外科医生数据的连续RARP。具有治疗意图的RARP治疗前列腺癌。结果是3个月和6个月的尿失禁恢复状态。可控性的定义是每天使用零个或一个安全垫。使用随机森林(SAS HPFOREST)。共有20名外科医生进行了193次RARP。56.7%(102/180)和73.3%(129/176)的患者在RARP后3个月和6个月达到尿控。模型预测的可控性恢复(3个月 = 曲线下面积0.74，95%可信区间[CI]0.66~0.81，6个月 = 曲线下面积0.6 7，95%CI 0.5 8~0.76)。临床因素，包括PT分期，在预测RARP术后3个月的可控性恢复时混淆了APMS(Δβ中位数-13.3%，四分位数范围[-28.2%~-6.5%])。在调整了临床因素后，11/20(55%)的顶级APM仍然是重要的独立预测因素(即膀胱输尿管吻合术中的速度和腕关节)。局限性包括不同机构之间外科医生/患者数据的异质性，尽管它在多变量分析中被考虑在内。在预测RARP术后尿失禁恢复的过程中，临床因素扰乱了外科医生的绩效指标。尽管如此，许多外科医生因素仍然是早期大小便恢复的独立预测因素。机器人前列腺切除术中记录的患者因素和外科医生的运动学指标都会影响机器人辅助前列腺癌根治术后早期尿控恢复。在这项队列研究中，机器学习使用外科医生的表现指标和临床数据预测3个月(曲线下面积0.74)的尿失禁恢复。虽然临床因素混淆了指标(Δβ中位数-13.3%，四分位数范围[-28.2%至-6.5%])，但排名靠前的外科医生指标仍然是独立的预测因素。
During robotic surgeries, kinematic metrics objectively quantify surgeon performance. To determine whether clinical factors confound the ability of surgeon performance metrics to anticipate urinary continence recovery after robot-assisted radical prostatectomies (RARPs). Clinical data (patient characteristics, continence recovery, and treatment factors) and surgeon data from RARPs performed between July 2016 and November 2018 were prospectively collected. Surgeon data included 40 automated performance metrics (APMs) derived from robot systems (instrument kinematics and events) and summarized over each standardized RARP step. The data were collected from two high-volume robotic centers in the USA and Germany. Surgeons from both institutions performed RARPs. The inclusion criteria were consecutive RARPs having both clinical and surgeon data. RARP with curative intent to treat prostate cancer. The outcome was 3- and 6-mo urinary continence recovery status. Continence was defined as the use of zero or one safety pad per day. Random forest (SAS HPFOREST) was utilized. A total of 193 RARPs performed by 20 surgeons were included. Of the patients, 56.7% (102/180) and 73.3% (129/176) achieved urinary continence by 3 and 6 mo after RARP, respectively. The model anticipated continence recovery (area under the curve = 0.74, 95% confidence interval [CI] 0.66–0.81 for 3-mo, and area under the curve = 0.67, 95% CI 0.58–0.76 for 6 mo). Clinical factors, including pT stage, confounded APMs during prediction of continence recovery at 3 mo after RARP (Δβ median –13.3%, interquartile range [–28.2% to –6.5%]). After adjusting for clinical factors, 11/20 (55%) top-ranking APMs remained significant and independent predictors (ie, velocity and wrist articulation during the vesicourethral anastomosis). Limitations included heterogeneity of surgeon/patient data between institutions, although it was accounted for during multivariate analysis. Clinical factors confound surgeon performance metrics during the prediction of urinary continence recovery after RARP. Nonetheless, many surgeon factors are still independent predictors of early continence recovery. Both patient factors and surgeon kinematic metrics, recorded during robotic prostatectomies, impact early urinary continence recovery after robot-assisted radical prostatectomy. In this cohort study, machine learning anticipates urinary continence recovery at 3 mo (area under the curve 0.74) using surgeon performance metrics and clinical data. While clinical factors confound metrics (Δβ median –13.3%, interquartile range [–28.2% to –6.5%]), top-ranked surgeon metrics remain independent predictors.
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DOI：
10.1016/j.juro.2017.07.081
发表时间：
2018-01
期刊：
The Journal of urology
影响因子：
--
作者：
Hung AJ;Chen J;Jarc A;Hatcher D;Djaladat H;Gill IS
通讯作者：
Gill IS
影响因子：
23.4
作者：
Joshi, Neil;de Blok, Willem;van der Poel, Henk
通讯作者：
van der Poel, Henk
影响因子：
4.5
作者：
Chen, Jian;Chu, Tiffany;Hung, Andrew J.
通讯作者：
Hung, Andrew J.
影响因子：
23.4
作者：
Ghani, Khurshid R.;Miller, David C.;Peabody, James O.
通讯作者：
Peabody, James O.
影响因子：
6.6
作者：
Wolin, Kathleen Y.;Luly, Jason;Kibel, Adam S.
通讯作者：
Kibel, Adam S.