Survival Analysis Using Surgeon Skill Metrics and Patient Factors to Predict Urinary Continence Recovery After Robot-assisted Radical Prostatectomy.
Survival Analysis Using Surgeon Skill Metrics and Patient Factors to Predict Urinary Continence Recovery After Robot-assisted Radical Prostatectomy.
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DOI：
10.1016/j.euf.2021.04.001
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发表时间：
2022-03
影响因子：
5.4
通讯作者：
Hung AJ
中科院分区：
文献类型：
--
作者：
Trinh L;Mingo S;Vanstrum EB;Sanford DI;Aastha;Ma R;Nguyen JH;Liu Y;Hung AJ
已经表明，在机器人手术期间记录的器械运动学的度量可以预测泌尿外科手术的结果。评价患者和治疗因素、外科医生效率指标和外科医生技术技能评分（尤其是膀胱尿道吻合术（VUA））对预测机器人辅助根治性膀胱切除术（RARP）后尿道恢复的模型的贡献。收集了2016年7月至2017年12月进行的RARP的自动性能指标（APM;仪器运动学和系统事件）和患者数据。手动评价VUA期间的机器人吻合能力评价（RACE）评分。训练数据集包括：（1）患者因素;（2）总结的APM（在RARP步骤中报告）;（3）详细的APM（在VUA的两个阶段中报告）;以及（4）技术技能（RACE）。特征选择用于压缩输入的维数。研究结果是尿失禁恢复，定义为每天使用0或1个安全垫。使用了两种预测模型（考克斯比例风险[CoxPH]和深度学习生存分析[DeepSurv]）。在115例接受RARP的患者中，89例（77.4%）恢复了排尿功能，中位恢复时间为166 d（四分位距[IQR] 82-337）。VUA由23名外科医生进行。中位RACE评分为28/30（IQR 27-29）。在各个数据集中，技术技能（RACE）产生了最好的模型（C指数：CoxPH 0.695，DeepSurv：0.708）。在总结的APM中，后/前VUA产生的模型性能上级其他RARP步骤（C指数0.543-0.592）。在详细的APM中，针驱动的指标产生了表现最好的模型（C指数0.614-0.655），超过了其他阶段。DeepSurv模型的表现一直优于CoxPH;当提供所有数据集时，这两种方法的表现最好。限制包括特征选择，这可能排除了相关信息，但防止过拟合。技术技能和“针驱动”杀伤人员地雷在VUA是最有贡献的。表现最好的模型使用来自所有数据集的协同数据。机器人辅助手术切除前列腺的步骤之一是将膀胱连接到尿道。有关外科医生这一步骤表现的详细信息提高了预测接受该前列腺癌手术的男性尿失禁恢复的准确性。
It has been shown that metrics recorded for instrument kinematics during robotic surgery can predict urinary continence outcomes. To evaluate the contributions of patient and treatment factors, surgeon efficiency metrics, and surgeon technical skill scores, especially for vesicourethral anastomosis (VUA), to models predicting urinary continence recovery following robot-assisted radical prostatectomy (RARP). Automated performance metrics (APMs; instrument kinematics and system events) and patient data were collected for RARPs performed from July 2016 to December 2017. Robotic Anastomosis Competency Evaluation (RACE) scores during VUA were manually evaluated. Training datasets included: (1) patient factors; (2) summarized APMs (reported over RARP steps); (3) detailed APMs (reported over suturing phases of VUA); and (4) technical skills (RACE). Feature selection was used to compress the dimensionality of the inputs. The study outcome was urinary continence recovery, defined as use of 0 or 1 safety pads per day. Two predictive models (Cox proportional hazards [CoxPH] and deep learning survival analysis [DeepSurv]) were used. Of 115 patients undergoing RARP, 89 (77.4%) recovered their urinary continence and the median recovery time was 166 d (interquartile range [IQR] 82–337). VUAs were performed by 23 surgeons. The median RACE score was 28/30 (IQR 27–29). Among the individual datasets, technical skills (RACE) produced the best models (C index: CoxPH 0.695, DeepSurv: 0.708). Among summary APMs, posterior/anterior VUA yielded superior model performance over other RARP steps (C index 0.543–0.592). Among detailed APMs, metrics for needle driving yielded top-performing models (C index 0.614–0.655) over other suturing phases. DeepSurv models consistently outperformed CoxPH; both approaches performed best when provided with all the datasets. Limitations include feature selection, which may have excluded relevant information but prevented overfitting. Technical skills and “needle driving” APMs during VUA were most contributory. The best-performing model used synergistic data from all datasets. One of the steps in robot-assisted surgical removal of the prostate involves joining the bladder to the urethra. Detailed information on surgeon performance for this step improved the accuracy of predicting recovery of urinary continence among men undergoing this operation for prostate cancer.
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影响因子：
2.7
作者：
Hung, Andrew J.;Chen, Jian;Liu, Yan
通讯作者：
Liu, Yan
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：
4.5
作者：
Hung, Andrew J.;Oh, Paul J.;Gill, Inderbir S.
通讯作者：
Gill, Inderbir S.
影响因子：
6.6
作者：
Goh, Alvin C.;Goldfarb, David W.;Dunkin, Brian J.
通讯作者：
Dunkin, Brian J.
影响因子：
2.7
作者：
Goldenberg, Mitchell G.;Goldenberg, Larry;Grantcharov, Teodor P.
通讯作者：
Grantcharov, Teodor P.