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确定机器人辅助根治性前列腺切除术基于组件的手术时间学习曲线。

Determining the component-based operative time learning curve for robotic-assisted radical prostatectomy.

作者信息

Ambinder David, Wang Shu, Siddiqui Mohummad Minhaj

机构信息

Division of Urology, Department of Surgery, University of Maryland School of Medicine, Baltimore, MD, USA.

出版信息

Curr Urol. 2022 Dec;16(4):240-245. doi: 10.1097/CU9.0000000000000119. Epub 2022 Aug 31.

Abstract

OBJECTIVES

To determine the learning curve (LC) of total operative time and the discrete components of the robotic-assisted radical prostatectomy (RARP) for a recent robotic fellowship-trained urologic surgeon.

MATERIALS AND METHODS

We performed a retrospective analysis of RARP procedures performed by a single new attending surgeon from August 2015 to April 2019. Patients' demographics and operative details were assessed. Total operative time was divided and prospectively recorded in 7 parts: () docking robot, () dissecting seminal vesicles (SVs) () dissecting endopelvic fascia (EPF), () incising bladder neck (BN), () completing the dissection, () lymph node dissection, and () urethrovesical anastomosis (UVA) and robot undocking. Cumulative sum analysis was used to ascertain the LC for total operative time and the 7 parts of the procedure.

RESULTS

One hundred twenty consecutive RARPs were performed. The LC was overcome at 25 cases for total operative time, 13 cases for docking the robot, 33 cases for dissecting SVs, 31 cases for dissecting EPF, 46 cases for incising BN, 38 cases for prostate dissection, 25 cases for lymph node dissection, and 52 cases for UVA. Total operative time was decreased 22.8% ( < 0.0001) and time for robot docking, dissecting SVs, dissecting EPF, incising BN, completing prostate dissection, lymph node dissection, and UVA were decreased 16.7%, 30.5%, 29.5%, 36.2%, 37.3%, 32.2%, and 26.9%, respectively (all < 0.05).

CONCLUSIONS

We observed a 25-case LC for a fellowship-trained urologist to achieve stable operative performance of RARP surgery. Procedural components demonstrated variable LCs including the UVA that required upward of 52 cases.

摘要

目的

确定一名近期完成机器人手术进修培训的泌尿外科医生在机器人辅助根治性前列腺切除术(RARP)中总手术时间及各个独立环节的学习曲线(LC)。

材料与方法

我们对一名新入职的主治医生在2015年8月至2019年4月期间所进行的RARP手术进行了回顾性分析。评估了患者的人口统计学资料和手术细节。总手术时间被分为7个部分并进行前瞻性记录:()对接机器人,()解剖精囊(SVs),()解剖盆腔内筋膜(EPF),()切开膀胱颈(BN),()完成解剖,()淋巴结清扫,以及()尿道膀胱吻合术(UVA)和机器人脱机。采用累积和分析来确定总手术时间及该手术7个部分的学习曲线。

结果

共连续进行了120例RARP手术。总手术时间在25例时克服学习曲线,对接机器人在13例时克服,解剖SVs在33例时克服,解剖EPF在31例时克服,切开BN在46例时克服,前列腺解剖在38例时克服,淋巴结清扫在25例时克服,UVA在52例时克服。总手术时间减少了22.8%(P<0.0001),对接机器人、解剖SVs、解剖EPF、切开BN、完成前列腺解剖、淋巴结清扫和UVA的时间分别减少了16.7%、30.5%、29.5%、36.2%、37.3%、32.2%和26.9%(均P<0.05)。

结论

我们观察到一名完成进修培训的泌尿外科医生在RARP手术中达到稳定手术表现的学习曲线为25例。手术各个环节的学习曲线各不相同,其中UVA需要超过52例。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54da/9875207/7cd113fba1c1/curr-urol-16-240-g001.jpg

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