医学
肾盂成形术
围手术期
外科
系统
腹腔镜检查
失血
机械人手术
普通外科
肾积水
泌尿系统
内分泌学
作者
Matthew T. Gettman,Richard Neururer,Georg Bartsch,Reinhard Peschel
出处
期刊:Urology
[Elsevier]
日期:2002-09-01
卷期号:60 (3): 509-513
被引量:227
标识
DOI:10.1016/s0090-4295(02)01761-2
摘要
To evaluate and describe the use of the da Vinci robotic system in performing laparoscopic Anderson-Hynes pyeloplasty.Between June 2001 and February 2002, 9 patients underwent laparoscopic Anderson-Hynes pyeloplasty with the da Vinci telerobotic surgical system. The diagnosis was based on the presenting symptoms and radiologic imaging findings. The technique for da Vinci-assisted Anderson-Hynes pyeloplasty followed the same steps as for conventional laparoscopy. Three transperitoneal laparoscopic ports were required for the robotic system, and a fourth laparoscopic port was used by the assistant for retraction, suction, and introduction of suture. The operative time, suturing time, perioperative complications, and success rates were prospectively evaluated. The mean operative time was 138.8 minutes (range 80 to 215), and the mean suturing time was 62.4 minutes (range 40 to 115). No intraoperative complications or open conversions were required. The estimated blood loss was less than 50 mL in all cases. The mean length of hospitalization was 4.7 days (range 4 to 11). Postoperatively, 1 (11.1%) of 9 patients required open exploration to repair a defect in the renal pelvis. At a mean follow-up of 4.1 months (range less than 1 to 8), all procedures were successful on the basis of the subjective and radiographic data.All aspects of laparoscopic Anderson-Hynes pyeloplasty were performed using the da Vinci robotic system. da Vinci-assisted procedures resulted in favorable overall operative times, suturing times, perioperative complications, and available success rates, but additional clinical experience is required. Ongoing clinical application of robotic technology in a controlled scientific manner is needed to gauge the effectiveness of this method completely.
科研通智能强力驱动
Strongly Powered by AbleSci AI