学习曲线对机器人活体肾切除结果的影响：回顾性分析。

IF 2.3 3区医学 Q2 SURGERY

International Journal of Medical Robotics and Computer Assisted Surgery Pub Date : 2025-01-21 DOI:10.1002/rcs.70041

Sarah Papa, Aleksandar Popovic, Reut Hod Dvorai, Rauf Shahbazov

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引用次数: 0

摘要

背景：我们的目的是研究不同时间点RDN后患者的预后。方法：对77例活体机器人活体供肾切除术（RDN）的预后进行研究。将捐赠者分为学习曲线期（LCP）、稳定期（SP）和教学期（TP）三组。结果：三组患者失血量及住院时间差异有统计学意义。LCP组手术时间明显短于SP组和TP组（282±51.6 min vs 308±38.7 min vs 314±28.7 min, p = 0.02）。然而，与LCP和SP组相比，TP组的热缺血时间更短（5.0±3.6 min vs. 3.4±3.2 min vs. 1.5±1.3 min）， p结论：本研究表明，学习期后RDN结果有所改善。

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The Impact of Learning Curve on Robotic Living Donor Nephrectomy Outcomes: Retrospective Analysis

Background

We aimed to investigate the outcome of patients after RDN at different time points.

Methods

We studied the outcomes of 77 living robotic living donor nephrectomies (RDN). Donors were separated into three groups: learning curve period (LCP), stabilisation period (SP), and teaching period (TP).

Results

There were significant differences in blood loss and hospitalisation times between the three groups. Operative time was significantly shorter in the LCP group compared with the SP and TP groups (282 ± 51.6 min vs. 308 ± 38.7 min vs. 314 ± 28.7, p = 0.02). However, warm ischaemia time was shorter in the TP group compared with the LCP and SP groups (5.0 ± 3.6 min vs. 3.4 ± 3.2 min vs. 1.5 ± 1.3 min, p < 0.01). Complication rates were higher in the LCP group compared with the SP and TP groups (p = 0.04).

Conclusion

This study demonstrated that RDN outcomes improve after the learning period.

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来源期刊

International Journal of Medical Robotics and Computer Assisted Surgery 医学-外科

CiteScore

4.50

自引率

12.00%

发文量

131

审稿时长

6-12 weeks

期刊介绍： The International Journal of Medical Robotics and Computer Assisted Surgery provides a cross-disciplinary platform for presenting the latest developments in robotics and computer assisted technologies for medical applications. The journal publishes cutting-edge papers and expert reviews, complemented by commentaries, correspondence and conference highlights that stimulate discussion and exchange of ideas. Areas of interest include robotic surgery aids and systems, operative planning tools, medical imaging and visualisation, simulation and navigation, virtual reality, intuitive command and control systems, haptics and sensor technologies. In addition to research and surgical planning studies, the journal welcomes papers detailing clinical trials and applications of computer-assisted workflows and robotic systems in neurosurgery, urology, paediatric, orthopaedic, craniofacial, cardiovascular, thoraco-abdominal, musculoskeletal and visceral surgery. Articles providing critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies, commenting on ease of use, or addressing surgical education and training issues are also encouraged. The journal aims to foster a community that encompasses medical practitioners, researchers, and engineers and computer scientists developing robotic systems and computational tools in academic and commercial environments, with the intention of promoting and developing these exciting areas of medical technology.