Learning curve of multiple surgeons for robot-assisted radical prostatectomy using the cumulative sum method: a retrospective single-institution study

Takashi Nagai ^1,2, Toshiki Etani ³, Nobuhiko Shimizu ¹

⁰Department of Nephro-Urology, Nagoya City University Graduate School of Medical Sciences, Kawasumi 1, Mizuho-Cho, Mizuho-Ku, Nagoya, 4678601, Japan.

Journal of Robotic Surgery +

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November 1, 2024

View abstract on PubMed

Summary

Robot-assisted radical prostatectomy (RARP) learning curves were assessed using the CUSUM method. Experienced surgeons required an average of 11 cases for initial anastomosis proficiency and 24 for consolidation.

Area Of Science

Urology
Surgical Oncology
Medical Technology

Background

Prostate cancer (PC) is a prevalent men's health issue.
Localized PC offers a favorable prognosis with effective treatment.
Robot-assisted radical prostatectomy (RARP) is a widely adopted surgical technique.

Purpose Of The Study

To evaluate the learning curve for RARP, specifically focusing on anastomosis time.
To apply the cumulative sum (CUSUM) method for assessing surgical proficiency in RARP.

Main Methods

Analysis of 469 RARP surgeries performed by experienced surgeons at Nagoya City University Hospital (May 2011–December 2018).
Utilized the cumulative sum (CUSUM) method to track and analyze surgical performance trends, particularly anastomosis time.

Main Results

An average of 11 RARP cases were needed to complete the initial phase of anastomosis proficiency.
An average of 24 RARP cases were required for the consolidation phase of anastomosis.
For complete resection of pT2c cases, 16 and 27 cases were needed for initial and consolidation phases, respectively.

Conclusions

The CUSUM method effectively visualizes surgical proficiency trends in RARP.
Surgical proficiency in RARP, assessed by anastomosis time, demonstrates distinct learning phases.
Limitations exist in evaluating surgical proficiency solely on time or margins; further bias considerations are noted.

Keywords:

Education Laparoscopic surgery Learning curve Prostate cancer Robot