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Updated: Aug 12, 2025
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Retzius-Sparing Robot-Assisted Radical Prostatectomy
Published on: May 19, 2022
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Determining the component-based operative time learning curve for robotic-assisted radical prostatectomy.
David Ambinder1, Shu Wang1, Mohummad Minhaj Siddiqui1
1Division of Urology, Department of Surgery, University of Maryland School of Medicine, Baltimore, MD, USA.
Current Urology
|January 30, 2023
Summary
A fellowship-trained urologist achieved a stable learning curve (LC) for robotic-assisted radical prostatectomy (RARP) within 25 cases. Specific procedural components, like urethrovesical anastomosis, required more cases to master.
Area of Science:
- Urology
- Surgical Technology
- Medical Education
Background:
- Robotic-assisted radical prostatectomy (RARP) is a complex procedure.
- Fellowship training aims to optimize surgical skills.
- Quantifying the learning curve is crucial for surgical proficiency.
Purpose of the Study:
- To determine the learning curve (LC) for total operative time in RARP.
- To analyze the LC for discrete components of RARP.
- To assess the learning trajectory of a new fellowship-trained urologic surgeon.
Main Methods:
- Retrospective analysis of 120 consecutive RARP procedures.
- Prospective recording of operative time in 7 distinct parts.
- Cumulative sum (CUSUM) analysis to identify the LC.
Main Results:
- The overall LC for total operative time was 25 cases.
- Component LCs varied, with urethrovesical anastomosis (UVA) having the longest LC at 52 cases.
- Significant reductions in operative times were observed across all components (p < 0.05).
Conclusions:
- A 25-case LC is sufficient for a fellowship-trained surgeon to achieve stable RARP performance.
- Individual procedural components, such as UVA, exhibit variable learning curves.
- This study provides benchmarks for RARP training and skill acquisition.
