Learning curve of single-port robot-assisted simple prostatectomy: a risk-adjusted CUSUM analysis
Lorenzo Santodirocco, Luca Morgantini, Marwan Alkassis, Alexandru Turcan, Flavia Tamborino, Filippo Carletti, Simone Crivellaro
- 发表年份
- 2026
- 引用次数
- 2
- 访问权限
- 开放获取
摘要
PURPOSE: To evaluate the learning curve of robotic-assisted simple prostatectomy (RASP) using a transvesical approach and to identify the impact of patient-related factors, such as prostatic volume and Body Mass Index (BMI), on operative efficiency. MATERIALS AND METHODS: A retrospective analysis of 103 consecutive RASP procedures performed by a single surgeon was conducted. A multiple linear regression model was developed to calculate predicted operative times adjusted for prostatic volume and BMI. Risk-adjusted Cumulative Sum (RA-CUSUM) analysis was applied to the residuals to characterize the learning curve. Perioperative outcomes were compared across the identified phases using the χ² test or Fisher's exact test for categorical variables and one-way ANOVA or the Kruskal-Wallis test for continuous variables. RESULTS: Both prostatic volume (p < 0.001) and BMI (p = 0.029) were independent predictors of operative duration. The RA-CUSUM analysis identified a learning phase (cases 1-33), a proficiency phase (34-73), and a consolidation phase (74-103). Significant improvements were observed across phases for median operative time (210 vs. 176 vs. 190 min, p = 0.011) and median length of stay (11 vs. 9 vs. 9 h, p < 0.001). No significant differences were found regarding complications or functional outcomes. CONCLUSIONS: Proficiency in robotic transvesical simple prostatectomy is achieved after approximately 33 cases, with technical consolidation occurring after the 75th procedure. The transvesical approach provides direct access that may mitigate the technical challenges associated with high BMI, although prostatic volume remains the primary driver of operative duration.
关键词
相关论文
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Fractional Differential Equations
Igor Podlubný
2025
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
Genetic Programming: On the Programming of Computers by Means of Natural Selection
John R. Koza
1992