Scholarlink

Background/objectives <p>Single-port robot-assisted radical prostatectomy (SP-RARP) represents an evolution in minimally invasive surgery but introduces unique technical challenges. Existing evidence on SP-RARP learning curves is limited to single-center or single-surgeon experiences. This multicenter study aimed to define proficiency milestones for SP-RARP across multiple surgeons using robust statistical methods.</p> Materials and methods <p>We retrospectively analyzed data from 327 patients who underwent SP-RARP performed by eight experienced multiport surgeons transitioning to the single-port platform across three U.S. centers (2019–2024). Learning curves for operative time (OT) and estimated blood loss (EBL) were evaluated using cumulative sum (CUSUM) analysis and piecewise linear regression. Piecewise multivariable logistic regression was used to adjust for confounders.</p> Results <p>Of the 327 cases, 111 were transperitoneal (TP) and 216 extraperitoneal (EP). No clear proficiency plateau was observed for the TP approach, with persistent variability in OT and EBL. In contrast, EP cases demonstrated breakpoints at 55 cases for OT (95% CI: 53.8–55.6; R² = 0.91) and 97 cases for EBL (95% CI: 92.3–101.1; R² = 0.97). Multivariable analysis confirmed a significant association between case volume and OT (<i>p</i> = 0.048), but not for EBL (<i>p</i> = 0.12).</p> Conclusion <p>This is the first multicenter study to evaluate SP-RARP learning curves using CUSUM and piecewise regression. Clear proficiency thresholds were identified for the EP approach (OT at 55, EBL at 97 cases), though only OT was confirmed after adjustment. No definitive plateau was reached for the TP approach, suggesting different learning dynamics.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Evaluating the learning curve for single-port radical prostatectomy: a multi-institutional cohort analysis

  • 期刊论文

  • 发表日期：

    2025年10月22日

查看全文
  • Alessio Finocchiaro,
  • Nicolò Buffi,
  • Muhannad Aljoulani,
  • Mustafa Almajedi,
  • Alex Stephens,
  • Silvia Viganò,
  • Alessandro Bertini,
  • Arianna Biasatti,
  • Fabio Maria Valenzi,
  • Vincenzo Ficarra,
  • Gabriele Sorce,
  • Andrea Salonia,
  • Alberto Briganti,
  • Francesco Montorsi,
  • Giovanni Lughezzani,
  • Srinivas Vourganti,
  • Riccardo Autorino,
  • Simone Crivellaro,
  • Ryan J. Nelson,
  • Craig Rogers,
  • Firas Abdollah

摘要

Background/objectives

Single-port robot-assisted radical prostatectomy (SP-RARP) represents an evolution in minimally invasive surgery but introduces unique technical challenges. Existing evidence on SP-RARP learning curves is limited to single-center or single-surgeon experiences. This multicenter study aimed to define proficiency milestones for SP-RARP across multiple surgeons using robust statistical methods.

Materials and methods

We retrospectively analyzed data from 327 patients who underwent SP-RARP performed by eight experienced multiport surgeons transitioning to the single-port platform across three U.S. centers (2019–2024). Learning curves for operative time (OT) and estimated blood loss (EBL) were evaluated using cumulative sum (CUSUM) analysis and piecewise linear regression. Piecewise multivariable logistic regression was used to adjust for confounders.

Results

Of the 327 cases, 111 were transperitoneal (TP) and 216 extraperitoneal (EP). No clear proficiency plateau was observed for the TP approach, with persistent variability in OT and EBL. In contrast, EP cases demonstrated breakpoints at 55 cases for OT (95% CI: 53.8–55.6; R² = 0.91) and 97 cases for EBL (95% CI: 92.3–101.1; R² = 0.97). Multivariable analysis confirmed a significant association between case volume and OT (p = 0.048), but not for EBL (p = 0.12).

Conclusion

This is the first multicenter study to evaluate SP-RARP learning curves using CUSUM and piecewise regression. Clear proficiency thresholds were identified for the EP approach (OT at 55, EBL at 97 cases), though only OT was confirmed after adjustment. No definitive plateau was reached for the TP approach, suggesting different learning dynamics.