Literature DB >> 28117215

Learning curve of minimally invasive radical prostatectomy: Comprehensive evaluation and cumulative summation analysis of oncological outcomes.

Arjun Sivaraman1, Rafael Sanchez-Salas2, Dominique Prapotnich1, Kaixin Yu3, Fabien Olivier3, Fernando P Secin4, Eric Barret1, Marc Galiano1, François Rozet1, Xavier Cathelineau1.   

Abstract

BACKGROUND AND
OBJECTIVE: The primary objective was to evaluate the learning curve of minimally invasive radical prostatectomy (MIRP) in our institution and analyze the salient learning curve transition points regarding oncological outcomes.
METHODS: Clinical, pathologic, and oncological outcome data were collected from our prospectively collected MIRP database to estimate positive surgical margin (PSM) and biochemical recurrence (BCR) trends during a 15-year period from 1998 to 2013. All the radical prostatectomies (laparoscopic prostatectomy [LRP]/robot-assisted laparoscopic radical prostatectomy [RARP]) were performed by 9 surgeons. PSM was defined as presence of cancer cells at inked margins. BCR was defined as serum prostate-specific antigen >0.2ng/ml and rising or start of secondary therapy. Surgical learning curve was assessed with the application of Kaplan-Meier curves, Cox regression model, cumulative summation, and logistic model to define the "transition point" of surgical improvement.
RESULTS: We identified 5,547 patients with localized prostate cancer treated with MIRP (3,846 LRP and 1,701 RARP). Patient characteristics of LRP and RARP were similar. The overall risk of PSM in LRP was 25%, 20%, and 17% for the first 50, 50 to 350, and>350 cases, respectively. For the same population, the 5-year BCR rate decreased from 30% to 16.7%. RARP started 3 years after the LRP program (after approximately 250 LRP). The PSM rate for RARP decreased from 21.8% to 20.4% and the corresponding 5-year BCR rate decreased from 17.6% to 7.9%. The cumulative summation analysis showed significantly lower PSM and BCR at 2 years occurred at the transition point of 350 cases for LRP and 100 cases for RARP. In multivariable analysis, predictors of BCR were prostate-specific antigen, Gleason score, extraprostatic disease, seminal vesicle invasion, and number of operations (P<0.05). Patients harboring PSM showed higher BCR risk (23% vs. 8%, P< 0.05).
CONCLUSIONS: Learning curve trends in our large, single-center experience show correlation between surgical experience and oncological outcomes in MIRP. Significant reduction in PSM and BCR risk at 2 years is noted after the initial 350 cases and 100 cases of LRP and RARP, respectively.
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  CUSUM analysis; Laparoscopic prostatectomy; Learning curve; Prostate cancer; Robotic prostatectomy

Mesh:

Year:  2017        PMID: 28117215     DOI: 10.1016/j.urolonc.2016.10.015

Source DB:  PubMed          Journal:  Urol Oncol        ISSN: 1078-1439            Impact factor:   3.498


  6 in total

1.  A systematic review of the learning curve in robotic surgery: range and heterogeneity.

Authors:  I Kassite; T Bejan-Angoulvant; H Lardy; A Binet
Journal:  Surg Endosc       Date:  2018-09-28       Impact factor: 4.584

2.  Improving patient safety during introduction of novel medical devices through cumulative summation analysis.

Authors:  Vejay N Vakharia; Roman Rodionov; Andrew W McEvoy; Anna Miserocchi; Rachel Sparks; Aidan G O'Keeffe; Sebastien Ourselin; John S Duncan
Journal:  J Neurosurg       Date:  2018-02-16       Impact factor: 5.115

3.  Recommendations on robotic-assisted radical prostatectomy: a Brazilian experts' consensus.

Authors:  Eliney Ferreira Faria; Carlos Vaz Melo Maciel; André Berger; Anuar Mitre; Breno Dauster; Celso Heitor Freitas; Clovis Fraga; Daher Chade; Marcos Dall'Oglio; Francisco Carvalho; Franz Campos; Gustavo Franco Carvalhal; Gustavo Caserta Lemos; Gustavo Guimarães; Hamilton Zampolli; Joao Ricardo Alves; Joao Pádua Manzano; Marco Antônio Fortes; Marcos Flavio Holanda Rocha; Mauricio Rubinstein; Murilo Luz; Pedro Romanelli; Rafael Coelho; Raphael Rocha; Roberto Dias Machado; Rodolfo Borges Dos Reis; Stenio Zequi; Romulo Guida; Valdair Muglia; Marcos Tobias-Machado
Journal:  J Robot Surg       Date:  2021-01-11

Review 4.  Systematic review of learning curves in robot-assisted surgery.

Authors:  N A Soomro; D A Hashimoto; A J Porteous; C J A Ridley; W J Marsh; R Ditto; S Roy
Journal:  BJS Open       Date:  2019-11-29

5.  Surgeon heterogeneity significantly affects functional and oncological outcomes after radical prostatectomy in the Swedish LAPPRO trial.

Authors:  Martin Nyberg; Daniel D Sjoberg; Sigrid V Carlsson; Ulrica Wilderäng; Stefan Carlsson; Johan Stranne; Peter Wiklund; Gunnar Steineck; Eva Haglind; Jonas Hugosson; Anders Bjartell
Journal:  BJU Int       Date:  2020-09-29       Impact factor: 5.588

6.  Development and validation of non-guided bladder-neck and neurovascular-bundle dissection modules of the RobotiX-Mentor® full-procedure robotic-assisted radical prostatectomy virtual reality simulation.

Authors:  Jan Ebbing; Peter N Wiklund; Olof Akre; Stefan Carlsson; Mats J Olsson; Jonas Höijer; Maurice Heimer; Justin W Collins
Journal:  Int J Med Robot       Date:  2020-11-13       Impact factor: 2.547

  6 in total

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