Literature DB >> 24053179

Learning curves for urological procedures: a systematic review.

Hamid Abboudi1, Mohammed Shamim Khan, Khurshid A Guru, Saied Froghi, Gunter de Win, Hendrik Van Poppel, Prokar Dasgupta, Kamran Ahmed.   

Abstract

OBJECTIVE: To determine the number of cases a urological surgeon must complete to achieve proficiency for various urological procedures. PATIENT AND METHODS: The MEDLINE, EMBASE and PsycINFO databases were systematically searched for studies published up to December 2011. Studies pertaining to learning curves of urological procedures were included. Two reviewers independently identified potentially relevant articles. Procedure name, statistical analysis, procedure setting, number of participants, outcomes and learning curves were analysed.
RESULTS: Forty-four studies described the learning curve for different urological procedures. The learning curve for open radical prostatectomy ranged from 250 to 1000 cases and for laparoscopic radical prostatectomy from 200 to 750 cases. The learning curve for robot-assisted laparoscopic prostatectomy (RALP) has been reported to be 40 procedures as a minimum number. Robot-assisted radical cystectomy has a documented learning curve of 16-30 cases, depending on which outcome variable is measured. Irrespective of previous laparoscopic experience, there is a significant reduction in operating time (P = 0.008), estimated blood loss (P = 0.008) and complication rates (P = 0.042) after 100 RALPs.
CONCLUSIONS: The available literature can act as a guide to the learning curves of trainee urologists. Although the learning curve may vary among individual surgeons, a consensus should exist for the minimum number of cases to achieve proficiency. The complexities associated with defining procedural competence are vast. The majority of learning curve trials have focused on the latest surgical techniques and there is a paucity of data pertaining to basic urological procedures.
© 2013 The Authors. BJU International © 2013 BJU International.

Entities:  

Keywords:  education; learning curves; training; urology

Mesh:

Year:  2013        PMID: 24053179     DOI: 10.1111/bju.12315

Source DB:  PubMed          Journal:  BJU Int        ISSN: 1464-4096            Impact factor:   5.588


  50 in total

1.  Development and validation of surgical training tool: cystectomy assessment and surgical evaluation (CASE) for robot-assisted radical cystectomy for men.

Authors:  Ahmed A Hussein; Kevin J Sexton; Paul R May; Maxwell V Meng; Abolfazl Hosseini; Daniel D Eun; Siamak Daneshmand; Bernard H Bochner; James O Peabody; Ronney Abaza; Eila C Skinner; Richard E Hautmann; Khurshid A Guru
Journal:  Surg Endosc       Date:  2018-04-13       Impact factor: 4.584

Review 2.  The safety of urologic robotic surgery depends on the skills of the surgeon.

Authors:  Erika Palagonia; Elio Mazzone; Geert De Naeyer; Frederiek D'Hondt; Justin Collins; Pawel Wisz; Fijs W B Van Leeuwen; Henk Van Der Poel; Peter Schatteman; Alexandre Mottrie; Paolo Dell'Oglio
Journal:  World J Urol       Date:  2019-08-19       Impact factor: 4.226

3.  Peri-operative efficacy and long-term survival benefit of robotic-assisted radical cystectomy in septuagenarian patients compared with younger patients: a nationwide multi-institutional study in Japan.

Authors:  Hideto Iwamoto; Shuichi Morizane; Takuya Koie; Ryoichi Shiroki; Mutsushi Kawakita; Tatsuo Gondo; Kazumasa Matsumoto; Tomonori Habuchi; Hiroshi Sunada; Yusuke Endo; Hisashi Noma; Atsushi Takenaka; Hiroomi Kanayama
Journal:  Int J Clin Oncol       Date:  2019-05-23       Impact factor: 3.402

4.  Predictive factors for immediate continence after radical prostatectomy.

Authors:  G Hatiboglu; D Teber; D Tichy; S Pahernik; B Hadaschik; J Nyarangi-Dix; M Hohenfellner
Journal:  World J Urol       Date:  2015-05-20       Impact factor: 4.226

5.  Development, validation and clinical application of Pelvic Lymphadenectomy Assessment and Completion Evaluation: intraoperative assessment of lymph node dissection after robot-assisted radical cystectomy for bladder cancer.

Authors:  Ahmed A Hussein; Nobuyuki Hinata; Shiva Dibaj; Paul R May; Justen D Kozlowski; Hassan Abol-Enein; Ronney Abaza; Daniel Eun; Mohamed S Khan; James L Mohler; Piyush Agarwal; Kamal Pohar; Richard Sarle; Ronald Boris; Sridhar S Mane; Alan Hutson; Khurshid A Guru
Journal:  BJU Int       Date:  2017-01-18       Impact factor: 5.588

Review 6.  Learning Curves for Robotic Surgery: a Review of the Recent Literature.

Authors:  Giorgio Mazzon; Ashwin Sridhar; Gerald Busuttil; James Thompson; Senthil Nathan; Tim Briggs; John Kelly; Greg Shaw
Journal:  Curr Urol Rep       Date:  2017-09-23       Impact factor: 3.092

7.  Positive surgical margin rates during the robot-assisted laparoscopic radical prostatectomy learning curve of an experienced laparoscopic surgeon.

Authors:  Anthony F Adili; Julia Di Giovanni; Emma Kolesar; Nathan C Wong; Jen Hoogenes; Shawn Dason; Bobby Shayegan
Journal:  Can Urol Assoc J       Date:  2017-11       Impact factor: 1.862

8.  Development and Validation of Objective Performance Metrics for Robot-Assisted Radical Prostatectomy: A Pilot Study.

Authors:  Andrew J Hung; Jian Chen; Anthony Jarc; David Hatcher; Hooman Djaladat; Inderbir S Gill
Journal:  J Urol       Date:  2017-07-29       Impact factor: 7.450

9.  Comparison of surgical, oncological, and functional outcomes of robot-assisted and laparoscopic radical prostatectomy in patients with prostate cancer.

Authors:  Abdurrahman İnkaya; Ahmet Tahra; Resul Sobay; Ali Kumcu; Eyüp Veli Küçük; Uğur Boylu
Journal:  Turk J Urol       Date:  2019-11-01

10.  The effect of wide resection during radical prostatectomy on surgical margins.

Authors:  Luke T Lavallée; Andrew Stokl; Sonya Cnossen; Ranjeeta Mallick; Chris Morash; Ilias Cagiannos; Rodney H Breau
Journal:  Can Urol Assoc J       Date:  2016 Jan-Feb       Impact factor: 1.862

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