The Surgical Learning Curve for Biochemical Recurrence After Robot-assisted Radical Prostatectomy.


Journal

European urology oncology
ISSN: 2588-9311
Titre abrégé: Eur Urol Oncol
Pays: Netherlands
ID NLM: 101724904

Informations de publication

Date de publication:
08 2023
Historique:
received: 26 05 2022
revised: 13 06 2022
accepted: 30 06 2022
medline: 31 7 2023
pubmed: 20 7 2022
entrez: 19 7 2022
Statut: ppublish

Résumé

Improved cancer control with increasing surgical experience-the learning curve-was demonstrated for open and laparoscopic prostatectomy. In a prior single-center study, we found that this might not be the case for robot-assisted radical prostatectomy (RARP). To investigate the relationship between prior experience of a surgeon and biochemical recurrence (BCR) after RARP. We retrospectively analyzed the data of 8101 patients with prostate cancer treated with RARP by 46 surgeons at nine institutions between 2003 and 2021. Surgical experience was coded as the total number of robotic prostatectomies performed by the surgeon before the patient operation. We evaluated the relationship of prior surgeon experience with the probability of BCR adjusting for preoperative prostate-specific antigen, pathologic stage, grade, lymph-node involvement, and year of surgery. Overall, 1047 patients had BCR. The median follow-up for patients without BCR was 33 mo (interquartile range: 14, 61). After adjusting for case mix, the relationship between surgical experience and the risk of BCR after surgery was not statistically significant (p = 0.2). The 5-yr BCR-free survival rates for a patient treated by a surgeon with prior 10, 250, and 1000 procedures performed were, respectively, 82.0%, 82.7%, and 84.8% (absolute difference between 10 and 1000 prior procedures: 1.6% [95% confidence interval: 0.4%, 3.3%). Results were robust to a number of sensitivity analyses. These findings suggest that, as opposed to open and laparoscopic radical prostatectomy, surgeons performing RARP achieve adequate cancer control in the early phase of their career. Further research should explore why the learning curve for robotic surgery differs from prior findings for open and laparoscopic radical prostatectomy. We hypothesize that surgical education, including simulation training and the adoption of objective performance metrics, is an important mechanism for flattening the learning curve. We investigated the relationship between biochemical recurrence after robot-assisted radical prostatectomy and surgeon's experience. Surgeons at an early stage of their career had similar outcomes to those of more experienced surgeons, and we hypothesized that surgical education in robotics might be an important determinant of such a finding.

Sections du résumé

BACKGROUND
Improved cancer control with increasing surgical experience-the learning curve-was demonstrated for open and laparoscopic prostatectomy. In a prior single-center study, we found that this might not be the case for robot-assisted radical prostatectomy (RARP).
OBJECTIVE
To investigate the relationship between prior experience of a surgeon and biochemical recurrence (BCR) after RARP.
DESIGN, SETTING, AND PARTICIPANTS
We retrospectively analyzed the data of 8101 patients with prostate cancer treated with RARP by 46 surgeons at nine institutions between 2003 and 2021. Surgical experience was coded as the total number of robotic prostatectomies performed by the surgeon before the patient operation.
OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS
We evaluated the relationship of prior surgeon experience with the probability of BCR adjusting for preoperative prostate-specific antigen, pathologic stage, grade, lymph-node involvement, and year of surgery.
RESULTS AND LIMITATIONS
Overall, 1047 patients had BCR. The median follow-up for patients without BCR was 33 mo (interquartile range: 14, 61). After adjusting for case mix, the relationship between surgical experience and the risk of BCR after surgery was not statistically significant (p = 0.2). The 5-yr BCR-free survival rates for a patient treated by a surgeon with prior 10, 250, and 1000 procedures performed were, respectively, 82.0%, 82.7%, and 84.8% (absolute difference between 10 and 1000 prior procedures: 1.6% [95% confidence interval: 0.4%, 3.3%). Results were robust to a number of sensitivity analyses.
CONCLUSIONS
These findings suggest that, as opposed to open and laparoscopic radical prostatectomy, surgeons performing RARP achieve adequate cancer control in the early phase of their career. Further research should explore why the learning curve for robotic surgery differs from prior findings for open and laparoscopic radical prostatectomy. We hypothesize that surgical education, including simulation training and the adoption of objective performance metrics, is an important mechanism for flattening the learning curve.
PATIENT SUMMARY
We investigated the relationship between biochemical recurrence after robot-assisted radical prostatectomy and surgeon's experience. Surgeons at an early stage of their career had similar outcomes to those of more experienced surgeons, and we hypothesized that surgical education in robotics might be an important determinant of such a finding.

Identifiants

pubmed: 35850976
pii: S2588-9311(22)00113-4
doi: 10.1016/j.euo.2022.06.010
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

414-421

Subventions

Organisme : NCI NIH HHS
ID : P30 CA008748
Pays : United States
Organisme : NCI NIH HHS
ID : P50 CA092629
Pays : United States

Informations de copyright

Copyright © 2022 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Auteurs

Carlo A Bravi (CA)

Department of Urology, Onze-Lieve-Vrouwziekenhuis Hospital, Aalst, Belgium; ORSI Academy, Ghent, Belgium; Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy. Electronic address: carloandrea.bravi@gmail.com.

Paolo Dell'Oglio (P)

Department of Urology, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy; Department of Urology, Antoni van Leeuwenhoek Hospital, The Netherlands Cancer Institute, Amsterdam, The Netherlands; Interventional Molecular Imaging Laboratory, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.

Elio Mazzone (E)

Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy.

Marcio C Moschovas (MC)

AdventHealth Global Robotics Institute, Celebration, FL, USA.

Ugo Falagario (U)

Urology and Renal Transplantation Unit, Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy.

Pietro Piazza (P)

Department of Urology, Onze-Lieve-Vrouwziekenhuis Hospital, Aalst, Belgium; Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.

Simone Scarcella (S)

Department of Urology, Onze-Lieve-Vrouwziekenhuis Hospital, Aalst, Belgium; Division of Urology, United Hospital of Ancona, School of Medicine Marche Polytechnic University, Ancona, Marche, Italy.

Christopher Bednarz (C)

Division of Urology, Virginia Commonwealth University, Richmond, VA, USA.

Luca Sarchi (L)

Department of Urology, Onze-Lieve-Vrouwziekenhuis Hospital, Aalst, Belgium.

Stefano Tappero (S)

Department of Urology, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy; Department of Urology, Policlinico San Martino Hospital, University of Genova, Genova, Italy.

Sophie Knipper (S)

Department of Urology, Onze-Lieve-Vrouwziekenhuis Hospital, Aalst, Belgium; Martini-Klinik Prostate Cancer Center, University Hospital Hamburg-Eppendorf, Hamburg, Germany.

Ruben De Groote (R)

Department of Urology, Onze-Lieve-Vrouwziekenhuis Hospital, Aalst, Belgium; ORSI Academy, Ghent, Belgium.

Daniel Sjoberg (D)

Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Riccardo Schiavina (R)

Division of Urology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy.

Nazareno Suardi (N)

Department of Urology, Policlinico San Martino Hospital, University of Genova, Genova, Italy.

Carlo Terrone (C)

Department of Urology, Policlinico San Martino Hospital, University of Genova, Genova, Italy.

Riccardo Autorino (R)

Division of Urology, Virginia Commonwealth University, Richmond, VA, USA.

Giuseppe Carrieri (G)

Urology and Renal Transplantation Unit, Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy.

Andrea Galosi (A)

Division of Urology, United Hospital of Ancona, School of Medicine Marche Polytechnic University, Ancona, Marche, Italy.

Antonio Galfano (A)

Department of Urology, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy.

Alberto Briganti (A)

Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy.

Francesco Montorsi (F)

Division of Oncology/Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy.

Vipul Patel (V)

AdventHealth Global Robotics Institute, Celebration, FL, USA.

Andrew Vickers (A)

Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Alexandre Mottrie (A)

Department of Urology, Onze-Lieve-Vrouwziekenhuis Hospital, Aalst, Belgium; ORSI Academy, Ghent, Belgium.

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