Role of Bed Assistant During Robot-assisted Radical Prostatectomy: The Effect of Learning Curve on Perioperative Variables.
Assistant surgeon
Learning curve
Prostate cancer
Radical prostatectomy
Robot assisted
Training
Journal
European urology focus
ISSN: 2405-4569
Titre abrégé: Eur Urol Focus
Pays: Netherlands
ID NLM: 101665661
Informations de publication
Date de publication:
15 03 2020
15 03 2020
Historique:
received:
24
07
2018
revised:
08
09
2018
accepted:
03
10
2018
pubmed:
15
10
2018
medline:
21
5
2021
entrez:
15
10
2018
Statut:
ppublish
Résumé
A remote interaction between a console surgeon (CS) and a bedside surgeon (BS) makes the role of the latter critical. No conclusive data are reported about the length of the learning curve of a BS. To highlight the role of a BS during robot-assisted radical prostatectomy (RARP) and to analyze the effect of the learning curve of a BS on intra- and postoperative outcomes. From June 2013 to September 2016, 129 RARPs were performed by one expert CS (>1000 RARPs) and two BSs (residents). According to the learning curve of the BS, the patients were divided into three groups: group 1 (first 20 procedures), group 2 (21-40 procedures), and group 3 (>40 procedures). Preoperative variables, pathological data, operating time (OT), blood loss (BL), number of lymph nodes excised (LE), length of hospital stay (LHS), and time to catheter removal (CR) were analyzed. Linear/logistic regression analyses tested the impact of BS experience on surgical outcomes. T test and chi-square test compared the outcomes of the two BSs. Perfect interaction between CSs and BSs are requested to obtain the optimal exposure and avoid any conflict. On the linear regression model, BS learning curve was not related to OT, BL, LHS, and CR, but was related to LE (r In this study, BS learning curve does not appear to influence the surgical outcomes; good experience of the CS was probably the explanation. In our experience, it is the primary surgeon who dictates the perioperative outcomes during robot-assisted radical prostatectomy.
Sections du résumé
BACKGROUND
A remote interaction between a console surgeon (CS) and a bedside surgeon (BS) makes the role of the latter critical. No conclusive data are reported about the length of the learning curve of a BS.
OBJECTIVE
To highlight the role of a BS during robot-assisted radical prostatectomy (RARP) and to analyze the effect of the learning curve of a BS on intra- and postoperative outcomes.
DESIGN, SETTING, AND PARTICIPANTS
From June 2013 to September 2016, 129 RARPs were performed by one expert CS (>1000 RARPs) and two BSs (residents). According to the learning curve of the BS, the patients were divided into three groups: group 1 (first 20 procedures), group 2 (21-40 procedures), and group 3 (>40 procedures).
OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS
Preoperative variables, pathological data, operating time (OT), blood loss (BL), number of lymph nodes excised (LE), length of hospital stay (LHS), and time to catheter removal (CR) were analyzed. Linear/logistic regression analyses tested the impact of BS experience on surgical outcomes. T test and chi-square test compared the outcomes of the two BSs.
RESULTS AND LIMITATIONS
Perfect interaction between CSs and BSs are requested to obtain the optimal exposure and avoid any conflict. On the linear regression model, BS learning curve was not related to OT, BL, LHS, and CR, but was related to LE (r
CONCLUSIONS
In this study, BS learning curve does not appear to influence the surgical outcomes; good experience of the CS was probably the explanation.
PATIENT SUMMARY
In our experience, it is the primary surgeon who dictates the perioperative outcomes during robot-assisted radical prostatectomy.
Identifiants
pubmed: 30316824
pii: S2405-4569(18)30298-0
doi: 10.1016/j.euf.2018.10.005
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
397-403Informations de copyright
Copyright © 2018 European Association of Urology. Published by Elsevier B.V. All rights reserved.