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Role of Bed Assistant During Robot-assisted Radical Prostatectomy: The Effect of Learning Curve on Perioperative Variables.
European Urology Focus 2018 October 11
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 (r2 =0.09; p=0.03). On multivariate analyses, no correlation between BS experience and OT, BL, LHS, CR, LE, margin status, and complications (all p>0.05) was found. Comparing the two BSs, no difference was found for the abovementioned outcomes in the first 40 surgeries (all p>0.05). Study limitations include the limited cohort of patients and its retrospective nature.
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.
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 (r2 =0.09; p=0.03). On multivariate analyses, no correlation between BS experience and OT, BL, LHS, CR, LE, margin status, and complications (all p>0.05) was found. Comparing the two BSs, no difference was found for the abovementioned outcomes in the first 40 surgeries (all p>0.05). Study limitations include the limited cohort of patients and its retrospective nature.
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.
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