Learning Curve in Robot-assisted Kidney Transplantation: Results from the European Robotic Urological Society Working Group

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Surgery in Motion

Learning Curve in Robot-assisted Kidney Transplantation: Results from the European Robotic Urological Society Working Group Andrea Gallioli a,b,y,*, Angelo Territo a,y, Romain Boissier a, Riccardo Campi c,d, Graziano Vignolini c,d, Mireia Musquera e, Antonio Alcaraz e, Karel Decaestecker f, Volkan Tugcu g, Davide Vanacore a,c, Sergio Serni c,d, Alberto Breda a a

Department of Urology, Fundaciò Puigvert, Autonomous University of Barcelona, Barcelona, Spain;

b

Fondazione IRCCS Ca’ Granda Ospedale Maggiore

Policlinico, Urology, Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy; c Department of Urological Robotic Surgery and Renal Transplantation, Careggi Hospital, University of Florence, Florence, Italy; d Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy; e Department of Urology, Hospital Clinic, Barcelona, Spain; f Department of Urology, Ghent University Hospital, Ghent, Belgium; g Department of Urology, Bakirkoy Dr. Sadi Konuk Training and Research Hospital, Istanbul, Turkey

Article info

Abstract

Article history: Accepted December 10, 2019

Background: Recently, robot-assisted kidney transplantation (RAKT) was recently introduced as renal replacement mini-invasive surgery. Objective: To report surgical technique, including tips and tricks, and the learning curve for RAKT. Design, setting, and participants: All consecutive RAKTs performed in the five highest-volume centers of the European Robotic Urological Society RAKT group were reviewed, and a step-bystep description of the technique was compiled. Surgical procedure: Surgeries were performed with Da Vinci Si/Xi. The patient was placed in the lithotomy position. The Trendelenburg position was set at 20–30 and the robot was docked between the legs. Measurements: Shewhart control charts and cumulative summation (CUSUM) graphs and trifecta were generated to assess the learning curve according to rewarming time (RWT), intra/postoperative complications, and renal graft function (glomerular filtration rate) on days 7 and 30, and at 1 yr. Linear regressions were performed to compare the learning curves of each surgeon. Results and limitations: Arterial anastomosis time was below the alarm/alert line in 93.3%/ 88.9% of RAKTs, while venous anastomosis time was below the alarm/alert line in 88.9%/73.9%. The nonanastomotic RWT exceeded +3 standard deviation (SD) in 24.7% of procedures and +2SD in 37.1%. In only 46% cases, the RWT was below the alert line. The ureteroneocystostomy time was below +2SD and +3SD in 87.9% and 90.2% of cases, respectively. CUSUM showed that the learning curve for arterial anastomosis required up to 35 (mean = 16) cases. Complications and delayed graft function rates decreased significantly and reached a plateau after the first 20 cases. Trifecta was achieved in 75% (24/32) of the cases after the first 34 RAKTs in each center. Conclusions: A minimum of 35 cases are necessary to reach reproducibility in terms of RWT, complications, and functional results. Patient summary: Robot-assisted kidney transplantation requires a learning curve of 35 cases to achieve reproducibility in terms of timing, complications, and functional results. Synergy between the surgeon and the assistant is crucial to reduce rewarming time. High-grade complications and delayed graft function are rare after ten surgeries. Hands-on training and proctorship are highly recommended. © 2019 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Associate Editor: Alexandre Mottrie Keywords: Kidney transplantation Learning curve Regional hypothermia Robot-assisted kidney transplantation Robotic surgery Vascular anastomosis Please visit www.europeanurology.com and www.urosource.com to view the accompanying video.

y

The authors Andrea Gallioli and Angelo Territo contributed equally in writing this paper. * Corresponding author. Department of Urology, Fundacio´ Puigvert, Calle Cartagena 340–350, Barcelona 08035, Spain. Tel. +34 934 169700.

https://doi.org/10.1016/j.eururo.2019.12.008 0302-2838/© 2019 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Please cite this article in press as: Gallioli A, et al. Learning Curve in Robot-assisted Kidney Transplantation: Results from the European Robotic Urological Society Working Group. Eur Urol (2020), https://doi.org/10.1016/j.eururo.2019.12.008

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1.

Introduction

Kidney transplantation (KT) is the best renal replacement treatment in patients with end-stage renal disease. The open approach is currently the preferred technique [1], but the robotic approach (robot-assisted KT [RAKT]) has been introduced recently [2–4]. The European Robotic Urological Society (ERUS) group has already demonstrated the feasibility of RAKT when the procedure is carried out by surgeons with experience in both open KT and robotic surgical skills [5,6]. However, RAKT remains a complex procedure. Technical mistakes may lead to graft loss, which were reported in the very early experience of each pioneer center in RAKT [5]. Nevertheless, RAKT has been proved to offer less morbidity, cosmetic benefit, and similar graft function results compared with open KT [6]. In this study, we reported the surgical technique, including tips and tricks, as refined during the initial experience gained in RAKT by the ERUS group. We also analyzed the learning curve in RAKT, evaluating surgical and functional results, and intra- and postoperative complications in the five highest-volume centers of the ERUS group. The second objective of the study was to evaluate the reproducibility of the learning curve. 2.

Patients and methods

2.1.

Study design

Data from the ERUS RAKT working group database were collected. The database received approval by the institutional review board of all the centers from 2015. From July 2015 to March 2019, five centers, each of which performed >20 RAKTs, were selected for the study: Fundaciò Puigvert (Barcelona), Hospital Clinic (Barcelona), Careggi Hospital (Florence), Ghent University Hospital (Ghent), and Bakirkoy Research Hospital (Istanbul). Inclusion criteria for the selection of eligible patients for RAKT have been reported previously [5]. In each center, the procedures were performed by a single surgeon skilled in both open KT and robotic surgeries (>100 procedures), such as partial nephrectomies, radical prostatectomies, and pyeloplasty. To compare the learning curves for RAKT, the surgical procedures were categorized as follows: group 1, first ten surgeries of each center; group 2, 11–20 surgeries; group 3, >20 surgeries.

2.2.1.

Preparation of the graft

Kidney grafts were provided mainly by living donation (92%); multiorgan retrieval in cadaveric donors occurred in 8% cases, as part of a recent RAKT program from deceased donors [9]. The graft vessels were carefully dissected on the back table. The angles of the vessels may be marked with a stitch. A 12-cm, 4.8-French, double-J stent was inserted in the ureter (Fig. 1A). The kidney was wrapped in gauze (with a central hole for the graft vessels) filled with ice slush to lower its temperature during the rewarming time (RWT), that is, the time between the peritoneal insertion of the kidney and the start of reperfusion (Fig. 1B and 1C) [7]. The anterior wall of the artery was shortened in order to avoid its kinking while rotating the kidney into the peritoneal pocket (Fig. 1D–F). 2.2.2.

Trocar positioning

Concurrently with living donor nephrectomy, a supraumbilical incision was performed in the recipient to introduce a 12-mm robotic camera port. The 8-mm robotic port for arm 2 was placed at least 9 cm from the camera, on a transverse line crossing the umbilicus. About 9 cm laterally to this port, a 12-mm port for the bed assistant was positioned at the same level as the camera port. The 8-mm port for arm 4 was placed contralaterally to the assistant port. In case of Da Vinci Si RAKT, the 8-mm port for arm 3 was positioned on the intersection between two lines: the line joining the pubis to arm 4 and the umbilicus– anterior superior iliac spine line (Fig. 2A). The usage of Da Vinci Xi allows positioning of all the robotic ports in line, avoiding the risks of conflicts of the robot Si and allowing the use of a very simple and reproducible port placement scheme (Fig. 2B). 2.2.3.

Creation of the peritoneal pouch and bladder dissection

After dissection of the exterior iliac vessels, the peritoneum was incised following a transverse line above the level of the appendix to allow proper retroperitonealization of the graft. Then, the bladder was filled with 250 ml of saline solution and prepared for ureteroneocystostomy. To choose the site of bladder incision, the scissors may be passed laterally to the vas deferens or round ligament to mimic the path of the graft ureter. 2.2.4.

Introduction of the graft in the abdominal cavity and

GelPOINT placement 2.2.

Surgical technique

Standardization of the surgical technique of RAKT was reported by Menon et al [7]. Subsequently, Breda et al [3] described technical variations that were adopted by the ERUS RAKT working group. Surgeries were performed with Da Vinci Si/Xi (Intuitive Surgical, Sunnyvale, CA, USA). The patient was placed in the lithotomy position [8]. The Trendelenburg position was set at 20–30 and the robot was docked between the legs. The tips and tricks of the procedure are summarized in Supplementary Table 1.

The technique provides a 6-cm periumbilical or a Pfannenstiel incision. The graft was inserted into the abdominal cavity with abundant (~200 ml) ice slush, to be reinjected every 10–15 min. The GelPOINT was placed, passed by a 12mm port and modified Toomey syringe for ice slush entry on the graft that was positioned and fixed using arm 4. In very selected cases, the graft was inserted transvaginally [10]. This indication may apply for its cosmetic and analgesic benefits, particularly for overweight/obese patients [11]. The main drawback of the technique is the impossibility to insert ice slush during RWT, which renders the vascular anastomosis time even more crucial. In order to

Please cite this article in press as: Gallioli A, et al. Learning Curve in Robot-assisted Kidney Transplantation: Results from the European Robotic Urological Society Working Group. Eur Urol (2020), https://doi.org/10.1016/j.eururo.2019.12.008

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Fig. 1 – (A–C) Preparation of the graft and (D–F) shortening of the anterior wall of the artery to reduce the risk of kinking after graft retroperitonealization.

maintain the pneumoperitoneum at 8 mmHg, the AirSeal system might be used. 2.2.5.

Venous anastomosis

The external iliac vein was clamped with Bulldog clamps. A venotomy was performed using the cold scissors. The graft renal vein was anastomosed in an end-to-side continuous fashion to the external iliac vein using a 6/0 Gore-Tex CV-6 (W.L. Gore and Associates Inc., Flagstaff, AZ, USA). The anastomosis was performed with a large needle driver on arm 2 and black diamond microforceps on arm 3 for righthanded surgeons. At the cranial angle of the anastomosis, the suture was knotted to fix the posterior wall of the anastomosis (Fig. 3A and 3B), and then the continuous suture is completed until the caudal angle. Just prior to knotting the anastomosis, the lumen was flushed with heparinized solution via a 4.8 F ureteric catheter. The graft vein was then clamped. 2.2.6.

Arterial anastomosis

After declamping of the iliac vein, the external iliac artery was clamped. Arteriotomy was done using a scalpel (for Da Vinci Si) at 1 o’clock to reduce the risk of arterial kinking and completed with an arterial punch. The arterial anastomosis technique resembles the venous one. In both arterial and venous anastomosis, at the caudal angle, the needle was passed in the external iliac vessel in an outside-inside direction, and then outside-inside through the graft vessel. At this point of the arterial anastomosis, the suture is not tied, and the needle is passed through the graft artery before tying the suture (Fig. 3C and 3D). A double graft artery is not considered a contraindication; the two branches may be anastomosed on the bench or separately [4,12]. A clamp was positioned on the graft artery while the external iliac artery was declamped. If no sign of leakage is observed, the graft vein and artery are declamped.

2.2.7.

Evaluation of graft perfusion

The graft perfusion is initially evaluated visually, observing the pulsatility of the graft artery and bleeding from the renal capsule. During the ureteral anastomosis, evidence of urine output from the graft is another sign of perfusion. Once the graft has been rotated for retroperitonealization, vascularization of the graft can be evaluated with Doppler ultrasound. The FireFly fluorescence with indocyanine green is a useful tool for the analysis of graft perfusion and, particularly, vascularization of the ureter, which is crucial to lower the risk of ureteral stenosis [13]. Then, the peritoneum was closed at a few locations with Hem-o-lok to keep the graft in the retroperitoneal position, thus permitting draining of lymph fluid into the peritoneal cavity. 2.2.8.

Ureteroneocystostomy

The ureteroneocystostomy was performed according to the Lich-Gregoir technique. After knotting the suture on the cranial angle, the end of the thread without the needle is moved by the Prograsp on arm 4 to expose the ureteral and bladder mucosa for a semicontinuous suture using a 4/0 Monocryl suture (Ethicon Inc., Cincinnati, OH, USA). The detrusor muscle antireflux tunnel was closed in a continuous fashion. Foley catheter was maintained for 1 wk, while double-J stent was usually removed on postoperative day 28. 2.3.

Study variables

The variables analyzed included the correlation between the learning curve and surgical results (console time, vascular anastomosis, and RWT), functional results, and intra/postoperative complications. The nonanastomotic time during RWT was defined as the time spent during RWT without performing the vascular anastomoses. The

Please cite this article in press as: Gallioli A, et al. Learning Curve in Robot-assisted Kidney Transplantation: Results from the European Robotic Urological Society Working Group. Eur Urol (2020), https://doi.org/10.1016/j.eururo.2019.12.008

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Fig. 2 – Trocar and patient positioning using (A) Si and (B) Xi RAKT in the right iliac fossa. Patient positioned in dorsal decubitus, legs in Allen stirrups (only Si/X), table in 20–30 Trendelenburg GelPOINT device at the level of the umbilicus through a 6-cm vertical periumbilical incision; camera trocar and ice applicator in the GelPOINT (eventually with 12-mm AirSeal port); three 8-mm robotic trocars in the lower abdomen, two in the left iliac fossa and one in the right iliac fossa. RAKT = robot-assisted kidney transplantation.

functional outcome taken into account was estimated glomerular filtration rate (eGFR) on postoperative days 7 and 30 and at 1 yr. The eGFR was calculated using the Modified Diet in Renal Disease equation (patient >18 yr old) [14,15]. The early (30-d) postoperative complication rate was reported according to the Clavien-Dindo classification [16]. To test the impact of the learning curve on the functional results (difference between pre- and postoperative eGFR on postoperative day 30) of RAKT, a trifecta was proposed (no major intra/postoperative complications, no delayed graft function (DGF) and RWT < mean + 2 standard deviation [SD] =48.6 min). 2.4.

values for Shewhart control charts were set referring to the functional outcomes of procedures reported by Breda et al [5] (+2SD = alert line, +3SD = alarm line). The values of RAKTs with RWT < 48 min were chosen (Table 1). Cumulative summation (CUSUM) graphs were generated to assess the learning curve, which is considered complete when the curve reaches a plateau. Linear regressions were performed to compare the learning curves of the different surgeons. All p values <0.05 were considered statistically significant. The statistical package SPSS V 25 and GraphPad Prism were used.

3.

Results

3.1.

Descriptive characteristics

Statistical evaluation

Continuous variables were presented as mean and SD, while absolute frequencies and percentages were used to describe the qualitative variables. Student t test was used for comparison of quantitative variables. The target and SD

A total of 183 patients submitted to RAKT were included in the study, as reported in Table 2. The median follow-up was 24 mo.

Please cite this article in press as: Gallioli A, et al. Learning Curve in Robot-assisted Kidney Transplantation: Results from the European Robotic Urological Society Working Group. Eur Urol (2020), https://doi.org/10.1016/j.eururo.2019.12.008

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Fig. 3 – (3A and 3B) Cranial angle of a venous anastomosis. (3C and 3D) caudal tying of an arterial anastomosis: the suture is passed through the iliac artery (A), the renal artery (B) and, again, through the graft artery in outside-inside fashion (C) to finally tight the knot (D)..

Table 1 – Target (mean), alert values (mean + 2SD), and alarm values (mean + 3SD). Minutes

Mean (SD)

Mean + SD

Mean + 2SD

Mean + 3SD

Rewarming time Arterial anastomosis Venous anastomosis Nonanastomotic time Ureteroneocystostomy eGFR (POD 30 – preoperative)

40.6 (4) 16.5 (3.6) 17.4 (2.7) 8.4 (3.2) 18.5 (4.8) 55 (10)

44.6 20.1 20.1 11.6 23.3 45

48.6 23.7 22.8 14.8 28.1 35

52.6 27.3 25.5 18 32.9 25

eGFR = estimated glomerular filtration rate; POD = postoperative day; SD = standard deviation.

3.2.

Shewhart control charts

Control charts are reported in Fig. 4. Arterial anastomosis time was below the alarm/alert line in 93.3%/88.9% of RAKTs, while venous anastomosis time was below the alarm/alert line in 88.9%/73.9%. The nonanastomotic RWT exceeded +3SD in 24.7% of procedures and +2SD in 37.1%. In only 46% of cases, the RWT was below the alert line. The ureteroneocystostomy time was below +2SD and +3SD in 87.9% and 90.2% of cases, respectively. The difference between preoperative eGFR and eGFR on postoperative day 30 was at least 25 ml/min in 86.4%. 3.3.

CUSUM analysis

CUSUM showed that the learning curve for arterial anastomosis required up to 35 (mean = 16) cases, with variation among the five centers (Fig. 5). A similar conclusion was reached for venous anastomosis, which may need >40 procedures (mean = 24). The plateau in the ureteroneocystostomy curve was reached within 30 RAKTs in four of the centers, and within 40 RAKTs in the remaining one (mean = 17). The plateau for RWT was reached within 23 procedures at center 1, 44 at center 2, and 38 at center 3 (mean 35 cases); centers 4 and 5 did not reach the plateau. Interestingly, the curves for nonanastomotic time during RWT resemble those for RWT. The learning curve in respect of kidney function was achieved after 20 cases in centers 1 and 5; in the other centers, no learning curve was observed as the slopes did not rise.

3.4.

Reproducibility

On the linear regression model, all the anastomotic times were comparable (Supplementary Fig. 1). The slopes in respect of nonanastomotic time during RWT were slightly different (p = 0.0006), as was also true for RWT itself (p = 0.007). 3.5.

Complications

Intraoperative complications occurred in 3/50 (6%) patients in group 1, 1/50 (2%) in group 2, and 3/83 (3.6%) in group 3 (p = 0.6); in 4/7 (57.1%) cases, conversion to open transplantation was required. In group 1, postoperative Clavien-Dindo grade III/IV complications were reported in 7/50 (14%) cases, while in groups 2 and 3 they occurred in 1/ 50 (2%) and 3/83 (3.6%) cases, respectively (p = 0.02; Supplementary Table 2). Three graft losses occurred in group 1, all because of massive arterial thrombosis in the 1 st postoperative week; none were reported in the other groups (3/183; 1.6%; p = 0.02). 3.6.

Graft function

The mean eGFR values on postoperative days 7 and 30 and at 1 yr were comparable between groups 1 and 2, and were better for group 3 versus 2 (all p < 0.05; Table 1). DGF was observed in 4% (2/50), 6% (3/50), and 2.4% (2/83) of cases in groups 1, 2, and 3, respectively (p = 0.5). In 4/7 (57.1%) patients, the graft came from a cadaveric donor.

Please cite this article in press as: Gallioli A, et al. Learning Curve in Robot-assisted Kidney Transplantation: Results from the European Robotic Urological Society Working Group. Eur Urol (2020), https://doi.org/10.1016/j.eururo.2019.12.008

Total cases

Baseline characteristics

Age (yr) Gender, n (%)

Male Female

2

BMI (kg/m ) Past surgical history, n (%)

Complications

Functional results

Pre-emptive renal transplantation, n (%) Total operative time (min) Warm ischemia time (min) Cold ischemia time (min) Rewarming time (min) Total ischemia time (min) Arterial anastomosis time (min) Venous anastomosis time (min) Vascular anastomosis time (min) Ureterovesical anastomosis time (min) Estimated blood loss (ml) Intraoperative Postoperative

Delayed graft function eGFR (ml/min/1.73 m2)

Clavien Clavien Clavien Clavien

I/II IIIa IIIb IV

Preoperative Day 7 Day 30 1 yr

Trifectaa BMI = body mass index; DGF = delayed graft function; RWT = rewarming time. Trifecta: no major intra/postoperative complications, no DGF and RWT < 48.6 min.

a

Group 2

Group 3

1–10 (n = 50)

11–20 (n = 50)

>20 (n = 83)

43 (13) 62% 38% 25 (4) 17% 73% 57% 228 (56) 3 (2) 56 (127) 51 (12) 106 (154) 17 (4) 18 (3) 36 (8) 20 (15) 153 (88) 7 (3.8%) 25 (13.7%) 1 (0.6%) 10 (5.5%) 0 7 (3.8%) 11 (4) 54 (22) 57 (21) 60 (20) 72 (39%)

41 (13) 54% 46% 24 (5) 70% 30% 68% 287 (75) 3 (2) 158 (280) 60 (16) 209 (268) 20 (7) 22 (7) 42 (13) 27 (10) 124 (71) 3 (6%) 5 (10%) 1 (2%) 6 (12%) 0 2 (4%) 10.36 (4) 51 (23) 58 (22) 61 (18) 6 (12%)

45 (14) 70% 30% 25 (4) 69% 31% 87% 240 (55) 3 (1) 237 (372) 50 (7) 279 (355) 17 (4) 18 (4) 35 (8) 20 (7) 119 (73) 1 (2%) 7 (14%) 0 1 (2%) 0 3 (6%) 10 (4) 50 (22) 52 (19) 53 (16) 19 (38%)

44 (13) 62% 38% 24 (4) 66% 33% 57% 228 (56) 2 (1.8) 56 (126) 46 (10) 106 (154) 17 (4) 18 (3) 36 (8.5) 20 (6) 150 (88) 3 (3.6%) 10 (14.4%) 1 (1.2%) 2 (2.4%) 0 2 (2.4%) 11 (4) 58 (21) 61 (21) 63 (23) 47 (56.6%)

Group 1 vs 2

Group 2 vs 3

0.15

0.45

0.14 0.99

0.07 0.42

0.65 <0.01 0.98 0.23 <0.01 0.27 0.01 <0.01 <0.01 <0.01 0.74 0.6 0.76 1 0.10 1 0.90 0.53 0.83 0.21 0.03 0.005

0.07 0.31 0.61 <0.01 <0.01 <0.01 0.67 0.74 0.98 0.22 0.03 0.9 0.90 1 0.90 1 0.36 0.40 0.03 0.03 <0.01 0.04

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Surgical results

Abdominal surgery Nonabdominal surgery

Group 1

(n = 183)

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Table 2 – Descriptive characteristics (demographic, surgical, and functional data) and group comparison.

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Fig. 4 – Shewhart control charts of (A and B) vascular anastomosis, (C) nonanastomotic time during RWT, and (D) RWT, (E) ureteroneocystostomy, and eGFR. Red line represents the target value, dashed lines + SD, +2SD (alert line), and +3SD (alarm line). eGFR = estimated glomerular filtration rate; POD = postoperative day; RWT = rewarming time; SD = standard deviation.

Fig. 5 – Cumulative summation analysis of (A and B) vascular anastomosis, (C) nonanastomotic time during RWT, and (D) RWT, (E) ureteroneocystostomy and eGFR. eGFR = estimated glomerular filtration rate; POD = postoperative day; RWT = rewarming time.

3.7.

Trifecta outcomes

On linear regression, the trifecta correctly predicted the functional outcomes of RAKT (B = 8.5, 95% confidence interval 1.23–15.8; p = 0.02). Trifecta was achieved in 6/50 (12%), 19/50 (38%), and 47/ 83 (56.6%) of groups 1, 2, and 3, respectively (Table 2). Of note, trifecta was attained in 24/32 (75%) patients after the first 34 RAKTs of each center versus 48/151 (31.7%) cases before that threshold (p < 0.0001). 4.

Discussion

In this study, we have reported the surgical technique resulting from the early experience of the highest-volume centers in the ERUS RAKT group, and determined that a minimum of 35 cases is necessary to reach reproducibility in the surgical time, complications, and functional results.

The anastomotic time and RWT can potentially affect the functional outcomes of KT [17,18]. For this reason, we studied a target population represented by cases with <48 min of RWT [5] and evaluated 183 consecutive patients who underwent RAKT in a multicenter prospective setting. To the best of our knowledge, this is the largest series in the literature. The results demonstrated that the learning curve in vascular anastomosis ranged between zero and 40 cases, with procedures being under control (below the alert line) in 73.9–88.9% of RAKTs. Similar results could be observed for ureteroneocystostomy (90.2% below the alert line), with a learning curve comprising 30 surgeries in four of the five centers. Furthermore, the curves were reproducible. The RWT and the nonanastomotic time curves were most variable, with similar learning curves and the highest percentage of procedures being out of control (46.9% and 24.7%, respectively). The nonanastomotic time spent during RWT is mainly represented by positioning of the graft and

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by the synergic work with the assistant. It depends on the surgical teamwork, which renders it more variable and highlights the importance of having an established surgical team for this demanding intervention. It appears to be as important as the vascular anastomosis time and to be the most improvable time. Although Sood et al [19] reported that there was no learning curve for experienced surgeons, it must be underlined that the SD values were significantly wider (45 + 15, +30, +45) for RWT, with the consequence that RWT was suggested to be under control in all surgeries. This may be questionable, as maintenance of low graft temperatures within the peritoneal cavity represents one of the main challenges of RAKT. More recently, Ahlawat et al [20] reported a short learning curve in RAKT for experienced surgeons, with continuing improvements in skill up to 20– 25 cases. The functional results do not seem to be affected by RWT (86.3% cases were under control), possibly due to regional hypothermia [7,19]. DGF was reported in <2% of cases. Intraoperative complications occurred in 6% of patients in group 1 and in 2–3.6% in groups 2 and 3. The rate of ClavienDindo grade III/IV complications was 14% during the first ten RAKTs, but only 3% after this threshold. Trifecta required a minimum of 35 RAKTs to be achieved in 75% of patients. Graft loss occurred during the first ten RAKTs due to arterial thrombosis. However, the rate of arterial graft thrombosis (1.6%) in our population is comparable with the open KT experience (0.5–3.5%) [21]. The present study is not devoid of limitations. First, it relates to very skilled surgeons treating a population mainly represented by pre-emptive patients. Second, evaluation of the learning curve analyzing the timing is open to some debate. The surgical technique, including tips and tricks, described above was acquired during the learning phase by each surgeon, and robot training has been proved to be significantly associated with improved task completion times [19]. With the twin aim of improving general robotic skills and providing specific hands-on training using porcine models, ORSI academy courses were organized, providing a good opportunity to reduce the risk of the first RAKTs. In view of the surgical complexity of the procedure, proctorship should be considered mandatory.

Author contributions: Andrea Gallioli had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Gallioli, Breda, Territo. Acquisition of data: Territo, Musquera, Campi, Vignolini, Decaestecker, Tugcu. Analysis and interpretation of data: Gallioli, Boissier. Drafting of the manuscript: Territo, Boissier, Vanacore. Critical revision of the manuscript for important intellectual content: Breda. Statistical analysis: Gallioli. Obtaining funding: None. Administrative, technical, or material support: None. Supervision: Breda, Serni, Alcaraz. Other: None. Financial disclosures: Andrea Gallioli certifies that all conflicts of interest, including specific financial interests and relationships and affiliations relevant to the subject matter or materials discussed in the manuscript (eg, employment/affiliation, grants or funding, consultancies, honoraria, stock ownership or options, expert testimony, royalties, or patents filed, received, or pending), are the following: None. Funding/Support and role of the sponsor: None. Acknowledgments: The authors would like to thank Dana Kuefner for voice over of the video.

Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j. eururo.2019.12.008.

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5.

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