The role of the assistant during robot-assisted partial nephrectomy: does experience matter?

Robotic-assisted laparoscopic radical hysterectomy for early-stage cervical cancer: The more experienced the bedside assistant, the better?

Background

Aim to investigate the impact of bedside assistant's work experience and learning curve on the short-term safety and efficacy in robotic-assisted laparoscopic radical hysterectomy for early-stage cervical cancer.

Methods

Our research retrospectively retrieved 120 cases of early-stage cervical cancer patients who underwent robotic-assisted laparoscopic radical hysterectomy at the First Affiliated Hospital of Guangxi Medical University. According to the different work experiences of the two bedside assistants (BA), patients were divided into a research group (inexperienced BA 1) and a control group (experienced BA 2). Furthermore, the learning curves of these BAs were plotted separately and divided into two distinct phases by cumulative summation: the first learning phase and the second master phase.

Result

In terms of work experience, comparing BA 1 with BA 2 who was more experienced, although the average operative time was prolonged by 29 min (P＜0.001), it did not increase the incidence of operative complication [24.4 % VS 29.1 %, P = 0.583], positive resection margin [4.9 % VS 7.6 %, P = 0.714], intraoperative organ damage [0 % VS 2.5 %, P = 0.546] and there was no significant difference in the number of lymph nodes [19 VS 15, P = 0.103]. Additionally, comparing two distinct phases of the same bedside assistant, there was no significant increasing rate in terms of operative complication, positive resection margin, intraoperative organ damage, and the number of lymph nodes (P＞0.05) neither BA 1 nor BA 2, except for a slight extension of operative time about 20 min in learning phase (P＜0.05).

Conclusion

In robotic-assisted laparoscopic radical hysterectomy for early-stage cervical cancer, work inexperience and the learning phase of BA only result in a slight extension of operative time, without causing worse short-term surgical outcomes.

Experience matters for robotic assistance: an analysis of case data

Many robotic procedures require active participation by assistants. Most prior work on assistants' effect on outcomes has been limited in procedural focus and scope, with studies reporting differing results. Knowing how assistant experience affects operating room time could inform operating room case scheduling and provide an impetus for additional assistant training. As such, this retrospective cohort study aimed to determine the association between assistant experience and operating room time for 2291 robotic-assisted operations performed from 2016 to 2022 at our institution. Linear regression showed a significant association between the presence of a junior resident and increased case length differential with an increase of 26.9 min (p = 0.01). There were no significant associations between the presence of a senior resident (p = 0.52), presence of a fellow (p = 0.20), or presence of a physician assistant (p = 0.43) and case length differential. The finding of increased operating room time in the presence of a junior resident during robotic cases supports consideration of the adoption of formal assistant training programs for residents to improve efficiency.

The effect of bedside assistant technical performance on outcomes in robotic surgery

Technical performance in surgery has been associated with patient outcomes. Robotic surgery is unique in that both a console surgeon and bedside surgeon are required. A systematic review according to PRISMA guidelines identified all pertinent literature regarding skill level of the bedside assistant with regards to patient outcomes in robotic surgery. 10 studies met inclusion criteria. In all studies, the skill level of the assistant was based on experience, either by post-graduate year of the resident or number of cases previously performed by the assistant. Five studies reported significant, shorter operative times with increasing experience of the bedside assistant. No study reported a significant difference in postoperative outcomes. The existing literature fails to show improved patient outcomes with more experienced bedside assistants in robotic surgery. Metrics should be developed to measure actual technical performance of the bedside assistant rather than using arbitrary assessments of experience in future studies.

Seniority of the assistant surgeon and perioperative outcomes in robotic-assisted proctectomy for rectal cancer

The background of this study is to evaluate the impact of the assistant surgeon's in robotic-assisted proctectomy (RAP) on perioperative outcomes. A retrospective analysis of all patients who underwent RAP for rectal adenocarcinoma between 2011 and 2020 was conducted. Patient cohort was divided into three groups based on the assistant surgeon's training level: post-graduate years (PGY) 1-3 surgical residents (Group 1), PGY 4-5 surgical residents (Group 2), and board-certified general surgeons (Group 3). Overall, 175 patients were included in the study: 29 patients (17%) in Group 1, 84 (48%) in Group 2, and 62 (35%) in Group 3. The median tumor distance from the anal verge was 8 cm in all groups (p = 0.73). The median operative time was similar across all groups: 290, 291, and 281 min in Groups 1, 2, and 3, respectively (p = 0.69). In a multivariable analysis, the lack of association between assistant training level and procedure time maintained when adjusting for the year of operation (p = 0.84). Patients operated with junior residents as assistant surgeons (Group 1) had a more postoperative complications (p = 0.01) and a slightly longer hospital length of stay [7 days, interquartile range (IQR) 3], compared to those operated by assistant surgeons that were senior residents or attendings (6 IQR 2.5, and 6 IQR 2 in Groups 2 and 3, respectively; p = 0.02). Conversion rates (p = 0.12), intraoperative complications (p = 0.39), major postoperative complications (Clavien-Dindo ≥ 3; p = 0.32), 30-day readmission (p = 0.45), and mortality (p = 0.99) were similar between the groups. Robotic-assisted proctectomy performed with the assistance of a junior resident was found to be correlated with worse postoperative outcomes compared to more experienced assistants. No difference was seen in intraoperative outcomes.

The role of the bedside assistant in robot-assisted surgery: A critical synthesis

INTRODUCTION

Robot-assisted surgery has grown exponentially since its inception and first approval in the United States in the year 2000. The surgeon operating with the assistance of the robot sits remotely to the patient and another practitioner assists at the bedside. The role of the bedside assistant and the training that is required to undertake this role are understudied topics.

AIM

To explore the functions, training and professional development of the bedside assistant in robot-assisted surgery and propose the necessary foundations for the safe enactment of the role in the United Kingdom.

METHODS

Through critical interpretative synthesis, relevant literature was systematically searched and analysed to inform integration of evidence.

RESULTS

Seventy-three studies were retrieved from the literature, across several health care disciplines and surgical specialities. These were critically analysed to inform a theoretically sound account grounded on evidence.

CONCLUSION

The role, functions and skills of the bedside assistant in robot-assisted surgery vary across contexts. These were analysed and critically synthetised to produce several keys to the success of bedside assistants in robot-assisted surgery in the context of the United Kingdom and of its national regulations.

Role and Training of the Bedside Surgeon in Robotic Surgery: A Survey Among French Urologists-in-Training

Introduction

Due to the development of robotic surgery, surgeons operating at the console are physically separated from the patient. They must rely on help from an assistant, also called bedside surgeon. This study aimed to investigate the habits and practices of French urologic residents when performing this role and to determine whether they expected specific training to qualify as bedside surgeons.

Materials and Methods

A web-based survey was sent to French urology residents and fellows using the mailing-list of the French Association of Urologists in Training (AFUF).

Results

Over a 3-month period, 86 residents and fellows responded to the survey. Seventy-five (87.2%) thought that an experience as bedside surgeon was useful to acquire console surgeon status and, more specifically, 48.2% of them indicated that this former experience was vital. Nearly 64% considered that learning robotic surgery was essential during residency and fellowship. Overall, 91.9% believed that bedside surgeons should receive a formal training. They were 69.7% to need 5 to 10 procedures to feel confident as bedside surgeon. Almost 75% underwent laparoscopic training on simulators. Having access to a surgery school significantly increased the probability of receiving laparoscopic training on simulators (p = 0.0033).

Conclusion

French urology residents and fellows expect a specific training program for bedside surgeons to increase their level of confidence and to get familiar with the device during their first robotic procedures. For them, a bedside surgeon program on a regional/national level would be required.

Learning curve for robotic bedside assistance for rectal cancer: application of the cumulative sum method

BACKGROUND

This investigation assesses the learning curve for dedicated bedside assistance at a facility that recently adopted robot-assisted rectal resection.

METHODS

Data from patients with rectal cancer who underwent robotic rectal resections from September 2019 through April 2020 were retrospectively analyzed. Before starting robotic surgery, we set the rule that a console surgeon would not enter the sterile field and all of those maneuvers would be left to a dedicated physician. Docking time was analyzed using the cumulative sum (CUSUM) method to evaluate the learning curve. Different phases in the learning curve were identified according to CUSUM plot configuration. A comparison was made of phases 1 and 2 combined, and phase 3.

RESULT

The procedures were performed in 30 patients. Median docking time, console time was 13 min. A total of nine patients had histories of abdominal surgery. CUSUM analysis of docking time demonstrated 3 phases. Each docking time was longer in Phase 1 (the first 3 cases) than the average docking time over the all cases. The docking time in Phase 2 (the 9 middle cases) approximated the average time over the all cases. Phase 3 (the remaining 18 cases) showed further improvement of the docking procedure and time was reduced. A comparison of Phases 1 and 2 combined, and Phase 3, revealed that Phase 3 had a significantly higher rate of history of abdominal surgery.

CONCLUSION

Docking manipulation proficiency was achieved in approximately 10 cases without the influence of surgical difficulty.

Implementation of a standardized robotic assistant surgical training curriculum

Since 2000, robotic-assisted surgery has rapidly expanded into almost every surgical sub-specialty. Despite the popularity of robotic surgery across the United States, a national consensus for standardized training and education of robotic surgeons or surgical teams remains absent. In this quality improvement initiative, a novel, stepwise iterative Robotic Assistant Surgical Training (RAST) curriculum was developed to broaden and standardize robotic bedside assistant training. Thirteen voluntary participants, capable of fulfilling the bedside assistant role, were evaluated to determine if RAST enhanced the learner's self-perceived level of confidence and comfort in their role as bedside assistant. A pre- and post-RAST training survey and a between-stages repeated-measures survey were conducted. All learner participants reported statistically significant increases in confidence and comfort after RAST training, (p =  < 0.001), and between each stage, F (2, 24 = 60.47, p < .001; [Formula: see text] = 0.834). Participant feedback regarding curriculum improvement was obtained, suggesting the desire for more training and practice, in smaller groups of 2-3 participants. One hundred percent of participants felt RAST was beneficial and that it should be implemented as standardized training during onboarding for all robotic bedside assistants. Thus, a standardized, stepwise iterative robotic bedside assistant curriculum increases learner preparedness, comfort, and confidence, safely away from the patient bedside.

Surgical benchmarks, mid-term oncological outcomes, and impact of surgical team composition on simultaneous enbloc robot-assisted radical cystectomy and nephroureterectomy

Background

Simultaneous urothelial cancer manifestation in the lower and upper urinary tract affects approximately 2% of patients. Data on the surgical benchmarks and mid-term oncological outcomes of enbloc robot-assisted radical cystectomy and nephro-ureterectomy are scarce.

Methods

After written informed consent was obtained, we prospectively enrolled consecutive patients undergoing enbloc radical cystectomy and nephro-ureterectomy with robotic assistance from the DaVinci Si-HD® system in a prospective institutional database and collected surgical benchmarks and oncological outcomes. Furthermore, as one console surgeon conducted all the procedures, whereas the team providing bedside assistance was composed ad hoc, we assessed the impact of this approach on the operative duration.

Results

Nineteen patients (9 women), with a mean age of 73 (SD: 7.5) years, underwent simultaneous enbloc robot-assisted radical cystectomy and nephro-ureterectomy. There were no cases of conversion to open surgery. In the postoperative period, we registered 2 Clavien-Dindo class 2 complications (transfusions) and 1 Clavien-Dindo class 3b complication (port hernia). After a median follow-up of 23 months, there were 3 cases of mortality and 1 case of metachronous urothelial cancer (contralateral kidney).The total operative duration did not decrease with increasing experience (r = 0.174, p = 0.534). In contrast, there was a significant, inverse, strong correlation between the console time relative to the total operative duration and the number of conducted procedures after adjusting for the degree of adhesions and the type of urinary diversion(r = -0.593, p = 0.02).

Conclusions

These data suggest that en bloc simultaneous robot-assisted radical cystectomy and nephro-ureterectomy can be safely conducted with satisfactory mid-term oncological outcomes. With increasing experience, improved performance was detectable for the console surgeon but not in terms of the total operative duration. Simulation training of all team members for highly complex procedures might be a suitable approach for improving team performance.

Trial registration: Not applicable.

Quantifying the "Assistant Effect" in Robotic-Assisted Radical Prostatectomy (RARP): Measures of Technical Performance

PURPOSE

Surgeons are reliant on the bedside assistant during robotic surgeries. Using a modified global rating scale (GRS), we aim to assess the association between an assistant's technical skill on surgeon performance in Robotic-Assisted Radical Prostatectomy (RARP).

METHODS

Prospective, intraoperative video from RARP cases at three centers were collected. Baseline demographic and RARP-experience data were collected from participating surgeons and trainees. The dissection of the prostatic pedicle and neurovascular bundle step (NVB) was analyzed. Expert analysts scored the console surgeon performance using the Global Evaluative Assessment of Robotic Skills (GEARS), and the bedside assistant performance using a modified Objective Structured Assessment of Technical Skills (aOSATS). The primary outcome is the association between console surgeon performance, as measured by GEARS, and assistant skill, as measured by aOSATS. Spearman's rho correlations were used to test the relationship between assistant and surgeon technical performance, and a multivariable linear regression model was created to test this association while controlling for patient factors.

RESULTS

92 RARP cases were available for the analysis, comprising 14 console surgeons and 22 different bedside assistants. In only 5 (5.4%) cases, the neurovascular bundle step was completed by a trainee, and in 13 (14.1%) of cases, a staff-level surgeon acted as the bedside assistant. aOSATS score was significantly associated with robotic console experience (P = 0.011), and prior laparoscopic experience (P < 0.001). Assistant aOSATS score showed a weak but significant correlation with surgeon GEARS score during the neurovascular bundle step (spearman's rho = 0.248, P = 0.028). On linear regression, aOSATS remained a significant predictor of console surgeon performance (P = 0.016), after controlling for patient age and BMI, prostate volume, tumor stage, and presence of nerve-sparing.

CONCLUSIONS

This is the first study to assess the association between assistant technical skill and surgeon performance in RARP. Additionally, we have provided validity evidence for a modified OSATS global rating scale for training and assessing bedside assistant performance.

Robot-assisted minimally invasive esophagectomy (RAMIE): tips and tricks from the bedside assistant view-expert experiences

The role of bedside assistants in robot-assisted minimally invasive esophagectomy is important. It includes knowledge of the procedure, knowledge of the da Vinci Surgical System, skills in laparoscopy, and good communicative skills. An experienced bedside assistant will likely improve efficiency and safety of robot-assisted minimally invasive esophagectomy.

The role of bedside assistant during robot assisted radical prostatectomy: Is more experience better? Analysis on perioperative and clinical outcomes

OBJECTIVES

To analyze the impact of the bedside assistant's experience during RARP. It is believed that the outcome of robotic surgery during Robot Assisted Radical Prostatectomy (RARP) for prostate cancer depends not only on the console surgeon's experience.

MATERIALS AND METHODS

All consecutive RARPs from January 2017 to March 2018 were sourced from a prospectively maintained database. All cases were performed by the same surgeon. He was supported by three bedside assistants: one with bedside and console experience, one only with relevant bedside experience, one basically inexperienced. The patient's parameters analyzed: age, Body Mass Index (BMI), previous abdominal surgery, prostate volume (by TRUS), pre-operative PSA, bioptic grading. Surgical outcomes analyzed included skin-to-skin operative time and estimated blood loss; clinical outcomes included length of hospital stay and time to catheter removal; the oncological outcome was represented by positive surgical margin rate.

RESULTS

A total of 116 RARPs were identified: 38 RARPs were performed with the console experienced bedside assistant, 38 with the experienced one, 40 with the novice one. The variables were similar between the three groups. As far as outcomes are concerned, there were no statistically significant differences between the three bedside assistants in terms of operative time, estimated blood loss, length of stay, days of catheterization, positive surgical margin rate.

Three Different Learning Curves Have an Independent Impact on Perioperative Outcomes After Robotic Partial Nephrectomy: A Comparative Analysis

Background

Robot-assisted partial nephrectomy (RAPN) has become widely accepted, but its different underlying types of learning curves have not been comparatively analyzed to date. This study aimed to determine and compare the impact that the learning curve of the department, the console surgeon, and the bedside assistant as well as patient-related factors has on the perioperative outcomes of RAPN.

Methods

The study retrospectively analyzed 500 consecutive transperitoneal RAPNs (2007–2018) performed in a tertiary referral center by 7 surgeons and 37 bedside assistants. Patient characteristics and surgical data were obtained. Experience (EXP) was defined as the current number of RAPNs performed by the department, the surgeon, and the assistant. As the primary outcome, the impact of EXP and patient-related factors on perioperative outcomes were analyzed and compared. As the secondary outcome, a cutoff between “experienced” and “inexperienced” was defined. Correlation and regression analysis, receiver operating characteristic curve analysis, Fisher’s exact test, and the Mann–Whitney U test were performed, with p values lower than 0.05 denoting significance.

Results

The EXP of the department, the surgeon, and the assistant each has a major influence on perioperative outcome in RAPN irrespective of patient-related factors. Perioperative outcomes improve significantly with EXP greater than 100 for the department, EXP greater than 35 for the surgeon, and EXP greater than 15 for the assistant.

Conclusions

The perioperative results of RAPN are influenced by three different types of learning curves including those for the surgical department, the console surgeon, and the assistant. The influence of the bedside assistant clearly has been underestimated to date because it has a significant impact on the perioperative outcomes of RAPN.

Electronic supplementary material

The online version of this article (10.1245/s10434-020-08856-1) contains supplementary material, which is available to authorized users.

Feasibility and necessity of the fourth arm of the da Vinci Si surgical system for robot-assisted partial nephrectomy

BACKGROUND

To investigate the feasibility of the fourth arm of the da Vinci Si system for robot-assisted partial nephrectomy (RAPN).

METHODS

Fifty-eight consecutive patients underwent RAPN with the same port placements. After reviewing the surgical videos and records, 38 patients showing usefulness of the fourth arm were categorized into Group A and those not showing usefulness into Group B. The background data, tumor characteristics, and perioperative outcomes were compared between the groups.

RESULTS

Group B had a larger proportion of tumors located on the inner side of the kidney, and the console time was significantly longer. Multivariable logistic regression analysis showed that tumors located on the inner side of the kidney were associated with the non-use of the fourth arm of the da Vinci Si system during RAPN.

CONCLUSIONS

Our findings suggested that use of fourth arm in RAPN by da Vinci Si should be considered for each tumor location.

Robotic cardiac surgery impact of a new patient-side assistant on outcomes

OBJECTIVE

Very few studies have focused on the experience of the patient-side assistant in robotic cardiac surgery. We investigated whether the retirement of a highly experienced robotic patient-side assistant and replacement with a new assistant had an effect on surgical outcomes.

METHODS

In 4/2016, the established patient-side assistant retired after spending 8 months training a new patient-side assistant. A retrospective analysis of 216 patients was performed; 108 patients over a 9 month period just prior to arrival of the new patient-side assistant (Group 1), and 108 patients over 8 months just subsequent to departure of the established assistant (Group 2). Case distribution, pre-operative characteristics, and surgical outcomes were collected and compared.

RESULTS

Case volume increased in Group 2 with the new assistant. The mean age and rate of pre-op CVA for Group 1 was lower, but, otherwise, patient demographics were not significantly different. Group 1 had more intracardiac cases and group 2 had more triple-vessel TECABs. Overall operative time was not different between the two groups. Perioperative outcomes including hospital length of stay major adverse cardiovascular events and mortality were similar between the two groups.

CONCLUSIONS

We conclude that the transition to a new robotic cardiac surgical patient-side assistant does not have to affect the progress of a busy robotic program. If adequate time for training and gradual assumption of responsibility is ensured, it is feasible to make this transition without loss of volume or compromise in patient outcomes.

Does the experience of the bedside assistant effect the results of robotic surgeons in the learning curve of robot assisted radical prostatectomy?

Introduction:

The success of the robot assisted radical prostatectomy (RARP) procedures depend on a successful team, however the literature focuses on the performance of a console surgeon. The aim of this study was to evaluate surgical outcomes of the surgeons during the learning curve in relation to the bedside assistant's experience level during RARP.

Materials and Methods:

We retrospectively reviewed two non - laparoscopic, beginner robotic surgeon's cases, and we divided the patients into two groups. The first surgeon completed the operations on 20 patients with a beginner bedside assistant in February - May 2009 (Group-1). The second surgeon completed operations on 16 patients with an experienced (at least 150 cases) bedside assistant in February 2015 - December 2015 (Group-2). The collected data included age, prostate volume, prostate specific antigen (PSA), estimated blood loss, complications and percent of positive surgical margins. In addition, the elapsed time for trocar insertion, robot docking, console surgery, specimen extraction and total anesthesia time were measured separately.

Results:

There were no significant differences between the groups in terms of age, comorbidity, prostate volume, PSA value, preoperative Gleason score, number of positive cores, postoperative Gleason score, pathological grade, protection rate of neurovascular bundles, surgical margin positivity, postoperative complications, length of hospital stay, or estimated blood loss. The robot docking, trocar placement, console surgery, anesthesia and specimen extraction times were significantly shorter in group 2 than they were in group 1 (17.75 ± 3.53 min vs. 30.20 ± 7.54 min, p ≤ 0.001; 9.63 ± 2.71 min vs. 14.40 ± 4.52 min, p = 0.001; 189.06 ± 27.70 min vs. 244.95 ± 80.58 min, p = 0.01; 230.94 ± 30.83 min vs. 306.75 ± 87.96 min, p = 0.002; 10.19 ± 2.54 min vs. 17.55 ± 8.79 min, p = 0.002; respectively).

Conclusion:

Although the bedside assistant's experience in RARP does not appear to influence the robotic surgeon's oncological outcomes during the learning curve, it may reduce the potential complications by shortening the total operation time.