Learning Curve For Robotic Surgery Research Articles

Learning curve of robotic surgery for lung cancer: analysis for two surgeons during the COVID-19 pandemic.

To describe and compare the RATS learning curve between two surgeons in one department for lung cancer surgery using the CUSUM method. Retrospective analysis using a prospective database on robotic-assisted lung resections performed by two different surgeons in one hospital. The CUSUM method was used to describe the learning curve. 366 consecutives cases were analysed (195 for the first surgeon and 171 for the second surgeon). A traditional 3-phase pattern learning curve was found with a diminution of the operating time throughout the different phases. For Surgeon 1, phase 1 was from case 1 to 59, phase 2 from case 60 to 99 and phase 3 started at case 100. For Surgeon 2, phase 1 was from 1 to 44, phase 2 from case 45 to 79 and phase 3 started at case 80. This study described our first experience with the Da Vinci Robotic System in our department. The curves had a similar shape which shows the learning curve of robotic surgery using the CUSUM method is reproducible. Furthermore, our results showed that the learning curve may improve after the programme starts in the department when the different team elements are all trained.

Feasibility and learning curve for robotic surgery in a small hospital: A retrospective cohort study.

Robotic surgery rates, typified by the use of the da Vinci Surgical System, have increased in recent years. However, robotic surgery is mostly performed in large hospitals and has not been fully implemented in small hospitals. Therefore, we aimed to verify the feasibility of robotic surgery in small hospitals and verify the number of cases in which perioperative preparation for robotic surgery is stable by creating a learning curve in small hospitals. Forty robot-assisted rectal cancer surgeries performed in large and small hospitals by a surgeon with extensive experience in robotic surgery were validated. Draping and docking times were recorded as perioperative preparation times. Unexpected surgical interruptions, intraoperative adverse events, conversion to laparoscopic or open surgery, and postoperative complications were recorded. Cumulative sum analysis was used to derive the learning curve for perioperative preparation time. Draping times were significantly longer in the small hospital group (7 vs 10 minutes, P = .0002), while docking times were not significantly different (12 vs 13 minutes, P = .098). Surgical interruptions, intraoperative adverse events, and conversions were not observed in either group. There were no significant differences in the incidence of severe complications (25% [5/20] vs 5% [1/20], P = .184). In the small hospital group, phase I of the draping learning curve was completed in 4 cases, while phase I of the docking learning curve was completed in 7 cases. Robotic surgery is feasible for small hospitals, and the preoperative preparation time required for robotic surgery stabilizes relatively early.

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

BackgroundImproved 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). ObjectiveTo investigate the relationship between prior experience of a surgeon and biochemical recurrence (BCR) after RARP. Design, setting, and participantsWe 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 analysisWe 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 limitationsOverall, 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. ConclusionsThese 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 summaryWe 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.

The robotic learning curve for a newly appointed colorectal surgeon.

Robotic colorectal surgery allows for better ergonomics, superior retraction, and fine movements in the narrow anatomy of the pelvis. Recent years have seen the uptake of robotic surgery in all pelvic surgeries specifically in low rectal malignancies. However, the learning curve of robotic surgery in this cohort is unclear as established training pathways are not formalized. This study looks at the experience and learning curve of a single laparoscopic trained surgeon in performing safe and effective resections, mainly for low rectal and anal malignancies using the da Vinci robotic system by evaluating metrics related to surgical process and patient outcome. A serial retrospective review of the robotic colorectal surgery database, in the University Hospital Coventry and Warwickshire (UHCW), was undertaken. All 48 consecutive cases, performed by a recently qualified colorectal surgeon, were included in our study. The surgical process was evaluated using both console and total operative time recorded in each case along with the adequacy of resections performed; in addition, patient-related outcomes including intraoperative and postoperative complications were analyzed to assess differences in the learning curve. Forty eight sequential recto-sigmoid resections were included in the study performed by a single surgeon. The cases were divided into four cohorts in chronological order with comparable demographics, tumour stage, location, and complexity of the operation (mean age 65, male 79%, and female 29%). The results showed that the mean console time dropped from 3 to 2.5h, while total operative time dropped from 6h to 5.5h as the surgeon became more experienced; however, this was not found to be statistically significant. In addition, no significant difference in pathological staging was seen over the study period. No major intra-op and post-op complications were observed and no 30-day mortality was recorded. Moreover, after 30 cases, the learning curve developed the plateau phase, suggesting the gain of maximum proficiency of skills required for robotic colorectal resections. The learning curve in robotic rectal surgery is short and flattens early; complication rates are low during the learning curve and continue to decrease with time. This shows that with proper training and proctoring, new colorectal surgeons can be trained in a short time to perform elective colorectal pelvic resections.

413 Evaluation of the Validity of Virtual Reality Simulators in the Preclinical Training of Robotic Surgery

Abstract Aim Robotic surgeries are a form of minimally invasive procedures that decrease a patient’s recovery time. They have been increasing in numbers and scope over the past decade. Yet, they do not have a standardized method of training nor accreditation. The significant learning curve associated with it makes it intimidating to approach despite its benefits. This structured review aims to evaluate the validity of virtual reality simulators–tools that help surgeons overcome the learning curve of robotic surgery in a pressure-free environment that ensures patients’ safety. Virtual reality simulators have the potential of increasing the efficiency of robotic surgery as well as its uptake in general. Method Literature electronic databases SCOPUS, MEDLINE and PubMed were searched and interrogated for studies related to the aim. Inclusion and exclusion criteria were then applied to filter out more relevant studies. Redundant studies were eliminated, and the s of the remaining studies were examined until relevant studies were selected. Results Four prospective clinical trials were found and critically appraised by the Support Unit for Research Evidence (SURE) checklist. All studies comprised the evaluation of the face, content, and construct validity of a VR simulator. Conclusions The studies confirmed face, content, and construct validation of specific VR simulators. Hence, they encourage the use of VR simulators in the development of training and accreditation programs for robotic surgery. However, more predictive, and concurrent validation studies must be performed in order to establish the legitimacy of VR simulators as valid tools of training and accreditation.

Does the learning curve in robotic rectal cancer surgery impact circumferential resection margin involvement and reoperation rates? A risk-adjusted cumulative sum analysis.

The aim of this study was to evaluate the impact of surgeons' learning curve in robotic surgery for rectal cancer on circumferential resection margin (CRM) involvement and reoperation rates. Learning curve data were prospectively collected from four centers. Patients undergoing robotic proctectomy for resectable rectal cancer were included. CRM was involved when ≥1 mm. TME quality was classified as complete, nearly complete, or incomplete. T-test and χ2 tests were used to compare continuous and categorical variables, respectively. Risk-adjusted cumulative sum (RA-CUSUM) analysis was utilized to evaluate the effect of the learning curve on primary endpoints. Univariate analysis of potential risk factors for CRM involvement and reoperation was performed. Factors with the P value ≤0.2 were included in the multivariate logistic regression model for further RA-CUSUM analysis. A total of 221 patients (80, 36, 62, and 43 patients operated on by surgeons 1, 2, 3, and 4, respectively) who underwent robotic surgery for rectal cancer during the surgeons' learning curves were included. CRM involvement rate was 0%, 11%, 3%, and 5% in surgeons 1, 2, 3, and 4, respectively. Reoperation rate was 3.7%, 8.3%, 4.8%, and 11.6%, respectively. RA-CUSUM analysis of CRM involvement (R2=0.9886) and reoperation (R2=0.9891) found a statistically significant decreasing trend in aggregate CUSUM values throughout the learning curve. This study found a continued significant decrease in CRM involvement and reoperation rates throughout the learning curve in robotic rectal cancer surgery.

Evolution of robotic-assisted kidney transplant: successes and barriers to overcome.

The aim of this study was to provide an updated review of robotic-assisted kidney transplant (RAKT) with an emphasis on advantages over the open kidney transplant (OKT), utility in special populations and resources available to overcome the learning curve of robotic surgery. The majority of the reported studies showed that RAKT and OKT have similar functional outcomes including similar ischemia times and time to postoperative normalization of creatinine. However, RAKT results in fewer wound complications, decreased estimated blood loss and pain. Given these benefits, RAKT is a promising approach for obese patient across BMI subtypes and several studies showed decreased wound complications in this population compared with the open approach. Moreover, new 3D-print techniques are promising resources for robotic simulation, which may decrease the learning curve of robotic surgery. Overall, RAKT is a feasible approach especially in obese patients. However, more data with long-term follow-up are needed to fully elucidate the advantages over OKT before universal implementation of this approach is possible.

Does the learning curve in robotic rectal cancer surgery impact circumferential resection margin involvement and reoperation rates? A risk-adjusted cumulative sum analysis

Background The aim of this study was to evaluate the impact of surgeons' learning curve in robotic surgery for rectal cancer on circumferential resection margin (CRM) involvement and reoperation rates. Methods Learning curve data were prospectively collected from four centers. Patients undergoing robotic proctectomy for resectable rectal cancer were included. CRM was involved when ≥1 mm. TME quality was classified as complete, nearly complete, or incomplete. T-test and Chi-square tests were used to compare continuous and categorical variables, respectively. Risk-adjusted cumulative sum (RA-CUSUM) analysis was utilized to evaluate the effect of the learning curve on primary endpoints. Univariate analysis of potential risk factors for CRM involvement and reoperation was performed. Factors with the p-value ≤0.2 were included in the multivariate logistic regression model for further RA-CUSUM analysis. Results A total of 221 patients (80, 36, 62, and 43 patients operated on by surgeons 1, 2, 3, and 4, respectively) who underwent robotic surgery for rectal cancer during the surgeons' learning curves were included. CRM involvement rate was 0%, 11%, 3%, and 5% in surgeons 1, 2, 3, and 4, respectively. Reoperation rate was 3.7%, 8.3%, 4.8%, and 11.6%, respectively. RA-CUSUM analysis of CRM involvement (R2=0.9886) and reoperation (R2=0.9891) found a statistically significant decreasing trend in aggregate CUSUM values throughout the learning curve. Conclusions This study found a continued significant decrease in CRM involvement and reoperation rates throughout the learning curve in robotic rectal cancer surgery.

Assessment of Feedback Mechanisms in Robotic Surgery Curriculum in Obstetrics and Gynecology Residency

Study Objective Overcoming the learning curve in robotic surgery requires appreciable effort. A structured curriculum in robotic gynecologic surgery was established for residents at an academic teaching hospital. This study aimed to explore resident experience with robotic surgery and training and to elicit resident preferences for feedback within the curriculum. Design A survey assessing demographics, surgical preparation methods, robotic surgical experience and comfort, and feedback preferences was distributed electronically to current obstetrics and gynecology (OB/GYN) residents. Responses were analyzed with descriptive statistics. Setting This study was conducted in the OB/GYN residency at Northwestern University in Chicago, IL. Patients or Participants The survey was sent to the 47 current OB/GYN residents in the academic year 2019-2020. A total of 29 responses were elicited. Interventions N/A Measurements and Main Results Eighteen residents (18/29; 62%) reported console experience in at least one robotic surgery. Most (26/29; 90%) stated that prior to operating on the robotic console they felt uncomfortable or very uncomfortable. Prior to console experience, residents primarily used online videos (62%), VR simulation (48%) or observation of live surgery (48%) as preparation. Of note, 7 (24%) residents reported no preparation prior to robotic surgery; these were PGY1 or PGY2 residents who also reported no prior console experience. After console experience, ongoing preparation techniques remained similar, although more residents reported in engaging in pre-operative preparation. All reported that they do receive feedback on surgical skills, but perception of usefulness, timeliness, and structure varied. Feedback was reported as useful and timely, but not well-structured. Most residents (28/29, 97%) indicated that they would prefer real-time feedback immediately after console experience. Conclusion OB/GYN residents report that they are not comfortable with robotic surgery training. Current practices for preparation include self-directed activities, but they do not begin until after surgical console experience. This study demonstrates that early initiation of structured learning with feedback is necessary.

Impact of laparoscopic surgical experience on the learning curve of robotic rectal cancer surgery.

Robotic surgery has advantages in terms of the ergonomic design and expectations of shortening the learning curve, which may reduce the number of patients with adverse outcomes during a surgeon's learning period. We investigated the differences in the learning curves of robotic surgery and clinical outcomes for rectal cancer among surgeons with differences in their experiences of laparoscopic rectal cancer surgery. Patients who underwent robotic surgery for colorectal cancer were reviewed retrospectively. Patients were divided into five groups by surgeons, and their clinical outcomes were analyzed. The learning curve of each surgeon with different volumes of laparoscopic experience was analyzed using the cumulative sum technique (CUSUM) for operation times, surgical failure (open conversion or anastomosis-related complications), and local failure (positive resection margins or local recurrence within 1year). A total of 662 patients who underwent robotic low anterior resection (LAR) for rectal cancer were included in the analysis. Number of laparoscopic LAR cases performed by surgeon A, B, C, D, and E prior to their first case of robotic surgery were 403, 40, 15, 5, and 0 cases, respectively. Based on CUSUM for operation time, surgeon A, B, C, D, and E's learning curve periods were 110, 39, 114, 55, and 23 cases, respectively. There were no significant differences in the surgical and oncological outcomes after robotic LAR among the surgeons. This study demonstrated the limited impact of laparoscopic surgical experience on the learning curve of robotic rectal cancer surgery, which was greater than previously reported curves.

605 STEPWISE APPROACH TO ROBOTIC SURGERY IN A HIGH-VOLUME CENTER WITHOUT PREVIOUS EXPERIENCE IN ROBOTIC ESOPHAGEAL SURGERY

Abstract Minimally Invasive esophagectomy has been introduced to reduce the trauma associated with open surgery while keeping the same oncological outcome. Robotic platforms could facilitate this. Even with the increasing availability of surgical robots and the standardization of the technique, it can be difficult to convince high volume centers to change their surgical approach. Our aim is to assess the impact of the learning curve for robotic surgery in a high-volume laparoscopic center. Methods We retrospectively analyzed a prospectively collected database in a tertiary referral center for esophagogastric surgery. We identified two groups composed by the patients who underwent Ivor-Lewis (IL) esophagectomy by a hybrid laparoscopic (HL) approach (standard laparoscopy + open thoracotomy) and those who underwent a hybrid robotic (HR) approach (robotic abdominal part + open thoracotomy) with the Davinci Xi surgical platform for lower esophageal or EGJ cancer from November 2018 to September 2019. Data regarding post-operative stay, complications, leaks, total number of lymph nodes and length of surgery were analyzed. Complications were reported according to the ECCG classification. Results We selected 50 consecutive IL cases among which there were our first 17 HR cases. There were no differences in terms of demographics, comorbidities and neoadjuvant treatments between the two groups. No statistical difference was observed in post-operative stay(median 12 vs 10 days in HL and HR respectively, p = 0.09), overall complication rate was 33% in HL and 35% in HR(p = 0.9), patients with leaks 1(3.03%) in HL and 1(5.88%) in HR(p = 0.6), total lymph nodes median of 38 in HL vs 30 in HR(p = 0.4). Only the total length of surgery was statistically longer in the HR group (median 446 vs 478 min, p = 0.01). Conclusion We showed that in the setting of a high-volume center with no previous robotic surgery expertise, robotically performing only the abdominal phase could represent an effective alternative to familiarize with the robotic system without compromising the clinical outcome and workflow. We understand that the main advantages would be seen when using the robot during the thoracic part. After these first cases we will proceed to adopt it during the most demanding part of the operation.

The intuitive da Vinci single port surgical system and feasibility of transoral thyroidectomy vestibular approach

: Remote-access thyroid surgery is increasingly implemented to reduce the morbidity of an anterior cervical incision resulting from traditional thyroid surgery. While most remote-access approaches leave a cutaneous scar, the recent development of the transoral vestibular approach (TOVA) enables the achievement of a truly scarless surgery, as all incisions are made in the oral vestibule. Unfortunately, the adoption of the robotic system in transoral thyroid surgery has been limited by the need for an additional axillary incision, prohibitive cost, and steep learning curve of robotic surgery. The Intuitive da Vinci single port (SP) robotic surgical system (Intuitive Surgical, Inc., Sunnyvale, CA, USA), introduced in 2018, may enable transoral robotic thyroid surgery without an axillary port. Additionally, the new SP system is equipped with two “joggle joints” in the robotic arms and endoscope, allowing for additional flexibility in a narrow workspace. Compared to previous generations, the SP robot offers a greater range of motion and more precise surgical control, a derivative of fully articulating instruments on a similar console like the Xi system. This review provides an overview of literature on the application of the intuitive da Vinci SP robotic surgical system via the TOVA in thyroid surgery.

Learning curve of robotic-assisted laparoscopic pyeloplasty (RALP) in children: how to reach a level of excellence?

It has been suggested that up to 40 cases of RALP are required to reach the operative results equal to open surgery. We have hypothesized that previous experience in open and laparoscopic surgeries might shorten the learning curve of robotic surgery. We have retrospectively evaluated the data of all children who underwent pyeloplasty in our institute by a single surgeon since 2003. The children were divided into three groups: open pyeloplasty (OP) of 72 children, laparoscopic pyeloplasty (LAP) of 22, and RALP of 33 patients subsequently. The data included: demographics, duration of surgery, length of stay, success of surgery, and complication rate according to the Clavien-Dindo score. The groups were ordered chronologically by the operation date and each group was divided into two different phases: early and late. A comparison was made between the data of the early and the late phase. There was no difference in the demographic data between the groups; however, the patients who underwent laparoscopic surgery were significantly older compared with the patients from the other groups. The median duration of surgery in the RALP group was significantly shorter than the OP group (65min vs 72.5min P < 0.01), while the first RALP case was already shorter than the median duration of surgery in OP group. There was no significant decrease in the duration of surgery of the RALP group over the study period, though there was a significant trend of decreasing operative time in the OP and LAP groups. There was no difference in the length of stay in the early vs late phases in the RALP group. There was no difference in the complication and success rate between the RALP and OP group, as well as the early and late phases of the RALP group. Our data show that previous experience in OP and LAP surgery may contribute to a shorter learning curve of robotic surgery required for the surgeon to achieve a similar outcome to that of OP.

Learning curve for robotic surgery in children: a systematic review of outcomes and fellowship programs

The learning curve (LC) of a new technique is fundamental for its application and assessment, and for the training. Literature was analysed to define the LCs of different surgical procedures and the impact of fellowship programs. MEDLINE, EMBASE and paediatric surgical journals' databases from January 1995 to December 2018 were systematically analysed. Two independent residents sought for articles providing description of robotic-assisted procedures' LCs in paediatric age/population. Seventeen articles were selected, describing LC of robotic-assisted pyeloplasty (n = 9), fundoplication (n = 4), cholecystectomy (n = 2), choledochal cyst resection (n = 1) and lingual tonsillectomy (n = 1), with 721 procedures. Ten studies refer to one single surgeon; six to more than one; one does not specify the number of operators. Eleven studies are unicentric retrospective, two multicentric retrospective, three prospective and one is a comparative analysis between a retrospective case series and a prospective cohort. The most recruited parameter is operative time alone in 3 articles, associated with complications in 12, length of hospital stay in 6, blood loss in 3, resolution in 4 and narcotic use in 2. The LC is described as impacting procedural planning (n = 17), training (n = 9) and economic costs (n = 2). To date, operative time is the most reported outcome to measure LC and proficiency. Efforts are needed to consider measures of surgical expertise and patient status. Robotic training should be standardized on targeted programs planned upon well-defined LCs.

Comprehensive Learning Curve of Robotic Surgery

To evaluate the complication-based learning curve and identify learning-associated complications of robotic gastrectomy. With the increased popularity of robotic surgery, a sound understanding of the learning curve in the surgical outcome of robotic surgery has taken on great importance. However, a multicenter prospective study analyzing learning-associated morbidity has never been conducted in robotic gastrectomy. Data on 502 robotic gastrectomy cases were prospectively collected from 5 surgeons. Risk-adjusted cumulative sum analysis was applied to visualize the learning curve of robotic gastrectomy on operation time and complications. Twenty-five cases, on average, were needed to overcome complications and operation time-learning curve sufficiently to gain proficiency in 3 surgeons. An additional 23 cases were needed to cross the transitional phase to progress from proficiency to mastery. The moderate complication rate (CD ≥ grade II) was 20% in phase 1 (cases 1-25), 10% in phase 2 (cases 26-65), 26.1% in phase 3 (cases 66-88), and 6.4% in phase 4 (cases 89-125) (P < 0.001). Among diverse complications, CD ≥ grade II intra-abdominal bleeding (P < 0.001) and abdominal pain (P = 0.01) were identified as major learning-associated morbidities of robotic gastrectomy. Previous experience on laparoscopic surgery and mode of training influenced progression in the learning curve. This is the first study suggesting that technical immaturity substantially affects the surgical outcomes of robotic gastrectomy and that robotic gastrectomy is a complex procedure with a significant learning curve that has implications for physician training and credentialing.

Credentialing for robotic lobectomy: what is the learning curve? A retrospective analysis of 272 consecutive cases by a single surgeon.

Credentialing processes for surgeons seeking robotic thoracic surgical privileges are not evidence-based, and the learning curve has not been reported. The goal of this study is to review our experience with robotic lobectomies and provide evidence for the development of a more uniform credentialing process. We performed a retrospective review of the first 272 consecutive robotic lobectomies performed between 2011 and 2017 by a single surgeon with prior video-assisted thoracoscopic (VATS) experience. Primary outcomes were operative duration, blood loss, chest tube duration, length of hospital stay, intraoperative complication, and conversion to thoracotomy. The patients were subdivided by surgical date into two cohorts of 120 consecutive patients to compare differences in outcomes, thereby illustrating the learning curve. Between 2011 and 2017, 272 patients (median age 67.5years) underwent a robotic lobectomy by a single surgeon. The majority of patients (157/272) had early stage (T1N0) adenocarcinoma. For the entire cohort, median operative time was 160min (83-317min). The median blood loss was 75mL (10-4000mL). Median chest tube duration was 2days (1-23days) and median hospital stay was 3days (1-25days). Intraoperative complications occurred in seven patients. Only six patients required conversion to thoracotomy. Using multivariable logistic regression, it was found that the age, gender, and stage do not factor into conversion to thoracotomy, but BMI was found to be a significant covariate (p 0.043). As the surgeon performs more surgeries, there is a significantly shorter operative time (p < 0.001), decreased blood loss (p < 0.001), and shorter hospital stay (p < 0.014). When the first 120 and last 120 surgeries were compared, there was significantly less blood loss (234.6 vs 78.69cc, p < 0.001), shorter operative time (181.9 vs 147.4min, p < 0.001), shorter tube duration (3.49 vs 3.11 days, p 0.007), and shorter length of stay (4.03 vs 3.48 days, p < 0.001), respectively. More intraoperative complications were observed during the first 120 surgeries (6/120) compared to the last 120 surgeries (0/120; Fischer exact p = 0.029). Regression model plots did not show any apparent and significant change points, but rather a steady improvement. The more cases the surgeon does, the better is the outcome in terms of operative duration, blood loss, post-operative length of stay and intraoperative complications. The learning curve for robotic surgery for a surgeon with prior VATS experience is that of a continuous improvement with experience instead of a particular change point. Since most thoracic surgeons who perform robotic-assisted surgery have already gotten past their VATS learning curves, they no longer have a definable learning curve for robotic surgery. Hence, if a surgeon is already proficient and credentialed to perform VATS lung resections, he or she is no longer faced with a significant learning curve for robotic lung resections, and should be credentialed to do so once he or she has undergone the appropriate training with the equipment and technology.

A systematic review of the learning curve in robotic surgery: range and heterogeneity.

With the rapid adoption of the robotic surgery, more and more learning curve (LC) papers are being published but there is no set definition of what should constitute a rigorous analysis and represent a true LC. A systematic review of the robotic surgical literature was undertaken to determine the range and heterogeneity of parameters reported in studies assessing the LC in robotic surgery. The search was conducted in July 2017 in PubMed. All studies reporting a LC in robotic surgery were included. 268 (25%) of the identified studies met the inclusion criteria. 102 (38%) studies did not define nor explicitly state the LC with appropriate evidence; 166 studies were considered for quantitative analysis. 46 different parameters of 6 different outcome domains were reported with a median of two parameters (1-8) and 1 domain (1-5) per study. Overall, three domains were only technical and three domains were both technical and clinical/patient-centered outcomes. The two most commonly reported domains were operative time [146 studies (88%)] and intraoperative outcomes [31 studies (19%)]. Postoperative outcomes [16 studies (9%)] and surgical success [11 studies (7%)] were reported infrequently. Purely technical outcomes were the most frequently used to assess LC [131 studies (79%)]. The outcomes reported in studies assessing LC in robotic surgery are extremely heterogeneous and are most often technical indicators of surgical performance rather than clinical and patient-centered outcomes. There is no single outcome that best represents the surgical success. A standardized multi-outcome approach to assessing LC is recommended.

Does Previous Laparoscopic Experience Influence Basic Robotic Surgical Skills?

Studies addressing the effect of laparoscopic experience on robotic skills have produced conflicting results. This study aimed to compare simulated robotic surgical tasks using the virtual reality simulator dV-Trainer between laparoscopically experienced surgeons and first-year surgical residents. A cross-sectional study. Participants completed 4 trials of the following tasks on the dV-Trainer: Peg Board 2, Ring and Rail 1, and Suture Sponge 1. Performance was recorded using a computerized built-in scoring algorithm. Scores and metrics were compared between groups 1 and 2 and between the first and subsequent trials. Hospital de Clínicas, Porto Alegre, Brazil, a tertiary care teaching hospital. Twenty laparoscopically experienced surgeons (group 1) and 20 first-year surgical residents (group 2). All participants completed the study. The overall scores for Peg Board 2 (738.04 ± 267.83 vs 730.39 ± 225.31; p = 0.57), Ring and Rail 1 (919.03 ± 242.69 vs 965.84 ± 222.96; p = 0.13), and Suture Sponge 1 (563.62 ± 185.50 vs 560.99 ± 152.71; p = 0.67) did not differ significantly between groups 1 and 2. Group 1 had better results for master workspace range in Peg Board 2 and Ring and Rail 1. Group 2 had higher scores for economy of motion in Peg Board 2 and Ring and Rail 1 and for excessive instrument force in Ring and Rail 1. In both groups, the overall scores in the third and fourth trials were significantly higher than those in the first trial. There are no significant differences in the performance of simulated robotic surgical tasks between laparoscopically experienced surgeons and laparoscopically naïve surgical residents. Some slight differences were observed in specific metrics, but these differences were not sufficient to change the final results. We may assume that laparoscopic experience should not be an essential step in the initial learning curve of robotic surgery.

Outcomes of robotic versus laparoscopic surgery for mid and low rectal cancer after neoadjuvant chemoradiation therapy and the effect of learning curve

Randomized controlled trials have demonstrated that laparoscopic surgery for rectal cancer is safe and can accelerate recovery without compromising oncological outcomes. However, such a surgery is technically demanding, limiting its application in nonspecialized centers. The operational features of a robotic system may facilitate overcoming this limitation. Studies have reported the potential advantages of robotic surgery. However, only a few of them have featured the application of this surgery in patients with advanced rectal cancer undergoing neoadjuvant chemoradiation therapy (nCRT).From January 2012 to April 2015, after undergoing nCRT, 40 patients with mid or low rectal cancer were operated using the robotic approach at our institution. Another 38 patients who were operated using the conventional laparoscopic approach were matched to patients in the robotic group by sex, age, the body mass index, and procedure. All operations were performed by a single surgical team. The clinicopathological characteristics and short-term outcomes of these patients were compared. To assess the effect of the learning curve on the outcomes, patients in the robotic group were further subdivided into 2 groups according to the sequential order of their procedures, with an equal number of patients in each group. Their outcome measures were compared.The robotic and laparoscopic groups were comparable with regard to pretreatment characteristics, rectal resection type, and pathological examination result. After undergoing nCRT, more patients in the robotic group exhibited clinically advanced diseases. The complication rate was similar between the 2 groups. The operation time and the time to the resumption of a soft diet were significantly prolonged in the robotic group. Further analysis revealed that the difference was mainly observed in the first robotic group. No significant difference was observed between the second robotic and laparoscopic groups.Although the robotic approach may offer potential advantages for rectal surgery, comparable short-term outcomes may be achieved when laparoscopic surgery is performed by experienced surgeons. However, our results suggested a shorter learning curve for robotic surgery for rectal cancer, even in patients who exhibited more advanced disease after undergoing nCRT.

Robotic and laparoscopic surgeries for colorectal cancer: learning curve and short-term outcomes

Objective: comparative evaluation of perioperative clinicopathologic characteristics and short-term outcomes of robotic and laparoscopic surgeries for colorectal cancer (CRC), depending on the phases of the learning curve. Materials and methods. Patients who underwent robotic or laparoscopic surgeries were compared retrospectively (n = 77 and n = 123 respectively). Cumulative sum (CUSUM) was used to evaluate the learning curve. Results. Robotic operations were comparable with laparoscopic in the average blood loss and conversion rate, but differed in the longer duration (237.5 ± 74.9 min versus 213.5 ± 73.1 min, p = 0.024). The short-term outcomes and pathomorphological characteristics were not significantly different in both procedures. According to CUSUM, the learning curve of robotic surgery was the 46 cases and of laparoscopic surgery was the 54 cases. Comparison between 1st phase and 2nd phase in the robotic group showed significant difference in the total operation time and in docking and surgeon console time. Other perioperative characteristics did not differ significantly between the phases of the learning curve. For the laparoscopic group, there was also a decrease in the operation time after the 1st phase of the learning curve, as well as a statistically significant decrease in intraoperative blood loss (140.9 ± 68.8 ml for 1st phase versus 108.1 ± 67.8 ml for 2nd phase, p = 0.009). All cases of conversion in the laparoscopic group were in the 1st phase of the learning curve (p = 0.047). Comparison between 1st phase and 2nd phase for laparoscopic surgery showed no significant difference for the short-term outcomes. Pathologic outcomes were not significantly different in both procedures and phases. Conclusions. The learning curve for robotic surgery was shorter than for laparoscopic surgery. There were no significant differences in perioperative clinicopathologic characteristics and short-term outcomes in both procedures and phases of the learning curve.