Robotic hepaticojejunostomy training in novices using robotic simulation and dry-lab suturing (ROSIM): randomized controlled crossover trial.

BACKGROUND: Robotic suturing training is in increasing demand and can be done using suture-pads or robotic simulation training. Robotic simulation is less cumbersome, whereas a robotic suture-pad approach could be more effective but is more costly. A training curriculum with crossover between both approaches may be a practical solution. However, studies assessing the impact of starting with robotic simulation or suture-pads in robotic suturing training are lacking. METHODS: This was a randomized controlled crossover trial conducted with 20 robotic novices from 3 countries who underwent robotic suturing training using an Intuitive Surgical® X and Xi system with the SimNow (robotic simulation) and suture-pads (dry-lab). Participants were randomized to start with robotic simulation (intervention group, n = 10) or suture-pads (control group, n = 10). After the first and second training, all participants completed a robotic hepaticojejunostomy (HJ) in biotissue. Primary endpoint was the objective structured assessment of technical skill (OSATS) score during HJ, scored by two blinded raters. Secondary endpoints were force measurements and a qualitative analysis. After training, participants were surveyed regarding their preferences. RESULTS: Overall, 20 robotic novices completed both training sessions and performed 40 robotic HJs. After both trainings, OSATS was scored higher in the robotic simulation-first group (3.3 ± 0.9 vs 2.5 ± 0.8; p = 0.049), whereas the median maximum force (N) (5.0 [3.2-8.0] vs 3.8 [2.3-12.8]; p = 0.739) did not differ significantly between the groups. In the survey, 17/20 (85%) participants recommended to include robotic simulation training, 14/20 (70%) participants preferred to start with robotic simulation, and 20/20 (100%) to include suture-pad training. CONCLUSION: Surgical performance during robotic HJ in robotic novices was significantly better after robotic simulation-first training followed by suture-pad training. A robotic suturing curriculum including both robotic simulation and dry-lab suturing should ideally start with robotic simulation.

Development of biotissue training models for anastomotic suturing in pancreatic surgery.

BACKGROUND: Anastomotic suturing is the Achilles heel of pancreatic surgery. Especially in laparoscopic and robotically assisted surgery, the pancreatic anastomosis should first be trained outside the operating room. Realistic training models are therefore needed. METHODS: Models of the pancreas, small bowel, stomach, bile duct, and a realistic training torso were developed for training of anastomoses in pancreatic surgery. Pancreas models with soft and hard textures, small and large ducts were incrementally developed and evaluated. Experienced pancreatic surgeons (n = 44) evaluated haptic realism, rigidity, fragility of tissues, and realism of suturing and knot tying. RESULTS: In the iterative development process the pancreas models showed high haptic realism and highest realism in suturing (4.6 ± 0.7 and 4.9 ± 0.5 on 1-5 Likert scale, soft pancreas). The small bowel model showed highest haptic realism (4.8 ± 0.4) and optimal wall thickness (0.1 ± 0.4 on -2 to +2 Likert scale) and suturing behavior (0.1 ± 0.4). The bile duct models showed optimal wall thickness (0.3 ± 0.8 and 0.4 ± 0.8 on -2 to +2 Likert scale) and optimal tissue fragility (0 ± 0.9 and 0.3 ± 0.7). CONCLUSION: The biotissue training models showed high haptic realism and realistic suturing behavior. They are suitable for realistic training of anastomoses in pancreatic surgery which may improve patient outcomes.

Predictors of Risk and Success of Obesity Surgery.

BACKGROUND: Obesity surgery has proven successful for weight loss and the resolution of comorbidities. There is, however, little evidence on its success and the risk of complications when considering age of onset of obesity (AOO), years of obesity (YOO), preoperative body mass index (BMI), Edmonton obesity staging system (EOSS) score, and age as possible predictors of weight loss, the resolution of comorbidities, and the risk of complications. METHODS: Patients who underwent Roux-en-Y gastric bypass (RYGB) and laparoscopic sleeve gastrectomy (LSG) from a prospective database were analyzed. Multiple regression analyses were used to predict comorbidities and their resolution, percentage excess weight loss (%EWL) and total weight loss (%TWL) 12 months after surgery, and the risk of complications using the predictors AOO, YOO, age, EOSS, and BMI. RESULTS: 180 patients aged 46.8 ± 11.1 years with a preoperative BMI 49.5 ± 7.5 were included. The number of preoperative comorbidities was higher with older age (ß = 0.054; p = 0.023) and a greater BMI (ß = 0.040; p = 0.036) but was not related to AOO and YOO. Patients with AOO as a child or adolescent were more likely to have an EOSS score of ≥2. Greater preoperative BMI was negatively associated with %EWL (ß = -1.236; p < 0.001) and older age was negatively associated with %TWL (ß = -0.344; p = 0.020). Postoperative complications were positively associated with EOSS score (odds ratio [OR] 1.147; p = 0.042) and BMI (OR 1.010; p = 0.020), but not with age. AOO and YOO were not related to postoperative outcome. CONCLUSION: Greater BMI was associated with a lower %EWL and age was associated with a low %TWL. YOO and AOO did not influence outcome. Age, BMI, and EOSS score were the most important predictors for risk and success after obesity surgery. Surgery should be performed early enough for optimal outcomes.

Skills in minimally invasive and open surgery show limited transferability to robotic surgery: results from a prospective study.

BACKGROUND: There is limited evidence on the transferability of conventional laparoscopic and open surgical skills to robotic-assisted surgery. The primary aim of this study was to evaluate the transferability of expertise in conventional laparoscopy and open surgery to robotic-assisted surgery using the da Vinci Skills Simulator (dVSS). Secondary aims included evaluating the influence of individual participants' characteristics. METHODS: Participants performed four tasks on the dVSS: Peg Board 1 (PB), Pick and Place (PP), Thread the Rings (TR), and Suture Sponge 1 (SS). Participants were classified into three groups (Novice, Intermediate, Experts) according to experience in laparoscopic and open surgery. All tasks were performed twice except for SS. Performance was assessed using the built-in scoring system. RESULTS: 37 medical students and 25 surgeons participated. Experts did not perform significantly better than less experienced participants on the dVSS. Specifically, with regard to laparoscopic experience, total simulator scores were: Novices 68.2 ± 28.8; Intermediates 65.1 ± 31.2; Experts 65.1 ± 30.0; p = 0.611. Regarding open surgical experience, scores were: Novices 68.6 ± 28.7; Intermediates 68.2 ± 30.8; Experts 63.2 ± 30.3; p = 0.305. Although there were some significant differences among groups for single parameters in specific tasks, there was no constant superiority of one group. Laparoscopic and open surgical Novices improved significantly in overall score and time for all three tasks (p < 0.05). Laparoscopic intermediates improved only in PP time (4.64 ± 3.42; p = 0.006), open Intermediates in PB score (11.98 ± 13.01; p = 0.025), and open Experts in PP score (6.69 ± 11.48; p = 0.048). Laparoscopic experts showed no improvement. Participants with gaming experience had better overall scores than non-gamers when comparing all second attempts (Gamer 83.62 ± 7.57; Non-Gamer 76.31 ± 12.78; p = 0.008) as well as first and second attempts together (Gamer 72.08 ± 8.86; Non-Gamer 65.45 ± 11.68; p = 0.039). Musical and sports experience showed no correlation with robotic performance. CONCLUSIONS: Robotic-assisted surgery requires skills distinct from conventional laparoscopy or open surgery. Basic robotic skills training prior to patient contact should be required.