Learning curve for robot-assisted lobectomy of lung cancer

Background: Robotic lobectomy is widely used for lung cancer treatment. So far, few studies have been performed to systematically analyze the learning curve. Our purpose is to define the learning curve to provide a training guideline of this technique. Methods: A total of 208 consecutive patients with primary lung cancer who underwent robotic-assisted lobectomy by our surgical team were enrolled in this study. Baseline information and postoperative outcomes were collected. Learning curves were then analyzed using the cumulative sum (CUSUM) method. Patients were divided into three groups according to the cut-off points of the learning curve. Intraoperative characteristics and short-term outcomes were compared among the three groups. Results: CUSUM plots revealed that the docking time, console time and total surgical time in patients were 20, 34 and 32 cases, respectively. Comparison of the surgical time among the 3 phases revealed that the total surgical time (197.03=27.67, 152.61 +/- 21.07, 141.35 +/- 29.11 min, P<0.(X)1), console time (150.97 +/- 26.13, 103.89=18.04, 97.49 +/- 24.80 min, P<0.001) and docking time (13.53=2.08, 11.95=1.10, 11.89 +/- 1.49 min, P<0.001) were decreased significantly. Estimated blood loss differed among groups (90.63 +/- 45.41, 87.63=59.84, 60.29=28.59 mL, P=0.001) and was associated with shorter operative time. There was no conversion or 30-day mortality. No significant differences were observed among other clinic-pathological characteristics among the groups. Conclusions: For a surgeon, the learning time of robotic lobectomy was in the 32th operation. For a bedside assistant, at least 20 cases were required to achieve the level of optimal docking.