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, 25 (3), 855-60

Learning Curve for Robotic-Assisted Laparoscopic Colorectal Surgery


Learning Curve for Robotic-Assisted Laparoscopic Colorectal Surgery

Malak B Bokhari et al. Surg Endosc.


Background: Robotic-assisted laparoscopic surgery (RALS) is evolving as an important surgical approach in the field of colorectal surgery. We aimed to evaluate the learning curve for RALS procedures involving resections of the rectum and rectosigmoid.

Methods: A series of 50 consecutive RALS procedures were performed between August 2008 and September 2009. Data were entered into a retrospective database and later abstracted for analysis. The surgical procedures included abdominoperineal resection (APR), anterior rectosigmoidectomy (AR), low anterior resection (LAR), and rectopexy (RP). Demographic data and intraoperative parameters including docking time (DT), surgeon console time (SCT), and total operative time (OT) were analyzed. The learning curve was evaluated using the cumulative sum (CUSUM) method.

Results: The procedures performed for 50 patients (54% male) included 25 AR (50%), 15 LAR (30%), 6 APR (12%), and 4 RP (8%). The mean age of the patients was 54.4 years, the mean BMI was 27.8 kg/m(2), and the median American Society of Anesthesiologists (ASA) classification was 2. The series had a mean DT of 14 min, a mean SCT of 115.1 min, and a mean OT of 246.1 min. The DT and SCT accounted for 6.3% and 46.8% of the OT, respectively. The SCT learning curve was analyzed. The CUSUM(SCT) learning curve was best modeled as a parabola, with equation CUSUM(SCT) in minutes equal to 0.73 × case number(2) - 31.54 × case number - 107.72 (R = 0.93). The learning curve consisted of three unique phases: phase 1 (the initial 15 cases), phase 2 (the middle 10 cases), and phase 3 (the subsequent cases). Phase 1 represented the initial learning curve, which spanned 15 cases. The phase 2 plateau represented increased competence with the robotic technology. Phase 3 was achieved after 25 cases and represented the mastery phase in which more challenging cases were managed.

Conclusions: The three phases identified with CUSUM analysis of surgeon console time represented characteristic stages of the learning curve for robotic colorectal procedures. The data suggest that the learning phase was achieved after 15 to 25 cases.


Fig. 1
Fig. 1
Surgeon console time (SCT). A SCT plotted against case number. B Cumulative sum (CUSUM)SCT plotted against case number (solid line). The dashed line represents the curve of best fit for the plot (a second-order polynomial with equation CUSUMSCT = 0.73 × case number2 − 31.54 × case number − 107.72 (R = 0.93)
Fig. 2
Fig. 2
Three phases of the surgeon console time (SCT) in terms of the cumulative sum (CUSUM) learning curve. The solid diamond represents abdominoperineal resection (APR), and the solid circle represents anterior resection (AR). The open circle represents low anterior resection (LAR), and the solid triangle represents rectopexy (RP)
Fig. 3
Fig. 3
Lines of best fit for each phase of the cumulative sum (CUSUM)SCT learning curve. A Phase 1 represents the initial learning curve. B Phase 2 represents the accumulation of additional experience. C Phase 3 represents increasing surgeon competence

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