title: Evaluation of the learning curve of total robotic hysterectomy with or without lymphadenectomy for a gynecologic oncology service.
abstract: We sought to determine the learning curve for total robotic hysterectomy, bilateral salpingo-oophorectomy (TRH, BSO) with/without lymphadenectomy (LND) for a gynecologic oncology service. Data was collected prospectively and included demographics, surgical data, and timed data points to calculate times for the following categories: total operating room (OR) time, setup time, hysterectomy (HYST) time, lymphadenectomy (LND) time, and console time. Cases were grouped into tens by chronological order and compared. A risk-adjusted cumulative sum (CUSUM) model was used to evaluate learning curves for hysterectomy and lymphadenectomy. The first 155 patients are reported. Average HYST time was 45.2 min and average LND time was 52.4 min. Cases were grouped by each consecutive 10 cases per surgeon (i.e. Group 1 = cases 1-10 for each surgeon). All groups were similar with respect to age, body mass index, stage, grade, cancer type, number of lymph nodes, and uterine weight. All times significantly improved with the increase in number of cases: total OR time (P < 0.001); setup time (P = 0.004); HYST time (P = 0.001); LND time (P = 0.05); console time (P = 0.05). CUSUM analysis demonstrated a learning curve of 14 cases for HYST time and 19 cases for lymphadenectomy. Our data describes the robotic laparoscopic learning curves for both hysterectomy and lymphadenectomy in a gynecologic oncology practice and could be utilized for hospital credentialing. The amount of experience required to achieve maximum time efficiency for robotic lymphadenectomy was greater than that for hysterectomy. A significant improvement was observed in all timed data points collected, and the time to proficiency appears reasonable.
authors.affiliation: Division of Gynecologic Oncology, University of South Alabama, Mitchell Cancer Institute, 1660 Springhill, Avenue, Mobile, AL, 36604, USA. rocconi@usouthal.edu.
authors.forename: Rodney P
authors.initials: RP
authors.lastname: Rocconi
authors.affiliation: Division of Gynecologic Oncology, University of South Alabama, Mitchell Cancer Institute, 1660 Springhill, Avenue, Mobile, AL, 36604, USA.
authors.forename: Courtney
authors.initials: C
authors.lastname: Meredith
authors.affiliation: Division of Gynecologic Oncology, University of South Alabama, Mitchell Cancer Institute, 1660 Springhill, Avenue, Mobile, AL, 36604, USA.
authors.forename: Michael A
authors.initials: MA
authors.lastname: Finan
keywords: CUSUM; Endometrial cancer; Learning curve; Robotic surgery