[Efficacy and learning curve of 69 cases of robot-assisted resection of retroperitoneal benign tumors].

Zhonghua Wai Ke Za Zhi

Department of Hepatobiliary and Pancreatic Surgery & Retroperitoneal Tumor Surgery, Affiliated Hospital of Qingdao University, Qingdao266001, China.

Published: January 2025

To explore the efficacy of robotic-assisted retroperitoneal benign tumor resection and to analyze its learning curve. This is a retrospective case series study. The data of patients who underwent robotic-assisted retroperitoneal benign tumor resection from August 2015 to February 2023 at the Department of Retroperitoneal Tumor Surgery was analyzed retrospectively. There were 24 males and 45 females, with an age of (46.3±10.6) years (range: 19 to 76 years). The perioperative data, postoperative pathological results, and follow-up data were recorded. The cumulative sum (CUSUM) method was used to analyze the robotic system setup time and operative time to plot the learning curve. A linear regression model was applied to determine the best-fit curve, selecting the model with the highest R² value. Based on the vertex of the learning curve for surgical time, the patients were divided into a learning group and a mastery group. The general data and perioperative conditions of the two groups were compared. Independent sample -tests, Mann-Whitney tests, and tests were used for comparisons. All 69 patients successfully completed the surgery without intraoperative complications. The diameter of tumors was (49.7±18.6) mm (range: 16 to 131 mm). The setup time for the robotic surgical system was (35.3±9.8) minutes (range: 20 to 61 minutes); the surgical time was (169.2±36.5) minutes (range: 70 to 305 minutes); intraoperative blood loss ((IQR)) was 10.0 (15.0) ml (range: 2.0 to 200.0 ml). The tumors in 32 patients (46.4%) were adherent to major blood vessels. All patients were discharged without complications. The follow-up period lasted until February 2024, and no patients required reoperation, readmission, or died due to retroperitoneal benign tumors. There were no severe long-term complications, and no radiological evidence of tumor recurrence was found. The best-fit equation for the learning curve based on surgical time was CUSUM=0.010X³-1.648X²-68.573X-61.091, and the best-fit equation for the learning curve based on robotic system setup time was CUSUM=0.0018X³-0.285X²+10.460X+57.541 (where X represents the number of surgeries). The R² values of 2 learning curve models were 0.953 and 0.957, respectively, and the fit model tests had <0.05. The inflection point of the learning curve based on surgical time was the 28th case, which is considered the minimum number of surgeries required to achieve proficiency in robotic-assisted retroperitoneal benign tumor resection. Based on this, the patients were divided into a learning group (cases 1 to 28) and a mastery group (cases 29 to 69). The surgical time for the learning group was significantly longer than that of the mastery group ((194.7±30.0) minutes (151.9±31.4) minutes, =4.126,<0.01). No statistically significant differences were found for other parameters (all >0.05). Robotic-assisted retroperitoneal benign tumor resection is feasible. The minimum number of surgeries required to achieve proficiency in overcoming the learning curve is about 28 cases.

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Source
http://dx.doi.org/10.3760/cma.j.cn112139-20240918-00428DOI Listing

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