Learning Curve in Robot-Assisted Laparoscopic Liver Resection.

BACKGROUND

The objective of this study was to evaluate the learning curve effect on the safety and feasibility of robot-assisted liver resection (RALR).

METHODS

In 140 consecutive cases, all data about demographic, surgical procedure, postoperative course were collected prospectively and analyzed. Risk-adjusted cumulative sum model was used for determining the learning curve based on the need for conversion.

RESULTS

Among all 140 patients, no patients suffered from any organ dysfunction postoperatively and the operative mortality was 0%. The CUSUM analysis showed that at the 30th consecutive patient, the open conversion rate reached to the average value, and it further improved thereafter. In the last 70 patients, only 3 patients (4.3%) required conversion and 7 patients (10%) needed blood transfusion. Only 1 patient (1.3%) out of 79 patients with HCC had a positive resection margin. Univariate analyses showed the following risk factors associated with significantly higher risks of conversion (P < 0.05): tumor number > 1, lesions in segments 1/4a/7/8, right posterior sectionectomy, and lesions which were beyond the indications of the Louisville statement. Multivariate logistic analysis revealed that both tumor number > 1 (OR: 2.10, P < 0.05) and right posterior sectionectomy (OR: 11.19, P < 0.01) were risk factors of conversion.

CONCLUSIONS

The robotic approach for hepatectomy is safe and feasible. A learning curve effect was demonstrated in this study after the 30th consecutive patient. The long-term oncological outcomes of robotic hepatectomy still need further investigation.