Analysis of training pathway to reach expert performance levels based on proficiency-based progression in robotic-assisted minimally invasive esophagectomy (RAMIE)

BACKGROUND

Robotic-assisted minimally invasive esophagectomy (RAMIE) was first introduced in 2003 and has since then shown to significantly improve the postoperative course. Previous studies have shown that a structured training pathway based on proficiency-based progression using individual skill levels as measures of reach of competence can enhance surgical performance. The aim of this study was to evaluate and help understand our pathway to reach surgical expert levels using a proficiency-based approach introducing RAMIE at our German high-volume center.

METHODS

All patients undergoing RAMIE performed by two experienced surgeons for esophageal cancer since the introduction of the robotic technique in 2017 was included in this analysis. Intraoperative outcomes and postoperative outcomes were included in the analysis. The cumulative sum method was used to analyze how many cases are needed to reach expert levels for different performance characteristics and skill sets during robotic-assisted minimally invasive esophagectomy.

RESULTS

From 06/2017 to 03/2022, a total of 154 patients underwent RAMIE at our facility and were included in the analysis. An advancement in performance level was observed for total operating time after 70 cases and for thoracic operative time after 79 cases. Lymph node yield showed an increase up until case 60 in the CUSUM analysis. Length of hospital stay stabilized after case 55. The CCI score inflection point was at case 55 in both CUSUM and regression analyses. Anastomotic leak rate stabilized at case 38 and showed another inflection point after case 83.

CONCLUSION

Our data and analysis showed the progression from proficient to expert performance levels during the implementation of RAMIE at a European high-volume center. Further analysis of surgeons, especially with a different training status has yet to reveal if the caseloads found in this study are universally applicable. However, skill acquisition and respective measures of such are diverse and as a great range of number of cases was observed, we believe that the learning curve and ascent in performance levels cannot be defined by one parameter alone.

Mapping the Lymphatic Drainage Pattern of Esophageal Cancer with Near-Infrared Fluorescent Imaging during Robotic Assisted Minimally Invasive Ivor Lewis Esophagectomy (RAMIE)-First Results of the Prospective ESOMAP Feasibility Trial

While the sentinel lymph node concept is routinely applied in other surgical fields, no established and valid modality for lymph node mapping for esophageal cancer surgery currently exists. Near-infrared light fluorescence (NIR) using indocyanine green (ICG) has been recently proven to be a safe technology for peritumoral injection and consecutive lymph node mapping in small surgical cohorts, mostly without the usage of robotic technology. The aim of this study was to identify the lymphatic drainage pattern of esophageal cancer during highly standardized RAMIE and to correlate the intraoperative images with the histopathological dissemination of lymphatic metastases. Patients with clinically advanced stage squamous cell carcinoma or adenocarcinoma of the esophagus undergoing a RAMIE at our Center of Excellence for Surgery of the Upper Gastrointestinal Tract were prospectively included in this study. Patients were admitted on the day prior to surgery, and an additional EGD with endoscopic injection of the ICG solution around the tumor was performed. Intraoperative imaging procedures were performed using the Stryker 1688 or the FIREFLY fluorescence imaging system, and resected lymph nodes were sent to pathology. A total of 20 patients were included in the study, and feasibility and safety for the application of NIR using ICG during RAMIE were shown. NIR imaging to detect lymph node metastases can be safely performed during RAMIE. Further analyses in our center will focus on pathological analyses of ICG-positive tissue and quantification using artificial intelligence tools with a correlation of long-term follow-up data.