Three Different Learning Curves Have an Independent Impact on Perioperative Outcomes After Robotic Partial Nephrectomy: A Comparative Analysis

• Published in: Annals of surgical oncology 2021-02, Vol.28 (2), p.1254-1261
• Main Authors: Zeuschner, Philip, Meyer, Irmengard, Siemer, Stefan, Stoeckle, Michael, Wagenpfeil, Gudrun, Wagenpfeil, Stefan, Saar, Matthias, Janssen, Martin
• Format: Article
• Language: English
• Subjects: Comparative analysis; Female; Hospitals; Humans; Ischemia; Kidney Neoplasms - surgery; Learning; Learning Curve; Male; Medicine; Medicine & Public Health; Nephrectomy; Oncology; Pediatrics; Regression analysis; Retrospective Studies; Robotic surgery; Robotic Surgical Procedures; Robotics; Robots; Surgery; Surgical Oncology; Surgical outcomes; Treatment Outcome; Tumors; Urologic Oncology; Urology; Values; Variables
• ISSN: 1068-9265; 1534-4681
• DOI: 10.1245/s10434-020-08856-1

Background Robot-assisted partial nephrectomy (RAPN) has become widely accepted, but its different underlying types of learning curves have not been comparatively analyzed to date. This study aimed to determine and compare the impact that the learning curve of the department, the console surgeon, and the bedside assistant as well as patient-related factors has on the perioperative outcomes of RAPN. Methods The study retrospectively analyzed 500 consecutive transperitoneal RAPNs (2007–2018) performed in a tertiary referral center by 7 surgeons and 37 bedside assistants. Patient characteristics and surgical data were obtained. Experience (EXP) was defined as the current number of RAPNs performed by the department, the surgeon, and the assistant. As the primary outcome, the impact of EXP and patient-related factors on perioperative outcomes were analyzed and compared. As the secondary outcome, a cutoff between “experienced” and “inexperienced” was defined. Correlation and regression analysis, receiver operating characteristic curve analysis, Fisher’s exact test, and the Mann–Whitney U test were performed, with p values lower than 0.05 denoting significance. Results The EXP of the department, the surgeon, and the assistant each has a major influence on perioperative outcome in RAPN irrespective of patient-related factors. Perioperative outcomes improve significantly with EXP greater than 100 for the department, EXP greater than 35 for the surgeon, and EXP greater than 15 for the assistant. Conclusions The perioperative results of RAPN are influenced by three different types of learning curves including those for the surgical department, the console surgeon, and the assistant. The influence of the bedside assistant clearly has been underestimated to date because it has a significant impact on the perioperative outcomes of RAPN.