The learning curve for robotic distal pancreatectomy: an analysis of outcomes of the first 100 consecutive cases at a high‐volume pancreatic centre. Issue 7 (July 2015)

• Record Type: Journal Article
• Authors: Shakir, Murtaza; Boone, Brian A.; Polanco, Patricio M.; Zenati, Mazen S.; Hogg, Melissa E.; Tsung, Allan; Choudry, Haroon A.; Moser, A. James; Bartlett, David L.; Zeh, Herbert J.; Zureikat, Amer H.
• Is Part Of: HPB. Volume 17:Issue 7(2015:Jul.)
• Page Start: 580
• Page End: 586
• Publication Date: 2015-07
• Subjects: Liver -- Diseases -- Periodicals; Biliary tract -- Diseases -- Periodicals; Pancreas -- Diseases -- Periodicals; 616.362005
• DOI: 10.1111/hpb.12412
• Languages: English
• ISSNs: 1365-182X

Abstract

Abstract: Background: Robotic distal pancreatectomy (RDP) is performed increasingly, but knowledge of the number of cases required to attain procedural proficiency is lacking. The aim of this study was to identify the learning curve associated with RDP at a high‐volume pancreatic centre. Methods: Metrics of perioperative safety and efficiency for all consecutive RDPs were evaluated. Outcomes were followed to 90 days. Cumulative sum (CUSUM) analysis was used to identify inflexion points corresponding to the learning curve. Results: Between 2008 and 2013, 100 patients underwent RDP. There was no 90‐day mortality. In two patients (2.0%), surgery was converted to laparotomy. Thirty procedures were performed for pancreatic adenocarcinoma. Precipitous operative time reductions from an initial operative time of 331 min were observed after the first 20 and 40 cases to 266 min and 210 min, respectively ( P < 0.0001). The likelihood of readmission was significantly lower after the first 40 cases ( P = 0.04), and non‐significant reductions were observed in incidences of major (Clavien–Dindo Grade II or higher) morbidity and Grade B and C leaks, and length of stay. Conclusions: In this experience, RDP outcomes were optimized after 40 cases. Familiarity with the platform and dedicated training are likely to contribute to significantly shorter learning curves in future adopters.