Literature DB >> 35927351

Robotic pancreatoduodenectomy: trends in technique and training challenges.

Catherine H Davis1,2, Miral S Grandhi1,2, Victor P Gazivoda1,2, Alissa Greenbaum1,2, Timothy J Kennedy1,2, Russell C Langan1,2,3, H Richard Alexander1,2, Henry A Pitt1,2, David A August4,5.   

Abstract

BACKGROUND: More complex cases are being performed robotically. This study aims to characterize trends in robotic pancreatoduodenectomy (RPD) over time and assess opportunities for advanced trainees.
METHODS: Using the ACS-NSQIP database from 2014 to 2019, PD cases were characterized by operative approach (open-OPN, laparoscopic-LAP, robotic-ROB). Proficiency and postoperative outcomes were described by approach over time.
RESULTS: 24,268 PDs were identified, with the ROB approach increasing from 2.8% to 7.5%. Unplanned conversion increased over time for LAP (27.7-39.0%, p = 0.003) but was unchanged for ROB cases (14.8-14.7%, p = 0.257). Morbidity increased for OPN PD (35.5-36.8%, p = 0.041) and decreased for ROB PD (38.7-30.3%, p = 0.010). Mean LOS was lower in ROB than LAP/OPN (9.5 vs. 10.9 vs. 10.9 days, p < 0.00001). Approximately, 100 AHPBA, SSO, and ASTS fellows are being trained each year in North America; however, only about 5 RPDs are available per trainee per year which is far below that recommended to achieve proficiency.
CONCLUSION: Over a 6-year period, a significant increase was observed in the use of RPD without a concomitant increase in conversion rates. RPD was associated with decreased morbidity and length of stay. Despite this shift, the number of cases being performed is not adequate for all fellows to achieve proficiency before graduation.
© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Entities:  

Keywords:  Minimally invasive surgery; Pancreas cancer; Pancreatoduodenectomy; Robotic surgery; Surgical education; Surgical training

Year:  2022        PMID: 35927351     DOI: 10.1007/s00464-022-09469-3

Source DB:  PubMed          Journal:  Surg Endosc        ISSN: 0930-2794            Impact factor:   3.453


  40 in total

1.  Predictors of morbidity and mortality after hepatectomy in elderly patients: analysis of 7621 NSQIP patients.

Authors:  Ching-Wei D Tzeng; Amanda B Cooper; Jean-Nicolas Vauthey; Steven A Curley; Thomas A Aloia
Journal:  HPB (Oxford)       Date:  2013-08-26       Impact factor: 3.647

Review 2.  The 2016 update of the International Study Group (ISGPS) definition and grading of postoperative pancreatic fistula: 11 Years After.

Authors:  Claudio Bassi; Giovanni Marchegiani; Christos Dervenis; Micheal Sarr; Mohammad Abu Hilal; Mustapha Adham; Peter Allen; Roland Andersson; Horacio J Asbun; Marc G Besselink; Kevin Conlon; Marco Del Chiaro; Massimo Falconi; Laureano Fernandez-Cruz; Carlos Fernandez-Del Castillo; Abe Fingerhut; Helmut Friess; Dirk J Gouma; Thilo Hackert; Jakob Izbicki; Keith D Lillemoe; John P Neoptolemos; Attila Olah; Richard Schulick; Shailesh V Shrikhande; Tadahiro Takada; Kyoichi Takaori; William Traverso; Charles R Vollmer; Christopher L Wolfgang; Charles J Yeo; Roberto Salvia; Marcus Buchler
Journal:  Surgery       Date:  2016-12-28       Impact factor: 3.982

3.  Training in Minimally Invasive Pancreatic Resections: a paradigm shift away from "See one, Do one, Teach one".

Authors:  Melissa E Hogg; Marc G Besselink; Pierre-Alain Clavien; Abe Fingerhut; D Rohan Jeyarajah; David A Kooby; A James Moser; Henry A Pitt; Oliver A Varban; Charles M Vollmer; Herbert J Zeh; Paul Hansen
Journal:  HPB (Oxford)       Date:  2017-02-10       Impact factor: 3.647

4.  Evolution of a Novel Robotic Training Curriculum in a Complex General Surgical Oncology Fellowship.

Authors:  L Mark Knab; Mazen S Zenati; Anton Khodakov; Maryjoe Rice; Amr Al-Abbas; David L Bartlett; Amer H Zureikat; Herbert J Zeh; Melissa E Hogg
Journal:  Ann Surg Oncol       Date:  2018-08-02       Impact factor: 5.344

5.  National Trends in Robotic Pancreas Surgery.

Authors:  Richard S Hoehn; Ibrahim Nassour; Mohamed A Adam; Sharon Winters; Alessandro Paniccia; Amer H Zureikat
Journal:  J Gastrointest Surg       Date:  2020-04-20       Impact factor: 3.452

Review 6.  Developing a robotic pancreas program: the Dutch experience.

Authors:  Carolijn L Nota; Maurice J Zwart; Yuman Fong; Jeroen Hagendoorn; Melissa E Hogg; Bas Groot Koerkamp; Marc G Besselink; I Quintus Molenaar
Journal:  J Vis Surg       Date:  2017-08-21

7.  The Learning Curve in Robotic Pancreaticoduodenectomy.

Authors:  N Napoli; E F Kauffmann; M Palmeri; M Miccoli; F Costa; F Vistoli; G Amorese; Ugo Boggi
Journal:  Dig Surg       Date:  2016-05-25       Impact factor: 2.588

8.  Formal robotic training diminishes the learning curve for robotic pancreatoduodenectomy: Implications for new programs in complex robotic surgery.

Authors:  Carl R Schmidt; Britney R Harris; Kelsey A Musgrove; Pavan Rao; J Wallis Marsh; Alan A Thomay; Melissa E Hogg; Herbert J Zeh; Amer H Zureikat; Brian A Boone
Journal:  J Surg Oncol       Date:  2020-11-02       Impact factor: 3.454

9.  Learning curves for robotic pancreatic surgery-from distal pancreatectomy to pancreaticoduodenectomy.

Authors:  Bor-Uei Shyr; Shih-Chin Chen; Yi-Ming Shyr; Shin-E Wang
Journal:  Medicine (Baltimore)       Date:  2018-11       Impact factor: 1.889

10.  Assessment of quality outcomes for robotic pancreaticoduodenectomy: identification of the learning curve.

Authors:  Brian A Boone; Mazen Zenati; Melissa E Hogg; Jennifer Steve; Arthur James Moser; David L Bartlett; Herbert J Zeh; Amer H Zureikat
Journal:  JAMA Surg       Date:  2015-05       Impact factor: 14.766

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