Mitigation of the Robotic Pancreaticoduodenectomy Learning Curve through comprehensive training.


Journal

HPB : the official journal of the International Hepato Pancreato Biliary Association
ISSN: 1477-2574
Titre abrégé: HPB (Oxford)
Pays: England
ID NLM: 100900921

Informations de publication

Date de publication:
10 2021
Historique:
received: 19 08 2020
revised: 05 11 2020
accepted: 15 03 2021
pubmed: 28 4 2021
medline: 27 1 2022
entrez: 27 4 2021
Statut: ppublish

Résumé

There is an associated lag in achieving competency for robotic pancreaticoduodenectomy (PD), resulting in a learning curve. We hypothesize that the reported learning curve can be mitigated through a comprehensive graduated training protocol. All patients (n = 237) who underwent an open (n = 197, 83.1%) or robotic (n = 40, 16.9%) PD between 2015-2019 were identified at The Ohio State University. The learning curve for operative time and surgical failure (defined as conversion to open, blood transfusion, or Clavien-Dindo complication grade ≥3) was analyzed using a risk adjusted cumulative summation technique. After 10 cases, operative time plateaued to a mean of 468.3 ± 96.3 minutes for robotic PD versus a mean of 332.5 ± 103.9 minutes for open PD (P < 0.001). There was no further apparent learning curve over time relative to rates of operative time or surgical failure. After propensity score-matching, patients undergoing robotic PD had a similar incidence of major complications, grade B/C postoperative pancreatic fistula, and delayed gastric emptying versus patients undergoing open PD (all P > 0.05). Completion of a comprehensive procedure-specific robotic training protocol for PD mitigated the learning curve for this operative approach by shifting the curve into the training/simulation phase rather than the live operating phase. These data hold important implications for the future training and accreditation of surgeons embarking on robotic PD.

Sections du résumé

BACKGROUND
There is an associated lag in achieving competency for robotic pancreaticoduodenectomy (PD), resulting in a learning curve. We hypothesize that the reported learning curve can be mitigated through a comprehensive graduated training protocol.
METHODS
All patients (n = 237) who underwent an open (n = 197, 83.1%) or robotic (n = 40, 16.9%) PD between 2015-2019 were identified at The Ohio State University. The learning curve for operative time and surgical failure (defined as conversion to open, blood transfusion, or Clavien-Dindo complication grade ≥3) was analyzed using a risk adjusted cumulative summation technique.
RESULTS
After 10 cases, operative time plateaued to a mean of 468.3 ± 96.3 minutes for robotic PD versus a mean of 332.5 ± 103.9 minutes for open PD (P < 0.001). There was no further apparent learning curve over time relative to rates of operative time or surgical failure. After propensity score-matching, patients undergoing robotic PD had a similar incidence of major complications, grade B/C postoperative pancreatic fistula, and delayed gastric emptying versus patients undergoing open PD (all P > 0.05).
CONCLUSION
Completion of a comprehensive procedure-specific robotic training protocol for PD mitigated the learning curve for this operative approach by shifting the curve into the training/simulation phase rather than the live operating phase. These data hold important implications for the future training and accreditation of surgeons embarking on robotic PD.

Identifiants

pubmed: 33903049
pii: S1365-182X(21)00092-7
doi: 10.1016/j.hpb.2021.03.010
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1550-1556

Informations de copyright

Copyright © 2021 International Hepato-Pancreato-Biliary Association Inc. Published by Elsevier Ltd. All rights reserved.

Auteurs

Da Y Ryoo (DY)

The Ohio State University, Columbus, OH, USA.

Mariam F Eskander (MF)

The Ohio State University, Columbus, OH, USA.

Ahmad Hamad (A)

The Ohio State University, Columbus, OH, USA.

Yaming Li (Y)

The Ohio State University, Columbus, OH, USA.

Jordan Cloyd (J)

The Ohio State University, Columbus, OH, USA.

Andrei Manilchuk (A)

The Ohio State University, Columbus, OH, USA.

Allan Tsung (A)

The Ohio State University, Columbus, OH, USA.

Timothy M Pawlik (TM)

The Ohio State University, Columbus, OH, USA.

Mary Dillhoff (M)

The Ohio State University, Columbus, OH, USA.

Carl Schmidt (C)

West Virginia University, Morgantown, WV, USA.

Aslam Ejaz (A)

The Ohio State University, Columbus, OH, USA. Electronic address: aslam.ejaz@osumc.edu.

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Classifications MeSH