International Expert Consensus on Metric-based Characterization of Robot-assisted Partial Nephrectomy.

Construct validation Proficiency-based metrics Proficiency-based training Robot-assisted partial nephrectomy Surgical training

Journal

European urology focus
ISSN: 2405-4569
Titre abrégé: Eur Urol Focus
Pays: Netherlands
ID NLM: 101665661

Informations de publication

Date de publication:
03 2023
Historique:
received: 17 07 2022
revised: 11 09 2022
accepted: 28 09 2022
medline: 4 4 2023
pubmed: 14 10 2022
entrez: 13 10 2022
Statut: ppublish

Résumé

Robot-assisted partial nephrectomy (RAPN) training usually takes place in vivo, and methods vary across countries/institutions. No common system exists to objectively assess trainee ability to perform RAPN at predetermined performance levels prior to in vivo practice. The identification of objective performance metrics for RAPN training is a crucial starting point to improve training and surgical outcomes. We sought to identify objective performance metrics that best characterize a reference approach to RAPN, and obtain face and content validity from procedure experts through a modified Delphi meeting. During a series of online meetings, a core metrics team of three RAPN experts and a senior behavioral scientist performed a detailed task deconstruction of a transperitoneal left-sided RAPN procedure. Based on published guidelines, manufacturers' instructions, and unedited videos of RAPN, the team identified performance metrics that constitute an optimal approach for training purposes. The metrics were then subjected to an in-person modified international Delphi panel meeting with 19 expert surgeons. Eleven procedure phases, with 64 procedure steps, 43 errors, and 39 critical errors, were identified. After the modified Delphi process, the international expert panel added 13 metrics (two steps), six were deleted, and three were modified; 100% panel consensus on the resulting metrics was obtained. Limitations are that the metrics are applicable only to left-sided RAPN cases and some might have been excluded. Performance metrics that accurately characterize RAPN procedure were developed by a core group of experts. The metrics were then presented to and endorsed by an international panel of very experienced peers. Reliable and valid metrics underpin effective, quality-assured, structured surgical training for RAPN. We organize a meeting among robot-assisted partial nephrectomy (RAPN) experts to identify and reach consensus on objective performance metrics for RAPN training. The metrics are a crucial starting point to improve and quality assure surgical training and patients' clinical outcomes.

Sections du résumé

BACKGROUND
Robot-assisted partial nephrectomy (RAPN) training usually takes place in vivo, and methods vary across countries/institutions. No common system exists to objectively assess trainee ability to perform RAPN at predetermined performance levels prior to in vivo practice. The identification of objective performance metrics for RAPN training is a crucial starting point to improve training and surgical outcomes.
OBJECTIVE
We sought to identify objective performance metrics that best characterize a reference approach to RAPN, and obtain face and content validity from procedure experts through a modified Delphi meeting.
DESIGN, SETTING, AND PARTICIPANTS
During a series of online meetings, a core metrics team of three RAPN experts and a senior behavioral scientist performed a detailed task deconstruction of a transperitoneal left-sided RAPN procedure.
OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS
Based on published guidelines, manufacturers' instructions, and unedited videos of RAPN, the team identified performance metrics that constitute an optimal approach for training purposes. The metrics were then subjected to an in-person modified international Delphi panel meeting with 19 expert surgeons.
RESULTS AND LIMITATIONS
Eleven procedure phases, with 64 procedure steps, 43 errors, and 39 critical errors, were identified. After the modified Delphi process, the international expert panel added 13 metrics (two steps), six were deleted, and three were modified; 100% panel consensus on the resulting metrics was obtained. Limitations are that the metrics are applicable only to left-sided RAPN cases and some might have been excluded.
CONCLUSIONS
Performance metrics that accurately characterize RAPN procedure were developed by a core group of experts. The metrics were then presented to and endorsed by an international panel of very experienced peers. Reliable and valid metrics underpin effective, quality-assured, structured surgical training for RAPN.
PATIENT SUMMARY
We organize a meeting among robot-assisted partial nephrectomy (RAPN) experts to identify and reach consensus on objective performance metrics for RAPN training. The metrics are a crucial starting point to improve and quality assure surgical training and patients' clinical outcomes.

Identifiants

pubmed: 36229343
pii: S2405-4569(22)00226-7
doi: 10.1016/j.euf.2022.09.017
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

388-395

Informations de copyright

Copyright © 2022 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Auteurs

Rui Farinha (R)

Orsi Academy, Melle, Belgium; Department of Urology, Onze-Lieve-Vrouw Ziekenhuis, Aalst, Belgium; Department of Urology, São José Hospital, Lisbon, Portugal; Lusíadas Hospital, Lisbon, Portugal. Electronic address: ruifarinhaurologia@gmail.com.

Alberto Breda (A)

Department of Urology, Fundación Puigvert, Universitat Autonoma de Barcelona, Spain.

James Porter (J)

Swedish Urology Group, Swedish Medical Center, Seattle, WA, USA.

Alexandre Mottrie (A)

Orsi Academy, Melle, Belgium; Department of Urology, Onze-Lieve-Vrouw Ziekenhuis, Aalst, Belgium.

Ben Van Cleynenbreugel (B)

Department of Urology, University Hospitals Leuven, Leuven, Belgium; Department of Development and Regeneration, KU Leuven, Leuven, Belgium.

Jozef Vander Sloten (J)

Department of Mechanical Engineering, Section of Biomechanics, KU Leuven, Leuven, Belgium.

Angelo Mottaran (A)

Division of Urology, IRCCS Azienda Ospedaliero-Universitaris di Bologna, Bologna, Italy; University of Bologna, Bologna, Italy.

Anthony G Gallagher (AG)

Orsi Academy, Melle, Belgium; Faculty of Medicine, KU Leuven, Leuven, Belgium; Faculty of Life and Health Sciences, Ulster University, Derry, Northern Ireland, UK.

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Classifications MeSH