Determining the component-based operative time learning curve for robotic-assisted radical prostatectomy Article Swipe
David Ambinder
,
Shu Wang
,
Mohummad Minhaj Siddiqui
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1097/cu9.0000000000000119
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1097/cu9.0000000000000119
Objectives To determine the learning curve (LC) of total operative time and the discrete components of the robotic-assisted radical prostatectomy (RARP) for a recent robotic fellowship‐trained urologic surgeon. Materials and methods We performed a retrospective analysis of RARP procedures performed by a single new attending surgeon from August 2015 to April 2019. Patients' demographics and operative details were assessed. Total operative time was divided and prospectively recorded in 7 parts: ( a ) docking robot, ( b ) dissecting seminal vesicles (SVs) ( c ) dissecting endopelvic fascia (EPF), ( d ) incising bladder neck (BN), ( e ) completing the dissection, ( f ) lymph node dissection, and ( g ) urethrovesical anastomosis (UVA) and robot undocking. Cumulative sum analysis was used to ascertain the LC for total operative time and the 7 parts of the procedure. Results One hundred twenty consecutive RARPs were performed. The LC was overcome at 25 cases for total operative time, 13 cases for docking the robot, 33 cases for dissecting SVs, 31 cases for dissecting EPF, 46 cases for incising BN, 38 cases for prostate dissection, 25 cases for lymph node dissection, and 52 cases for UVA. Total operative time was decreased 22.8% ( p < 0.0001) and time for robot docking, dissecting SVs, dissecting EPF, incising BN, completing prostate dissection, lymph node dissection, and UVA were decreased 16.7%, 30.5%, 29.5%, 36.2%, 37.3%, 32.2%, and 26.9%, respectively (all p < 0.05). Conclusions We observed a 25-case LC for a fellowship-trained urologist to achieve stable operative performance of RARP surgery. Procedural components demonstrated variable LCs including the UVA that required upward of 52 cases.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1097/cu9.0000000000000119
- OA Status
- diamond
- Cited By
- 6
- References
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- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4293585741Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1097/cu9.0000000000000119Digital Object Identifier
- Title
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Determining the component-based operative time learning curve for robotic-assisted radical prostatectomyWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-08-30Full publication date if available
- Authors
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David Ambinder, Shu Wang, Mohummad Minhaj SiddiquiList of authors in order
- Landing page
-
https://doi.org/10.1097/cu9.0000000000000119Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1097/cu9.0000000000000119Direct OA link when available
- Concepts
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Medicine, Prostatectomy, Dissection (medical), Lymph node, Prostate cancer, Surgery, Urology, Robotic surgery, Demographics, Fascia, Cancer, Internal medicine, Sociology, DemographyTop concepts (fields/topics) attached by OpenAlex
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6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 2, 2023: 1Per-year citation counts (last 5 years)
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17Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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