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Determining the component-based operative time learning curve for robotic-assisted radical prostatectomy
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 performe...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Lippincott Williams & Wilkins
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9875207/ https://www.ncbi.nlm.nih.gov/pubmed/36714228 http://dx.doi.org/10.1097/CU9.0000000000000119 |
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author | Ambinder, David Wang, Shu Siddiqui, Mohummad Minhaj |
author_facet | Ambinder, David Wang, Shu Siddiqui, Mohummad Minhaj |
author_sort | Ambinder, David |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9875207 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-98752072023-01-26 Determining the component-based operative time learning curve for robotic-assisted radical prostatectomy Ambinder, David Wang, Shu Siddiqui, Mohummad Minhaj Curr Urol Special Topic: Advances in Prostate Cancer Therapy: Original Articles 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. Lippincott Williams & Wilkins 2022-12 2022-08-31 /pmc/articles/PMC9875207/ /pubmed/36714228 http://dx.doi.org/10.1097/CU9.0000000000000119 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) , where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. |
spellingShingle | Special Topic: Advances in Prostate Cancer Therapy: Original Articles Ambinder, David Wang, Shu Siddiqui, Mohummad Minhaj Determining the component-based operative time learning curve for robotic-assisted radical prostatectomy |
title | Determining the component-based operative time learning curve for robotic-assisted radical prostatectomy |
title_full | Determining the component-based operative time learning curve for robotic-assisted radical prostatectomy |
title_fullStr | Determining the component-based operative time learning curve for robotic-assisted radical prostatectomy |
title_full_unstemmed | Determining the component-based operative time learning curve for robotic-assisted radical prostatectomy |
title_short | Determining the component-based operative time learning curve for robotic-assisted radical prostatectomy |
title_sort | determining the component-based operative time learning curve for robotic-assisted radical prostatectomy |
topic | Special Topic: Advances in Prostate Cancer Therapy: Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9875207/ https://www.ncbi.nlm.nih.gov/pubmed/36714228 http://dx.doi.org/10.1097/CU9.0000000000000119 |
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