Successful incorporation of robotic surgery into gynecologic oncology fellowship training

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Background The increasing role of robotic surgery in gynecologic oncology may impact fellowship training. The purpose of this study was to review the proportion of robotic procedures performed by fellows at the console, and compare operative times and lymph node yields to faculty surgeons. Methods A prospective database of women undergoing robotic gynecologic surgery has been maintained since 2008. Intra-operative datasheets completed include surgical times and primary surgeon at the console. Operative times were compared between faculty and fellows for simple hysterectomy (SH), bilateral salpingo- oophorectomy (BSO), pelvic (PLND) and paraaortic lymph node dissection (PALND) and vaginal cuff closure (VCC). Lymph nodes counts were also compared. Results Times were recorded for 239 SH, 43 BSOs, 105 right PLNDs, 104 left PLNDs, 34 PALND and 269 VCC. Comparing 2008 to 2011, procedures performed by the fellow significantly increased; SH 16% to 83% (p < 0.001), BSO 7% to 75% (p = 0.005), right PLND 4% to 44% (p < 0.001), left PLND 0% to 56% (p < 0.001), and VCC 59% to 82% (p = 0.024). Console times (min) were similar for SH (60 vs. 63, p = 0.73), BSO (48 vs. 43, p = 0.55), and VCC (20 vs. 22, p = 0.26). Faculty times (min) were shorter for PLND (right 26 vs. 30, p = 0.04, left 23 vs. 27, p = 0.02). Nodal counts were not significantly different (right 7 vs. 8, p = 0.17 or left 7 vs. 7, p = 0.87). Conclusions Robotic surgery can be successfully incorporated into gynecologic oncology fellowship training. With increased exposure to robotic surgery, fellows had similar operative times and lymph node yields as faculty surgeons.

Original languageEnglish (US)
Pages (from-to)730-733
Number of pages4
JournalGynecologic oncology
Volume131
Issue number3
DOIs
StatePublished - Dec 1 2013

Fingerprint

Gynecologic Surgical Procedures
Robotics
Operative Time
Hysterectomy
Ovariectomy
Lymph Nodes
Lymph Node Excision
Databases
Surgeons

Keywords

  • Fellowship training
  • Gynecologic cancers
  • Learning curve
  • Minimally invasive surgery
  • Robotic surgery

ASJC Scopus subject areas

  • Obstetrics and Gynecology
  • Oncology

Cite this

Successful incorporation of robotic surgery into gynecologic oncology fellowship training. / Soliman, Pamela Therese; Iglesias, David; Munsell, Mark F.; Frumovitz, Michael; Westin, Shannon Neville; Nick, Alpa Manchandia; Schmeler, Kathleen M; Ramirez, Pedro Tomas.

In: Gynecologic oncology, Vol. 131, No. 3, 01.12.2013, p. 730-733.

Research output: Contribution to journalArticle

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abstract = "Background The increasing role of robotic surgery in gynecologic oncology may impact fellowship training. The purpose of this study was to review the proportion of robotic procedures performed by fellows at the console, and compare operative times and lymph node yields to faculty surgeons. Methods A prospective database of women undergoing robotic gynecologic surgery has been maintained since 2008. Intra-operative datasheets completed include surgical times and primary surgeon at the console. Operative times were compared between faculty and fellows for simple hysterectomy (SH), bilateral salpingo- oophorectomy (BSO), pelvic (PLND) and paraaortic lymph node dissection (PALND) and vaginal cuff closure (VCC). Lymph nodes counts were also compared. Results Times were recorded for 239 SH, 43 BSOs, 105 right PLNDs, 104 left PLNDs, 34 PALND and 269 VCC. Comparing 2008 to 2011, procedures performed by the fellow significantly increased; SH 16{\%} to 83{\%} (p < 0.001), BSO 7{\%} to 75{\%} (p = 0.005), right PLND 4{\%} to 44{\%} (p < 0.001), left PLND 0{\%} to 56{\%} (p < 0.001), and VCC 59{\%} to 82{\%} (p = 0.024). Console times (min) were similar for SH (60 vs. 63, p = 0.73), BSO (48 vs. 43, p = 0.55), and VCC (20 vs. 22, p = 0.26). Faculty times (min) were shorter for PLND (right 26 vs. 30, p = 0.04, left 23 vs. 27, p = 0.02). Nodal counts were not significantly different (right 7 vs. 8, p = 0.17 or left 7 vs. 7, p = 0.87). Conclusions Robotic surgery can be successfully incorporated into gynecologic oncology fellowship training. With increased exposure to robotic surgery, fellows had similar operative times and lymph node yields as faculty surgeons.",
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author = "Soliman, {Pamela Therese} and David Iglesias and Munsell, {Mark F.} and Michael Frumovitz and Westin, {Shannon Neville} and Nick, {Alpa Manchandia} and Schmeler, {Kathleen M} and Ramirez, {Pedro Tomas}",
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AU - Soliman, Pamela Therese

AU - Iglesias, David

AU - Munsell, Mark F.

AU - Frumovitz, Michael

AU - Westin, Shannon Neville

AU - Nick, Alpa Manchandia

AU - Schmeler, Kathleen M

AU - Ramirez, Pedro Tomas

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N2 - Background The increasing role of robotic surgery in gynecologic oncology may impact fellowship training. The purpose of this study was to review the proportion of robotic procedures performed by fellows at the console, and compare operative times and lymph node yields to faculty surgeons. Methods A prospective database of women undergoing robotic gynecologic surgery has been maintained since 2008. Intra-operative datasheets completed include surgical times and primary surgeon at the console. Operative times were compared between faculty and fellows for simple hysterectomy (SH), bilateral salpingo- oophorectomy (BSO), pelvic (PLND) and paraaortic lymph node dissection (PALND) and vaginal cuff closure (VCC). Lymph nodes counts were also compared. Results Times were recorded for 239 SH, 43 BSOs, 105 right PLNDs, 104 left PLNDs, 34 PALND and 269 VCC. Comparing 2008 to 2011, procedures performed by the fellow significantly increased; SH 16% to 83% (p < 0.001), BSO 7% to 75% (p = 0.005), right PLND 4% to 44% (p < 0.001), left PLND 0% to 56% (p < 0.001), and VCC 59% to 82% (p = 0.024). Console times (min) were similar for SH (60 vs. 63, p = 0.73), BSO (48 vs. 43, p = 0.55), and VCC (20 vs. 22, p = 0.26). Faculty times (min) were shorter for PLND (right 26 vs. 30, p = 0.04, left 23 vs. 27, p = 0.02). Nodal counts were not significantly different (right 7 vs. 8, p = 0.17 or left 7 vs. 7, p = 0.87). Conclusions Robotic surgery can be successfully incorporated into gynecologic oncology fellowship training. With increased exposure to robotic surgery, fellows had similar operative times and lymph node yields as faculty surgeons.

AB - Background The increasing role of robotic surgery in gynecologic oncology may impact fellowship training. The purpose of this study was to review the proportion of robotic procedures performed by fellows at the console, and compare operative times and lymph node yields to faculty surgeons. Methods A prospective database of women undergoing robotic gynecologic surgery has been maintained since 2008. Intra-operative datasheets completed include surgical times and primary surgeon at the console. Operative times were compared between faculty and fellows for simple hysterectomy (SH), bilateral salpingo- oophorectomy (BSO), pelvic (PLND) and paraaortic lymph node dissection (PALND) and vaginal cuff closure (VCC). Lymph nodes counts were also compared. Results Times were recorded for 239 SH, 43 BSOs, 105 right PLNDs, 104 left PLNDs, 34 PALND and 269 VCC. Comparing 2008 to 2011, procedures performed by the fellow significantly increased; SH 16% to 83% (p < 0.001), BSO 7% to 75% (p = 0.005), right PLND 4% to 44% (p < 0.001), left PLND 0% to 56% (p < 0.001), and VCC 59% to 82% (p = 0.024). Console times (min) were similar for SH (60 vs. 63, p = 0.73), BSO (48 vs. 43, p = 0.55), and VCC (20 vs. 22, p = 0.26). Faculty times (min) were shorter for PLND (right 26 vs. 30, p = 0.04, left 23 vs. 27, p = 0.02). Nodal counts were not significantly different (right 7 vs. 8, p = 0.17 or left 7 vs. 7, p = 0.87). Conclusions Robotic surgery can be successfully incorporated into gynecologic oncology fellowship training. With increased exposure to robotic surgery, fellows had similar operative times and lymph node yields as faculty surgeons.

KW - Fellowship training

KW - Gynecologic cancers

KW - Learning curve

KW - Minimally invasive surgery

KW - Robotic surgery

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