The multiphasic learning curve for robot-assisted rectal surgery

Kevin Kaity Sng, Masayasu Hara, Jae Won Shin, Byung Eun Yoo, Kyung Sook Yang, Seon Hahn Kim

Research output: Contribution to journalArticle

61 Citations (Scopus)

Abstract

Background: Robotic rectal surgery is gaining in popularity. We aimed to define the learning curve of an experienced laparoscopic colorectal surgeon in performing robot-assisted rectal surgery. We hypothesized that there are multiple phases in this learning process. Methods: We performed a retrospective analysis. Consecutive patients who underwent robot-assisted rectal surgery between July 2007 and August 2011 were identified. Operating times were analyzed using the CUSUM (cumulative sum) technique. CUSUMs were model fitted as a fourth-order polynomial. χ2, Fisher's exact, two independent samples t test, one-way ANOVA, Kruskal-Wallis, and Mann-Whitney tests were used. A p value of <0.05 was considered statistically significant. Results: We identified 197 patients. The median (range) total operative, robot, console, and docking times (min) were 265 (145-515), 140 (59-367), 135 (50-360), and 5 (3-40), respectively. CUSUM analysis of docking time showed a learning curve of 35 cases. CUSUM analysis of total operative, robot, and console times demonstrated three phases. The first phase (35 patients) represented the initial learning curve. The second phase (93 patients) involved more challenging cases with increased operative time. The third phase (69 patients) represented the concluding phase in the learning curve. There was increased complexity of cases in the latter two phases. Of phase 1 patients, 45.7 % had tumors ≤7 cm from the anal verge compared to 64.2 % in phases 2 and 3 (p = 0.042). Of phase 1 patients, 2.9 % had neoadjuvant chemoradiotherapy compared to 32.7 % of patients in phases 2 and 3 (p < 0.001). Splenic flexure was mobilized in 8.6 % of phase 1 patients compared to 56.8 % of patients in phases 2 and 3 (p < 0.001). Median blood loss was <50 ml in all three phases. The patients in phases 2 and 3 had a longer hospital stay compared to those in phase 1 (9 vs. 8 days, p = 0.002). There were no conversions. Conclusion: At least three phases in the learning curve for robot-assisted rectal surgery are defined in our study.

Original languageEnglish
Pages (from-to)3297-3307
Number of pages11
JournalSurgical Endoscopy and Other Interventional Techniques
Volume27
Issue number9
DOIs
Publication statusPublished - 2013 Sep 1

Fingerprint

Learning Curve
Transverse Colon
Robotics
Chemoradiotherapy
Operative Time
Length of Stay
Analysis of Variance
Learning

Keywords

  • Colorectal
  • Cumulative sum
  • Learning curve
  • Minimally invasive surgery
  • Rectal cancer
  • Robot-assisted

ASJC Scopus subject areas

  • Surgery

Cite this

The multiphasic learning curve for robot-assisted rectal surgery. / Sng, Kevin Kaity; Hara, Masayasu; Shin, Jae Won; Yoo, Byung Eun; Yang, Kyung Sook; Kim, Seon Hahn.

In: Surgical Endoscopy and Other Interventional Techniques, Vol. 27, No. 9, 01.09.2013, p. 3297-3307.

Research output: Contribution to journalArticle

Sng, Kevin Kaity ; Hara, Masayasu ; Shin, Jae Won ; Yoo, Byung Eun ; Yang, Kyung Sook ; Kim, Seon Hahn. / The multiphasic learning curve for robot-assisted rectal surgery. In: Surgical Endoscopy and Other Interventional Techniques. 2013 ; Vol. 27, No. 9. pp. 3297-3307.
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abstract = "Background: Robotic rectal surgery is gaining in popularity. We aimed to define the learning curve of an experienced laparoscopic colorectal surgeon in performing robot-assisted rectal surgery. We hypothesized that there are multiple phases in this learning process. Methods: We performed a retrospective analysis. Consecutive patients who underwent robot-assisted rectal surgery between July 2007 and August 2011 were identified. Operating times were analyzed using the CUSUM (cumulative sum) technique. CUSUMs were model fitted as a fourth-order polynomial. χ2, Fisher's exact, two independent samples t test, one-way ANOVA, Kruskal-Wallis, and Mann-Whitney tests were used. A p value of <0.05 was considered statistically significant. Results: We identified 197 patients. The median (range) total operative, robot, console, and docking times (min) were 265 (145-515), 140 (59-367), 135 (50-360), and 5 (3-40), respectively. CUSUM analysis of docking time showed a learning curve of 35 cases. CUSUM analysis of total operative, robot, and console times demonstrated three phases. The first phase (35 patients) represented the initial learning curve. The second phase (93 patients) involved more challenging cases with increased operative time. The third phase (69 patients) represented the concluding phase in the learning curve. There was increased complexity of cases in the latter two phases. Of phase 1 patients, 45.7 {\%} had tumors ≤7 cm from the anal verge compared to 64.2 {\%} in phases 2 and 3 (p = 0.042). Of phase 1 patients, 2.9 {\%} had neoadjuvant chemoradiotherapy compared to 32.7 {\%} of patients in phases 2 and 3 (p < 0.001). Splenic flexure was mobilized in 8.6 {\%} of phase 1 patients compared to 56.8 {\%} of patients in phases 2 and 3 (p < 0.001). Median blood loss was <50 ml in all three phases. The patients in phases 2 and 3 had a longer hospital stay compared to those in phase 1 (9 vs. 8 days, p = 0.002). There were no conversions. Conclusion: At least three phases in the learning curve for robot-assisted rectal surgery are defined in our study.",
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AU - Kim, Seon Hahn

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N2 - Background: Robotic rectal surgery is gaining in popularity. We aimed to define the learning curve of an experienced laparoscopic colorectal surgeon in performing robot-assisted rectal surgery. We hypothesized that there are multiple phases in this learning process. Methods: We performed a retrospective analysis. Consecutive patients who underwent robot-assisted rectal surgery between July 2007 and August 2011 were identified. Operating times were analyzed using the CUSUM (cumulative sum) technique. CUSUMs were model fitted as a fourth-order polynomial. χ2, Fisher's exact, two independent samples t test, one-way ANOVA, Kruskal-Wallis, and Mann-Whitney tests were used. A p value of <0.05 was considered statistically significant. Results: We identified 197 patients. The median (range) total operative, robot, console, and docking times (min) were 265 (145-515), 140 (59-367), 135 (50-360), and 5 (3-40), respectively. CUSUM analysis of docking time showed a learning curve of 35 cases. CUSUM analysis of total operative, robot, and console times demonstrated three phases. The first phase (35 patients) represented the initial learning curve. The second phase (93 patients) involved more challenging cases with increased operative time. The third phase (69 patients) represented the concluding phase in the learning curve. There was increased complexity of cases in the latter two phases. Of phase 1 patients, 45.7 % had tumors ≤7 cm from the anal verge compared to 64.2 % in phases 2 and 3 (p = 0.042). Of phase 1 patients, 2.9 % had neoadjuvant chemoradiotherapy compared to 32.7 % of patients in phases 2 and 3 (p < 0.001). Splenic flexure was mobilized in 8.6 % of phase 1 patients compared to 56.8 % of patients in phases 2 and 3 (p < 0.001). Median blood loss was <50 ml in all three phases. The patients in phases 2 and 3 had a longer hospital stay compared to those in phase 1 (9 vs. 8 days, p = 0.002). There were no conversions. Conclusion: At least three phases in the learning curve for robot-assisted rectal surgery are defined in our study.

AB - Background: Robotic rectal surgery is gaining in popularity. We aimed to define the learning curve of an experienced laparoscopic colorectal surgeon in performing robot-assisted rectal surgery. We hypothesized that there are multiple phases in this learning process. Methods: We performed a retrospective analysis. Consecutive patients who underwent robot-assisted rectal surgery between July 2007 and August 2011 were identified. Operating times were analyzed using the CUSUM (cumulative sum) technique. CUSUMs were model fitted as a fourth-order polynomial. χ2, Fisher's exact, two independent samples t test, one-way ANOVA, Kruskal-Wallis, and Mann-Whitney tests were used. A p value of <0.05 was considered statistically significant. Results: We identified 197 patients. The median (range) total operative, robot, console, and docking times (min) were 265 (145-515), 140 (59-367), 135 (50-360), and 5 (3-40), respectively. CUSUM analysis of docking time showed a learning curve of 35 cases. CUSUM analysis of total operative, robot, and console times demonstrated three phases. The first phase (35 patients) represented the initial learning curve. The second phase (93 patients) involved more challenging cases with increased operative time. The third phase (69 patients) represented the concluding phase in the learning curve. There was increased complexity of cases in the latter two phases. Of phase 1 patients, 45.7 % had tumors ≤7 cm from the anal verge compared to 64.2 % in phases 2 and 3 (p = 0.042). Of phase 1 patients, 2.9 % had neoadjuvant chemoradiotherapy compared to 32.7 % of patients in phases 2 and 3 (p < 0.001). Splenic flexure was mobilized in 8.6 % of phase 1 patients compared to 56.8 % of patients in phases 2 and 3 (p < 0.001). Median blood loss was <50 ml in all three phases. The patients in phases 2 and 3 had a longer hospital stay compared to those in phase 1 (9 vs. 8 days, p = 0.002). There were no conversions. Conclusion: At least three phases in the learning curve for robot-assisted rectal surgery are defined in our study.

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