Meta-Analysis of the Impact of the Learning Curve in Robotic Rectal Cancer Surgery on Histopathologic Outcomes

Mahir Gachabayov, Karen You, Seon Hahn Kim, Tomohiro Yamaguchi, Rosa Jimenez-Rodriguez, Li Jen Kuo, Fabio Cianchi, Fabio Staderini, Roberto Bergamaschi

Research output: Contribution to journalArticle

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Abstract

INTRODUCTION: Although the process of learning robotic surgery for rectal cancer is associated with a prolonged operating time and higher complication rates, its impact on histopathologic outcomes is unknown. The aim of this meta-analysis was to evaluate the impact of the learning curve in robotic surgery for rectal cancer on histopathologic outcomes. METHODS: The PubMed, EMBASE, Cochrane Library, MEDLINE via Ovid, CINAHL, and Web of Science databases were systematically searched. The inclusion criterion was any clinical study comparing the outcomes of robotic surgery for rectal cancer between different phases of the learning curve (LC) including competence (C). The primary endpoint was the circumferential resection margin (CRM) involvement rate defined as CRM ≤1 mm. The Mantel-Haenszel method with odds ratios with 95% confidence intervals (OR (95%CI)) was used for dichotomous variables. RESULTS: Ten studies including a total of 907 patients (521 LC and 386 C) were selected. Nine studies were found to have a low risk of bias, and one had a moderate risk of bias. The CRM involvement rate was 2.9% (13/441) for learning curve vs. 4.6% (13/284) for competence. This difference was not significant (OR (95%CI) = 0.70 (0.30, 1.60); p=0.39; I2=0%). CONCLUSION: A surgeon's learning curve seems to have no impact on CRM involvement rates compared to surgeon competence in robotic surgery for rectal cancer.

Original languageEnglish
Pages (from-to)139-155
Number of pages17
JournalSurgical technology international
Volume34
Publication statusPublished - 2019 May 15

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Learning Curve
Robotics
Rectal Neoplasms
Meta-Analysis
Mental Competency
Odds Ratio
Confidence Intervals
PubMed
MEDLINE
Libraries
Learning
Databases
Margins of Excision

ASJC Scopus subject areas

  • Medicine(all)

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Gachabayov, M., You, K., Kim, S. H., Yamaguchi, T., Jimenez-Rodriguez, R., Kuo, L. J., ... Bergamaschi, R. (2019). Meta-Analysis of the Impact of the Learning Curve in Robotic Rectal Cancer Surgery on Histopathologic Outcomes. Surgical technology international, 34, 139-155.

Meta-Analysis of the Impact of the Learning Curve in Robotic Rectal Cancer Surgery on Histopathologic Outcomes. / Gachabayov, Mahir; You, Karen; Kim, Seon Hahn; Yamaguchi, Tomohiro; Jimenez-Rodriguez, Rosa; Kuo, Li Jen; Cianchi, Fabio; Staderini, Fabio; Bergamaschi, Roberto.

In: Surgical technology international, Vol. 34, 15.05.2019, p. 139-155.

Research output: Contribution to journalArticle

Gachabayov, M, You, K, Kim, SH, Yamaguchi, T, Jimenez-Rodriguez, R, Kuo, LJ, Cianchi, F, Staderini, F & Bergamaschi, R 2019, 'Meta-Analysis of the Impact of the Learning Curve in Robotic Rectal Cancer Surgery on Histopathologic Outcomes', Surgical technology international, vol. 34, pp. 139-155.
Gachabayov, Mahir ; You, Karen ; Kim, Seon Hahn ; Yamaguchi, Tomohiro ; Jimenez-Rodriguez, Rosa ; Kuo, Li Jen ; Cianchi, Fabio ; Staderini, Fabio ; Bergamaschi, Roberto. / Meta-Analysis of the Impact of the Learning Curve in Robotic Rectal Cancer Surgery on Histopathologic Outcomes. In: Surgical technology international. 2019 ; Vol. 34. pp. 139-155.
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AB - INTRODUCTION: Although the process of learning robotic surgery for rectal cancer is associated with a prolonged operating time and higher complication rates, its impact on histopathologic outcomes is unknown. The aim of this meta-analysis was to evaluate the impact of the learning curve in robotic surgery for rectal cancer on histopathologic outcomes. METHODS: The PubMed, EMBASE, Cochrane Library, MEDLINE via Ovid, CINAHL, and Web of Science databases were systematically searched. The inclusion criterion was any clinical study comparing the outcomes of robotic surgery for rectal cancer between different phases of the learning curve (LC) including competence (C). The primary endpoint was the circumferential resection margin (CRM) involvement rate defined as CRM ≤1 mm. The Mantel-Haenszel method with odds ratios with 95% confidence intervals (OR (95%CI)) was used for dichotomous variables. RESULTS: Ten studies including a total of 907 patients (521 LC and 386 C) were selected. Nine studies were found to have a low risk of bias, and one had a moderate risk of bias. The CRM involvement rate was 2.9% (13/441) for learning curve vs. 4.6% (13/284) for competence. This difference was not significant (OR (95%CI) = 0.70 (0.30, 1.60); p=0.39; I2=0%). CONCLUSION: A surgeon's learning curve seems to have no impact on CRM involvement rates compared to surgeon competence in robotic surgery for rectal cancer.

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