Tu1591 Learning Curve for Combined Thoracoscopic and Laparoscopic Robotic-Assisted Minimally Invasive Esophagectomy Using a Four-Arm Platform

132 Background: Minimally invasive esophagectomy(MIE) has demonstrated superior outcomes compared to open approaches. The myriad of techniques has precluded the recommendation of a standard approach. The robotic approach has increased steadily. We have previously published our series defining the learning curve for this approach. The purpose of this study is to redefine the learning curve for robotic-assisted esophagogastrectomy with respect to operative time, conversion rates, and patient safety. Methods: We have prospectively followed all patients undergoing robotic-assisted esophagogastrectomy and compared operations performed at our institutions by a single surgeon in successive cohorts. Our measures of proficiency included: operative times, conversion rates, and complications. Statistical analyses were undertaken utilizing Spearman regression analysis and Mann-Whitney U test. Significance was accepted with 95 % confidence. Results: We identified 203 patients (166 [81.8%] male: 37 [18.2%] female) of median age of 67.2 (30-90) years who underwent robotic-assisted esophagogastrectomies for malignant esophageal disease. One-hundred sixty six were adenocarcinoma, 26 were squamous cell carcinoma and 11 were other. R0 resections was performed in 202 (99.5%) of patients. The median lymph node harvest was 18 (6-63). Neoadjuvant chemoradiation was administered to 157 (77.4%) patients. A significant reduction in operative times (p <0.005) following completion of 20 procedures was identified (514 ± 106 min vs. 415± 91 min compared to subsequent 80 cases and further reduced with the subsequent 100 cases 397 ± 71.9 min) p<0.001. Complications decreased after the initial learning curve of 29 cases, p=0.04. However there was an increase in complications after 90 cases in which there was an increase in the Charleson morbidity index, p<0.01 indicating higher risk patients which tapered after case 115. Conclusions: For surgeons proficient in performing minimally-invasive esophagogastrectomies, the learning curve for a robotic-assisted procedure appears to begin near proficiency after 20 cases however as more complex cases are undertake there appears to be an additional learning curve which is surpassed after 90 cases.

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411 LEARNING CURVES IN MINIMALLY INVASIVE ESOPHAGECTOMY: A SYSTEMATIC REVIEW

Diseases of the Esophagus ◽

10.1093/dote/doaa087.103 ◽

2020 ◽

Vol 33 (Supplement_1) ◽

Author(s):

P Prasad ◽

L Wallace ◽

M Navidi ◽

S Wahed ◽

A Immanuel ◽

...

Keyword(s):

Minimally Invasive ◽

Learning Curve ◽

Minimally Invasive Esophagectomy ◽

Learning Curves ◽

Minimally Invasive Techniques ◽

Robotic Assisted ◽

Invasive Esophagectomy ◽

Training Programmes ◽

Meta Analyses ◽

Invasive Techniques

Abstract Minimally invasive techniques are being increasingly used in the treatment of esophageal cancer. The learning curve for minimally invasive esophagectomy (MIE) is variable and can have an impact upon training delivered within residency and fellowship programmes. The aims of this review are to critically appraise current literature on the learning curve for MIE, identify what parameter(s) is used to quantify achieving competence and determine if there is evidence of resultant impact on surgical training. Methods A search of the major reference databases (MEDLINE, EMBASE, Cochrane) was performed with no time limits up to the date of the search (February 2020). Results were screened in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and study quality assessed using the Newcastle-Ottawa Scale for cohort studies. Results Twenty-one studies comprising 2720 patients were included- 17 studies reported on a combination of thoracoscopic, hybrid and total MIE, 3 studies reported robotic assisted alone and 1 study evaluated robotic assisted and thoracoscopic esophagectomy. 3 studies used a cumulative sum (CUSUM) analysis to define learning, 1 study used CUSUM and another parameter and 17 studies used one or more parameters. Quantification of surgical competence was variable and ranged from 12–80 cases for robotic surgery and 12–60 cases for other modes of MIE. One study reported trainees achieving MIE skills quicker if mentoring surgeons had attained proficiency on the learning curve. Conclusion Learning curves in MIE remain ill-defined with limited evidence on impact upon training received by residents and fellows. Additionally, the parameters used to define achievement of surgical competency is heterogenous. As minimally invasive techniques are increasingly adopted, specific standards to help define competence need to be identified and agreed on. This could help in designing training programmes and improve the rate of achieving competency.

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