411 LEARNING CURVES IN MINIMALLY INVASIVE ESOPHAGECTOMY: A SYSTEMATIC REVIEW

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.

Redefining the learning curve for robotic esophagectomy.

2018 ◽  
Vol 36 (4_suppl) ◽  
pp. 132-132
Author(s):  
Ken Lee Meredith ◽  
Jamie Huston ◽  
Pedro Briceno ◽  
Ravi Shridhar
Keyword(s):  
Minimally Invasive ◽  
Learning Curve ◽  
Minimally Invasive Esophagectomy ◽  
Neoadjuvant Chemoradiation ◽  
Esophageal Disease ◽  
Robotic Assisted ◽  
Invasive Esophagectomy ◽  
Robotic Esophagectomy ◽  
Conversion Rates ◽  
Morbidity Index

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.

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

2014 ◽  
Vol 146 (5) ◽  
pp. S-1083
Author(s):  
Inderpal S. Sarkaria ◽  
Nabil P. Rizk ◽  
Arjun Chandrasekaran ◽  
Manjit Bains ◽  
David J. Finley ◽  
...  
Keyword(s):  
Minimally Invasive ◽  
Learning Curve ◽  
Minimally Invasive Esophagectomy ◽  
Robotic Assisted ◽  
Invasive Esophagectomy

scholarly journals P-OGC26 Learning curves in minimally invasive esophagectomy- a systematic review and evaluation of benchmarking parameters

2021 ◽  
Vol 108 (Supplement_9) ◽  
Author(s):  
Pooja Prasad ◽  
Lauren Wallace ◽  
Maziar Navidi ◽  
Alexander Phillips
Keyword(s):  
Minimally Invasive ◽  
Learning Curve ◽  
Patient Outcomes ◽  
Minimally Invasive Esophagectomy ◽  
Operating Time ◽  
Learning Curves ◽  
Curve Progression ◽  
Current Evidence ◽  
Invasive Esophagectomy ◽  
Multiple Parameters

Abstract Background Minimally invasive techniques are increasingly used in the treatment of esophageal cancer. The learning curve for minimally invasive oesophagectomy (MIO) is variable and can impact on patient outcomes. The aim of this study was to review the current evidence on learning curves in MIO and identify which parameters are used for benchmarking. Methods A search of the major reference databases (PubMed, Medline, Cochrane) was performed with no time limits up to February 2020. Results were screened in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Studies were included if an assessment of the learning curve was reported on, regardless of which (if any) statistical method was used.  Results Twenty-nine studies comprising 3741 patients were included. Twenty-two studies reported on a combination of thoracoscopic, hybrid and total MIO, 6 studies reported robotic assisted MIO (RAMIE) alone and 1 study evaluated both RAMIE and thoracoscopic esophagectomies. Operating time was the most frequently used parameter to determine learning curve progression (23/39 studies), with number of resected lymph nodes, morbidity and blood loss also frequently used. Learning curves were found to plateau at 7-60 cases for thoracoscopic esophagectomy, 12-175 cases for total and thoracoscopic/hybrid esophagectomy and 9-85 cases for RAMIE.  Conclusions Multiple parameters are employed to gauge MIO learning curve progression. However, there are no validated or approved sets of outcomes. Further work is required to determine the optimum parameters that should be utilised to ensure best patient outcomes and required length of proctoring. 

Two-Stage Indicators to Assess Learning Curves for Minimally Invasive Ivor Lewis Esophagectomy

2016 ◽  
Vol 66 (05) ◽  
pp. 362-369 ◽  
Author(s):  
Qi Wang ◽  
Zixiang Wu ◽  
Gang Chen ◽  
Sai Zhang ◽  
Gang Shen ◽  
...  
Keyword(s):  
Minimally Invasive ◽  
Learning Curve ◽  
Minimally Invasive Esophagectomy ◽  
Learning Curves ◽  
Differential Analysis ◽  
Ivor Lewis ◽  
Invasive Esophagectomy ◽  
Three Phase ◽  
Relevant Variables ◽  
General Competence

Background Minimally invasive esophagectomy (MIE) Ivor Lewis has been increasingly performed over the last two decades. To guide the implementation of this technically demanding procedure, a comprehensive assessment of MIE-Ivor Lewis learning curves should include both the general competence to accomplish the procedure and the ability to generate oncological benefits. These objectives are believed to be associated with different phases of the learning curve. Methods A retrospective review of the first 109 patients who underwent MIE-Ivor Lewis by a single qualified surgeon was conducted. Relevant variables were collected and assessed by regression analysis to identify suitable indicators for patient stratification and learning curve assessment. Thereafter, the differential analysis was performed among groups to validate the learning curve model. Results Two variables, intrathoracic gastroesophageal anastomosis time and bilateral recurrent laryngeal nerve (RLN) lymphadenectomy number, which plateaued, respectively, after the 26th and 88th cases, were selected as meaningful indicators to identify different competence levels. Therefore, 109 patients were chronologically subcategorized into three groups (the first 26 MIEs as the early group, the next 62 cases as the middle group, and 21 most recent cases as the late group). Perioperative data were compared between groups with positive results to indicate a three-phase model for a learning curve for MIE-Ivor Lewis. Conclusions An MIE-Ivor Lewis learning curve should include three discrete phases that indicate, successively, unskilled operation (general competence to accomplish, less proficiency), surgical proficiency, and oncological efficacy. Intrathoracic anastomosis time and bilateral RLN lymphadenectomy were identified as suitable indicators delineate the different stages of an MIE-Ivor Lewis learning curve.

P99 A STRUCTURED TRAINING PATHWAY TO IMPLEMENT ROBOT-ASSISTED MINIMALLY INVASIVE ESOPHAGECTOMY (RAMIE): LEARNING CURVE RESULTS FROM A HIGH VOLUME CENTER

2019 ◽  
Vol 32 (Supplement_2) ◽  
Author(s):  
Kingma BF ◽  
Hadzijusufovic E ◽  
van der Sluis PC ◽  
Lang H ◽  
Ruurda JP ◽  
...  
Keyword(s):  
Blood Loss ◽  
Minimally Invasive ◽  
Learning Curve ◽  
Minimally Invasive Esophagectomy ◽  
Operating Time ◽  
Learning Curves ◽  
Perioperative Outcomes ◽  
Intraoperative Blood Loss ◽  
Robot Assisted ◽  
Invasive Esophagectomy

Abstract Aim The aim of this study was to describe the results of a structured training pathway that was developed to implement robot-assisted minimally invasive esophagectomy (RAMIE) in new centers. Background & Methods To safely and effectively implement RAMIE in new centers, the learning process needs to be optimized. In this context, a structured training pathway was created (Table 1). The results of this training pathway were investigated by evaluating consecutive patients who underwent RAMIE by a single surgeon who followed the structured training pathway. These patients were included from the trainee center’s prospective database. Cumulative sum (CUSUM) learning curves were plotted for thoracic operating time and intraoperative blood loss. Perioperative outcomes were compared between patients who underwent surgery before and after a learning curve plateau occurred. Results Between 2017-2018, the trainee team adhered to the structured training pathway and a total of 70 patients were included. The learning curves showed plateaus after 22 cases. In cases 23-70, the operating time was shorter for both the thoracic phase (median 215 vs. 249 minutes, P=0.001) and overall procedure (median 394 vs. 440 minutes, P=0.005), intraoperative blood loss was less (median 210 vs. 400 milliliters, P=0.029), and lymph node yield was higher (median 32 vs. 23 nodes, P=0.001) when compared to the first 22 cases. No significant differences were found for conversion rates, postoperative complications, length of hospital stay, radicality, or mortality. Conclusions The structured RAMIE training pathway results in a short learning curve and is an effective way to introduce RAMIE without compromising the oncological outcomes and complication rates. The pathway is therefore advised to surgeons who are willing to adopt this technique.

Learning Curve and Associated Morbidity of Minimally Invasive Esophagectomy

2019 ◽  
Vol 269 (1) ◽  
pp. 88-94 ◽  
Author(s):  
Frans van Workum ◽  
Marianne H. B. C. Stenstra ◽  
Gijs H. K. Berkelmans ◽  
Annelijn E. Slaman ◽  
Mark I. van Berge Henegouwen ◽  
...  
Keyword(s):  
Minimally Invasive ◽  
Learning Curve ◽  
Minimally Invasive Esophagectomy ◽  
Invasive Esophagectomy

scholarly journals Robotic assisted minimally invasive esophagectomy: Ivor-Lewis approach

2021 ◽  
Vol 5 ◽  
pp. 21-21
Author(s):  
Kelsey Musgrove ◽  
Charlotte R. Spear ◽  
Jahnavi Kakuturu ◽  
Britney R. Harris ◽  
Fazil Abbas ◽  
...  
Keyword(s):  
Minimally Invasive ◽  
Minimally Invasive Esophagectomy ◽  
Ivor Lewis ◽  
Robotic Assisted ◽  
Invasive Esophagectomy

Minimally invasive esophagectomy

2021 ◽  
Keyword(s):  
Esophageal Cancer ◽  
Minimally Invasive ◽  
Minimally Invasive Esophagectomy ◽  
Respiratory Complications ◽  
Invasive Esophagectomy ◽  
Video Tutorial ◽  
Complex Procedure ◽  
Resectable Esophageal ◽  
Resectable Esophageal Cancer ◽  
Invasive Techniques

Minimally invasive esophagectomy is increasingly becoming the surgical treatment of choice for esophageal cancer. The goal of this technique is to reduce the rate of respiratory complications associated with thoracotomy while taking advantage of the benefits of reduced mortality associated with minimally invasive techniques. However, minimally invasive esophagectomy is still not considered the gold standard for resectable esophageal cancer worldwide because it is a highly technical and complex procedure. The goal of this video tutorial is to present an easy step-by-step approach to a minimally invasive esophagectomy and to address technical considerations and potential pitfalls.

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