Learning curve for endoscopic submucosal dissection in early gastric neoplasm using a multibending endoscope

Abstract

Background: Endoscopic submucosal dissection (ESD) is a representative treatment modality for early gastric neoplasms. However, the learning curve for beginners performing ESD using a multibending endoscope has not been introduced.

Objective: This study aimed to evaluate the learning curves of operators undergoing intensive training using a multibending endoscope.

Design: This was a retrospective single center study.

Methods: We retrospectively analyzed data of over 1500 consecutive gastric ESDs performed by two operators using a multibending endoscope. A change-point analysis with 50 cases of moving average speeds was used to determine the new target resection speed. Cumulative sum (CUSUM) analysis was used to identify the cases required for proficiency in ESD. Risk-adjusted CUSUM (RA-CUSUM) analysis was performed for each operator after adjusting for confounding factors influencing the resection speed.

Results: In total, 1491 cases were enrolled, with early gastric cancer accounting for 43.2% (n = 644). Overall, the en bloc resection, R0 resection, and curability rates were 97.7%, 96.0%, and 92.3%, respectively. The mean resection speed was 19.8 cm²/h. Because both operators surpassed the commonly used benchmark resection speed of 9 cm²/h in the first 50-case block, we established a new target benchmark of 17.9 cm²/h in the change-point analysis. CUSUM analysis indicated that performing 166 cases overall was required to achieve the benchmark, with the 2 operators needing 153 and 69 cases to meet this target speed after RA-CUSUM analysis.

Conclusion: Using a multibending endoscope for gastric ESD can help beginners achieve safe and excellent outcomes. These findings will serve as a useful guide for beginners attempting to use a multibending endoscope.

Keywords: CUSUM; early gastric neoplasm; endoscopic submucosal dissection; learning curve; multibending endoscope.

Figure 1.

Flow chart of the enrolled patients and procedure outcomes.

Figure 2.

(a) Annual case volume for the two operators. (b) Trends of en bloc, R0, and curative resection rates over sequential blocks of 50 procedures for operator 1 and (c) operator 2. (d) The overall trend of resection speed over sequential blocks of 50 cases. (e) Trends of resection speed over sequential blocks of 50 cases in operator 1 and (f) operator 2.

Figure 3.

(a) Fifty-case moving average of resection speed (red line: overall, green line: operator 1, and blue line: operator 2). (b) CUSUM analysis plot of cases required to reach the new target resection speed of 17.9 cm²/h. (c) RA-CUSUM analysis plots of cases required to reach the new target resection speed of 17.9 cm²/h in operator 1 and (d) operator 2. The gray line was drawn by curve fitting. The degree of curve fitting was determined on the basis of adjusted R-squared values. The gray line in (b) represents the fitted curve generated from the regression model (curve fitting). RA-CUSUM, risk-adjusted cumulative sum.