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DOI: 10.1055/a-1293-7327
Efficacy of Endocuff Vision compared to first-generation Endocuff in adenoma detection rate and polyp detection rate in high-definition colonoscopy: a systematic review and network meta-analysis
Authors
Abstract
Background and study aims Recently, the newer Endocuff Vision (ECV) has been evaluated for improving colonoscopy outcome metrics such as adenoma detection rate (ADR) and polyp detection rate (PDR). Due to lack of direct comparative studies between ECV and original Endocuff (ECU), we performed a systematic review and network meta-analysis to evaluate these outcomes.
Methods The following databases were searched: PubMed, Embase, Cochrane, and Web of Sciences to include randomized controlled trials (RCTs) comparing ECV or ECU colonoscopy to high-definition (HD) colonoscopy. Direct as well as network meta-analyses comparing ADR and PDR were performed using a random effects model. Relative-risk (RR) with 95 % confidence interval (CI) was calculated.
Results A total of 12 RCTs with 8638 patients were included in the final analysis. On direct meta-analysis, ECV did not demonstrate statistically improved ADR compared to HD colonoscopy (RR: 1.12, 95 % CI 0.99–1.27). A clinically and statistically improved PDR was noted for ECV compared to HD (RR: 1.15, 95 % CI 1.03–1.28) and ECU compared to HD (RR: 1.26, 95 % CI 1.09–1.46) as well as improved ADR (RR: 1.22, 95 % CI 1.05–1.43) was observed for ECU colonoscopy when compared to HD colonoscopy. These results were also consistent on network meta-analysis. Lower overall complication rates (RR: 0.14, 95 % CI 0.02–0.84) and particularly lacerations/erosions (RR: 0.11, 95 % CI 0.02–0.70) were noted with ECV compared to ECU colonoscopy.
Conclusions Although safe, the newer ECV did not significantly improve ADR compared to ECU and HD colonoscopy. Further device modification is needed to increase the overall ADR and PDR.
Publication History
Received: 16 July 2020
Accepted: 30 September 2020
Article published online:
01 January 2021
© 2021. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commecial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)
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