WeChat Mini Program
Old Version Features

    • Patient Selection in One Anastomosis/Mini Gastric Bypass—an Expert Modified Delphi Consensus

    Obesity Surgery（2022）

    Iran University of Medical Sciences

    Cited 35|Views45
    Abstract
    One anastomosis/mini gastric bypass (OAGB/MGB) is up to date the third most performed obesity and metabolic procedure worldwide, which recently has been endorsed by ASMBS. The main criticisms are the risk of bile reflux, esophageal cancer, and malnutrition. Although IFSO has recognized this procedure, guidance is needed regarding selection criteria. To give clinicians a daily support in performing the right patient selection in OAGB/MGB, the aim of this paper is to generate clinical guidelines based on an expert modified Delphi consensus. A committee of 57 recognized bariatric surgeons from 24 countries created 69 statements. Modified Delphi consensus voting was performed in two rounds. An agreement/disagreement among ≥ 70.0% of the experts was considered to indicate a consensus. Consensus was achieved for 56 statements. Remarkably, ≥ 90.0% of the experts felt that OAGB/MGB is an acceptable and suitable option “in patients with Body mass index (BMI) > 70, BMI > 60, BMI > 50 kg/m2 as a one-stage procedure,” “as the second stage of a two-stage bariatric surgery after Sleeve Gastrectomy for BMI > 50 kg/m2 (instead of BPD/DS),” and “in patients with weight regain after restrictive procedures. No consensus was reached on the statement that OAGB/MGB is a suitable option in case of resistant Helicobacter pylori. This is likely as there is a concern that this procedure is associated with reflux and its related long-term complications including risk of cancer in the esophagus or stomach. Also no consensus reached on OAGB/MGB as conversional surgery in patients with GERD after restrictive procedures. Consensus for disagreement was predominantly achieved “in case of intestinal metaplasia of the stomach” (74.55%), “in patients with severe Gastro Esophageal Reflux Disease (GERD)(C,D)” (75.44%), “in patients with Barrett’s metaplasia” (89.29%), and “in documented insulinoma” (89.47%). Patient selection in OAGB/MGB is still a point of discussion among experts. There was consensus that OAGB/MGB is a suitable option in elderly patients, patients with low BMI (30–35 kg/m2) with associated metabolic problems, and patients with BMIs more than 50 kg/m2 as one-stage procedure. OAGB/MGB can also be a safe procedure in vegetarian and vegan patients. Although OAGB/MGB can be a suitable procedure in patients with large hiatal hernia with concurrent hiatal hernia, it should not be offered to patients with grade C or D esophagitis or Barrett’s metaplasia.
    More
    Translated text
    Key words
    OAGB/MGB,Patient selection,Metabolic surgery,Bariatric surgery
    上传PDF
    View via Publisher
    Bibtex
    收藏
    AI Read Science
    AI Summary
    AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
    Example
    Background
    Key content
    Introduction
    Methods
    Results
    Related work
    Fund
    Key content
    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
    Upload PDF to Generate Summary
    Must-Reading Tree
    Example
    Generate MRT to find the research sequence of this paper
    Data Disclaimer
    The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
    Chat Paper
    Summary is being generated by the instructions you defined