Do pre-surgery eating habits affect weight loss one year following a sleeve gastrectomy?

Objectives This systematic review aimed to compare the weight change in people with or without binge eating who underwent various weight loss treatments. Methods We searched for studies in PubMed, American Psychological Association, and Embase from inception to January 2022. The studies selected included assessment of binge eating and body weight before and after weight loss treatment in people of any age. The meta-analyses were conducted using Comprehensive Meta-Analysis (CMA). We used Egger’s regression test, the funnel plot, and the Trim and Fill test to assess the risk of publication bias. Results Thirty-four studies were included in the systematic review, with a total of 10.184 participants. The included studies were divided into three categories according to types of weight loss treatments, namely, (1) bariatric surgery; (2) pharmacotherapy isolated or combined with behavioral interventions; and (3) behavioral and/or nutritional interventions. The meta-analyses showed no significant difference in weight loss between people with or without binge eating engaged in weight loss treatments, with an overall effect size of − 0.117 (95% CI − 0.405 to 0.171; P = 0.426). Conclusions Our findings showed no difference in weight loss in people with or without pre-treatment binge eating who received various weight loss treatments. Weight loss treatments should not be withheld on the basis that they will not be effective in people with pre-treatment binge eating, albeit their safety and longer term impacts are unclear. Level of evidence: Level I, at least one properly designed randomized controlled trials; systematic reviews and meta-analyses; experimental studies.

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Electrical impedance characterization of in vivo porcine tissue using machine learning

Chiang

Eschbach

Knapp

et al. 2021

Journal of Electrical Bioimpedance

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The incorporation of sensors onto the stapling platform has been investigated to overcome the disconnect in our understanding of tissue handling by surgical staplers. The goal of this study was to explore the feasibility of in vivo porcine tissue differentiation using bioimpedance data and machine learning methods. In vivo electrical impedance measurements were obtained in 7 young domestic pigs, using a logarithmic sweep of 50 points over a frequency range of 100 Hz to 1 MHz. Tissues studied included lung, liver, small bowel, colon, and stomach, which was further segmented into fundus, body, and antrum. The data was then parsed through MATLAB's classification learner to identify the best algorithm for tissue type differentiation. The most effective classification scheme was found to be cubic support vector machines with 86.96% accuracy. When fundus, body and antrum were aggregated together as stomach, the accuracy improved to 88.03%. The combination of stomach, small bowel, and colon together as GI tract improved accuracy to 99.79% using fine k nearest neighbors. The results suggest that bioimpedance data can be effectively used to differentiate tissue types in vivo. This study is one of the first that combines in vivo bioimpedance tissue data across multiple tissue types with machine learning methods.

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Do pre-surgery eating habits affect weight loss one year following a sleeve gastrectomy?

Cited by 5 publications

References 27 publications

Preoperative Binge Eating and Weight Loss After Bariatric Surgery: A Systematic Review and Meta-analysis

Preoperative Binge Eating and Weight Loss After Bariatric Surgery: A Systematic Review and Meta-analysis

Is binge eating associated with poor weight loss outcomes in people with a high body weight? A systematic review with meta-analyses

Electrical impedance characterization of in vivo porcine tissue using machine learning

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