Tailoring Endoscopic sleeve gastroplasty: computational biomechanics for the evaluation and prediction of post-surgical outcomes

Abstract

Endoscopic Sleeve Gastroplasty (ESG) is currently being used successfully in people with obesity. However, potential long-term side effects are still unknown. Computational biomechanics has emerged as a valid tool to improve the intervention effectiveness.

The aim of this work is to provide an in silico framework to estimate stomach mechanics, as volumetric capacity, structural stiffness, and wall tissue strain, in response to food intake before and after ESG. A cohort of patients who underwent ESG was studied to rationally analyze the reduction in gastric volume and the changes in structural response and strain distribution. Computational predictions were compared with Magnetic Resonance Imaging (MRI) data from post-operative stomachs, allowing the reliability and reproducibility of the methodology to be assessed. Significant differences in stomach mechanics before and after surgery were observed, considering both structural stiffness and tissue strain distribution. This difference may lead to improper activation of mechanoreceptors and thus to variations in satiety after ESG.

The results confirm the suitability of the in silico approach for evaluating bariatric surgery in the short-term, because it shed light on the reduction of stomach capacity and pressurization depending on the amount of food ingested, on the variation of tissue strain distribution, giving to the surgeon information that are currently not available. Leveraging computational modeling may help prevent complications, such as reflux or misplacement of sutures, and enhance outcomes by prescribing gastric-wall loading conditions associated with lower postoperative weight-regain rates.