Gastric wall thickness and stapling in laparoscopic sleeve gastrectomy - a literature review.

Barski, Krzysztof; Binda, Artur; Kudlicka, Emilia; Jaworski, Paweł; Tarnowski, Wiesław

doi:10.5114/wiitm.2018.73362

Cited by 19 publications

(16 citation statements)

References 17 publications

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“…Other elements may be responsible, like ischemia and morphology. Buttressing material has been shown to more evenly distribute the staple pressure over a wider surface area thus resulting in higher burst pressures and lower bleed rates [173][174][175][176][177][178][179]. As such, we hypothesize that the lower leak rate associated with the use of a thin buttressing material, such as APM (0.5 mm maximum total thickness), is related to improved staple compression, given, of course, appropriate staple height selection.…”

Section: Discussionmentioning

confidence: 99%

Comparison of laparoscopic sleeve gastrectomy leak rates in five staple-line reinforcement options: a systematic review

2019

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Background Staple-line leaks following laparoscopic sleeve gastrectomy (LSG) remain a concerning complication. Stapleline buttressing is largely adopted as an acceptable reinforcement but data regarding leaks have been equivocal. This study compared staple-line leaks in five reinforcement options during LSG: no reinforcement (NO-SLR), oversewing (suture), nonabsorbable bovine pericardial strips (BPS), tissue sealant or fibrin glue (Seal), or absorbable polymer membrane (APM). Methods This systematic review study of articles published between 2012 and 2016 regarding LSG leak rates aligned with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Variables of interest included leak rates, bleeding, and complications in addition to surgical and population parameters. An independent Fisher's exact test was used to compare the number of patients with and without leaks for the different reinforcement options. Results Of the 1633 articles identified, 148 met inclusion criteria and represented 40,653 patients. Differences in age (older in APM; p = 0.001), starting body mass index (lower in Suture; p = 0.008), and distance from pylorus (closer in BPS; p = 0.04) were observed between groups, but mean bougie size was equivalent. The overall leak rate of 1.5% (607 leaks) ranged from 0.7% for APM (significantly lower than all groups; p ≤ 0.007 for next lowest leak rate) to 2.7% (BPS). Conclusions This systematic review of staple-line leaks following LSG demonstrated a significantly lower rate using APM staple-line reinforcement as compared to oversewing, use of sealants, BPS reinforcement, or no reinforcement. Variation in surgical technique may also contribute to leak rates.

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Section: Discussionmentioning

confidence: 99%

Comparison of laparoscopic sleeve gastrectomy leak rates in five staple-line reinforcement options: a systematic review

2019

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“…1 Sleeve gastrectomy an average thickness of 2.5 mm. An analysis of data from the literature on animal and human samples [23,24] suggested considering that the muscularis layer is thicker than the mucosa. The estimated thicknesses for the mucosa and muscularis layers were 1 mm and 1.5 mm, respectively.…”

Section: Methodsmentioning

confidence: 99%

Computational evaluation of laparoscopic sleeve gastrectomy

et al. 2021

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LSG is one of the most performed bariatric procedures worldwide. It is a safe and effective operation with a low complication rate. Unsatisfactory weight loss/regain may occur, suggesting that the operation design could be improved. A bioengineering approach might significantly help in avoiding the most common complications. Computational models of the sleeved stomach after LSG were developed according to bougie size (range 27–54 Fr). The endoluminal pressure and the basal volume were computed at different intragastric pressures. At an inner pressure of 22.5 mmHg, the basal volume of the 54 Fr configuration was approximately 6 times greater than that of the 27 Fr configuration (57.92 ml vs 9.70 ml). Moreover, the elongation distribution of the gastric wall was assessed to quantify the effect on mechanoreceptors impacting satiety by differencing regions and layers. An increasing trend in elongation strain with increasing bougie size was observed in all cases. The most stressed region and layer were the antrum (approximately 25% higher stress than that in the corpus at 37.5 mmHg) and mucosa layer (approximately 7% higher stress than that in the muscularis layer at 22.5 mmHg), respectively. In addition, the pressure–volume behaviors were reported. Computational models and bioengineering methods can help to quantitatively identify some critical aspects of the “design” of bariatric operations to plan interventions, and predict and increase the success rate. Moreover, computational tools can support the development of innovative bariatric procedures, potentially skipping invasive approaches.

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“…It was difficult to differentiate the antrum from small bowel or colon, but it was never confused with the fundus. Identifying the various segments of stomach may provide clinical benefit in gastric resections as differences in gastric wall thickness have been postulated as a cause for staple line leaks [ 33 ]. The antrum, small bowel, and colon had comparable bioimpedance profiles which may be the result of similar architecture.…”

Section: Discussionmentioning

confidence: 99%

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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Gastric wall thickness and stapling in laparoscopic sleeve gastrectomy - a literature review.

Cited by 19 publications

References 17 publications

Comparison of laparoscopic sleeve gastrectomy leak rates in five staple-line reinforcement options: a systematic review

Comparison of laparoscopic sleeve gastrectomy leak rates in five staple-line reinforcement options: a systematic review

Computational evaluation of laparoscopic sleeve gastrectomy

Electrical impedance characterization of in vivo porcine tissue using machine learning

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