Long-Term Results of Laparoscopic Sleeve Gastrectomy: a Review of Studies Reporting 10+ Years Outcomes

Long-Term Outcomes of Patients with Staple Line Leaks Following Sleeve Gastrectomy

PURPOSE

The long-term outcomes of patients with staple line leaks (SLL) after SG are under-reported. The purpose of this study is to evaluate the long-term outcomes of patients with SLL after SG.

MATERIALS AND METHODS

A retrospective analysis of a maintained patient registry of patients undergoing SG between January 2012 and December 2020 in a single bariatric center was analyzed and included patients with SLL. Outcomes were compared to a group without SLL.

RESULTS

During this period, 1985 patients underwent SG of which 61 patients (3.1%) developed leak. 26 patients (1.3%) had overt SLL and the rest had organ space infection around the staple line. The mean age and body mass index (BMI) were 39.8 ± 11.3 and 41.5 ± 4.7, respectively. 7 patients (11.4%) underwent concomitant surgery and 12 patients (19.7%) had intraoperative complications. Leak was complicated by Clavien-Dindo ≥ 3 in 31 patients (50.8%). Reoperation rate was 27.8% (n = 17). ≥ 2 interventions were required in 26% of patients (n = 16). Two patients died during hospitalization due to septic complications. Long-term follow-up of median 121 months was available in 78% of the cohort (n = 48). The median total weight loss and BMI were 27% and 30.1 kg/m2, respectively. Weight loss outcomes were higher in comparison to patients without SLL. Seven patients (14.6%) underwent SG revision. All associated medical problems improved during follow-up except for gastroesophageal reflux which was found in 50% of cohort.

CONCLUSION

Long-term outcomes of SG patients with SLL are satisfactory in terms of weight loss, resolution of comorbidities, and requirement for surgical revision.

Implementation of Artificial Intelligence-Based Computer Vision Model for Sleeve Gastrectomy: Experience in One Tertiary Center

INTRODUCTION

Sleeve gastrectomy (SG) is the most common metabolic and bariatric procedure performed. Leveraging artificial intelligence (AI) for automated real-time data structuring and annotations of surgical videos has immense potential of clinical applications. This study presents initial real-world implementation of AI-based computer vision model in sleeve gastrectomy (SG) and external validation of accuracy of safety milestone annotations.

METHODS

A retrospective single-center study of 49 consecutive SG videos was captured and analyzed by the AI platform (December 2020-August 2023). A bariatric surgeon viewed all videos and assessed safety milestones adherence, compared to the AI annotations. Patients' data were retrieved from the bariatric unit registry.

RESULTS

SG total duration was 47.5 min (interquartile range 36-64). Main steps included preparation (12.2%), dissection of the greater curvature (30.8%), gastric transection (28.5%), specimen extraction (7.2%), and final inspection (14.4%). Out of body time comprised 6.9% of the total video. Safety milestones components and AI-surgeon agreements included the following: bougie insertion (100%), distance from pylorus ≥ 2 cm (100%), parallel to lesser curvature (98%), fundus mobilization (100%), and distance from esophagus ≥ 1 cm (true-100%, false-13.6%; kappa coefficient 0.2, p = 0.006). Intraoperative complications included notable hemorrhage (n = 4) and parenchymal injury (n = 1).

CONCLUSIONS

The AI model provides a fully automated SG video analysis. Outcomes suggest its accuracy in four of five safety milestone annotations. This data is valuable, as it reflects objective performance measures which can help us improve the surgical quality and efficiency of SG. Larger cohorts will enable SG standardization and clinical correlations with outcomes, aiming to improve patients' safety.