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Background: Sleeve gastrectomy (SG) increased in popularity after 2010 but recent data suggest it has concerning rates of gastroesophageal reflux and need for conversions. This study aims to evaluate recent trends in the utilization of bariatric procedures, associated complications, and conversions using an administrative claims database in the United States., Methods: We included adults who had bariatric procedures from 2000 to 2020 with continuous enrollment for at least 6 months in the MarketScan Commercial Claims and Encounters database. Index bariatric procedures and subsequent revisions or conversions were identified using CPT codes. Baseline comorbidities and postoperative complications were identified with ICD-9-CM and ICD-10 codes. Cumulative incidences of complications were estimated at 30-days, 6-months, and 1-year and compared with stabilized inverse probability of treatment weighted Kaplan-Meier analysis., Results: We identified 349,411 bariatric procedures and 5521 conversions or revisions. The sampled SG volume appeared to begin declining in 2018 while Roux-en-Y gastric bypass (RYGB) remained steady. Compared to RYGB, SG was associated with lower 1-year incidence [aHR, (95% CIs)] for 30-days readmission [0.65, (0.64-0.68)], dehydration [0.75, (0.73-0.78)], nausea or vomiting [0.70, (0.69-0.72)], dysphagia [0.55, (0.53-0.57)], and gastrointestinal hemorrhage [0.43, (0.40-0.46)]. Compared to RYGB, SG was associated with higher 1-year incidence [aHR, (95% CIs)] of esophagogastroduodenoscopy [1.13, (1.11-1.15)], heartburn [1.38, (1.28-1.49)], gastritis [4.28, (4.14-4.44)], portal vein thrombosis [3.93, (2.82-5.48)], and hernias of all types [1.36, (1.34-1.39)]. There were more conversions from SG to RYGB than re-sleeving procedures. SG had a significantly lower 1-year incidence of other non-revisional surgical interventions when compared to RYGB., Conclusions: The overall volume of bariatric procedures within the claims database appeared to be declining over the last 10 years. The decreasing proportion of SG and the increasing proportion of RYGB suggest the specific complications of SG may be driving this trend. Clearly, RYGB should remain an important tool in the bariatric surgeon's armamentarium., (© 2024. The Author(s).)

Bariatric surgery is currently the most effective long-term treatment for morbid obesity as well as type-2 diabetes mellitus. The field of metabolic and bariatric surgery has seen tremendous growth over the past decade with dramatically reduced risks. This article aims to provide an update on bariatric surgery, highlighting the latest outcomes, improvements, and challenges in the field. Recently, the American Society of Metabolic and Bariatric Surgery (ASMBS) and the International Federation for the Surgery of Obesity and Metabolic Disorders (IFSO) released a major update to the indications for bariatric surgery at BMI ≥35 kg/m 2 regardless of co-morbidities and 30-34.9 kg/m 2 with obesity-related comorbidities. Sleeve gastrectomy has emerged as the most popular bariatric procedure in the last 10 years with its remarkable efficacy and safety profile. The implementation of the Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program (MBSAQIP) and Enhanced Recovery After Surgery (ERAS) protocols have significantly improved the quality of care for all bariatric patients. The recent introduction and FDA approval of Glucagon-Like Peptide-1 (GLP-1) agonists for chronic obesity has garnered significant media coverage and popularity, but no guidelines exist regarding its use in relation to bariatric surgery. This update underscores the need for tailored approaches, ongoing research, and the integration of evidence-based medicine and innovations to enhance patient care., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: Postoperative gastrointestinal bleeding (GIB) is a rare but serious complication of bariatric surgery. The recent rise in extended venous thromboembolism regimens as well as outpatient bariatric surgery may increase the risk of postoperative GIB or lead to delay in diagnosis. This study seeks to use machine learning (ML) to create a model that predicts postoperative GIB to aid surgeon decision-making and improve patient counseling for postoperative bleeds., Methods: The Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program (MBSAQIP) database was used to train and validate three types of ML methods: random forest (RF), gradient boosting (XGB), and deep neural networks (NN), and compare them with logistic regression (LR) regarding postoperative GIB. The dataset was split using fivefold cross-validation into training and validation sets, in an 80/20 ratio. The performance of the models was assessed using area under the receiver operating characteristic curve (AUROC) and compared with the DeLong test. Variables with the strongest effect were identified using Shapley additive explanations (SHAP)., Results: The study included 159,959 patients. Postoperative GIB was identified in 632 (0.4%) patients. The three ML methods, RF (AUROC 0.764), XGB (AUROC 0.746), and NN (AUROC 0.741) all outperformed LR (AUROC 0.709). The best ML method, RF, was able to predict postoperative GIB with a specificity and sensitivity of 70.0% and 75.4%, respectively. Using DeLong testing, the difference between RF and LR was determined to be significant with p < 0.01. Type of bariatric surgery, pre-op hematocrit, age, duration of procedure, and pre-op creatinine were the 5 most important features identified by ML retrospectively., Conclusions: We have developed a ML model that outperformed LR in predicting postoperative GIB. Using ML models for risk prediction can be a helpful tool for both surgeons and patients undergoing bariatric procedures but more interpretable models are needed., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

While bariatric surgery is an effective method for achieving long-term weight loss, postoperative readmissions are associated with negative clinical outcomes and significant costs. We aimed to use machine learning (ML) algorithms to predict readmissions and compare results to logistic regression. Hospitals participating in the Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program, United States. Patients who underwent sleeve gastrectomy (SG), Roux-en-Y gastric bypass (RYGB), and biliopancreatic diversion with duodenal switch between 2016 and 2020 were selected from the Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program (MBSAQIP) database. Patient variables reported by the MBSAQIP database were analyzed by ML algorithms random forest (RF), gradient boosting (XGB), and deep neural networks (NN), and the results of the predictive models were compared to logistic regression using area under the receiver operating characteristic curve (AUROC). Our study included 863,348 patients, of which 39,068 (4.52%) were readmitted. AUROC scores were XGB.785 (95% CI.784–.786), RF.785 (95% CI.784–.785), and NN.754 (95% CI.753–.754), compared with.62 (95% CI.62–.621) for logistic regression (LR) (P <.001). The sensitivity and specificity for XGB, the best performing model, were 73.81% and 70%, compared with 52.94% and 70% for logistic regression. The most important variables were intervention or reoperation prior to discharge, unplanned ICU admission, initial procedure, and the intraoperative transfusion. ML demonstrates significant advantages over logistic regression when predicting 30-day readmission following bariatric surgery. With external validation, models could identify the best candidates for early discharge or targeted postdischarge resources. • Machine learning (ML) predicted readmissions after bariatric surgery better than logistic regression when using all predischarge information. • All model types showed low predictive ability for readmissions based on pre and intraoperative factors alone. [ABSTRACT FROM AUTHOR]

Summary Laparoscopic sleeve gastrectomy (LSG) is the most frequently performed bariatric procedure. To study the safety of LSG done with Nissen Fundoplication (NF) in patients affected by obesity and GERD and assess the complication rate. A retrospective cohort study using the Nationwide Readmissions Database from 2016 to 2019. A total of 236111 patients underwent LSG with and without NF. A matched cohort of 1096 without NF and 548 with NF was obtained. The median age of patients was 47 years old. Median length of hospital stay was higher in the LSG with NF group. Median total charge was higher in the LSG with NF group. There was no statistically significant difference in 30‐day readmission rates in patients with obesity and GERD who received LSG with NF compared to those who received LSG alone. Complications after both procedures were low, which highlights the safety of both procedures. [ABSTRACT FROM AUTHOR]