ArticleObesity surgery2022
Development and Validation of an Artificial Intelligence-Based Model to Predict Gastroesophageal Reflux Disease After Sleeve Gastrectomy.
Article in Obesity surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05416645 (Prevalence, Predictors, and Management of Gastroesophageal Reflux After Laparoscopic Sleeve Gastrectomy Multicentric Cohort Study), which is not on this map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Prevalence, Predictors, and Management of Gastroesophageal Reflux After Laparoscopic Sleeve Gastrectomy Multicentric Cohort Study
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Does the use of staple line reinforcement during sleeve gastrectomy and Roux-en-Y gastric bypass affect ClinicalOutcomes?: Systematic review and Meta-analysis.Langenbeck's archives of surgery · 2025Pooled it
- Post-Bariatric Surgery Complications and the Role of Endoscopic Intervention.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence for surgical management of benign esophageal disease: scoping review and evidence mapping.Langenbeck's archives of surgery · 2026Article
- Evidence-Based Prevention of De Novo GERD after Bariatric Surgery: Comparing Human and AI Inference.Obesity surgery · 2026Article
- Article
- Development and External Validation of a Machine Learning-Based Risk Score for Stent Outcomes in Post-Bariatric Leak Management: The "Alexandria-Bari-Stent" Tool.Obesity surgery · 2025Article
- The Impact of GERD Symptoms and Endoscopic RE on Early Postoperative GERD After LSG.Obesity surgery · 2025Article
- Application of Machine Learning to Predict Postoperative Nausea and Vomiting in Laparoscopic Sleeve Gastrectomy.Obesity surgery · 2025Article
- Review
- The performance of artificial intelligence large language model-linked chatbots in surgical decision-making for gastroesophageal reflux disease.Surgical endoscopy · 2024Article
- Computer-Based Intelligent Solutions for the Diagnosis of Gastroesophageal Reflux Disease Phenotypes and Chicago Classification 3.0.Healthcare (Basel, Switzerland) · 2023Article
- Insights into the Paradox of the Weak Association Between Sleeve Gastrectomy and Barrett's Esophagus.Obesity surgery · 2023Review
- How Good is Stratification and Prediction Model Analysis Between Primary and Revisional Roux-en-Y Gastric Bypass Surgery? A Multi-center Study and Narrative Review.Obesity surgery · 2023Review
- Machine learning-based improvement of an online rheumatology referral and triage system.Frontiers in medicine · 2022Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposePrediction of the onset of de novo gastroesophageal reflux disease (GERD) after sleeve gastrectomy (SG) would be helpful in decision-making and selection of the optimal bariatric procedure for every patient. The present study aimed to develop an artificial intelligence (AI)-based model to predict the onset of GERD after SG to help clinicians and surgeons in decision-making. MATERIALS AND
methodsA prospectively maintained database of patients with severe obesity who underwent SG was used for the development of the AI model using all the available data points. The dataset was arbitrarily split into two parts: 70% for training and 30% for testing. Then ranking of the variables was performed in two steps. Different learning algorithms were used, and the best model that showed maximum performance was selected for the further steps of machine learning. A multitask AI platform was used to determine the cutoff points for the top numerical predictors of GERD.
resultsIn total, 441 patients (76.2% female) of a mean age of 43.7 ± 10 years were included. The ensemble model outperformed the other models. The model achieved an AUC of 0.93 (95%CI 0.88-0.99), sensitivity of 79.2% (95% CI 57.9-92.9%), and specificity of 86.1% (95%CI 70.5-95.3%). The top five ranked predictors were age, weight, preoperative GERD, size of orogastric tube, and distance of first stapler firing from the pylorus.
conclusionAn AI-based model for the prediction of GERD after SG was developed. The model had excellent accuracy, yet a moderate sensitivity and specificity. Further prospective multicenter trials are needed to externally validate the model developed.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.