Development and validation of a simple risk score to predict gastroesophageal reflux disease after sleeve gastrectomy: a multicenter retrospective cohort study
Year: 2025
Type: article
Study design: Observational Study
Abstract: BACKGROUND: Laparoscopic sleeve gastrectomy (LSG) is the most commonly performed bariatric procedure worldwide. However, the development of gastroesophageal reflux disease (GERD) following LSG remains a significant clinical concern that can compromise long-term patient outcomes and satisfaction. MATERIALS AND METHODS: In this multicenter retrospective cohort study, a total of 968 patients were screened, of whom 851 met eligibility criteria and were included in the final analysis. Baseline characteristics, perioperative variables, and 12-month follow-up outcomes were collected. A predictive risk score for postoperative GERD was developed using LASSO regression and logistic modeling in a training cohort (n = 595) and subsequently validated in an independent cohort (n = 256). A nomogram was constructed based on the final model. RESULTS: The study included 313 male and 538 female patients, with a mean age of 31.8 years and mean BMI of 38.4 kg/m 2 . Among the entire cohort, 27.3% developed GERD after LSG. Key associated factors included BMI, The American Foregut Society (AFS) classification stratifies EGJ integrity (AFS grade), preoperative reflux symptoms, distance from the angle of His, complete mobilization of the angle of His, and duration of postoperative PPI therapy. The final model incorporated 6 predictors and demonstrated high discrimination with an AUC of 0.948 in the training set and 0.912 in the validation set. CONCLUSION: We established and validated a robust, user-friendly risk score and nomogram to predict GERD after LSG. This tool enables early identification of high-risk patients and supports individualized perioperative decision-making, thereby enhancing long-term outcomes and optimizing patient care.
Source: International Journal of Surgery
Authors: Xiang Gao, Jiahao Li, Zhi Song, Weizheng Li, Lei Zhao, Youwu Wen, Henggui Luo, Tongli Yuan, Zhen Li, Pengzhou Li, Liyong Zhu
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,450
OpenAlex ID: W4413418904
