Development and Internal Validation of a Nomogram for Predicting Reflux Esophagitis in Candidates for Metabolic...

Reflux esophagitis (RE) is common among individuals with obesity and candidates for Metabolic Bariatric Surgery (MBS). Therefore, this study aimed to develop and internally evaluate a clinical nomogram to estimate the risk of RE in candidates for MBS.

A total of 694 patients scheduled for MBS between July 2020 and December 2023 were retrospectively enrolled and randomly split into a training set and an internal test set at a 7:3 ratio. All patients underwent preoperative upper gastrointestinal endoscopy to assess RE status. The least absolute shrinkage and selection operator (LASSO) regression was applied to identify candidate predictors. Variables with non-zero coefficients were subsequently entered into multivariable logistic regression analysis, and independent factors were used to construct the nomogram. Model discrimination, calibration, and clinical utility were evaluated using the concordance index (C-index), calibration curves, and decision curve analysis (DCA), respectively.

The overall prevalence of RE was 20.03%. Multivariable logistic regression analysis identified age, BMI, HDL-C, sex, and Helicobacter pylori (H. pylori) infection status as independent factors associated with RE. The nomogram constructed based on these variables demonstrated acceptable predictive performance in both sets. The area under the curve (AUC) was 0.796 in the training set and 0.79 in the internal test set. After bootstrap internal validation with 1,000 resamples in the training set, the bias-corrected C-index was 0.786. Calibration curves showed acceptable agreement between predicted probabilities and the observed incidence of RE.

We developed a concise nomogram incorporating preoperative clinical variables to estimate the risk of RE in candidates for MBS. The model demonstrated acceptable discrimination and calibration based on internal validation; however, its clinical applicability requires further external validation.