Nomogram for Predicting Suboptimal Weight Loss at Three Years after Roux-en-Y Gastric Bypass Surgery in Chinese Patients with Obesity and Type 2 Diabetes.

Obes Facts

Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center of Diabetes, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Key Clinical Center for Metabolic Disease, Shanghai, China.

Published: January 2025

Introduction: Strategies to address suboptimal weight loss after Roux-en-Y gastric bypass (RYGB) can be developed if at-risk patients are identified in advance. This study aimed to build a pre-surgery prediction nomogram for early prediction of insufficient weight loss (IWL) or weight regain (WR) after bariatric surgery in Chinese patients.

Methods: In this retrospective study, 187 patients with obesity and type 2 diabetes who underwent laparoscopic RYGB were followed yearly for 3 years. Suboptimal weight loss included IWL and WR. IWL was defined as a total weight loss percentage of <25% at 1 year postoperatively, and WR was defined as a maximum weight loss percentage of >20% at 3 years postoperatively. Multivariate logistic regression was performed to identify independent predictors and to establish a nomogram to predict the occurrence of suboptimal weight loss.

Results: Multivariate logistic regression revealed that male sex (OR 4.268, 95% CI: 1.413-12.890), body mass index (OR 0.816, 95% CI: 0.705-0.946), and glycated hemoglobin (OR 1.493, 95% CI: 1.049-2.126) were independent predictors of IWL/WR. The AUC value of the nomogram constructed from the above three factors was 0.781. The Hosmer-Lemeshow test showed that the model had a good fit (p = 0.143). The calibration curve of the nomogram is close to an ideal diagonal line. Furthermore, the decision curve analysis demonstrated the good net benefits of the model.

Conclusions: A nomogram based on pre-surgery factors was developed to predict postoperative IWL/WR. This provides a convenient and useful tool for predicting suboptimal weight loss before surgery.

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Source
http://dx.doi.org/10.1159/000542923DOI Listing

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