Establishing a Prediction Model for Weight Loss Outcomes After LSG in Chinese Obese Patients with BMI ≥ 32.5 Kg/m2 Using Body Composition Data

Liang Wang,* Yilan Sun,* Qing Sang,* Zheng Wang, Chengyuan Yu, Zhehong Li, Mingyue Shang, Nengwei Zhang, Dexiao Du Surgery Centre of Diabetes Mellitus, Capital Medical University Affiliated Beijing Shijitan Hospital, Beijing, 100038, People’s Republic of China*These authors contributed equally to this workCorrespondence: Nengwei Zhang, Shijitan Hospital, Tieyi Road, Haidian District, Beijing, People’s Republic of China, Tel +8613801068802, Email [email protected] Dexiao Du, Shijitan Hospital, Tieyi Road, Haidian District, Beijing, People’s Republic of China, Tel +8613581753721, Email [email protected]: Laparoscopic sleeve gastrectomy (LSG) is associated with sustained and substantial weight loss. However, suboptimal results are observed in certain patients.Objective: Drawing from body composition data at our center, clinically accessible predictive factors for weight loss outcomes were identified, leading to the development and validation of a preoperative predictive model for weight loss following LSG.Methods and Materials: A retrospective analysis was conducted on the general clinical baseline and body composition data of obese patients (body mass index [BMI] ≥ 32.5 kg/m2) who underwent LSG between December 2016 and December 2022. Independent predictors for weight loss outcomes were selected through univariate logistic regression, random forest analysis, and multivariate logistic regression. Subsequently, a nomogram was developed to predict weight loss outcomes and was evaluated for discrimination, accuracy, and clinical utility, with validation performed in a separate cohort.Results: A total of 473 patients with mean BMI were included. The preoperative resting energy expenditure to body weight ratio (REE/BW), fat-free mass index (FFMI), and waist circumference (WC) emerged as independent predictive factors for weight loss outcomes at one year post-LSG. These body composition parameters were incorporated into the construction of an Inbody predictive nomogram, which yielded area under the curve (AUC) values of 0.868 (95% CI: 0.826– 0.902) for the modeling cohort and 0.829 (95% CI: 0.756– 0.887) for the validation cohort. Calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC) from both groups demonstrated the model’s robust discrimination, accuracy, and clinical utility.Conclusion: In obese Chinese patients with a BMI ≥ 32.5 kg/m2, the Inbody-based nomogram integrating REE/BW, FFMI, and WC offers an effective preoperative tool for predicting weight loss outcomes one year after LSG, facilitating surgical planning and postoperative management.Keywords: laparoscopic sleeve gastrectomy, prognostic prediction, metabolic bariatric surgery, obesity, body composition data