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

Laparoscopic sleeve gastrectomy (LSG) is associated with sustained and substantial weight loss. However, suboptimal results are observed in certain patients. 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. 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. 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. 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.