Nomogram-Based Prediction Model for Suboptimal Weight Loss Outcomes After Metabolic Bariatric Surgery Using CT-Derived Body Composition Parameters: A Prospective Chinese Cohort Study

Background and aim: Metabolic bariatric surgery (MBS) provides potent and sustained efficacy in managing obesity and its associated comorbidities. Nevertheless, suboptimal weight loss outcomes persist in a notable proportion of patients after MBS. This study aimed to identify influencing risk factors and develop a predictive model to predict 1-year suboptimal weight loss outcomes following MBS in a prospective Chinese cohort.

Methods

This study involved a prospective cohort of 120 patients who underwent MBS at Beijing Friendship Hospital from November 2021 to September 2023. Optimal weight loss outcome was defined as a total weight loss percentage (%TWL) ≥ 20% at the 1-year follow-up. Patient's clinical, biochemical, and CT-derived body composition parameters were collected at baseline and 1 year postoperatively. Independent predictors of suboptimal weight loss outcome were identified through univariate and multivariate logistic regression. Subsequently, a nomogram was constructed and internally validated with its discrimination, calibration, and clinical utility.