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Nomogram-Based Prediction Model for Suboptimal Weight Loss Outcomes After Metabolic Bariatric Surgery Using
Wei Zhu1,2,3, Xiaoqing Yuan1,2,3, Zhen Hao4
1Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, People's Republic of China.
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.
Results:
The study included 120 patients (77.5% female) with a mean age of 33.4 ± 8.7 years and a baseline body mass index (BMI) of 38.5 ± 7.1 kg/m2. Patients' weight, BMI, glycemic control, lipid profiles, and body composition parameters were improved significantly after MBS (P < 0.05). Twenty‑two patients (18.3%) were categorized as suboptimal outcome. Univariate and multivariate logistic regression analyses revealed that higher fasting blood glucose (FBG, OR 1.284, P = 0.037), lower HDL-C (OR 0.426, P = 0.024), larger subcutaneous adipose tissue (SAT; OR 1.006, P = 0.018), and lower paraspinal muscle (PSM) area (OR 0.938, P = 0.033) were independent predictors of suboptimal outcomes. The resulting nomogram demonstrated good discrimination with an AUC of 0.801 (95% CI: 0.708-0.914) and calibration, with decision curve analysis confirming its clinical utility.
Conclusion:
Preoperative body composition parameters, specifically SAT and PSM, are critical determinants of weight loss outcome following MBS. The nomogram model constructed based on biochemical and body composition factors in this prospective Chinese cohort may offers great significance for preoperative assessment for MBS candidates in China. Nevertheless, further external validation in independent cohorts is warranted before its routine clinical application.