This study aims to establish a classification system for patients undergoing metabolic surgery for severe obesity by constructing a prospective cohort of 2,000 patients and collecting clinical and biological data at multiple time points before and after surgery. By analyzing clinical, laboratory, and multi-omics characteristics, the study will identify indicators associated with postoperative adverse events and develop a risk warning model using machine learning algorithms. Ultimately, an intelligent digital system will be developed based on the classification criteria and risk model, integrating surgical classification and risk alert functions to provide real-time feedback, supporting clinicians and patients in optimizing postoperative treatment and risk management.
Patients who meet the clinical indications for bariatric/metabolic surgery;
Adults aged 18 to 50 years; ③ Stable body weight (change within ±5% over the past 3 months); ④ Undergoing either laparoscopic sleeve gastrectomy (LSG) or laparoscopic Roux-en-Y gastric bypass (LRYGB).
① Patients with conditions affecting the immune or metabolic systems (e.g., endocrine disorders such as untreated hypothyroidism/hyperthyroidism, cancer);
Patients with renal or hepatic impairment;
Patients who have taken medications that may affect metabolism within the past 3 months (e.g., weight-loss drugs, asthma medications, psychiatric medications, corticosteroids);
Patients who have previously undergone bariatric surgery and are undergoing revisional surgery; ⑤ Patients with psychiatric disorders, especially those with comorbid behavioral or personality disorders (e.g., binge eating disorder);
Patients undergoing bariatric/metabolic surgery
Cohort
Prospective
Not listed
Number of Participants Stratified into Distinct Clusters Using Unsupervised Clustering Algorithm Based on BMI, Comorbidity Count, Inflammatory Markers, and Proteomics Profiles
Participants will be stratified at baseline using an unsupervised clustering algorithm (e.g., k-means) based on the following input variables:（1）Body mass index (BMI, kg/m²)；（2）Comorbidity count (number of chronic diseases at enrollment)；（3）Inflammatory markers (e.g., CRP in mg/L, IL-6 in pg/mL)；（4）Proteomics features (relative expression intensity from LC-MS) The clustering procedure will produce a categorical variable assigning each participant to one of 3-5 data-driven subtypes. The total number of participants in each subtype group will be reported.
Accuracy, Sensitivity, and Specificity of the Postoperative Risk Prediction Model for Adverse Outcomes
Model performance will be assessed for predicting postoperative adverse outcomes within 12 months after bariatric surgery. Predictors include demographic data, intraoperative parameters, early postoperative recovery data, and stratification subtype. Model discrimination will be evaluated using area under the receiver operating characteristic curve (AUC-ROC), and calibration will be assessed with calibration plots and Hosmer-Lemeshow test. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) will be reported using confusion matrix analysis in internal validation (e.g., bootstrapping or 10-fold cross-validation).
User Satisfaction Score of the Digital Postoperative Management System Measured by System Usability Scale (SUS)
User experience and satisfaction with the digital management system will be evaluated using the validated 10-item System Usability Scale (SUS). SUS scores range from 0 to 100, with higher scores indicating greater usability. Both patients and clinical staff will be surveyed at 3 months post-deployment.
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