Bariatric Surgery, Bariatric Surgery (Sleeve Gastrectomy ), Obesity
Conditions
Keywords
Severe obesity, bariatric surgery, Classification System, Postoperative Risk Warning Model
Brief summary
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.
Detailed description
Establishment of a Prospective Disease-Specific Follow-up Cohort of 2,000 Patients Based on the Following Inclusion and Exclusion Criteria All participants will undergo metabolic surgery. A prospective, disease-specific follow-up cohort will be established, and baseline data will be collected. Patients will be followed up at multiple postoperative time points: days 3 and 7, and months 1, 3, 6, 12, and 24. Follow-up assessments will include the occurrence of postoperative complications and adverse events, as well as the degree of metabolic improvement and prognosis. A multidimensional data platform will be used to integrate and analyze diverse indicators, identifying those strongly associated with postoperative adverse events. Clustering analysis will be applied to establish a classification system for patients undergoing metabolic surgery for severe obesity. Targeted assays will be performed on time-series biospecimens to identify novel risk biomarkers. A risk warning model will be constructed, validated, and evaluated. Finally, an intelligent digital system integrating patient classification and real-time risk alert functions will be developed to optimize long-term outcomes and enhance the precision and timeliness of classification and risk warning for healthcare professionals and patients.
Interventions
None listed
Sponsors
The Third Xiangya Hospital of Central South University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 50 Years
Healthy volunteers
No
Inclusion criteria
* 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).
Exclusion criteria
* 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 currently participating in other clinical studies that may conflict with this study or those who refuse to sign the informed consent form.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Number of Participants Stratified into Distinct Clusters Using Unsupervised Clustering Algorithm Based on BMI, Comorbidity Count, Inflammatory Markers, and Proteomics Profiles | From enrollment to the end of follow-up at 2 years | 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 | Up to 24 months after surgery | 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). |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| User Satisfaction Score of the Digital Postoperative Management System Measured by System Usability Scale (SUS) | At 24 months after initiation of digital system use | 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. |
Countries
China
Contacts
Primary ContactLiyong Zhu
Outcome results
None listed