Establishment of a Classification System and Postoperative Risk Warning Model for Patients Undergoing Bariatric Metabolic Surgery for Severe Obesity

Establishment of a Classification System and Postoperative Risk Warning Model for Patients Undergoing Bariatric Metabolic Surgery for Severe Obesity

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.

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.

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.