Clinical trial

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

Recruiting now · Not applicable · 1 countries · Registry ID NCT07093502

Recruiting nowNot applicableObservational

What this study is about

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.

A promising-looking record is not the same as confirmed eligibility. The study team must review the full criteria and current recruitment status.

Basic eligibility

Age18 Years to 50 Years

SexAll

Healthy volunteersNot accepted

ConditionObesity, Bariatric Surgery, Bariatric Surgery (Sleeve Gastrectomy )

Full registry criteria

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.

Treatments and study arms

The registry does not list a named intervention.

Primary outcomes

Number of Participants Stratified into Distinct Clusters Using Unsupervised Clustering Algorithm Based on BMI, Comorbidity Count, Inflammatory Markers, and Proteomics ProfilesFrom 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 OutcomesUp 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).

Study locations

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Third Xiangya Hospital of Central South University🇨🇳 Changsha, Hunan, China

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Liyong ZhuContact

Email · Call +8613975879453