ObesityNCT07093502
Establishment of a Classification System and Postoperative Risk Warning Model for Patients Undergoing Bariatric Metabolic Surgery for Severe Obesity
An observational study of Obesity and Bariatric Surgery, run by The Third Xiangya Hospital of Central South University at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 50 years
- Conditions the study is for
- Obesity, Bariatric Surgery, Bariatric Surgery (Sleeve Gastrectomy )
- How many people
- 2,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Apr 2025to 31 Dec 2035
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 50 years
- All sexes
- No healthy volunteers
- Adult
Study population: Patients undergoing bariatric/metabolic surgery
Who may be able to join
- 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).
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
Third Xiangya Hospital of Central South University
Changsha, Hunan, 410013
Liyong Zhu, +8613975879453, zly8128@csu.edu.cn
About this study
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.
Read the 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.
- Keywords
- Severe obesity, bariatric surgery, Classification System, Postoperative Risk Warning Model
- Medical subject headings
- Obesity, Obesity, Morbid
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: From enrollment to the end of follow-up at 2 years
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).
Measured: Up to 24 months after surgery
Secondary outcomes (1)
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.
Measured: At 24 months after initiation of digital system use
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Jul 2025
- Study start1 Apr 2025
- Primary completion31 Dec 2035 (estimated)
- Study completion31 Dec 2035 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for ObesityStudies of the same condition, open and under way.
- More trials run by The Third Xiangya Hospital of Central South UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 2,000 participants (estimated)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07093502
- Sponsor's protocol ID
- ThirdXiangyaR25017
- Status last verified
- Jul 2025
- First submitted
- 15 Jul 2025
- Last updated
- 30 Jul 2025
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: Undecided. Data sharing is currently under consideration. The decision will depend on institutional policy, ethical review board approval, and the establishment of appropriate data-use agreements to protect participant confidentiality.
Study at a glance
From the registry record, last updated by the sponsor on 30 Jul 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Establishment of a Classification System and Postoperative Risk Warning Model for Patients Undergoing Bariatric Metabolic Surgery for Severe Obesity
- ClinicalTrials.gov ID
- NCT07093502
- Acronym / sponsor ID
- ThirdXiangyaR25017
- Keywords
- Severe obesity, bariatric surgery, Classification System, Postoperative Risk Warning Model
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- The Third Xiangya Hospital of Central South UniversityUniversity, hospital or other
- Study start
- 1 Apr 2025 (actual)
- Primary completion
- 31 Dec 2035 (estimated)
- Study completion
- 31 Dec 2035 (estimated)
- First posted
- 30 Jul 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 to 50 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult
- Study population
- Patients undergoing bariatric/metabolic surgery
Key inclusion criteria (2 listed)
- Patients who meet the clinical indications for bariatric/metabolic surgery;
Key exclusion criteria (5 listed)
- ① Patients with conditions affecting the immune or metabolic systems (e.g., endocrine disorders such as untreated hypothyroidism/hyperthyroidism, cancer);
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Number of Participants Stratified into Distinct Clusters Using Unsupervised Clustering Algorithm Based on BMI, Comorbidity Count, Inflammatory Markers, and Proteomics Profiles
Time frame: 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
Time frame: 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).
1 secondary outcome
User Satisfaction Score of the Digital Postoperative Management System Measured by System Usability Scale (SUS)
Time frame: At 24 months after initiation of digital system use
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Jul 2025.
- Individual participant dataSharing planned: Undecided. Undecided. Data sharing is currently under consideration. The decision will depend on institutional policy, ethical review board approval, and the establishment of appropriate data-use agreements to protect participant confidentiality.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Obesity and Bariatric Surgery, run by The Third Xiangya Hospital of Central South University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 30 Jul 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Liyong Zhu (+86 13975879453, zly8128@csu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Number of Participants Stratified into Distinct Clusters Using Unsupervised Clustering Algorithm Based on BMI, Comorbidity Count, Inflammatory Markers, and Proteomics Profiles (time frame: From enrollment to the end of follow-up at 2 years). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 30 Jul 2025.