Identification of Influencing Factors and Construction of a Prediction Model for New-Onset Diabetes After PancreatectomyNCT07436273
Development and Validation of a Risk Prediction Model for New-Onset Diabetes After Pancreatectomy
An observational study of Identification of Influencing Factors and Construction of a Prediction Model for New-Onset Diabetes After Pancreatectomy, run by Ningbo No. 1 Hospital at 1 site in China.
Trial summary
New-onset diabetes is one of the major complications after pancreatectomy. Without timely diagnosis, treatment and management, it can lead to adverse outcomes such as cardiovascular and cerebrovascular events and severe infections, increasing mortality. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Identification of Influencing Factors and Construction of a Prediction Model for New-Onset Diabetes After Pancreatectomy
- What is being tested?
- No Biologics
- How many people
- 575 participants
- Where
- 1 siteChina
- Study dates
- 1 Jun 2023to 30 Jun 2025
- How long will I take part?
- 12 Months
- Phase
- Observational
- Are there results?
- None posted in the registry
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 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: patients after pancreatectomy
Who may be able to join
- Patients undergoing pancreatectomy via open, laparoscopic, or robot-assisted approach.
- Age ≥ 18 years.
Who cannot join
- Patients who underwent total pancreatectomy.
- Patients with a preoperative diagnosis of diabetes.
- Patients with incomplete clinical or follow-up data.
Trial locations and contacts
1 site in 1 country.
China1 site
The First Affiliated Hospital of Ningbo University
Ningbo, Zhejiang, 312000
About this study
New-onset diabetes is one of the major complications after pancreatectomy. Without timely diagnosis, treatment and management, it can lead to adverse outcomes such as cardiovascular and cerebrovascular events and severe infections, increasing mortality. Moving the management threshold forward, early risk identification, and precise and effective intervention are the keys to its management.
Existing risk prediction studies show high heterogeneity across different populations and surgical procedures. Risk factors available for early identification remain unclear, and most included factors are non-interventional. There are limitations in study design, modeling and validation methods, and the research results provide limited value for guiding risk prediction, prevention and management.
Read more
This study adopts a mixed-methods design. We retrospectively analyze the general and clinical data of patients who underwent pancreatectomy at two institutions, follow up the subjects to determine the incidence of postoperative diabetes and postoperative self-management status, and use regression analysis to identify independent risk factors for new-onset diabetes after surgery.
We separately establish a nomogram model, a random forest model, and a deep learning model, perform internal and external validation, compare the performance of the three models, and select the model with optimal clinical performance. Based on the prediction model and evidence-based medicine, we formulate intervention strategies, evaluate their feasibility through clinical trials, refine the intervention items, and finally establish a precise intervention strategy for new-onset diabetes after pancreatectomy to achieve comprehensive, dynamic, efficient and precise management.
Implementation of this project can reduce the incidence and slow the progression of diabetes after pancreatectomy, improve clinical outcomes, lower readmission and mortality rates, save social medical resources, and provide decision-making guidance and practical evidence for the prevention and control of chronic diseases.
- Being tested
- No Biologics
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No BiologicsOther
Observational Study
What is being measured
The primary outcome is the main question the study is designed to answer.
Whether new-onset diabetes occurred
Measured: 12months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Feb 2026
- Study start1 Jun 2023
- Primary completion30 Jun 2025
- Study completion30 Jun 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Identification of Influencing Factors and Construction of a Prediction Model for New-Onset Diabetes After PancreatectomyStudies of the same condition, open and under way.
- 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
- 575 participants (actual)
- Target duration
- 12 Months
Sponsor and investigators
- Lead sponsor
- Ningbo No. 1 Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07436273
- Sponsor's protocol ID
- XiaoyingHeNingbo1
- Status last verified
- Jun 2023
- First submitted
- 21 Feb 2026
- Last updated
- 27 Feb 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 27 Feb 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of a Risk Prediction Model for New-Onset Diabetes After Pancreatectomy
- ClinicalTrials.gov ID
- NCT07436273
- Acronym / sponsor ID
- XiaoyingHeNingbo1
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 575 participants (actual)
- Lead sponsor
- Ningbo No. 1 HospitalUniversity, hospital or other
- Study start
- 1 Jun 2023 (actual)
- Primary completion
- 30 Jun 2025 (actual)
- Study completion
- 30 Jun 2025 (actual)
- First posted
- 27 Feb 2026 (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.
New-onset diabetes is one of the major complications after pancreatectomy. Without timely diagnosis, treatment and management, it can lead to adverse outcomes such as cardiovascular and cerebrovascular events and severe infections, increasing mortality. Moving the management threshold forward, early risk identification, and precise and effective intervention are the keys to its management.
Existing risk prediction studies show high heterogeneity across different populations and surgical procedures. Risk factors available for early identification remain unclear, and most included factors are non-interventional. There are limitations in study design, modeling and validation methods, and the research results provide limited value for guiding risk prediction, prevention and management.
This study adopts a mixed-methods design. We retrospectively analyze the general and clinical data of patients who underwent pancreatectomy at two institutions, follow up the subjects to determine the incidence of postoperative diabetes and postoperative self-management status, and use regression analysis to identify independent risk factors for new-onset diabetes after surgery.
We separately establish a nomogram model, a random forest model, and a deep learning model, perform internal and external validation, compare the performance of the three models, and select the model with optimal clinical performance. Based on the prediction model and evidence-based medicine, we formulate intervention strategies, evaluate their feasibility through clinical trials, refine the intervention items, and finally establish a precise intervention strategy for new-onset diabetes after pancreatectomy to achieve comprehensive, dynamic, efficient and precise management.
Implementation of this project can reduce the incidence and slow the progression of diabetes after pancreatectomy, improve clinical outcomes, lower readmission and mortality rates, save social medical resources, and provide decision-making guidance and practical evidence for the prevention and control of chronic diseases.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 12 Months
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- patients after pancreatectomy
Key inclusion criteria (2 listed)
- Patients undergoing pancreatectomy via open, laparoscopic, or robot-assisted approach.
- Age ≥ 18 years.
Key exclusion criteria (3 listed)
- Patients who underwent total pancreatectomy.
- Patients with a preoperative diagnosis of diabetes.
- Patients with incomplete clinical or follow-up data.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| No BiologicsObservational Study | Other | – |
Primary outcome
Whether new-onset diabetes occurred
Time frame: 12months
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 27 Feb 2026.
- Individual participant dataSharing planned: No
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. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
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 Identification of Influencing Factors and Construction of a Prediction Model for New-Onset Diabetes After Pancreatectomy, run by Ningbo No. 1 Hospital 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 3 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: finished, as of the sponsor's last update on 27 Feb 2026. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Whether new-onset diabetes occurred (time frame: 12months). 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 27 Feb 2026.