Development and Validation of a Risk Prediction Model for New-Onset Diabetes After Pancreatectomy

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 Overview

• Status: Completed
• Conditions: Identification of Influencing Factors and Construction of a Prediction Model for New-Onset Diabetes After Pancreatectomy
• Intervention / Treatment: Other: No Biologics

• Study Type: Observational
• Enrollment (Actual): 575

Contacts and Locations

Study Locations

• China
  • Zhejiang
    • Ningbo, Zhejiang, China, 312000
      • The First Affiliated Hospital of Ningbo University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Probability Sample

Study Population

patients after pancreatectomy

Description

Inclusion Criteria:

1. Patients undergoing pancreatectomy via open, laparoscopic, or robot-assisted approach.
2. Age ≥ 18 years.

Exclusion Criteria:

1. Patients who underwent total pancreatectomy.
2. Patients with a preoperative diagnosis of diabetes.
3. Patients with incomplete clinical or follow-up data.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Time Frame
Whether new-onset diabetes occurred	12months

Collaborators and Investigators

• Sponsor: Ningbo No. 1 Hospital

Study record dates

Study Major Dates

• Study Start (Actual): June 1, 2023
• Primary Completion (Actual): June 30, 2025
• Study Completion (Actual): June 30, 2025

Study Registration Dates

• First Submitted: February 21, 2026
• First Submitted That Met QC Criteria: February 21, 2026
• First Posted (Actual): February 27, 2026

Study Record Updates

• Last Update Posted (Actual): February 27, 2026
• Last Update Submitted That Met QC Criteria: February 21, 2026
• Last Verified: June 1, 2023

More Information

Terms related to this study

• Other Study ID Numbers: XiaoyingHeNingbo1

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No