Building an Artificial Intelligence-Driven Early Warning System and Home Management Protocol for a Diabetic Foot Ulcer Recurrence Cohort
Trial ID: NCT07277010
Status: NOT_YET_RECRUITING
Study type: OBSERVATIONAL
Start date: 2026-01-01
Completion date: 2028-12-31
Sponsor: Peking University Third Hospital
Conditions: Diabetic Foot Ulcer (DFU)
Interventions: Individualized follow-up care
Locations: Peking University Third Hospital
Countries: China
Enrollment: 200
Age range: 18 Years - None
Sex: ALL

Summary:
Diabetes is one of the major chronic diseases, and diabetic foot ulcer (DFU) is a significant adverse prognosis of diabetes. The recurrence of DFU after healing involves multiple risk factors, such as changes in foot loading patterns, patient compliance, family care capacity, blood glucose monitoring, the degree of ischemia, and control of systemic diseases. Early identification of signs of DFU recurrence and timely follow-up interventions are crucial for improving prognosis, reducing disability rates, and lowering healthcare costs. However, traditional follow-up systems lack individualized strategies (e.g., insufficient risk stratification, rigid follow-up intervals, inadequate compliance management), often resulting in low follow-up efficacy. High-risk patients prone to recurrence may not receive frequent enough follow-ups for early detection, while low-risk patients unlikely to recur may undergo multiple unnecessary visits, increasing the burden on both patients and healthcare providers. This inefficiency is a key reason for the persistently high rates of disability and mortality among patients with recurrent DFU. Establishing individualized follow-up strategies for DFU, leveraging advanced technologies to address core bottlenecks such as delayed recurrence warnings and insufficient home management, represents an effective technical approach to solving these problems.

Our center aims to establish and refine a specialized cohort for active DFU follow-up, along with a multimodal database with comprehensive indicators. We plan to explore a high-risk foot grading system for preventing DFU recurrence and develop targeted follow-up protocols. Using AI technology, we will create a wound alert system capable of identifying DFU recurrence and explore a remote healthcare and AI-assisted prevention and control system for DFU recurrence, centered on patient self-management at home.

Eligibility criteria:
Inclusion Criteria:

* A diagnosis of type 1 or type 2 diabetes in accordance with the definitions set by the World Health Organization；
* The wound was caused by diabetic foot\*. After treatment, the wound healed. The criteria for successful healing were as follows: the wound was dry with no exudate; the wound bed and edges were completely epithelialized; there were no signs of redness or swelling in the surrounding area; and the wound had sufficient tensile strength to withstand pressure without cracking；
* Voluntarily participated in this study and signed the informed consent form.

Exclusion Criteria:

* The patient is unable to cooperate or has a mental disorder；
* At the discretion of the researchers, the subject is not suitable for this study or is unable to comply with the requirements of this study.

Outcomes:
1-year recurrence rate of diabetic foot