Artificial Intelligence-based Prediction of Radio-cephalic Arteriovenous Fistula Maturation Using Preoperative Duplex Examination
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Renal Insufficiency, Chronic
- Sponsor
- Seoul National University Hospital
- Enrollment
- 494
- Locations
- 1
- Primary Endpoint
- Maturation of the fistula
- Status
- Completed
- Last Updated
- last year
Overview
Brief Summary
The goal of this observational study is to assess the efficacy of AI-driven models in analyzing comprehensive ultrasonographic variables across multiple forearm locations to predict successful AVF maturation. The main question it aims to answer is:
Can AI-driven models analyzing comprehensive ultrasonographic variables accurately predict the successful maturation of arteriovenous fistulas (AVFs)?
Participants who underwent radiocephalic arteriovenous fistula (AVF) creation had their preoperative ultrasonographic data analyzed using AI-driven models to predict successful AVF maturation over a four-year retrospective period.
Investigators
Ara Cho
Professor
Seoul National University Hospital
Eligibility Criteria
Inclusion Criteria
- •patients who underwent RCAVF due to advanced chronic kidney disease from 2018 to 2022
Exclusion Criteria
- •Patients who did not have follow-up data available
Outcomes
Primary Outcomes
Maturation of the fistula
Time Frame: 90 days
Fistula maturation was defined as an arteriovenous fistula that matures and is usable for dialysis with two-needle cannulation for hemodialysis for at least 90 days without the need for endovascular or surgical interventions.