Artificial Intelligence-based Prediction of Radio-cephalic Arteriovenous Fistula Maturation Using Preoperative Duplex Examination

Seoul National University Hospital494 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

494

Conditions

Renal Insufficiency, Chronic Arteriovenous Fistula Artificial Intelligence (AI)Machine Learning

Interventions

Patients who underwent Radiocephalic arteriovenous fistula surgery

Eligibility

Sex: ALL

Medical Language ↔ Plain English

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

Locations (1)

Seoul National University Hospital

Seoul, Seoul, South Korea

Outcomes

Primary Outcomes

Maturation of the fistula

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.

Time frame: 90 days

Data from ClinicalTrials.gov

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